\n",
- "If you have already cloned the material, please issue `git pull` now and reload the notebook to ensure that you have the latest updates.\n",
- "
"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "remove-input",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "%config InlineBackend.figure_format = 'retina'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Images are numpy arrays"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Images are represented in ``scikit-image`` using standard ``numpy`` arrays. This allows maximum inter-operability with other libraries in the scientific Python ecosystem, such as ``matplotlib`` and ``scipy``.\n",
- "\n",
- "Let's see how to build a grayscale image as a 2D array:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from matplotlib import pyplot as plt\n",
- "\n",
- "random_image = np.random.random([500, 500])\n",
- "\n",
- "plt.imshow(random_image, cmap='gray')\n",
- "plt.colorbar();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The same holds for \"real-world\" images:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage import data\n",
- "\n",
- "coins = data.coins()\n",
- "\n",
- "print('Type:', type(coins))\n",
- "print('dtype:', coins.dtype)\n",
- "print('shape:', coins.shape)\n",
- "\n",
- "plt.imshow(coins, cmap='gray');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "A color image is a 3D array, where the last dimension has size 3 and represents the red, green, and blue channels:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "cat = data.chelsea()\n",
- "print(\"Shape:\", cat.shape)\n",
- "print(\"Values min/max:\", cat.min(), cat.max())\n",
- "\n",
- "plt.imshow(cat);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "These are *just NumPy arrays*. E.g., we can make a red square by using standard array slicing and manipulation:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "cat[10:110, 10:110, :] = [255, 0, 0] # [red, green, blue]\n",
- "plt.imshow(cat);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Images can also include transparent regions by adding a 4th dimension, called an *alpha layer*."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Other shapes, and their meanings\n",
- "\n",
- "|Image type|Coordinates|\n",
- "|:---|:---|\n",
- "|2D grayscale|(row, column)|\n",
- "|2D multichannel|(row, column, channel)|\n",
- "|3D grayscale (or volumetric) |(plane, row, column)|\n",
- "|3D multichannel|(plane, row, column, channel)|"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Displaying images using matplotlib"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage import data\n",
- "\n",
- "img0 = data.chelsea()\n",
- "img1 = data.rocket()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(20, 10))\n",
- "\n",
- "ax0.imshow(img0)\n",
- "ax0.set_title('Cat', fontsize=18)\n",
- "ax0.axis('off')\n",
- "\n",
- "ax1.imshow(img1)\n",
- "ax1.set_title('Rocket', fontsize=18)\n",
- "ax1.set_xlabel(r'Launching position $\\alpha=320$')\n",
- "\n",
- "ax1.vlines([202, 300], 0, img1.shape[0], colors='magenta', linewidth=3, label='Side tower position')\n",
- "ax1.plot([168, 190, 200], [400, 200, 300], color='white', linestyle='--', label='Side angle')\n",
- "\n",
- "ax1.legend();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "For more on plotting, see the [Matplotlib documentation](https://matplotlib.org/gallery/index.html#images-contours-and-fields) and [pyplot API](https://matplotlib.org/api/pyplot_summary.html)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Data types and image values\n",
- "\n",
- "In literature, one finds different conventions for representing image values:\n",
- "\n",
- "```\n",
- " 0 - 255 where 0 is black, 255 is white\n",
- " 0 - 1 where 0 is black, 1 is white\n",
- "```\n",
- "\n",
- "``scikit-image`` supports both conventions--the choice is determined by the\n",
- "data-type of the array.\n",
- "\n",
- "E.g., here, I generate two valid images:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "linear0 = np.linspace(0, 1, 2500).reshape((50, 50))\n",
- "linear1 = np.linspace(0, 255, 2500).reshape((50, 50)).astype(np.uint8)\n",
- "\n",
- "print(\"Linear0:\", linear0.dtype, linear0.min(), linear0.max())\n",
- "print(\"Linear1:\", linear1.dtype, linear1.min(), linear1.max())\n",
- "\n",
- "fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 15))\n",
- "ax0.imshow(linear0, cmap='gray')\n",
- "ax1.imshow(linear1, cmap='gray');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The library is designed in such a way that any data-type is allowed as input,\n",
- "as long as the range is correct (0-1 for floating point images, 0-255 for unsigned bytes,\n",
- "0-65535 for unsigned 16-bit integers)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can convert images between different representations by using ``img_as_float``, ``img_as_ubyte``, etc.:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage import img_as_float, img_as_ubyte\n",
- "\n",
- "image = data.chelsea()\n",
- "\n",
- "image_ubyte = img_as_ubyte(image)\n",
- "image_float = img_as_float(image)\n",
- "\n",
- "print(\"type, min, max:\", image_ubyte.dtype, image_ubyte.min(), image_ubyte.max())\n",
- "print(\"type, min, max:\", image_float.dtype, image_float.min(), image_float.max())\n",
- "print()\n",
- "print(\"231/255 =\", 231/255.)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Your code would then typically look like this:\n",
- "\n",
- "```python\n",
- "def my_function(any_image):\n",
- " float_image = img_as_float(any_image)\n",
- " # Proceed, knowing image is in [0, 1]\n",
- "```\n",
- "\n",
- "We recommend using the floating point representation, given that\n",
- "``scikit-image`` mostly uses that format internally."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Image I/O\n",
- "\n",
- "Mostly, we won't be using input images from the scikit-image example data sets. Those images are typically stored in JPEG or PNG format. Since scikit-image operates on NumPy arrays, *any* image reader library that provides arrays will do. Options include imageio, matplotlib, pillow, etc.\n",
- "\n",
- "scikit-image conveniently wraps many of these in the `io` submodule, and will use whichever of the libraries mentioned above are installed:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage import io\n",
- "\n",
- "image = io.imread('../images/balloon.jpg')\n",
- "\n",
- "print(type(image))\n",
- "print(image.dtype)\n",
- "print(image.shape)\n",
- "print(image.min(), image.max())\n",
- "\n",
- "plt.imshow(image);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We also have the ability to load multiple images, or multi-layer TIFF images:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ic = io.ImageCollection('../images/*.png:../images/*.jpg')\n",
- "\n",
- "print('Type:', type(ic))\n",
- "\n",
- "ic.files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "\n",
- "f, axes = plt.subplots(nrows=3, ncols=len(ic) // 3 + 1, figsize=(20, 5))\n",
- "\n",
- "# subplots returns the figure and an array of axes\n",
- "# we use `axes.ravel()` to turn these into a list\n",
- "axes = axes.ravel()\n",
- "\n",
- "for ax in axes:\n",
- " ax.axis('off')\n",
- "\n",
- "for i, image in enumerate(ic):\n",
- " axes[i].imshow(image, cmap='gray')\n",
- " axes[i].set_title(os.path.basename(ic.files[i]))\n",
- " \n",
- "plt.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Aside: `enumerate`\n",
- "\n",
- "`enumerate` gives us each element in a container, along with its position."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "animals = ['cat', 'dog', 'leopard']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for i, animal in enumerate(animals):\n",
- " print('The animal in position {} is {}'.format(i, animal))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise: draw the letter H\n",
- "\n",
- "Define a function that takes as input an RGB image and a pair of coordinates (row, column), and returns a copy with a green letter H overlaid at those coordinates. The coordinates point to the top-left corner of the H.\n",
- "\n",
- "The arms and strut of the H should have a width of 3 pixels, and the H itself should have a height of 24 pixels and width of 20 pixels.\n",
- "\n",
- "Start with the following template:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "hide-output"
- ]
- },
- "outputs": [],
- "source": [
- "def draw_H(image, coords, color=(0, 255, 0)):\n",
- " out = image.copy()\n",
- " \n",
- " ...\n",
- " \n",
- " return out "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Test your function like so:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "cat = data.chelsea()\n",
- "cat_H = draw_H(cat, (50, -50))\n",
- "plt.imshow(cat_H);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise: visualizing RGB channels\n",
- "\n",
- "Display the different color channels of the image along (each as a gray-scale image). Start with the following template:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "# --- read in the image ---\n",
- "\n",
- "image = plt.imread('../images/Bells-Beach.jpg')\n",
- "\n",
- "# --- assign each color channel to a different variable ---\n",
- "\n",
- "r = ... # FIXME: grab channel from image...\n",
- "g = ... # FIXME\n",
- "b = ... # FIXME\n",
- "\n",
- "# --- display the image and r, g, b channels ---\n",
- "\n",
- "f, axes = plt.subplots(1, 4, figsize=(16, 5))\n",
- "\n",
- "for ax in axes:\n",
- " ax.axis('off')\n",
- "\n",
- "(ax_r, ax_g, ax_b, ax_color) = axes\n",
- " \n",
- "ax_r.imshow(r, cmap='gray')\n",
- "ax_r.set_title('red channel')\n",
- "\n",
- "ax_g.imshow(g, cmap='gray')\n",
- "ax_g.set_title('green channel')\n",
- "\n",
- "ax_b.imshow(b, cmap='gray')\n",
- "ax_b.set_title('blue channel')\n",
- "\n",
- "# --- Here, we stack the R, G, and B layers again\n",
- "# to form a color image ---\n",
- "ax_color.imshow(np.stack([r, g, b], axis=2))\n",
- "ax_color.set_title('all channels');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now, take a look at the following R, G, and B channels. How would their combination look? (Write some code to confirm your intuition.)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage import draw\n",
- "\n",
- "red = np.zeros((300, 300))\n",
- "green = np.zeros((300, 300))\n",
- "blue = np.zeros((300, 300))\n",
- "\n",
- "r, c = draw.circle(100, 100, 100)\n",
- "red[r, c] = 1\n",
- "\n",
- "r, c = draw.circle(100, 200, 100)\n",
- "green[r, c] = 1\n",
- "\n",
- "r, c = draw.circle(200, 150, 100)\n",
- "blue[r, c] = 1\n",
- "\n",
- "f, axes = plt.subplots(1, 3)\n",
- "for (ax, channel) in zip(axes, [red, green, blue]):\n",
- " ax.imshow(channel, cmap='gray')\n",
- " ax.axis('off')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise: Convert to grayscale (\"black and white\")\n",
- "\n",
- "The *relative luminance* of an image is the intensity of light coming from each point. Different colors contribute differently to the luminance: it's very hard to have a bright, pure blue, for example. So, starting from an RGB image, the luminance is given by:\n",
- "\n",
- "$$\n",
- "Y = 0.2126R + 0.7152G + 0.0722B\n",
- "$$\n",
- "\n",
- "Use Python 3.5's matrix multiplication, `@`, to convert an RGB image to a grayscale luminance image according to the formula above.\n",
- "\n",
- "Compare your results to that obtained with `skimage.color.rgb2gray`.\n",
- "\n",
- "Change the coefficients to 1/3 (i.e., take the mean of the red, green, and blue channels, to see how that approach compares with `rgb2gray`)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "from skimage import color, img_as_float\n",
- "\n",
- "image = img_as_float(io.imread('../images/balloon.jpg'))\n",
- "\n",
- "gray = color.rgb2gray(image)\n",
- "my_gray = ... # FIXME\n",
- "\n",
- "# --- display the results ---\n",
- "\n",
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 6))\n",
- "\n",
- "ax0.imshow(gray, cmap='gray')\n",
- "ax0.set_title('skimage.color.rgb2gray')\n",
- "\n",
- "ax1.imshow(my_gray, cmap='gray')\n",
- "ax1.set_title('my rgb2gray')"
- ]
- }
- ],
- "metadata": {
- "celltoolbar": "Tags",
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/lectures/00_images_are_arrays.md b/lectures/00_images_are_arrays.md
new file mode 100644
index 0000000..5f3a0d7
--- /dev/null
+++ b/lectures/00_images_are_arrays.md
@@ -0,0 +1,376 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+```{code-cell}
+---
+tags: [remove-input, remove-output]
+---
+
+%matplotlib inline
+%config InlineBackend.figure_format = 'retina'
+```
+
+# Images are numpy arrays
+
++++
+
+Images are represented in ``scikit-image`` using standard ``numpy`` arrays. This allows maximum inter-operability with other libraries in the scientific Python ecosystem, such as ``matplotlib`` and ``scipy``.
+
+Let's see how to build a grayscale image as a 2D array:
+
+```{code-cell}
+import numpy as np
+from matplotlib import pyplot as plt
+
+random_image = np.random.random([500, 500])
+
+plt.imshow(random_image, cmap='gray')
+plt.colorbar();
+```
+
+The same holds for "real-world" images:
+
+```{code-cell}
+from skimage import data
+
+coins = data.coins()
+
+print('Type:', type(coins))
+print('dtype:', coins.dtype)
+print('shape:', coins.shape)
+
+plt.imshow(coins, cmap='gray');
+```
+
+A color image is a 3D array, where the last dimension has size 3 and represents the red, green, and blue channels:
+
+```{code-cell}
+cat = data.chelsea()
+print("Shape:", cat.shape)
+print("Values min/max:", cat.min(), cat.max())
+
+plt.imshow(cat);
+```
+
+These are *just NumPy arrays*. E.g., we can make a red square by using standard array slicing and manipulation:
+
+```{code-cell}
+cat[10:110, 10:110, :] = [255, 0, 0] # [red, green, blue]
+plt.imshow(cat);
+```
+
+Images can also include transparent regions by adding a 4th dimension, called an *alpha layer*.
+
++++
+
+## Other shapes, and their meanings
+
+|Image type|Coordinates|
+|:---|:---|
+|2D grayscale|(row, column)|
+|2D multichannel|(row, column, channel)|
+|3D grayscale (or volumetric) |(plane, row, column)|
+|3D multichannel|(plane, row, column, channel)|
+
++++
+
+## Displaying images using matplotlib
+
+```{code-cell}
+from skimage import data
+
+img0 = data.chelsea()
+img1 = data.rocket()
+```
+
+```{code-cell}
+import matplotlib.pyplot as plt
+
+f, (ax0, ax1) = plt.subplots(1, 2, figsize=(20, 10))
+
+ax0.imshow(img0)
+ax0.set_title('Cat', fontsize=18)
+ax0.axis('off')
+
+ax1.imshow(img1)
+ax1.set_title('Rocket', fontsize=18)
+ax1.set_xlabel(r'Launching position $\alpha=320$')
+
+ax1.vlines([202, 300], 0, img1.shape[0], colors='magenta', linewidth=3, label='Side tower position')
+ax1.plot([168, 190, 200], [400, 200, 300], color='white', linestyle='--', label='Side angle')
+
+ax1.legend();
+```
+
+For more on plotting, see the [Matplotlib documentation](https://matplotlib.org/gallery/index.html#images-contours-and-fields) and [pyplot API](https://matplotlib.org/api/pyplot_summary.html).
+
++++
+
+## Data types and image values
+
+In literature, one finds different conventions for representing image values:
+
+```
+ 0 - 255 where 0 is black, 255 is white
+ 0 - 1 where 0 is black, 1 is white
+```
+
+``scikit-image`` supports both conventions--the choice is determined by the
+data-type of the array.
+
+E.g., here, I generate two valid images:
+
+```{code-cell}
+linear0 = np.linspace(0, 1, 2500).reshape((50, 50))
+linear1 = np.linspace(0, 255, 2500).reshape((50, 50)).astype(np.uint8)
+
+print("Linear0:", linear0.dtype, linear0.min(), linear0.max())
+print("Linear1:", linear1.dtype, linear1.min(), linear1.max())
+
+fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 15))
+ax0.imshow(linear0, cmap='gray')
+ax1.imshow(linear1, cmap='gray');
+```
+
+The library is designed in such a way that any data-type is allowed as input,
+as long as the range is correct (0-1 for floating point images, 0-255 for unsigned bytes,
+0-65535 for unsigned 16-bit integers).
+
++++
+
+You can convert images between different representations by using ``img_as_float``, ``img_as_ubyte``, etc.:
+
+```{code-cell}
+from skimage import img_as_float, img_as_ubyte
+
+image = data.chelsea()
+
+image_ubyte = img_as_ubyte(image)
+image_float = img_as_float(image)
+
+print("type, min, max:", image_ubyte.dtype, image_ubyte.min(), image_ubyte.max())
+print("type, min, max:", image_float.dtype, image_float.min(), image_float.max())
+print()
+print("231/255 =", 231/255.)
+```
+
+Your code would then typically look like this:
+
+```{code-cell}
+def my_function(any_image):
+ float_image = img_as_float(any_image)
+ # Proceed, knowing image is in [0, 1]
+```
+
+We recommend using the floating point representation, given that
+``scikit-image`` mostly uses that format internally.
+
++++
+
+## Image I/O
+
+Mostly, we won't be using input images from the scikit-image example data sets. Those images are typically stored in JPEG or PNG format. Since scikit-image operates on NumPy arrays, *any* image reader library that provides arrays will do. Options include imageio, matplotlib, pillow, etc.
+
+scikit-image conveniently wraps many of these in the `io` submodule, and will use whichever of the libraries mentioned above are installed:
+
+```{code-cell}
+from skimage import io
+
+image = io.imread('../images/balloon.jpg')
+
+print(type(image))
+print(image.dtype)
+print(image.shape)
+print(image.min(), image.max())
+
+plt.imshow(image);
+```
+
+We also have the ability to load multiple images, or multi-layer TIFF images:
+
+```{code-cell}
+ic = io.ImageCollection('../images/*.png:../images/*.jpg')
+
+print('Type:', type(ic))
+
+ic.files
+```
+
+```{code-cell}
+import os
+
+f, axes = plt.subplots(nrows=3, ncols=len(ic) // 3 + 1, figsize=(20, 5))
+
+# subplots returns the figure and an array of axes
+# we use `axes.ravel()` to turn these into a list
+axes = axes.ravel()
+
+for ax in axes:
+ ax.axis('off')
+
+for i, image in enumerate(ic):
+ axes[i].imshow(image, cmap='gray')
+ axes[i].set_title(os.path.basename(ic.files[i]))
+
+plt.tight_layout()
+```
+
+## Aside: `enumerate`
+
+`enumerate` gives us each element in a container, along with its position.
+
+```{code-cell}
+animals = ['cat', 'dog', 'leopard']
+```
+
+```{code-cell}
+for i, animal in enumerate(animals):
+ print('The animal in position {} is {}'.format(i, animal))
+```
+
+## Exercise: draw the letter H
+
+Define a function that takes as input an RGB image and a pair of coordinates (row, column), and returns a copy with a green letter H overlaid at those coordinates. The coordinates point to the top-left corner of the H.
+
+The arms and strut of the H should have a width of 3 pixels, and the H itself should have a height of 24 pixels and width of 20 pixels.
+
+Start with the following template:
+
+```{code-cell}
+---
+tags: [hide-output]
+---
+
+def draw_H(image, coords, color=(0, 255, 0)):
+ out = image.copy()
+
+ ...
+
+ return out
+```
+
+Test your function like so:
+
+```{code-cell}
+---
+tags: [remove-output]
+---
+
+cat = data.chelsea()
+cat_H = draw_H(cat, (50, -50))
+plt.imshow(cat_H);
+```
+
+## Exercise: visualizing RGB channels
+
+Display the different color channels of the image along (each as a gray-scale image). Start with the following template:
+
+```{code-cell}
+---
+tags: [raises-exception, remove-output]
+---
+
+# --- read in the image ---
+
+image = plt.imread('../images/Bells-Beach.jpg')
+
+# --- assign each color channel to a different variable ---
+
+r = ... # FIXME: grab channel from image...
+g = ... # FIXME
+b = ... # FIXME
+
+# --- display the image and r, g, b channels ---
+
+f, axes = plt.subplots(1, 4, figsize=(16, 5))
+
+for ax in axes:
+ ax.axis('off')
+
+(ax_r, ax_g, ax_b, ax_color) = axes
+
+ax_r.imshow(r, cmap='gray')
+ax_r.set_title('red channel')
+
+ax_g.imshow(g, cmap='gray')
+ax_g.set_title('green channel')
+
+ax_b.imshow(b, cmap='gray')
+ax_b.set_title('blue channel')
+
+# --- Here, we stack the R, G, and B layers again
+# to form a color image ---
+ax_color.imshow(np.stack([r, g, b], axis=2))
+ax_color.set_title('all channels');
+```
+
+Now, take a look at the following R, G, and B channels. How would their combination look? (Write some code to confirm your intuition.)
+
+```{code-cell}
+from skimage import draw
+
+red = np.zeros((300, 300))
+green = np.zeros((300, 300))
+blue = np.zeros((300, 300))
+
+r, c = draw.circle_perimeter(100, 100, 100, shape=red.shape)
+red[r, c] = 1
+
+r, c = draw.circle_perimeter(100, 200, 100, shape=green.shape)
+green[r, c] = 1
+
+r, c = draw.circle_perimeter(200, 150, 100, shape=blue.shape)
+blue[r, c] = 1
+
+f, axes = plt.subplots(1, 3)
+for (ax, channel) in zip(axes, [red, green, blue]):
+ ax.imshow(channel, cmap='gray')
+ ax.axis('off')
+```
+
+## Exercise: Convert to grayscale ("black and white")
+
+The *relative luminance* of an image is the intensity of light coming from each point. Different colors contribute differently to the luminance: it's very hard to have a bright, pure blue, for example. So, starting from an RGB image, the luminance is given by:
+
+$$
+Y = 0.2126R + 0.7152G + 0.0722B
+$$
+
+Use Python 3.5's matrix multiplication, `@`, to convert an RGB image to a grayscale luminance image according to the formula above.
+
+Compare your results to that obtained with `skimage.color.rgb2gray`.
+
+Change the coefficients to 1/3 (i.e., take the mean of the red, green, and blue channels, to see how that approach compares with `rgb2gray`).
+
+```{code-cell}
+---
+tags: [raises-exception, remove-output]
+---
+
+from skimage import color, img_as_float
+
+image = img_as_float(io.imread('../images/balloon.jpg'))
+
+gray = color.rgb2gray(image)
+my_gray = ... # FIXME
+
+# --- display the results ---
+
+f, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 6))
+
+ax0.imshow(gray, cmap='gray')
+ax0.set_title('skimage.color.rgb2gray')
+
+ax1.imshow(my_gray, cmap='gray')
+ax1.set_title('my rgb2gray')
+```
diff --git a/lectures/1_image_filters.ipynb b/lectures/1_image_filters.ipynb
deleted file mode 100644
index 454d14c..0000000
--- a/lectures/1_image_filters.ipynb
+++ /dev/null
@@ -1,1686 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "%config InlineBackend.figure_format = 'retina'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "# Image filtering"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "## Image filtering theory"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Filtering is one of the most basic and common image operations in image processing. You can filter an image to remove noise or to enhance features; the filtered image could be the desired result or just a preprocessing step. Regardless, filtering is an important topic to understand."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "### Local filtering"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "skip"
- }
- },
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "\n",
- "plt.rcParams['image.cmap'] = 'gray'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "The \"local\" in local filtering simply means that a pixel is adjusted by values in some surrounding neighborhood. These surrounding elements are identified or weighted based on a \"footprint\", \"structuring element\", or \"kernel\".\n",
- "\n",
- "Let's go to back to basics and look at a 1D step-signal"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "step_signal = np.zeros(100)\n",
- "step_signal[50:] = 1\n",
- "fig, ax = plt.subplots()\n",
- "ax.plot(step_signal)\n",
- "ax.margins(y=0.1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Now add some noise to this signal:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "# Just to make sure we all see the same results\n",
- "np.random.seed(0)\n",
- "\n",
- "\n",
- "noisy_signal = (step_signal\n",
- " + np.random.normal(0, 0.35, step_signal.shape))\n",
- "fig, ax = plt.subplots()\n",
- "ax.plot(noisy_signal);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "The simplest way to recover something that looks a bit more like the original signal is to take the average between neighboring \"pixels\":"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "# Take the mean of neighboring pixels\n",
- "smooth_signal = (noisy_signal[:-1] + noisy_signal[1:]) / 2.0\n",
- "fig, ax = plt.subplots()\n",
- "ax.plot(smooth_signal);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What happens if we want to take the *three* neighboring pixels? We can do the same thing:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "smooth_signal3 = (noisy_signal[:-2] + noisy_signal[1:-1]\n",
- " + noisy_signal[2:]) / 3\n",
- "fig, ax = plt.subplots()\n",
- "ax.plot(smooth_signal, label='mean of 2')\n",
- "ax.plot(smooth_signal3, label='mean of 3')\n",
- "ax.legend(loc='upper left');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "For averages of more points, the expression keeps getting hairier. And you have to worry more about what's going on in the margins. Is there a better way?\n",
- "\n",
- "It turns out there is. This same concept, nearest-neighbor averages, can be expressed as a *convolution* with an *averaging kernel*. Note that the operation we did with `smooth_signal3` can be expressed as follows:\n",
- "\n",
- "* Create an output array called `smooth_signal3`, of the same length as `noisy_signal`.\n",
- "* At each element in `smooth_signal3` starting at point 1, and ending at point -2, place the average of the sum of: 1/3 of the element to the left of it in `noisy_signal`, 1/3 of the element at the same position, and 1/3 of the element to the right.\n",
- "* discard the leftmost and rightmost elements.\n",
- "\n",
- "This is called a *convolution* between the input image and the array `[1/3, 1/3, 1/3]`. (We'll give a more in-depth explanation of convolution in the next section)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "# Same as above, using a convolution kernel\n",
- "# Neighboring pixels multiplied by 1/3 and summed\n",
- "mean_kernel3 = np.full((3,), 1/3)\n",
- "smooth_signal3p = np.convolve(noisy_signal, mean_kernel3,\n",
- " mode='valid')\n",
- "fig, ax = plt.subplots()\n",
- "ax.plot(smooth_signal3p)\n",
- "\n",
- "print('smooth_signal3 and smooth_signal3p are equal:',\n",
- " np.allclose(smooth_signal3, smooth_signal3p))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def convolve_demo(signal, kernel):\n",
- " ksize = len(kernel)\n",
- " convolved = np.correlate(signal, kernel)\n",
- " def filter_step(i):\n",
- " fig, ax = plt.subplots()\n",
- " ax.plot(signal, label='signal')\n",
- " ax.plot(convolved[:i+1], label='convolved')\n",
- " ax.legend()\n",
- " ax.scatter(np.arange(i, i+ksize),\n",
- " signal[i : i+ksize])\n",
- " ax.scatter(i, convolved[i])\n",
- " return filter_step\n",
- "\n",
- "from ipywidgets import interact, widgets\n",
- "\n",
- "i_slider = widgets.IntSlider(min=0, max=len(noisy_signal) - 3,\n",
- " value=0)\n",
- "\n",
- "interact(convolve_demo(noisy_signal, mean_kernel3),\n",
- " i=i_slider);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The advantage of convolution is that it's just as easy to take the average of 11 points as 3:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mean_kernel11 = np.full((11,), 1/11)\n",
- "smooth_signal11 = np.convolve(noisy_signal, mean_kernel11,\n",
- " mode='valid')\n",
- "fig, ax = plt.subplots()\n",
- "ax.plot(smooth_signal11);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "i_slider = widgets.IntSlider(min=0, max=len(noisy_signal) - 11,\n",
- " value=0)\n",
- "\n",
- "interact(convolve_demo(noisy_signal, mean_kernel11),\n",
- " i=i_slider);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Of course, to take the mean of 11 values, we have to move further and further away from the edges, and this starts to be noticeable. You can use `mode='same'` to pad the edges of the array and compute a result of the same size as the input:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "smooth_signal3same = np.convolve(noisy_signal, mean_kernel3,\n",
- " mode='same')\n",
- "smooth_signal11same = np.convolve(noisy_signal, mean_kernel11,\n",
- " mode='same')\n",
- "\n",
- "fig, ax = plt.subplots(1, 2)\n",
- "ax[0].plot(smooth_signal3p)\n",
- "ax[0].plot(smooth_signal11)\n",
- "ax[0].set_title('mode=valid')\n",
- "ax[1].plot(smooth_signal3same)\n",
- "ax[1].plot(smooth_signal11same)\n",
- "ax[1].set_title('mode=same');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "But now we see edge effects on the ends of the signal...\n",
- "\n",
- "This is because `mode='same'` actually pads the signal with 0s and then applies `mode='valid'` as before."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def convolve_demo_same(signal, kernel):\n",
- " ksize = len(kernel)\n",
- " padded_signal = np.pad(signal, ksize // 2,\n",
- " mode='constant')\n",
- " convolved = np.correlate(padded_signal, kernel)\n",
- " def filter_step(i):\n",
- " fig, ax = plt.subplots()\n",
- " x = np.arange(-ksize // 2,\n",
- " len(signal) + ksize // 2)\n",
- " ax.plot(signal, label='signal')\n",
- " ax.plot(convolved[:i+1], label='convolved')\n",
- " ax.legend()\n",
- " start, stop = i, i + ksize\n",
- " ax.scatter(x[start:stop]+1,\n",
- " padded_signal[start : stop])\n",
- " ax.scatter(i, convolved[i])\n",
- " ax.set_xlim(-ksize // 2,\n",
- " len(signal) + ksize // 2)\n",
- " return filter_step\n",
- "\n",
- "\n",
- "i_slider = widgets.IntSlider(min=0, max=len(noisy_signal)-1,\n",
- " value=0)\n",
- "\n",
- "interact(convolve_demo_same(noisy_signal, mean_kernel11),\n",
- " i=i_slider);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Exercise** Look up the documentation of `scipy.ndimage.convolve`. Apply the same convolution, but using a different `mode=` keyword argument to avoid the edge effects we see here."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "#### A difference filter"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Let's look again at our simplest signal, the step signal from before:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots()\n",
- "ax.plot(step_signal)\n",
- "ax.margins(y=0.1) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Exercise:** Can you predict what a convolution with the kernel `[-1, 0, 1]` does? Try thinking about it before running the cells below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "result_corr = np.correlate(step_signal, np.array([-1, 0, 1]),\n",
- " mode='valid')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "result_conv = np.convolve(step_signal, np.array([-1, 0, 1]),\n",
- " mode='valid')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots()\n",
- "ax.plot(step_signal, label='signal')\n",
- "ax.plot(result_conv, linestyle='dashed', label='convolved')\n",
- "ax.plot(result_corr, linestyle='dashed', label='correlated',\n",
- " color='C3')\n",
- "ax.legend(loc='upper left')\n",
- "ax.margins(y=0.1) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "(For technical signal processing reasons, convolutions actually occur \"back to front\" between the input array and the kernel. Correlations occur in the signal order, so we'll use correlate from now on.)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Whenever neighboring values are close, the filter response is close to 0. Right at the boundary of a step, we're subtracting a small value from a large value and and get a spike in the response. This spike \"identifies\" our edge."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Commutativity and assortativity of filters"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What if we try the same trick with our noisy signal?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "noisy_change = np.correlate(noisy_signal, np.array([-1, 0, 1]))\n",
- "fig, ax = plt.subplots()\n",
- "ax.plot(noisy_signal, label='signal')\n",
- "ax.plot(noisy_change, linestyle='dashed', label='change')\n",
- "ax.legend(loc='upper left')\n",
- "ax.margins(0.1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Oops! We lost our edge!\n",
- "\n",
- "But recall that we smoothed the signal a bit by taking its neighbors. Perhaps we can do the same thing here. Actually, it turns out that we can do it *in any order*, so we can create a filter that combines both the difference and the mean:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mean_diff = np.correlate([-1, 0, 1], [1/3, 1/3, 1/3], mode='full')\n",
- "print(mean_diff)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "*Note:* we use `np.convolve` here, because it has the option to output a *wider* result than either of the two inputs."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we can use this to find our edge even in a noisy signal:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "smooth_change = np.correlate(noisy_signal, mean_diff,\n",
- " mode='same')\n",
- "fig, ax = plt.subplots()\n",
- "ax.plot(noisy_signal, label='signal')\n",
- "ax.plot(smooth_change, linestyle='dashed', label='change')\n",
- "ax.margins(0.1)\n",
- "ax.hlines([-0.5, 0.5], 0, 100, linewidth=0.5, color='gray');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Exercise:** The Gaussian filter with variance $\\sigma^2$ is given by:\n",
- "\n",
- "$$\n",
- "k_i = \\frac{1}{\\sqrt{2\\pi}\\sigma}\\exp{\\left(-\\frac{(x_i - x_0)^2}{2\\sigma^2}\\right)}\n",
- "$$\n",
- "\n",
- "1. Create this filter (for example, with width 9, center 4, sigma 1). (Plot it)\n",
- "2. Convolve it with the difference filter (with appropriate mode). (Plot the result)\n",
- "3. Convolve it with the noisy signal. (Plot the result)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "xi = np.arange(9)\n",
- "x0 = 9 // 2 # 4\n",
- "x = xi - x0\n",
- "... # complete this code"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "## Local filtering of images"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Now let's apply all this knowledge to 2D images instead of a 1D signal. Let's start with an incredibly simple image:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "\n",
- "bright_square = np.zeros((7, 7), dtype=float)\n",
- "bright_square[2:5, 2:5] = 1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "This gives the values below:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "print(bright_square)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "and looks like a white square centered on a black square:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots()\n",
- "ax.imshow(bright_square);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "### The mean filter"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "For our first example of a filter, consider the following filtering array, which we'll call a \"mean kernel\". For each pixel, a kernel defines which neighboring pixels to consider when filtering, and how much to weight those pixels."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "mean_kernel = np.full((3, 3), 1/9)\n",
- "\n",
- "print(mean_kernel)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Now, let's take our mean kernel and apply it to every pixel of the image."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Applying a (linear) filter essentially means:\n",
- "* Center a kernel on a pixel\n",
- "* Multiply the pixels *under* that kernel by the values *in* the kernel\n",
- "* Sum all the those results\n",
- "* Replace the center pixel with the summed result"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "This process is known as convolution."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "Let's take a look at the numerical result:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "import scipy.ndimage as ndi\n",
- "\n",
- "%precision 2\n",
- "print(bright_square)\n",
- "print(ndi.correlate(bright_square, mean_kernel))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "The meaning of \"mean kernel\" should be clear now: Each pixel was replaced with the mean value within the 3x3 neighborhood of that pixel. When the kernel was over `n` bright pixels, the pixel in the kernel's center was changed to n/9 (= n * 0.111). When no bright pixels were under the kernel, the result was 0."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "This filter is a simple smoothing filter and produces two important results:\n",
- "1. The intensity of the bright pixel decreased.\n",
- "2. The intensity of the region near the bright pixel increased."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's see a convolution in action.\n",
- "\n",
- "(Execute the following cell, but don't try to read it; its purpose is to generate an example.)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#--------------------------------------------------------------------------\n",
- "# Convolution Demo\n",
- "#--------------------------------------------------------------------------\n",
- "from skimage import color\n",
- "from scipy import ndimage as ndi\n",
- "from matplotlib import patches\n",
- "\n",
- "def mean_filter_demo(image, vmax=1):\n",
- " mean_factor = 1.0 / 9.0 # This assumes a 3x3 kernel.\n",
- " iter_kernel_and_subimage = iter_kernel(image)\n",
- "\n",
- " image_cache = []\n",
- "\n",
- " def mean_filter_step(i_step):\n",
- " while i_step >= len(image_cache):\n",
- " filtered = image if i_step == 0 else image_cache[-1][-1][-1]\n",
- " filtered = filtered.copy()\n",
- "\n",
- " (i, j), mask, subimage = next(iter_kernel_and_subimage)\n",
- " filter_overlay = color.label2rgb(mask, image, bg_label=0,\n",
- " colors=('cyan', 'red'))\n",
- " filtered[i, j] = np.sum(mean_factor * subimage)\n",
- " image_cache.append(((i, j), (filter_overlay, filtered)))\n",
- "\n",
- " (i, j), images = image_cache[i_step]\n",
- " fig, axes = plt.subplots(1, len(images), figsize=(10, 5))\n",
- " \n",
- " for ax, imc in zip(axes, images):\n",
- " ax.imshow(imc, vmax=vmax)\n",
- " rect = patches.Rectangle([j - 0.5, i - 0.5], 1, 1, color='yellow', fill=False)\n",
- " ax.add_patch(rect)\n",
- " \n",
- " plt.show()\n",
- " return mean_filter_step\n",
- "\n",
- "\n",
- "def mean_filter_interactive_demo(image):\n",
- " from ipywidgets import IntSlider, interact\n",
- " mean_filter_step = mean_filter_demo(image)\n",
- " step_slider = IntSlider(min=0, max=image.size-1, value=0)\n",
- " interact(mean_filter_step, i_step=step_slider)\n",
- "\n",
- "\n",
- "def iter_kernel(image, size=1):\n",
- " \"\"\" Yield position, kernel mask, and image for each pixel in the image.\n",
- "\n",
- " The kernel mask has a 2 at the center pixel and 1 around it. The actual\n",
- " width of the kernel is 2*size + 1.\n",
- " \"\"\"\n",
- " width = 2*size + 1\n",
- " for (i, j), pixel in iter_pixels(image):\n",
- " mask = np.zeros(image.shape, dtype='int16')\n",
- " mask[i, j] = 1\n",
- " mask = ndi.grey_dilation(mask, size=width)\n",
- " #mask[i, j] = 2\n",
- " subimage = image[bounded_slice((i, j), image.shape[:2], size=size)]\n",
- " yield (i, j), mask, subimage\n",
- "\n",
- "\n",
- "def iter_pixels(image):\n",
- " \"\"\" Yield pixel position (row, column) and pixel intensity. \"\"\"\n",
- " height, width = image.shape[:2]\n",
- " for i in range(height):\n",
- " for j in range(width):\n",
- " yield (i, j), image[i, j]\n",
- "\n",
- "\n",
- "def bounded_slice(center, xy_max, size=1, i_min=0):\n",
- " slices = []\n",
- " for i, i_max in zip(center, xy_max):\n",
- " slices.append(slice(max(i - size, i_min), min(i + size + 1, i_max)))\n",
- " return tuple(slices)\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mean_filter_interactive_demo(bright_square)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Incidentally, the above filtering is the exact same principle behind the *convolutional neural networks*, or CNNs, that you might have heard much about over the past few years. The only difference is that while above, the simple mean kernel is used, in CNNs, the values inside the kernel are *learned* to find a specific feature, or accomplish a specific task. Time permitting, we'll demonstrate this in an exercise at the end of the notebook."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Slight aside:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "outputs": [],
- "source": [
- "print(np.sum(mean_kernel))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Note that all the values of the kernel sum to 1. Why might that be important?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "### Downsampled image"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Let's consider a real image now. It'll be easier to see some of the filtering we're doing if we downsample the image a bit. We can slice into the image using the \"step\" argument to sub-sample it (don't scale images using this method for real work; use `skimage.transform.rescale`):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "from skimage import data\n",
- "\n",
- "image = data.camera()\n",
- "pixelated = image[::10, ::10]\n",
- "fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 5))\n",
- "ax0.imshow(image)\n",
- "ax1.imshow(pixelated) ;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Here we use a step of 10, giving us every tenth column and every tenth row of the original image. You can see the highly pixelated result on the right."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We are actually going to be using the pattern of plotting multiple images side by side quite often, so we are going to make the following helper function:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage import img_as_float\n",
- "\n",
- "def imshow_all(*images, titles=None):\n",
- " images = [img_as_float(img) for img in images]\n",
- "\n",
- " if titles is None:\n",
- " titles = [''] * len(images)\n",
- " vmin = min(map(np.min, images))\n",
- " vmax = max(map(np.max, images))\n",
- " ncols = len(images)\n",
- " height = 5\n",
- " width = height * len(images)\n",
- " fig, axes = plt.subplots(nrows=1, ncols=ncols,\n",
- " figsize=(width, height))\n",
- " for ax, img, label in zip(axes.ravel(), images, titles):\n",
- " ax.imshow(img, vmin=vmin, vmax=vmax)\n",
- " ax.set_title(label)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "### Mean filter on a real image"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Now we can apply the filter to this downsampled image:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "filtered = ndi.correlate(pixelated, mean_kernel)\n",
- "imshow_all(pixelated, filtered, titles=['pixelated', 'mean filtered'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Comparing the filtered image to the pixelated image, we can see that this filtered result is smoother: Sharp edges (which are just borders between dark and bright pixels) are smoothed because dark pixels reduce the intensity of neighboring pixels and bright pixels do the opposite."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "## Essential filters"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "If you read through the last section, you're already familiar with the essential concepts of image filtering. But, of course, you don't have to create custom filter kernels for all of your filtering needs. There are many standard filter kernels pre-defined from half a century of image and signal processing."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "### Gaussian filter"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "The classic image filter is the Gaussian filter. This is similar to the mean filter, in that it tends to smooth images. The Gaussian filter, however, doesn't weight all values in the neighborhood equally. Instead, pixels closer to the center are weighted more than those farther away."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "outputs": [],
- "source": [
- "# Rename module so we don't shadow the builtin function\n",
- "from skimage import filters\n",
- "\n",
- "smooth_mean = ndi.correlate(bright_square, mean_kernel)\n",
- "sigma = 1\n",
- "smooth = filters.gaussian(bright_square, sigma)\n",
- "imshow_all(bright_square, smooth_mean, smooth,\n",
- " titles=['original', 'result of mean filter', 'result of gaussian filter'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "For the Gaussian filter, `sigma`, the standard deviation, defines the size of the neighborhood.\n",
- "\n",
- "For a real image, we get the following:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "from skimage import img_as_float\n",
- "# The Gaussian filter returns a float image, regardless of input.\n",
- "# Cast to float so the images have comparable intensity ranges.\n",
- "pixelated_float = img_as_float(pixelated)\n",
- "smooth = filters.gaussian(pixelated_float, sigma=1)\n",
- "imshow_all(pixelated_float, smooth)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "This doesn't look drastically different than the mean filter, but the Gaussian filter is typically preferred because of the distance-dependent weighting, and because it does not have any sharp transitions (consider what happens in the Fourier domain!). For a more detailed image and a larger filter, you can see artifacts in the mean filter since it doesn't take distance into account:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "size = 20\n",
- "structuring_element = np.ones((3*size, 3*size))\n",
- "smooth_mean = filters.rank.mean(image, structuring_element)\n",
- "smooth_gaussian = filters.gaussian(image, size)\n",
- "titles = ['mean', 'gaussian']\n",
- "imshow_all(smooth_mean, smooth_gaussian, titles=titles)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "(Above, we've tweaked the size of the structuring element used for the mean filter and the standard deviation of the Gaussian filter to produce an approximately equal amount of smoothing in the two results.)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Incidentally, for reference, let's have a look at what the Gaussian filter actually looks like. Technically, the value of the kernel at a pixel that is $r$ rows and $c$ cols from the center is:\n",
- "\n",
- "$$\n",
- "k_{r, c} = \\frac{1}{2\\pi \\sigma^2} \\exp{\\left(-\\frac{r^2 + c^2}{2\\sigma^2}\\right)}\n",
- "$$\n",
- "\n",
- "Practically speaking, this value is pretty close to zero for values more than $4\\sigma$ away from the center, so practical Gaussian filters are truncated at about $4\\sigma$:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "sidelen = 45\n",
- "sigma = (sidelen - 1) // 2 // 4\n",
- "spot = np.zeros((sidelen, sidelen), dtype=float)\n",
- "spot[sidelen // 2, sidelen // 2] = 1\n",
- "kernel = filters.gaussian(spot, sigma=sigma)\n",
- "\n",
- "imshow_all(spot, kernel / np.max(kernel))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Exercise** (Chapter 0 reminder!) Plot the profile of the gaussian kernel at its midpoint, i.e. the values under the line shown here:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots()\n",
- "\n",
- "ax.imshow(kernel, cmap='inferno')\n",
- "ax.vlines(22, -100, 100, color='C9')\n",
- "ax.set_ylim((sidelen - 1, 0))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "... # add your plotting code here"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "### Basic edge filtering"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "For images, edges are boundaries between light and dark values. The detection of edges can be useful on its own, or it can be used as preliminary step in other algorithms (which we'll see later)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "#### Difference filters in 2D"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "For images, you can think of an edge as points where the gradient is large in one direction. We can approximate gradients with difference filters."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "vertical_kernel = np.array([\n",
- " [-1],\n",
- " [ 0],\n",
- " [ 1],\n",
- "])\n",
- "\n",
- "gradient_vertical = ndi.correlate(pixelated.astype(float),\n",
- " vertical_kernel)\n",
- "fig, ax = plt.subplots()\n",
- "ax.imshow(gradient_vertical);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "## Exercise:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "- Add a horizontal kernel to the above example to also compute the horizontal gradient, $g_y$\n",
- "- Compute the magnitude of the image gradient at each point: $\\left|g\\right| = \\sqrt{g_x^2 + g_y^2}$"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "... # add your horizontal and gradient magnitude code here"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "### Sobel edge filter"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "The Sobel filter, the most commonly used edge filter, should look pretty similar to what you developed above. Take a look at the vertical and horizontal components of the Sobel kernel to see how they differ from your earlier implementation:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "* http://scikit-image.org/docs/dev/api/skimage.filters.html#skimage.filters.sobel_v\n",
- "* http://scikit-image.org/docs/dev/api/skimage.filters.html#skimage.filters.sobel_h"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "imshow_all(bright_square, filters.sobel(bright_square))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Notice that the size of the output matches the input, and the edges aren't preferentially shifted to a corner of the image. Furthermore, the weights used in the Sobel filter produce diagonal edges with reponses that are comparable to horizontal or vertical edges.\n",
- "\n",
- "Like any derivative, noise can have a strong impact on the result:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "pixelated_gradient = filters.sobel(pixelated)\n",
- "imshow_all(pixelated, pixelated_gradient)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Smoothing is often used as a preprocessing step in preparation for feature detection and image-enhancement operations because sharp features can distort results."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "gradient = filters.sobel(smooth)\n",
- "titles = ['gradient before smoothing', 'gradient after smoothing']\n",
- "# Scale smoothed gradient up so they're of comparable brightness.\n",
- "imshow_all(pixelated_gradient, gradient*1.8, titles=titles)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Notice how the edges look more continuous in the smoothed image."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Exercise: the simplest neural network.** Let's pretend we have an image and a \"ground truth\" image of what we want to detect:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "target = (filters.sobel_h(image) > 0.07)\n",
- "imshow_all(image, target, titles=['source', 'target'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Can we use machine learning to find a 3x3 convolutional filter that recovers this target?\n",
- "\n",
- "- use `skimage.util.view_as_windows` and `np.reshape` to view the image as a set of (approximately) `npixels` 3x3 patches. (Hint: why is it only approximate? Think of `mode=valid` convolutions.)\n",
- "- use `np.reshape` again to see it as `npixels` \"linear\" patches of 9 pixels.\n",
- "- Now you have an `(npixels, 9)` \"feature\" matrix, `X`.\n",
- "- Use slicing and `np.ravel` to get an `npixels`-length array of target values.\n",
- "- Use `sklearn.linear_model.LogisticRegression` to learn the relationship between our pixel neighborhoods (of size 9) and the target.\n",
- "- Look at your `model.coef_`. How do they compare to the Sobel coefficients?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "## Denoising filters"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "At this point, we make a distinction. The earlier filters were implemented as a *linear dot-product* of values in the filter kernel and values in the image. The following kernels implement an *arbitrary* function of the local image neighborhood. Denoising filters in particular are filters that preserve the sharpness of edges in the image.\n",
- "\n",
- "As you can see from our earlier examples, mean and Gaussian filters smooth an image rather uniformly, including the edges of objects in an image. When denoising, however, you typically want to preserve features and just remove noise. The distinction between noise and features can, of course, be highly situation-dependent and subjective."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "### Median Filter"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "The median filter is the classic edge-preserving filter. As the name implies, this filter takes a set of pixels (i.e. the pixels within a kernel or \"structuring element\") and returns the median value within that neighborhood. Because regions near a sharp edge will have many dark values and many light values (but few values in between) the median at an edge will most likely be either light or dark, rather than some value in between. In that way, we don't end up with edges that are smoothed."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "from skimage.morphology import disk\n",
- "neighborhood = disk(radius=1) # \"selem\" is often the name used for \"structuring element\"\n",
- "median = filters.rank.median(pixelated, neighborhood)\n",
- "titles = ['image', 'gaussian', 'median']\n",
- "imshow_all(pixelated, smooth, median, titles=titles)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "This difference is more noticeable with a more detailed image."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "neighborhood = disk(10)\n",
- "coins = data.coins()\n",
- "mean_coin = filters.rank.mean(coins, neighborhood)\n",
- "median_coin = filters.rank.median(coins, neighborhood)\n",
- "titles = ['image', 'mean', 'median']\n",
- "imshow_all(coins, mean_coin, median_coin, titles=titles)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "Notice how the edges of coins are preserved after using the median filter."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "## Further reading"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "notes"
- }
- },
- "source": [
- "`scikit-image` also provides more sophisticated denoising filters:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "from skimage.restoration import denoise_tv_bregman\n",
- "denoised = denoise_tv_bregman(image, 4)\n",
- "d = disk(4)\n",
- "median = filters.rank.median(image, d)\n",
- "titles = ['image', 'median', 'denoised']\n",
- "imshow_all(image, median, denoised, titles=titles)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "* [Denoising examples](http://scikit-image.org/docs/dev/auto_examples/plot_denoise.html)\n",
- "* [Rank filters example](http://scikit-image.org/docs/dev/auto_examples/applications/plot_rank_filters.html)\n",
- "* [Restoration API](http://scikit-image.org/docs/stable/api/skimage.restoration.html)\n",
- "\n",
- "Take a look at this [neat feature](https://github.com/scikit-image/scikit-image/pull/2647) merged last year:\n",
- "\n",
- ""
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/lectures/1_image_filters.md b/lectures/1_image_filters.md
new file mode 100644
index 0000000..1ed3c08
--- /dev/null
+++ b/lectures/1_image_filters.md
@@ -0,0 +1,937 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+```{code-cell} ipython3
+%matplotlib inline
+%config InlineBackend.figure_format = 'retina'
+```
+
++++ {"slideshow": {"slide_type": "slide"}}
+
+# Image filtering
+
++++ {"slideshow": {"slide_type": "slide"}}
+
+## Image filtering theory
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Filtering is one of the most basic and common image operations in image processing. You can filter an image to remove noise or to enhance features; the filtered image could be the desired result or just a preprocessing step. Regardless, filtering is an important topic to understand.
+
++++ {"slideshow": {"slide_type": "fragment"}}
+
+### Local filtering
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: skip
+---
+import matplotlib.pyplot as plt
+import numpy as np
+
+plt.rcParams['image.cmap'] = 'gray'
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+The "local" in local filtering simply means that a pixel is adjusted by values in some surrounding neighborhood. These surrounding elements are identified or weighted based on a "footprint", "structuring element", or "kernel".
+
+Let's go to back to basics and look at a 1D step-signal
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+step_signal = np.zeros(100)
+step_signal[50:] = 1
+fig, ax = plt.subplots()
+ax.plot(step_signal)
+ax.margins(y=0.1)
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Now add some noise to this signal:
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+# Just to make sure we all see the same results
+np.random.seed(0)
+
+
+noisy_signal = (step_signal
+ + np.random.normal(0, 0.35, step_signal.shape))
+fig, ax = plt.subplots()
+ax.plot(noisy_signal);
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+The simplest way to recover something that looks a bit more like the original signal is to take the average between neighboring "pixels":
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+# Take the mean of neighboring pixels
+smooth_signal = (noisy_signal[:-1] + noisy_signal[1:]) / 2.0
+fig, ax = plt.subplots()
+ax.plot(smooth_signal);
+```
+
+What happens if we want to take the *three* neighboring pixels? We can do the same thing:
+
+```{code-cell} ipython3
+smooth_signal3 = (noisy_signal[:-2] + noisy_signal[1:-1]
+ + noisy_signal[2:]) / 3
+fig, ax = plt.subplots()
+ax.plot(smooth_signal, label='mean of 2')
+ax.plot(smooth_signal3, label='mean of 3')
+ax.legend(loc='upper left');
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+For averages of more points, the expression keeps getting hairier. And you have to worry more about what's going on in the margins. Is there a better way?
+
+It turns out there is. This same concept, nearest-neighbor averages, can be expressed as a *convolution* with an *averaging kernel*. Note that the operation we did with `smooth_signal3` can be expressed as follows:
+
+* Create an output array called `smooth_signal3`, of the same length as `noisy_signal`.
+* At each element in `smooth_signal3` starting at point 1, and ending at point -2, place the average of the sum of: 1/3 of the element to the left of it in `noisy_signal`, 1/3 of the element at the same position, and 1/3 of the element to the right.
+* discard the leftmost and rightmost elements.
+
+This is called a *convolution* between the input image and the array `[1/3, 1/3, 1/3]`. (We'll give a more in-depth explanation of convolution in the next section).
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+# Same as above, using a convolution kernel
+# Neighboring pixels multiplied by 1/3 and summed
+mean_kernel3 = np.full((3,), 1/3)
+smooth_signal3p = np.convolve(noisy_signal, mean_kernel3,
+ mode='valid')
+fig, ax = plt.subplots()
+ax.plot(smooth_signal3p)
+
+print('smooth_signal3 and smooth_signal3p are equal:',
+ np.allclose(smooth_signal3, smooth_signal3p))
+```
+
+```{code-cell} ipython3
+def convolve_demo(signal, kernel):
+ ksize = len(kernel)
+ convolved = np.correlate(signal, kernel)
+ def filter_step(i):
+ fig, ax = plt.subplots()
+ ax.plot(signal, label='signal')
+ ax.plot(convolved[:i+1], label='convolved')
+ ax.legend()
+ ax.scatter(np.arange(i, i+ksize),
+ signal[i : i+ksize])
+ ax.scatter(i, convolved[i])
+ return filter_step
+
+from ipywidgets import interact, widgets
+
+i_slider = widgets.IntSlider(min=0, max=len(noisy_signal) - 3,
+ value=0)
+
+interact(convolve_demo(noisy_signal, mean_kernel3),
+ i=i_slider);
+```
+
+The advantage of convolution is that it's just as easy to take the average of 11 points as 3:
+
+```{code-cell} ipython3
+mean_kernel11 = np.full((11,), 1/11)
+smooth_signal11 = np.convolve(noisy_signal, mean_kernel11,
+ mode='valid')
+fig, ax = plt.subplots()
+ax.plot(smooth_signal11);
+```
+
+```{code-cell} ipython3
+i_slider = widgets.IntSlider(min=0, max=len(noisy_signal) - 11,
+ value=0)
+
+interact(convolve_demo(noisy_signal, mean_kernel11),
+ i=i_slider);
+```
+
+Of course, to take the mean of 11 values, we have to move further and further away from the edges, and this starts to be noticeable. You can use `mode='same'` to pad the edges of the array and compute a result of the same size as the input:
+
+```{code-cell} ipython3
+smooth_signal3same = np.convolve(noisy_signal, mean_kernel3,
+ mode='same')
+smooth_signal11same = np.convolve(noisy_signal, mean_kernel11,
+ mode='same')
+
+fig, ax = plt.subplots(1, 2)
+ax[0].plot(smooth_signal3p)
+ax[0].plot(smooth_signal11)
+ax[0].set_title('mode=valid')
+ax[1].plot(smooth_signal3same)
+ax[1].plot(smooth_signal11same)
+ax[1].set_title('mode=same');
+```
+
+But now we see edge effects on the ends of the signal...
+
+This is because `mode='same'` actually pads the signal with 0s and then applies `mode='valid'` as before.
+
+```{code-cell} ipython3
+def convolve_demo_same(signal, kernel):
+ ksize = len(kernel)
+ padded_signal = np.pad(signal, ksize // 2,
+ mode='constant')
+ convolved = np.correlate(padded_signal, kernel)
+ def filter_step(i):
+ fig, ax = plt.subplots()
+ x = np.arange(-ksize // 2,
+ len(signal) + ksize // 2)
+ ax.plot(signal, label='signal')
+ ax.plot(convolved[:i+1], label='convolved')
+ ax.legend()
+ start, stop = i, i + ksize
+ ax.scatter(x[start:stop]+1,
+ padded_signal[start : stop])
+ ax.scatter(i, convolved[i])
+ ax.set_xlim(-ksize // 2,
+ len(signal) + ksize // 2)
+ return filter_step
+
+
+i_slider = widgets.IntSlider(min=0, max=len(noisy_signal)-1,
+ value=0)
+
+interact(convolve_demo_same(noisy_signal, mean_kernel11),
+ i=i_slider);
+```
+
+**Exercise** Look up the documentation of `scipy.ndimage.convolve`. Apply the same convolution, but using a different `mode=` keyword argument to avoid the edge effects we see here.
+
+```{code-cell} ipython3
+
+```
+
++++ {"slideshow": {"slide_type": "slide"}}
+
+#### A difference filter
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Let's look again at our simplest signal, the step signal from before:
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+fig, ax = plt.subplots()
+ax.plot(step_signal)
+ax.margins(y=0.1)
+```
+
+**Exercise:** Can you predict what a convolution with the kernel `[-1, 0, 1]` does? Try thinking about it before running the cells below.
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+result_corr = np.correlate(step_signal, np.array([-1, 0, 1]),
+ mode='valid')
+```
+
+```{code-cell} ipython3
+result_conv = np.convolve(step_signal, np.array([-1, 0, 1]),
+ mode='valid')
+```
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+fig, ax = plt.subplots()
+ax.plot(step_signal, label='signal')
+ax.plot(result_conv, linestyle='dashed', label='convolved')
+ax.plot(result_corr, linestyle='dashed', label='correlated',
+ color='C3')
+ax.legend(loc='upper left')
+ax.margins(y=0.1)
+```
+
+(For technical signal processing reasons, convolutions actually occur "back to front" between the input array and the kernel. Correlations occur in the signal order, so we'll use correlate from now on.)
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Whenever neighboring values are close, the filter response is close to 0. Right at the boundary of a step, we're subtracting a small value from a large value and and get a spike in the response. This spike "identifies" our edge.
+
++++
+
+#### Commutativity and assortativity of filters
+
++++
+
+What if we try the same trick with our noisy signal?
+
+```{code-cell} ipython3
+noisy_change = np.correlate(noisy_signal, np.array([-1, 0, 1]))
+fig, ax = plt.subplots()
+ax.plot(noisy_signal, label='signal')
+ax.plot(noisy_change, linestyle='dashed', label='change')
+ax.legend(loc='upper left')
+ax.margins(0.1)
+```
+
+Oops! We lost our edge!
+
+But recall that we smoothed the signal a bit by taking its neighbors. Perhaps we can do the same thing here. Actually, it turns out that we can do it *in any order*, so we can create a filter that combines both the difference and the mean:
+
+```{code-cell} ipython3
+mean_diff = np.correlate([-1, 0, 1], [1/3, 1/3, 1/3], mode='full')
+print(mean_diff)
+```
+
+*Note:* we use `np.convolve` here, because it has the option to output a *wider* result than either of the two inputs.
+
++++
+
+Now we can use this to find our edge even in a noisy signal:
+
+```{code-cell} ipython3
+smooth_change = np.correlate(noisy_signal, mean_diff,
+ mode='same')
+fig, ax = plt.subplots()
+ax.plot(noisy_signal, label='signal')
+ax.plot(smooth_change, linestyle='dashed', label='change')
+ax.margins(0.1)
+ax.hlines([-0.5, 0.5], 0, 100, linewidth=0.5, color='gray');
+```
+
+**Exercise:** The Gaussian filter with variance $\sigma^2$ is given by:
+
+$$
+k_i = \frac{1}{\sqrt{2\pi}\sigma}\exp{\left(-\frac{(x_i - x_0)^2}{2\sigma^2}\right)}
+$$
+
+1. Create this filter (for example, with width 9, center 4, sigma 1). (Plot it)
+2. Convolve it with the difference filter (with appropriate mode). (Plot the result)
+3. Convolve it with the noisy signal. (Plot the result)
+
+```{code-cell} ipython3
+xi = np.arange(9)
+x0 = 9 // 2 # 4
+x = xi - x0
+... # complete this code
+```
+
++++ {"slideshow": {"slide_type": "slide"}}
+
+## Local filtering of images
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Now let's apply all this knowledge to 2D images instead of a 1D signal. Let's start with an incredibly simple image:
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+import numpy as np
+
+bright_square = np.zeros((7, 7), dtype=float)
+bright_square[2:5, 2:5] = 1
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+This gives the values below:
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+print(bright_square)
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+and looks like a white square centered on a black square:
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+fig, ax = plt.subplots()
+ax.imshow(bright_square);
+```
+
++++ {"slideshow": {"slide_type": "slide"}}
+
+### The mean filter
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+For our first example of a filter, consider the following filtering array, which we'll call a "mean kernel". For each pixel, a kernel defines which neighboring pixels to consider when filtering, and how much to weight those pixels.
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+mean_kernel = np.full((3, 3), 1/9)
+
+print(mean_kernel)
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Now, let's take our mean kernel and apply it to every pixel of the image.
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Applying a (linear) filter essentially means:
+* Center a kernel on a pixel
+* Multiply the pixels *under* that kernel by the values *in* the kernel
+* Sum all the those results
+* Replace the center pixel with the summed result
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+This process is known as convolution.
+
++++ {"slideshow": {"slide_type": "fragment"}}
+
+Let's take a look at the numerical result:
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+import scipy.ndimage as ndi
+
+%precision 2
+print(bright_square)
+print(ndi.correlate(bright_square, mean_kernel))
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+The meaning of "mean kernel" should be clear now: Each pixel was replaced with the mean value within the 3x3 neighborhood of that pixel. When the kernel was over `n` bright pixels, the pixel in the kernel's center was changed to n/9 (= n * 0.111). When no bright pixels were under the kernel, the result was 0.
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+This filter is a simple smoothing filter and produces two important results:
+1. The intensity of the bright pixel decreased.
+2. The intensity of the region near the bright pixel increased.
+
++++
+
+Let's see a convolution in action.
+
+(Execute the following cell, but don't try to read it; its purpose is to generate an example.)
+
+```{code-cell} ipython3
+#--------------------------------------------------------------------------
+# Convolution Demo
+#--------------------------------------------------------------------------
+from skimage import color
+from scipy import ndimage as ndi
+from matplotlib import patches
+
+def mean_filter_demo(image, vmax=1):
+ mean_factor = 1.0 / 9.0 # This assumes a 3x3 kernel.
+ iter_kernel_and_subimage = iter_kernel(image)
+
+ image_cache = []
+
+ def mean_filter_step(i_step):
+ while i_step >= len(image_cache):
+ filtered = image if i_step == 0 else image_cache[-1][-1][-1]
+ filtered = filtered.copy()
+
+ (i, j), mask, subimage = next(iter_kernel_and_subimage)
+ filter_overlay = color.label2rgb(mask, image, bg_label=0,
+ colors=('cyan', 'red'))
+ filtered[i, j] = np.sum(mean_factor * subimage)
+ image_cache.append(((i, j), (filter_overlay, filtered)))
+
+ (i, j), images = image_cache[i_step]
+ fig, axes = plt.subplots(1, len(images), figsize=(10, 5))
+
+ for ax, imc in zip(axes, images):
+ ax.imshow(imc, vmax=vmax)
+ rect = patches.Rectangle([j - 0.5, i - 0.5], 1, 1, color='yellow', fill=False)
+ ax.add_patch(rect)
+
+ plt.show()
+ return mean_filter_step
+
+
+def mean_filter_interactive_demo(image):
+ from ipywidgets import IntSlider, interact
+ mean_filter_step = mean_filter_demo(image)
+ step_slider = IntSlider(min=0, max=image.size-1, value=0)
+ interact(mean_filter_step, i_step=step_slider)
+
+
+def iter_kernel(image, size=1):
+ """ Yield position, kernel mask, and image for each pixel in the image.
+
+ The kernel mask has a 2 at the center pixel and 1 around it. The actual
+ width of the kernel is 2*size + 1.
+ """
+ width = 2*size + 1
+ for (i, j), pixel in iter_pixels(image):
+ mask = np.zeros(image.shape, dtype='int16')
+ mask[i, j] = 1
+ mask = ndi.grey_dilation(mask, size=width)
+ #mask[i, j] = 2
+ subimage = image[bounded_slice((i, j), image.shape[:2], size=size)]
+ yield (i, j), mask, subimage
+
+
+def iter_pixels(image):
+ """ Yield pixel position (row, column) and pixel intensity. """
+ height, width = image.shape[:2]
+ for i in range(height):
+ for j in range(width):
+ yield (i, j), image[i, j]
+
+
+def bounded_slice(center, xy_max, size=1, i_min=0):
+ slices = []
+ for i, i_max in zip(center, xy_max):
+ slices.append(slice(max(i - size, i_min), min(i + size + 1, i_max)))
+ return tuple(slices)
+
+```
+
+```{code-cell} ipython3
+mean_filter_interactive_demo(bright_square)
+```
+
+Incidentally, the above filtering is the exact same principle behind the *convolutional neural networks*, or CNNs, that you might have heard much about over the past few years. The only difference is that while above, the simple mean kernel is used, in CNNs, the values inside the kernel are *learned* to find a specific feature, or accomplish a specific task. Time permitting, we'll demonstrate this in an exercise at the end of the notebook.
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Slight aside:
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: notes
+---
+print(np.sum(mean_kernel))
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Note that all the values of the kernel sum to 1. Why might that be important?
+
++++ {"slideshow": {"slide_type": "slide"}}
+
+### Downsampled image
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Let's consider a real image now. It'll be easier to see some of the filtering we're doing if we downsample the image a bit. We can slice into the image using the "step" argument to sub-sample it (don't scale images using this method for real work; use `skimage.transform.rescale`):
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+from skimage import data
+
+image = data.camera()
+pixelated = image[::10, ::10]
+fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 5))
+ax0.imshow(image)
+ax1.imshow(pixelated) ;
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Here we use a step of 10, giving us every tenth column and every tenth row of the original image. You can see the highly pixelated result on the right.
+
++++
+
+We are actually going to be using the pattern of plotting multiple images side by side quite often, so we are going to make the following helper function:
+
+```{code-cell} ipython3
+from skimage import img_as_float
+
+def imshow_all(*images, titles=None):
+ images = [img_as_float(img) for img in images]
+
+ if titles is None:
+ titles = [''] * len(images)
+ vmin = min(map(np.min, images))
+ vmax = max(map(np.max, images))
+ ncols = len(images)
+ height = 5
+ width = height * len(images)
+ fig, axes = plt.subplots(nrows=1, ncols=ncols,
+ figsize=(width, height))
+ for ax, img, label in zip(axes.ravel(), images, titles):
+ ax.imshow(img, vmin=vmin, vmax=vmax)
+ ax.set_title(label)
+```
+
++++ {"slideshow": {"slide_type": "slide"}}
+
+### Mean filter on a real image
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Now we can apply the filter to this downsampled image:
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+filtered = ndi.correlate(pixelated, mean_kernel)
+imshow_all(pixelated, filtered, titles=['pixelated', 'mean filtered'])
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Comparing the filtered image to the pixelated image, we can see that this filtered result is smoother: Sharp edges (which are just borders between dark and bright pixels) are smoothed because dark pixels reduce the intensity of neighboring pixels and bright pixels do the opposite.
+
++++ {"slideshow": {"slide_type": "slide"}}
+
+## Essential filters
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+If you read through the last section, you're already familiar with the essential concepts of image filtering. But, of course, you don't have to create custom filter kernels for all of your filtering needs. There are many standard filter kernels pre-defined from half a century of image and signal processing.
+
++++ {"slideshow": {"slide_type": "fragment"}}
+
+### Gaussian filter
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+The classic image filter is the Gaussian filter. This is similar to the mean filter, in that it tends to smooth images. The Gaussian filter, however, doesn't weight all values in the neighborhood equally. Instead, pixels closer to the center are weighted more than those farther away.
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: notes
+---
+# Rename module so we don't shadow the builtin function
+from skimage import filters
+
+smooth_mean = ndi.correlate(bright_square, mean_kernel)
+sigma = 1
+smooth = filters.gaussian(bright_square, sigma)
+imshow_all(bright_square, smooth_mean, smooth,
+ titles=['original', 'result of mean filter', 'result of gaussian filter'])
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+For the Gaussian filter, `sigma`, the standard deviation, defines the size of the neighborhood.
+
+For a real image, we get the following:
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+from skimage import img_as_float
+# The Gaussian filter returns a float image, regardless of input.
+# Cast to float so the images have comparable intensity ranges.
+pixelated_float = img_as_float(pixelated)
+smooth = filters.gaussian(pixelated_float, sigma=1)
+imshow_all(pixelated_float, smooth)
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+This doesn't look drastically different than the mean filter, but the Gaussian filter is typically preferred because of the distance-dependent weighting, and because it does not have any sharp transitions (consider what happens in the Fourier domain!). For a more detailed image and a larger filter, you can see artifacts in the mean filter since it doesn't take distance into account:
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+size = 20
+structuring_element = np.ones((3*size, 3*size))
+smooth_mean = filters.rank.mean(image, structuring_element)
+smooth_gaussian = filters.gaussian(image, size)
+titles = ['mean', 'gaussian']
+imshow_all(smooth_mean, smooth_gaussian, titles=titles)
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+(Above, we've tweaked the size of the structuring element used for the mean filter and the standard deviation of the Gaussian filter to produce an approximately equal amount of smoothing in the two results.)
+
++++
+
+Incidentally, for reference, let's have a look at what the Gaussian filter actually looks like. Technically, the value of the kernel at a pixel that is $r$ rows and $c$ cols from the center is:
+
+$$
+k_{r, c} = \frac{1}{2\pi \sigma^2} \exp{\left(-\frac{r^2 + c^2}{2\sigma^2}\right)}
+$$
+
+Practically speaking, this value is pretty close to zero for values more than $4\sigma$ away from the center, so practical Gaussian filters are truncated at about $4\sigma$:
+
+```{code-cell} ipython3
+sidelen = 45
+sigma = (sidelen - 1) // 2 // 4
+spot = np.zeros((sidelen, sidelen), dtype=float)
+spot[sidelen // 2, sidelen // 2] = 1
+kernel = filters.gaussian(spot, sigma=sigma)
+
+imshow_all(spot, kernel / np.max(kernel))
+```
+
+**Exercise** (Chapter 0 reminder!) Plot the profile of the gaussian kernel at its midpoint, i.e. the values under the line shown here:
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+ax.imshow(kernel, cmap='inferno')
+ax.vlines(22, -100, 100, color='C9')
+ax.set_ylim((sidelen - 1, 0))
+```
+
+```{code-cell} ipython3
+... # add your plotting code here
+```
+
++++ {"slideshow": {"slide_type": "slide"}}
+
+### Basic edge filtering
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+For images, edges are boundaries between light and dark values. The detection of edges can be useful on its own, or it can be used as preliminary step in other algorithms (which we'll see later).
+
++++ {"slideshow": {"slide_type": "slide"}}
+
+#### Difference filters in 2D
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+For images, you can think of an edge as points where the gradient is large in one direction. We can approximate gradients with difference filters.
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+vertical_kernel = np.array([
+ [-1],
+ [ 0],
+ [ 1],
+])
+
+gradient_vertical = ndi.correlate(pixelated.astype(float),
+ vertical_kernel)
+fig, ax = plt.subplots()
+ax.imshow(gradient_vertical);
+```
+
++++ {"slideshow": {"slide_type": "fragment"}}
+
+## Exercise:
+
++++
+
+- Add a horizontal kernel to the above example to also compute the horizontal gradient, $g_y$
+- Compute the magnitude of the image gradient at each point: $\left|g\right| = \sqrt{g_x^2 + g_y^2}$
+
+```{code-cell} ipython3
+... # add your horizontal and gradient magnitude code here
+```
+
++++ {"slideshow": {"slide_type": "slide"}}
+
+### Sobel edge filter
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+The Sobel filter, the most commonly used edge filter, should look pretty similar to what you developed above. Take a look at the vertical and horizontal components of the Sobel kernel to see how they differ from your earlier implementation:
+
++++ {"slideshow": {"slide_type": "fragment"}}
+
+* http://scikit-image.org/docs/dev/api/skimage.filters.html#skimage.filters.sobel_v
+* http://scikit-image.org/docs/dev/api/skimage.filters.html#skimage.filters.sobel_h
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+imshow_all(bright_square, filters.sobel(bright_square))
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Notice that the size of the output matches the input, and the edges aren't preferentially shifted to a corner of the image. Furthermore, the weights used in the Sobel filter produce diagonal edges with reponses that are comparable to horizontal or vertical edges.
+
+Like any derivative, noise can have a strong impact on the result:
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+pixelated_gradient = filters.sobel(pixelated)
+imshow_all(pixelated, pixelated_gradient)
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Smoothing is often used as a preprocessing step in preparation for feature detection and image-enhancement operations because sharp features can distort results.
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+gradient = filters.sobel(smooth)
+titles = ['gradient before smoothing', 'gradient after smoothing']
+# Scale smoothed gradient up so they're of comparable brightness.
+imshow_all(pixelated_gradient, gradient*1.8, titles=titles)
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Notice how the edges look more continuous in the smoothed image.
+
++++
+
+**Exercise: the simplest neural network.** Let's pretend we have an image and a "ground truth" image of what we want to detect:
+
+```{code-cell} ipython3
+target = (filters.sobel_h(image) > 0.07)
+imshow_all(image, target, titles=['source', 'target'])
+```
+
+Can we use machine learning to find a 3x3 convolutional filter that recovers this target?
+
+- use `skimage.util.view_as_windows` and `np.reshape` to view the image as a set of (approximately) `npixels` 3x3 patches. (Hint: why is it only approximate? Think of `mode=valid` convolutions.)
+- use `np.reshape` again to see it as `npixels` "linear" patches of 9 pixels.
+- Now you have an `(npixels, 9)` "feature" matrix, `X`.
+- Use slicing and `np.ravel` to get an `npixels`-length array of target values.
+- Use `sklearn.linear_model.LogisticRegression` to learn the relationship between our pixel neighborhoods (of size 9) and the target.
+- Look at your `model.coef_`. How do they compare to the Sobel coefficients?
+
+```{code-cell} ipython3
+
+```
+
+---
+
++++ {"slideshow": {"slide_type": "slide"}}
+
+## Denoising filters
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+At this point, we make a distinction. The earlier filters were implemented as a *linear dot-product* of values in the filter kernel and values in the image. The following kernels implement an *arbitrary* function of the local image neighborhood. Denoising filters in particular are filters that preserve the sharpness of edges in the image.
+
+As you can see from our earlier examples, mean and Gaussian filters smooth an image rather uniformly, including the edges of objects in an image. When denoising, however, you typically want to preserve features and just remove noise. The distinction between noise and features can, of course, be highly situation-dependent and subjective.
+
++++ {"slideshow": {"slide_type": "fragment"}}
+
+### Median Filter
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+The median filter is the classic edge-preserving filter. As the name implies, this filter takes a set of pixels (i.e. the pixels within a kernel or "structuring element") and returns the median value within that neighborhood. Because regions near a sharp edge will have many dark values and many light values (but few values in between) the median at an edge will most likely be either light or dark, rather than some value in between. In that way, we don't end up with edges that are smoothed.
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+from skimage.morphology import disk
+neighborhood = disk(radius=1) # "selem" is often the name used for "structuring element"
+median = filters.rank.median(pixelated, neighborhood)
+titles = ['image', 'gaussian', 'median']
+imshow_all(pixelated, smooth, median, titles=titles)
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+This difference is more noticeable with a more detailed image.
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+neighborhood = disk(10)
+coins = data.coins()
+mean_coin = filters.rank.mean(coins, neighborhood)
+median_coin = filters.rank.median(coins, neighborhood)
+titles = ['image', 'mean', 'median']
+imshow_all(coins, mean_coin, median_coin, titles=titles)
+```
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+Notice how the edges of coins are preserved after using the median filter.
+
++++ {"slideshow": {"slide_type": "slide"}}
+
+## Further reading
+
++++ {"slideshow": {"slide_type": "notes"}}
+
+`scikit-image` also provides more sophisticated denoising filters:
+
+```{code-cell} ipython3
+---
+slideshow:
+ slide_type: fragment
+---
+from skimage.restoration import denoise_tv_bregman
+denoised = denoise_tv_bregman(image, 4)
+d = disk(4)
+median = filters.rank.median(image, d)
+titles = ['image', 'median', 'denoised']
+imshow_all(image, median, denoised, titles=titles)
+```
+
++++ {"slideshow": {"slide_type": "fragment"}}
+
+* [Denoising examples](http://scikit-image.org/docs/dev/auto_examples/plot_denoise.html)
+* [Rank filters example](http://scikit-image.org/docs/dev/auto_examples/applications/plot_rank_filters.html)
+* [Restoration API](http://scikit-image.org/docs/stable/api/skimage.restoration.html)
+
+Take a look at this [neat feature](https://github.com/scikit-image/scikit-image/pull/2647) merged last year:
+
+
diff --git a/lectures/3_morphological_operations.ipynb b/lectures/3_morphological_operations.ipynb
deleted file mode 100644
index 763ad14..0000000
--- a/lectures/3_morphological_operations.ipynb
+++ /dev/null
@@ -1,229 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Morphological operations\n",
- "\n",
- "Morphology is the study of shapes. In image processing, some simple operations can get you a long way. The first things to learn are *erosion* and *dilation*. In erosion, we look at a pixelโs local neighborhood and replace the value of that pixel with the minimum value of that neighborhood. In dilation, we instead choose the maximum."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from matplotlib import pyplot as plt, cm\n",
- "import skdemo"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "image = np.array([[0, 0, 0, 0, 0, 0, 0],\n",
- " [0, 0, 0, 0, 0, 0, 0],\n",
- " [0, 0, 1, 1, 1, 0, 0],\n",
- " [0, 0, 1, 1, 1, 0, 0],\n",
- " [0, 0, 1, 1, 1, 0, 0],\n",
- " [0, 0, 0, 0, 0, 0, 0],\n",
- " [0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)\n",
- "plt.imshow(image);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The documentation for scikit-image's morphology module is\n",
- "[here](http://scikit-image.org/docs/0.10.x/api/skimage.morphology.html).\n",
- "\n",
- "Importantly, we must use a *structuring element*, which defines the local\n",
- "neighborhood of each pixel. To get every neighbor (up, down, left, right, and\n",
- "diagonals), use `morphology.square`; to avoid diagonals, use\n",
- "`morphology.diamond`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage import morphology\n",
- "sq = morphology.square(width=3)\n",
- "dia = morphology.diamond(radius=1)\n",
- "print(sq)\n",
- "print(dia)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The central value of the structuring element represents the pixel being considered, and the surrounding values are the neighbors: a 1 value means that pixel counts as a neighbor, while a 0 value does not. So:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "skdemo.imshow_all(image, morphology.erosion(image, sq), shape=(1, 2))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "and"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "skdemo.imshow_all(image, morphology.dilation(image, sq))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "and"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "skdemo.imshow_all(image, morphology.dilation(image, dia))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Erosion and dilation can be combined into two slightly more sophisticated operations, *opening* and *closing*. Here's an example:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "image = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
- " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
- " [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n",
- " [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n",
- " [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n",
- " [0, 0, 1, 1, 1, 0, 0, 1, 0, 0],\n",
- " [0, 0, 1, 1, 1, 0, 0, 1, 0, 0],\n",
- " [0, 0, 1, 1, 1, 0, 0, 1, 0, 0],\n",
- " [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n",
- " [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n",
- " [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n",
- " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
- " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], np.uint8)\n",
- "plt.imshow(image);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What happens when run an erosion followed by a dilation of this image?\n",
- "\n",
- "What about the reverse?\n",
- "\n",
- "Try to imagine the operations in your head before trying them out below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "skdemo.imshow_all(image, morphology.opening(image, sq)) # erosion -> dilation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "skdemo.imshow_all(image, morphology.closing(image, sq)) # dilation -> erosion"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Exercise**: use morphological operations to remove noise from a binary image."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage import data, color\n",
- "hub = color.rgb2gray(data.hubble_deep_field()[350:450, 90:190])\n",
- "plt.imshow(hub);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Remove the smaller objects to retrieve the large galaxy."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/lectures/3_morphological_operations.md b/lectures/3_morphological_operations.md
new file mode 100644
index 0000000..be41028
--- /dev/null
+++ b/lectures/3_morphological_operations.md
@@ -0,0 +1,114 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+```{code-cell} ipython3
+%matplotlib inline
+```
+
+# Morphological operations
+
+Morphology is the study of shapes. In image processing, some simple operations can get you a long way. The first things to learn are *erosion* and *dilation*. In erosion, we look at a pixelโs local neighborhood and replace the value of that pixel with the minimum value of that neighborhood. In dilation, we instead choose the maximum.
+
+```{code-cell} ipython3
+import numpy as np
+from matplotlib import pyplot as plt, cm
+import skdemo
+```
+
+```{code-cell} ipython3
+image = np.array([[0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 1, 1, 1, 0, 0],
+ [0, 0, 1, 1, 1, 0, 0],
+ [0, 0, 1, 1, 1, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
+plt.imshow(image);
+```
+
+The documentation for scikit-image's morphology module is
+[here](http://scikit-image.org/docs/0.10.x/api/skimage.morphology.html).
+
+Importantly, we must use a *structuring element*, which defines the local
+neighborhood of each pixel. To get every neighbor (up, down, left, right, and
+diagonals), use `morphology.square`; to avoid diagonals, use
+`morphology.diamond`:
+
+```{code-cell} ipython3
+from skimage import morphology
+sq = morphology.square(width=3)
+dia = morphology.diamond(radius=1)
+print(sq)
+print(dia)
+```
+
+The central value of the structuring element represents the pixel being considered, and the surrounding values are the neighbors: a 1 value means that pixel counts as a neighbor, while a 0 value does not. So:
+
+```{code-cell} ipython3
+skdemo.imshow_all(image, morphology.erosion(image, sq), shape=(1, 2))
+```
+
+and
+
+```{code-cell} ipython3
+skdemo.imshow_all(image, morphology.dilation(image, sq))
+```
+
+and
+
+```{code-cell} ipython3
+skdemo.imshow_all(image, morphology.dilation(image, dia))
+```
+
+Erosion and dilation can be combined into two slightly more sophisticated operations, *opening* and *closing*. Here's an example:
+
+```{code-cell} ipython3
+image = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+ [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+ [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+ [0, 0, 1, 1, 1, 0, 0, 1, 0, 0],
+ [0, 0, 1, 1, 1, 0, 0, 1, 0, 0],
+ [0, 0, 1, 1, 1, 0, 0, 1, 0, 0],
+ [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+ [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+ [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], np.uint8)
+plt.imshow(image);
+```
+
+What happens when run an erosion followed by a dilation of this image?
+
+What about the reverse?
+
+Try to imagine the operations in your head before trying them out below.
+
+```{code-cell} ipython3
+skdemo.imshow_all(image, morphology.opening(image, sq)) # erosion -> dilation
+```
+
+```{code-cell} ipython3
+skdemo.imshow_all(image, morphology.closing(image, sq)) # dilation -> erosion
+```
+
+**Exercise**: use morphological operations to remove noise from a binary image.
+
+```{code-cell} ipython3
+from skimage import data, color
+hub = color.rgb2gray(data.hubble_deep_field()[350:450, 90:190])
+plt.imshow(hub);
+```
+
+Remove the smaller objects to retrieve the large galaxy.
diff --git a/lectures/4_segmentation.ipynb b/lectures/4_segmentation.ipynb
deleted file mode 100644
index 4eefa4a..0000000
--- a/lectures/4_segmentation.ipynb
+++ /dev/null
@@ -1,883 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "remove-input",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "%config InlineBackend.figure_format = 'retina'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Segmentation\n",
- "\n",
- "--------------\n",
- "\n",
- "## Separating an image into one or more regions of interest.\n",
- "\n",
- "Everyone has heard or seen Photoshop or a similar graphics editor take a person from one image and place them into another. The first step of doing this is *identifying where that person is in the source image*.\n",
- "\n",
- "In popular culture, the Terminator's vision segments humans:\n",
- "\n",
- "\n",
- "\n",
- "### Segmentation contains two major sub-fields\n",
- "\n",
- "* **Supervised** segmentation: Some prior knowledge, possibly from human input, is used to guide the algorithm. Supervised algorithms currently included in scikit-image include\n",
- " * Thresholding algorithms which require user input (`skimage.filters.threshold_*`)\n",
- " * `skimage.segmentation.random_walker`\n",
- " * `skimage.segmentation.active_contour`\n",
- " * `skimage.segmentation.watershed`\n",
- "* **Unsupervised** segmentation: No prior knowledge. These algorithms attempt to subdivide into meaningful regions automatically. The user may be able to tweak settings like number of regions.\n",
- " * Thresholding algorithms which require no user input.\n",
- " * `skimage.segmentation.slic`\n",
- " * `skimage.segmentation.chan_vese`\n",
- " * `skimage.segmentation.felzenszwalb`\n",
- " * `skimage.segmentation.quickshift`\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "First, some standard imports and a helper function to display our results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "import skimage.data as data\n",
- "import skimage.segmentation as seg\n",
- "from skimage import filters\n",
- "from skimage import draw\n",
- "from skimage import color\n",
- "from skimage import exposure\n",
- "\n",
- "\n",
- "def image_show(image, nrows=1, ncols=1, cmap='gray', **kwargs):\n",
- " fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16))\n",
- " ax.imshow(image, cmap='gray')\n",
- " ax.axis('off')\n",
- " return fig, ax"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Thresholding\n",
- "\n",
- "In some images, global or local contrast may be sufficient to separate regions of interest. Simply choosing all pixels above or below a certain *threshold* may be sufficient to segment such an image.\n",
- "\n",
- "Let's try this on an image of a textbook."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "text = data.page()\n",
- "\n",
- "image_show(text);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Histograms\n",
- "\n",
- "A histogram simply plots the frequency (number of times) values within a certain range appear against the data values themselves. It is a powerful tool to get to know your data - or decide where you would like to threshold."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots(1, 1)\n",
- "ax.hist(text.ravel(), bins=256, range=[0, 255])\n",
- "ax.set_xlim(0, 256);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Experimentation: supervised thresholding\n",
- "\n",
- "Try simple NumPy methods and a few different thresholds on this image. Because *we* are setting the threshold, this is *supervised* segmentation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "text_segmented = ... # Your code here\n",
- "\n",
- "image_show(text_segmented);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Not ideal results! The shadow on the left creates problems; no single global value really fits.\n",
- "\n",
- "What if we don't want to set the threshold every time? There are several published methods which look at the histogram and choose what should be an optimal threshold without user input. These are unsupervised. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Experimentation: unsupervised thresholding\n",
- "\n",
- "Here we will experiment with a number of automatic thresholding methods available in scikit-image. Because these require no input beyond the image itself, this is *unsupervised* segmentation.\n",
- "\n",
- "These functions generally return the threshold value(s), rather than applying it to the image directly.\n",
- "\n",
- "Try `otsu` and `li`, then take a look at `local` or `sauvola`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "text_threshold = filters.threshold_ # Hit tab with the cursor after the underscore, try several methods\n",
- "\n",
- "image_show(text < text_threshold);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Supervised segmentation\n",
- "\n",
- "Thresholding can be useful, but is rather basic and a high-contrast image will often limit its utility. For doing more fun things - like removing part of an image - we need more advanced tools.\n",
- "\n",
- "For this section, we will use the `astronaut` image and attempt to segment Eileen Collins' head using supervised segmentation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Our source image\n",
- "astronaut = data.astronaut()\n",
- "image_show(astronaut);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The contrast is pretty good in this image for her head against the background, so we will simply convert to grayscale with `rgb2gray`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "astronaut_gray = color.rgb2gray(astronaut)\n",
- "image_show(astronaut_gray);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We will use two methods, which segment using very different approaches:\n",
- "\n",
- "* **Active Contour**: Initializes using a user-defined contour or line, which then is attracted to edges and/or brightness. Can be tweaked for many situations, but mixed contrast may be problematic.\n",
- "* **Random walker**: Initialized using any labeled points, fills the image with the label that seems least distant from the origin point (on a path weighted by pixel differences). Tends to respect edges or step-offs, and is surprisingly robust to noise. Only one parameter to tweak."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Active contour segmentation\n",
- "\n",
- "We must have a set of initial parameters to 'seed' our segmentation this. Let's draw a circle around the astronaut's head to initialize the snake.\n",
- "\n",
- "This could be done interactively, with a GUI, but for simplicity we will start at the point [100, 220] and use a radius of 100 pixels. Just a little trigonometry in this helper function..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def circle_points(resolution, center, radius):\n",
- " \"\"\"Generate points defining a circle on an image.\"\"\"\n",
- " radians = np.linspace(0, 2*np.pi, resolution)\n",
- "\n",
- " c = center[1] + radius*np.cos(radians)\n",
- " r = center[0] + radius*np.sin(radians)\n",
- " \n",
- " return np.array([c, r]).T\n",
- "\n",
- "# Exclude last point because a closed path should not have duplicate points\n",
- "points = circle_points(200, [100, 220], 100)[:-1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "snake = seg.active_contour(astronaut_gray, points)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = image_show(astronaut)\n",
- "ax.plot(points[:, 0], points[:, 1], '--r', lw=3)\n",
- "ax.plot(snake[:, 0], snake[:, 1], '-b', lw=3);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Random walker\n",
- "\n",
- "One good analogy for random walker uses graph theory. \n",
- "\n",
- "* The distance from each pixel to its neighbors is weighted by how similar their values are; the more similar, the lower the cost is to step from one to another\n",
- "* The user provides some seed points\n",
- "* The algorithm finds the cheapest paths from each point to each seed value. \n",
- "* Pixels are labeled with the cheapest/lowest path.\n",
- "\n",
- "We will re-use the seed values from our previous example."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "astronaut_labels = np.zeros(astronaut_gray.shape, dtype=np.uint8)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The random walker algorithm expects a label image as input. Any label above zero will be treated as a seed; all zero-valued locations will be filled with labels from the positive integers available.\n",
- "\n",
- "There is also a masking feature where anything labeled -1 will never be labeled or traversed, but we will not use it here."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "indices = draw.circle_perimeter(100, 220, 25)\n",
- "\n",
- "astronaut_labels[indices] = 1\n",
- "astronaut_labels[points[:, 1].astype(np.int), points[:, 0].astype(np.int)] = 2\n",
- "\n",
- "image_show(astronaut_labels);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "astronaut_segmented = seg.random_walker(astronaut_gray, astronaut_labels)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Check our results\n",
- "fig, ax = image_show(astronaut_gray)\n",
- "ax.imshow(astronaut_segmented == 1, alpha=0.3);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Flood fill\n",
- "\n",
- "A basic but effective segmentation technique was recently added to scikit-image: `segmentation.flood` and `segmentation.flood_fill`. These algorithms take a seed point and iteratively find and fill adjacent points which are equal to or within a tolerance of the initial point. `flood` returns the region; `flood_fill` returns a modified image with those points changed to a new value.\n",
- "\n",
- "This approach is most suited for areas which have a relatively uniform color or gray value, and/or high contrast relative to adjacent structures.\n",
- "\n",
- "Can we accomplish the same task with flood fill?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "seed_point = (100, 220) # Experiment with the seed point\n",
- "flood_mask = seg.flood(astronaut_gray, seed_point, tolerance=0.3) # Experiment with tolerance"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = image_show(astronaut_gray)\n",
- "ax.imshow(flood_mask, alpha=0.3);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Not ideal! The flood runs away into the background through the right earlobe.\n",
- "\n",
- "Let's think outside the box. \n",
- "* What if instead of segmenting the head, we segmented the background around it and the collar?\n",
- "* Is there any way to increase the contrast between the background and skin?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "seed_bkgnd = (100, 350) # Background\n",
- "seed_collar = (200, 220) # Collar\n",
- "\n",
- "better_contrast = # Your idea to improve contrast here\n",
- "tol_bkgnd = # Experiment with tolerance for background\n",
- "tol_collar = # Experiment with tolerance for the collar\n",
- "\n",
- "flood_background = seg.flood(better_contrast, seed_bkgnd, tolerance=tol_bkgnd)\n",
- "flood_collar = seg.flood(better_contrast, seed_collar, tolerance=tol_collar)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "fig, ax = image_show(better_contrast)\n",
- "\n",
- "# Combine the two floods with binary OR operator\n",
- "ax.imshow(flood_background | flood_collar, alpha=0.3);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "flood_mask2 = seg.flood(astronaut[..., 2], (200, 220), tolerance=40)\n",
- "fig, ax = image_show(astronaut[..., 2])\n",
- "ax.imshow(flood_mask | flood_mask2, alpha=0.3);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Unsupervised segmentation\n",
- "\n",
- "Sometimes, human input is not possible or feasible - or, perhaps your images are so large that it is not feasible to consider all pixels simultaneously. Unsupervised segmentation can then break the image down into several sub-regions, so instead of millions of pixels you have tens to hundreds of regions.\n",
- "\n",
- "### SLIC\n",
- "\n",
- "There are many analogies to machine learning in unsupervised segmentation. Our first example directly uses a common machine learning algorithm under the hood - K-Means."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# SLIC works in color, so we will use the original astronaut\n",
- "astronaut_slic = seg.slic(astronaut)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# label2rgb replaces each discrete label with the average interior color\n",
- "image_show(color.label2rgb(astronaut_slic, astronaut, kind='avg'));"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We've reduced this image from 512*512 = 262,000 pixels down to 100 regions!\n",
- "\n",
- "And most of these regions make some logical sense."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Chan-Vese\n",
- "\n",
- "This algorithm iterates a level set, which allows it to capture complex and even disconnected features. However, its result is binary - there will only be one region - and it requires a grayscale image.\n",
- "\n",
- "This algorithm takes a few seconds to run."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "chan_vese = seg.chan_vese(astronaut_gray)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = image_show(astronaut_gray)\n",
- "ax.imshow(chan_vese == 0, alpha=0.3);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Chan-Vese has a number of paremeters, which you can try out! In the interest of time, we may move on."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Felzenszwalb\n",
- "\n",
- "This method oversegments an RGB image (requires color, unlike Chan-Vese) using another machine learning technique, a minimum-spanning tree clustering. The number of segments is not guaranteed and can only be indirectly controlled via `scale` parameter."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "astronaut_felzenszwalb = seg.felzenszwalb(astronaut) # Color required"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "image_show(astronaut_felzenszwalb);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Whoa, lots of regions! How many is that?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Find the number of unique labels\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's see if they make sense; label them with the region average (see above with SLIC)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "astronaut_felzenszwalb_colored = # Your code here\n",
- "\n",
- "image_show(astronaut_felzenszwalb_colored);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Actually reasonable small regions. If we wanted fewer regions, we could change the `scale` parameter (try it!) or start here and combine them.\n",
- "\n",
- "This approach is sometimes called **oversegmentation**.\n",
- "\n",
- "But when there are too many regions, they must be combined somehow."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Combining regions with a Region Adjacency Graph (RAG)\n",
- "\n",
- "Remember how the concept behind random walker was functionally looking at the difference between each pixel and its neighbors, then figuring out which were most alike? A RAG is essentially the same, except between regions.\n",
- "\n",
- "We have RAGs now in scikit-image, but we have to import *from the future*; this functionality is exposed in the `future.graph` submodule meaning it is stable and robust enough to ship, but the API may change."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import skimage.future.graph as graph\n",
- "\n",
- "rag = graph.rag_mean_color(astronaut, astronaut_felzenszwalb + 1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we show just one application of a very useful tool - `skimage.measure.regionprops` - to determine the centroid of each labeled region and pass that to the graph. \n",
- "\n",
- "`regionprops` has many, many other uses; see the API documentation for all of the features that can be quantified per-region! \n",
- "http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import skimage.measure as measure\n",
- "\n",
- "# Regionprops ignores zero, but we want to include it, so add one\n",
- "regions = measure.regionprops(astronaut_felzenszwalb + 1) \n",
- "\n",
- "# Pass centroid data into the graph\n",
- "for region in regions:\n",
- " rag.nodes[region['label']]['centroid'] = region['centroid']"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "`display_edges` is a helper function to assist in visualizing the graph."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def display_edges(image, g, threshold):\n",
- " \"\"\"Draw edges of a RAG on its image\n",
- " \n",
- " Returns a modified image with the edges drawn.Edges are drawn in green\n",
- " and nodes are drawn in yellow.\n",
- " \n",
- " Parameters\n",
- " ----------\n",
- " image : ndarray\n",
- " The image to be drawn on.\n",
- " g : RAG\n",
- " The Region Adjacency Graph.\n",
- " threshold : float\n",
- " Only edges in `g` below `threshold` are drawn.\n",
- " \n",
- " Returns:\n",
- " out: ndarray\n",
- " Image with the edges drawn.\n",
- " \"\"\"\n",
- " image = image.copy()\n",
- " for edge in g.edges():\n",
- " n1, n2 = edge\n",
- " \n",
- " r1, c1 = map(int, rag.nodes[n1]['centroid'])\n",
- " r2, c2 = map(int, rag.nodes[n2]['centroid'])\n",
- " \n",
- " line = draw.line(r1, c1, r2, c2)\n",
- " circle = draw.circle(r1,c1,2)\n",
- " \n",
- " if g[n1][n2]['weight'] < threshold :\n",
- " image[line] = 0,255,0\n",
- " image[circle] = 255,255,0\n",
- " \n",
- " return image"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "# All edges are drawn with threshold at infinity\n",
- "edges_drawn_all = display_edges(astronaut_felzenszwalb_colored, rag, np.inf)\n",
- "image_show(edges_drawn_all);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Try a range of thresholds out, see what happens."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "threshold = ... # Experiment\n",
- "\n",
- "edges_drawn_few = display_edges(astronaut_felzenszwalb_colored, rag, threshold)\n",
- "image_show(edges_drawn_few);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Finally, cut the graph\n",
- "\n",
- "Once you are happy with the (dis)connected regions above, the graph can be cut to merge the regions which are still connected."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "final_labels = graph.cut_threshold(astronaut_felzenszwalb + 1, rag, threshold)\n",
- "final_label_rgb = color.label2rgb(final_labels, astronaut, kind='avg')\n",
- "\n",
- "image_show(final_label_rgb);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "How many regions exist now?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "np.unique(final_labels).size"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise: Cat picture\n",
- "\n",
- "The data directory also has an excellent image of Stรฉfan's cat, Chelsea. With what you've learned, can you segment the cat's nose? How about the eyes? Why is the eye particularly challenging?\n",
- "\n",
- "Hint: the cat's nose is located close to [240, 270] and the right eye center is near [110, 172] in row, column notation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "fig, ax = image_show(data.chelsea())\n",
- "\n",
- "ax.plot(270, 240, marker='o', markersize=15, color=\"g\")\n",
- "ax.plot(172, 110, marker='o', markersize=15, color=\"r\");"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "celltoolbar": "Tags",
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/lectures/4_segmentation.md b/lectures/4_segmentation.md
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+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+```{code-cell} ipython3
+:tags: [remove-input, remove-output]
+
+%matplotlib inline
+%config InlineBackend.figure_format = 'retina'
+```
+
+# Segmentation
+
++++
+
+## Separating an image into one or more regions of interest.
+
+Everyone has heard or seen Photoshop or a similar graphics editor take a person from one image and place them into another. The first step of doing this is *identifying where that person is in the source image*.
+
+In popular culture, the Terminator's vision segments humans:
+
+
+
+### Segmentation contains two major sub-fields
+
+* **Supervised** segmentation: Some prior knowledge, possibly from human input, is used to guide the algorithm. Supervised algorithms currently included in scikit-image include
+ * Thresholding algorithms which require user input (`skimage.filters.threshold_*`)
+ * `skimage.segmentation.random_walker`
+ * `skimage.segmentation.active_contour`
+ * `skimage.segmentation.watershed`
+* **Unsupervised** segmentation: No prior knowledge. These algorithms attempt to subdivide into meaningful regions automatically. The user may be able to tweak settings like number of regions.
+ * Thresholding algorithms which require no user input.
+ * `skimage.segmentation.slic`
+ * `skimage.segmentation.chan_vese`
+ * `skimage.segmentation.felzenszwalb`
+ * `skimage.segmentation.quickshift`
+
++++
+
+First, some standard imports and a helper function to display our results
+
+```{code-cell} ipython3
+import numpy as np
+import matplotlib.pyplot as plt
+
+import skimage.data as data
+import skimage.segmentation as seg
+from skimage import filters
+from skimage import draw
+from skimage import color
+from skimage import exposure
+
+
+def image_show(image, nrows=1, ncols=1, cmap='gray', **kwargs):
+ fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16))
+ ax.imshow(image, cmap='gray')
+ ax.axis('off')
+ return fig, ax
+```
+
+## Thresholding
+
+In some images, global or local contrast may be sufficient to separate regions of interest. Simply choosing all pixels above or below a certain *threshold* may be sufficient to segment such an image.
+
+Let's try this on an image of a textbook.
+
+```{code-cell} ipython3
+text = data.page()
+
+image_show(text);
+```
+
+### Histograms
+
+A histogram simply plots the frequency (number of times) values within a certain range appear against the data values themselves. It is a powerful tool to get to know your data - or decide where you would like to threshold.
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(1, 1)
+ax.hist(text.ravel(), bins=256, range=[0, 255])
+ax.set_xlim(0, 256);
+```
+
+### Experimentation: supervised thresholding
+
+Try simple NumPy methods and a few different thresholds on this image. Because *we* are setting the threshold, this is *supervised* segmentation.
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+text_segmented = ... # Your code here
+
+image_show(text_segmented);
+```
+
+Not ideal results! The shadow on the left creates problems; no single global value really fits.
+
+What if we don't want to set the threshold every time? There are several published methods which look at the histogram and choose what should be an optimal threshold without user input. These are unsupervised.
+
++++
+
+### Experimentation: unsupervised thresholding
+
+Here we will experiment with a number of automatic thresholding methods available in scikit-image. Because these require no input beyond the image itself, this is *unsupervised* segmentation.
+
+These functions generally return the threshold value(s), rather than applying it to the image directly.
+
+Try `otsu` and `li`, then take a look at `local` or `sauvola`.
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+text_threshold = filters.threshold_ # Hit tab with the cursor after the underscore, try several methods
+
+image_show(text < text_threshold);
+```
+
+```{code-cell} ipython3
+
+```
+
+## Supervised segmentation
+
+Thresholding can be useful, but is rather basic and a high-contrast image will often limit its utility. For doing more fun things - like removing part of an image - we need more advanced tools.
+
+For this section, we will use the `astronaut` image and attempt to segment Eileen Collins' head using supervised segmentation.
+
+```{code-cell} ipython3
+# Our source image
+astronaut = data.astronaut()
+image_show(astronaut);
+```
+
+The contrast is pretty good in this image for her head against the background, so we will simply convert to grayscale with `rgb2gray`.
+
+```{code-cell} ipython3
+astronaut_gray = color.rgb2gray(astronaut)
+image_show(astronaut_gray);
+```
+
+We will use two methods, which segment using very different approaches:
+
+* **Active Contour**: Initializes using a user-defined contour or line, which then is attracted to edges and/or brightness. Can be tweaked for many situations, but mixed contrast may be problematic.
+* **Random walker**: Initialized using any labeled points, fills the image with the label that seems least distant from the origin point (on a path weighted by pixel differences). Tends to respect edges or step-offs, and is surprisingly robust to noise. Only one parameter to tweak.
+
++++
+
+### Active contour segmentation
+
+We must have a set of initial parameters to 'seed' our segmentation this. Let's draw a circle around the astronaut's head to initialize the snake.
+
+This could be done interactively, with a GUI, but for simplicity we will start at the point [100, 220] and use a radius of 100 pixels. Just a little trigonometry in this helper function...
+
+```{code-cell} ipython3
+def circle_points(resolution, center, radius):
+ """Generate points defining a circle on an image."""
+ radians = np.linspace(0, 2*np.pi, resolution)
+
+ c = center[1] + radius*np.cos(radians)
+ r = center[0] + radius*np.sin(radians)
+
+ return np.array([c, r]).T
+
+# Exclude last point because a closed path should not have duplicate points
+points = circle_points(200, [100, 220], 100)[:-1]
+```
+
+```{code-cell} ipython3
+snake = seg.active_contour(astronaut_gray, points)
+```
+
+```{code-cell} ipython3
+fig, ax = image_show(astronaut)
+ax.plot(points[:, 0], points[:, 1], '--r', lw=3)
+ax.plot(snake[:, 0], snake[:, 1], '-b', lw=3);
+```
+
+```{code-cell} ipython3
+
+```
+
+### Random walker
+
+One good analogy for random walker uses graph theory.
+
+* The distance from each pixel to its neighbors is weighted by how similar their values are; the more similar, the lower the cost is to step from one to another
+* The user provides some seed points
+* The algorithm finds the cheapest paths from each point to each seed value.
+* Pixels are labeled with the cheapest/lowest path.
+
+We will re-use the seed values from our previous example.
+
+```{code-cell} ipython3
+astronaut_labels = np.zeros(astronaut_gray.shape, dtype=np.uint8)
+```
+
+The random walker algorithm expects a label image as input. Any label above zero will be treated as a seed; all zero-valued locations will be filled with labels from the positive integers available.
+
+There is also a masking feature where anything labeled -1 will never be labeled or traversed, but we will not use it here.
+
+```{code-cell} ipython3
+indices = draw.circle_perimeter(100, 220, 25)
+
+astronaut_labels[indices] = 1
+astronaut_labels[points[:, 1].astype(np.int), points[:, 0].astype(np.int)] = 2
+
+image_show(astronaut_labels);
+```
+
+```{code-cell} ipython3
+astronaut_segmented = seg.random_walker(astronaut_gray, astronaut_labels)
+```
+
+```{code-cell} ipython3
+# Check our results
+fig, ax = image_show(astronaut_gray)
+ax.imshow(astronaut_segmented == 1, alpha=0.3);
+```
+
+```{code-cell} ipython3
+
+```
+
+## Flood fill
+
+A basic but effective segmentation technique was recently added to scikit-image: `segmentation.flood` and `segmentation.flood_fill`. These algorithms take a seed point and iteratively find and fill adjacent points which are equal to or within a tolerance of the initial point. `flood` returns the region; `flood_fill` returns a modified image with those points changed to a new value.
+
+This approach is most suited for areas which have a relatively uniform color or gray value, and/or high contrast relative to adjacent structures.
+
+Can we accomplish the same task with flood fill?
+
+```{code-cell} ipython3
+seed_point = (100, 220) # Experiment with the seed point
+flood_mask = seg.flood(astronaut_gray, seed_point, tolerance=0.3) # Experiment with tolerance
+```
+
+```{code-cell} ipython3
+fig, ax = image_show(astronaut_gray)
+ax.imshow(flood_mask, alpha=0.3);
+```
+
+Not ideal! The flood runs away into the background through the right earlobe.
+
+Let's think outside the box.
+* What if instead of segmenting the head, we segmented the background around it and the collar?
+* Is there any way to increase the contrast between the background and skin?
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+seed_bkgnd = (100, 350) # Background
+seed_collar = (200, 220) # Collar
+
+better_contrast = # Your idea to improve contrast here
+tol_bkgnd = # Experiment with tolerance for background
+tol_collar = # Experiment with tolerance for the collar
+
+flood_background = seg.flood(better_contrast, seed_bkgnd, tolerance=tol_bkgnd)
+flood_collar = seg.flood(better_contrast, seed_collar, tolerance=tol_collar)
+```
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+fig, ax = image_show(better_contrast)
+
+# Combine the two floods with binary OR operator
+ax.imshow(flood_background | flood_collar, alpha=0.3);
+```
+
+```{code-cell} ipython3
+flood_mask2 = seg.flood(astronaut[..., 2], (200, 220), tolerance=40)
+fig, ax = image_show(astronaut[..., 2])
+ax.imshow(flood_mask | flood_mask2, alpha=0.3);
+```
+
+## Unsupervised segmentation
+
+Sometimes, human input is not possible or feasible - or, perhaps your images are so large that it is not feasible to consider all pixels simultaneously. Unsupervised segmentation can then break the image down into several sub-regions, so instead of millions of pixels you have tens to hundreds of regions.
+
+### SLIC
+
+There are many analogies to machine learning in unsupervised segmentation. Our first example directly uses a common machine learning algorithm under the hood - K-Means.
+
+```{code-cell} ipython3
+# SLIC works in color, so we will use the original astronaut
+astronaut_slic = seg.slic(astronaut)
+```
+
+```{code-cell} ipython3
+# label2rgb replaces each discrete label with the average interior color
+image_show(color.label2rgb(astronaut_slic, astronaut, kind='avg'));
+```
+
+We've reduced this image from 512*512 = 262,000 pixels down to 100 regions!
+
+And most of these regions make some logical sense.
+
++++
+
+### Chan-Vese
+
+This algorithm iterates a level set, which allows it to capture complex and even disconnected features. However, its result is binary - there will only be one region - and it requires a grayscale image.
+
+This algorithm takes a few seconds to run.
+
+```{code-cell} ipython3
+chan_vese = seg.chan_vese(astronaut_gray)
+```
+
+```{code-cell} ipython3
+fig, ax = image_show(astronaut_gray)
+ax.imshow(chan_vese == 0, alpha=0.3);
+```
+
+Chan-Vese has a number of paremeters, which you can try out! In the interest of time, we may move on.
+
+```{code-cell} ipython3
+
+```
+
+### Felzenszwalb
+
+This method oversegments an RGB image (requires color, unlike Chan-Vese) using another machine learning technique, a minimum-spanning tree clustering. The number of segments is not guaranteed and can only be indirectly controlled via `scale` parameter.
+
+```{code-cell} ipython3
+astronaut_felzenszwalb = seg.felzenszwalb(astronaut) # Color required
+```
+
+```{code-cell} ipython3
+image_show(astronaut_felzenszwalb);
+```
+
+Whoa, lots of regions! How many is that?
+
+```{code-cell} ipython3
+# Find the number of unique labels
+```
+
+Let's see if they make sense; label them with the region average (see above with SLIC)
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+astronaut_felzenszwalb_colored = # Your code here
+
+image_show(astronaut_felzenszwalb_colored);
+```
+
+Actually reasonable small regions. If we wanted fewer regions, we could change the `scale` parameter (try it!) or start here and combine them.
+
+This approach is sometimes called **oversegmentation**.
+
+But when there are too many regions, they must be combined somehow.
+
+```{code-cell} ipython3
+
+```
+
+## Combining regions with a Region Adjacency Graph (RAG)
+
+Remember how the concept behind random walker was functionally looking at the difference between each pixel and its neighbors, then figuring out which were most alike? A RAG is essentially the same, except between regions.
+
+We have RAGs now in scikit-image, but we have to import *from the future*; this functionality is exposed in the `future.graph` submodule meaning it is stable and robust enough to ship, but the API may change.
+
+```{code-cell} ipython3
+import skimage.future.graph as graph
+
+rag = graph.rag_mean_color(astronaut, astronaut_felzenszwalb + 1)
+```
+
+Now we show just one application of a very useful tool - `skimage.measure.regionprops` - to determine the centroid of each labeled region and pass that to the graph.
+
+`regionprops` has many, many other uses; see the API documentation for all of the features that can be quantified per-region!
+http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops
+
+```{code-cell} ipython3
+import skimage.measure as measure
+
+# Regionprops ignores zero, but we want to include it, so add one
+regions = measure.regionprops(astronaut_felzenszwalb + 1)
+
+# Pass centroid data into the graph
+for region in regions:
+ rag.nodes[region['label']]['centroid'] = region['centroid']
+```
+
+`display_edges` is a helper function to assist in visualizing the graph.
+
+```{code-cell} ipython3
+def display_edges(image, g, threshold):
+ """Draw edges of a RAG on its image
+
+ Returns a modified image with the edges drawn.Edges are drawn in green
+ and nodes are drawn in yellow.
+
+ Parameters
+ ----------
+ image : ndarray
+ The image to be drawn on.
+ g : RAG
+ The Region Adjacency Graph.
+ threshold : float
+ Only edges in `g` below `threshold` are drawn.
+
+ Returns:
+ out: ndarray
+ Image with the edges drawn.
+ """
+ image = image.copy()
+ for edge in g.edges():
+ n1, n2 = edge
+
+ r1, c1 = map(int, rag.nodes[n1]['centroid'])
+ r2, c2 = map(int, rag.nodes[n2]['centroid'])
+
+ line = draw.line(r1, c1, r2, c2)
+ circle = draw.circle(r1,c1,2)
+
+ if g[n1][n2]['weight'] < threshold :
+ image[line] = 0,255,0
+ image[circle] = 255,255,0
+
+ return image
+```
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+# All edges are drawn with threshold at infinity
+edges_drawn_all = display_edges(astronaut_felzenszwalb_colored, rag, np.inf)
+image_show(edges_drawn_all);
+```
+
+Try a range of thresholds out, see what happens.
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+threshold = ... # Experiment
+
+edges_drawn_few = display_edges(astronaut_felzenszwalb_colored, rag, threshold)
+image_show(edges_drawn_few);
+```
+
+### Finally, cut the graph
+
+Once you are happy with the (dis)connected regions above, the graph can be cut to merge the regions which are still connected.
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+final_labels = graph.cut_threshold(astronaut_felzenszwalb + 1, rag, threshold)
+final_label_rgb = color.label2rgb(final_labels, astronaut, kind='avg')
+
+image_show(final_label_rgb);
+```
+
+How many regions exist now?
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+np.unique(final_labels).size
+```
+
+## Exercise: Cat picture
+
+The data directory also has an excellent image of Stรฉfan's cat, Chelsea. With what you've learned, can you segment the cat's nose? How about the eyes? Why is the eye particularly challenging?
+
+Hint: the cat's nose is located close to [240, 270] and the right eye center is near [110, 172] in row, column notation.
+
+```{code-cell} ipython3
+fig, ax = image_show(data.chelsea())
+
+ax.plot(270, 240, marker='o', markersize=15, color="g")
+ax.plot(172, 110, marker='o', markersize=15, color="r");
+```
+
+```{code-cell} ipython3
+
+```
+
+```{code-cell} ipython3
+
+```
diff --git a/lectures/5_tophat_filters.ipynb b/lectures/5_tophat_filters.ipynb
deleted file mode 100644
index df0e21e..0000000
--- a/lectures/5_tophat_filters.ipynb
+++ /dev/null
@@ -1,589 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "c04ba556-e6bd-4bbb-b6aa-492b7ad2aa02",
- "metadata": {},
- "source": [
- "# Correcting brightness nonuniformity with tophat filters\n",
- "\n",
- "Thresholding, usually the first image segmentation technique to be taught, labels individual pixels based on intensity. No information\n",
- "from a surrounding region is used.\n",
- "\n",
- "Basic concepts of segmentation, including thresholding, for skimage are introduced [here](https://scikit-image.org/docs/0.14.x/user_guide/tutorial_segmentation.html) and [here](https://scikit-image.org/docs/stable/auto_examples/segmentation/plot_niblack_sauvola.html) and [here](https://github.com/scikit-image/skimage-tutorials/blob/main/lectures/solutions/4_segmentation.ipynb)\n",
- "\n",
- "In this tutorial we'll focus on extending the classical thresholding techniques via processes that correct nonuniformity. Brightness nonuniformity is a common problem in many forms of imaging, with varying causes. Having a collection of tools to help deal with nonuniformity can simplify subsequent analysis steps considerably.\n",
- "\n",
- "## Recap\n",
- "\n",
- "The _page_ data, shown below, contains some scanned text and the aim is to select a threshold that separates printing from paper (dark from light). The tutorials linked above introduce manual selection, selection based on histograms, and adaptive thresholding, where a different threshold is selected for each pixel based on parameters of the local neighborhood. The results of these methods are shown at the end of the tutorial for reference.\n",
- "\n",
- "The approach we will introduce is similar to adaptive filtering, but is built using existing filtering procedures to which the manual or histogram approaches can be applied\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "86586f49-3cba-464d-9ba9-eb889de98d97",
- "metadata": {},
- "source": [
- "# Set up libraries and display functions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "471781bf-4a38-4691-a35f-f9517efa53fb",
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "import skimage.data as data\n",
- "import skimage.segmentation as seg\n",
- "from skimage import filters\n",
- "from skimage import draw\n",
- "from skimage import color\n",
- "from skimage import exposure\n",
- "import skimage.morphology as morph\n",
- "\n",
- "\n",
- "def image_show(image, nrows=1, ncols=1, cmap='gray', **kwargs):\n",
- " fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16))\n",
- " ax.imshow(image, cmap='gray')\n",
- " ax.axis('off')\n",
- " return fig, ax\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b6b126ed-b149-4649-a65b-b53c91fc612f",
- "metadata": {},
- "source": [
- "# Load and display the test data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "8c4b4e8b-469a-447f-b6f9-cbc312026ae6",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(, )"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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N72B0rGiNISq1hfZXWgMpDiheNK/onajunaZPn558aK3/8pe/3LR/8IMfJB/am2q80vn8pJNOSjZ9J/LYY48lny9+8YvJps8zZsyY5HPfffcl2xFHHNG0999//+RDdeOrX/1qx3vr31Zo/aL1qgLtr7pKV/tQuvdf7c7GGGOMMcYYY4wxxhhjjDHGmL8Z/IdFY4wxxhhjjDHGGGOMMcYYY0xH/IdFY4wxxhhjjDHGGGOMMcYYY0xH/IdFY4wxxhhjjDHGGGOMMcYYY0xHskpoERITVbFSEiUnoVcVKyXRU0LFS7UdwQK8KtJNz6Ji1CQOT0LBKuY7bty45KOC7hFZAJyehcQ21UYitiQYq6Lcw4YNSz4k/K73f/HFF5MPic/usMMOTZsEY3UeFi9enHzoWVTMl36frlM/ElklkW4VqCURaxWjfTWbQn2YP39+01Zx2IiIv//7v082Ffcmoek3v/nNTVsFuiNYTLx3795Nm8aABOsVFYt/tXvpXJEQM/3efvvt17S32Wab5EN5pfn45JNPJp8VK1Ykm47nzJkzkw+JHqt4MI3Ls88+27Sff/755EO1U8dKa1sE143KmFPt1HupcHlEHqeILHZPQsWU2+pH15FN5536pGuY9jGCx0CFnyk/DzvssGSbOHFi09a1KiLipz/9abLdcMMNTftDH/pQ8nnkkUeSTcWnDz744ORz/PHHJ5vWifPPPz/5XHLJJR3vdcsttySf8ePHJ9v73//+pr169erk8+ijjzbtVatWJZ++ffsm24UXXti0SXCcxlPn5rLLLks+v/71r5PtzDPPbNqUs1STNPbJh/ZXut9Yv3598qE+6DpD86I1SMcyIsd0RMQee+zR8fdJGP3mm29u2tttt13yIdH1NWvWNG2aY9qrac0jUflLL7002T7wgQ80bdpfzZkzJ9l0P0XxSuujzg1dt2TJkqatwvcRXKu/+93vNu0777wz+RxzzDHJNmLEiKb94IMPJp8f//jHyaZ7dr1PRM71iIi77767affs2TP5DBw4MNl+85vfNG3ag3Xv3j3ZdF9E/dTY0DpN94mI6NGjR9PW+I3g8Rw0aFDTHj16dPJ56aWXkk2fj2qL7rMjcg285pprks+MGTOSrU+fPk2b9m60v9J90qmnnpp8Zs2alWzf+ta3mvbgwYOTj565qN8777xzsulZhuoWrY+6x6MzyoABA5Lts5/9bNPWOY/g9VHHmM6YVId1X0Trx+WXX55s8+bN+4v3ich7WtonTZs2Ldk++clPNu2hQ4cmnyeeeCLZvv/97zfto48+OvnQHOvz3Xrrrcnna1/7WrJp3ae94le/+tVkW758edP+xje+kXyoTun6S7V66dKlTZvW9W233TbZvvCFLzRtOp8PGTIk2eg9gkL7lm9/+9tN+8Ybb0w+69atSzZdP7bffvvk8973vjfZtO+0fyS0llEeX3/99cl2zz33NO37778/+WjdonE69NBDk03XY8o9qpNf+cpXmjbVQOqD5uhnPvOZ5EM1V9+h6ZxH8LlFax7VJMrjD3/4w02bzlc///nPm7bmS0TEv/zLvySbrqsnnXRS8jnxxBOTTfcpFD9UF3XfQOvlvffem2xz585t2p/4xCeSD71juvbaa5v2HXfckXzOO++8ZNM17Be/+EXyobqo65zmS0Sud7S3offQmtsU53pOi8h5RGcN2itqDaQ+0Z5L9w005vQeQe9/0003JR86A2nd1/e2ETmH6L0i5bE+y4QJE5IPoTlK9e6II45INl2fvvzlLyef2267Ldn0vEr7Xn1/HpHfq9G7Kn0nG5H3YfTuj97ZaU2gNXvs2LFN++mnn04+9A5G+077c6qdGzdubNoUP7RX0zMtxSZdp2daGjvaB+o5/ic/+UnHPkXkPRftz9WH3j3SOftvCf/HojHGGGOMMcYYY4wxxhhjjDGmI/7DojHGGGOMMcYYY4wxxhhjjDGmI/7DojHGGGOMMcYYY4wxxhhjjDGmI13WWCS9LbWRD31vlnTsFPo2sX5Xnb5RTd/u1u/y0ne59Vu9dB9Cv2lM3ypXzbyI3Hf6vc022yzZdDzpWUhvp6K7VNGepG+F070WLFjQtEkXRH+P4oLmWKHv0ZOGnOqJ0HesScNBvz+t35Umn4ja99np+/qqr7PjjjsmH3pm/XY3fTNaryM9DNIA0tymuaJ41etI04W0XzSuq/qt+r37ilYBXVfV7VMb6R5UdB5V4zEij1Xle+10L9J4pXjVOaV7E9pPmmMaO71/dY4rmrwVjUV6Ph0Dqm2k5aXfpFcdlgheG/ReFJv0e9p3ig3S5NR5pzwmPSEdc6otquURkWsgrVc0xqqJQXsL1VMlXQDSHNI4IK00ykd9ZtIqIi041U+i7/TTN/i1dtEYVLQ7SCeUxurYY4/t2M+77rqraT/wwAPJh9ar4447ruPv01qvGnVUy0hnQbXZaJ0j3Qwd46lTpyafhx9+ONl0Haf4oZq00047Ne23v/3tyYfyX/cupH2tWihU83/7298m20MPPdS099lnn+Rz4IEHJps+H+mwUWyo/hXVTtIFUT1D0rCkvbDGHml5HHDAAcmm6zjtMa+77rqmTfu7z33uc8mm+2OqpaSXonNMfaIar3WD1lnKK40h0tp83/ve17GftF5973vfSzatN6QJPGbMmGTTcaAzmGr3UEzT2qD781/96lfJ54QTTkg2ra+LFi1KPqqxFpF1e0n/lzTVdM2u6jzpHFMte+aZZ5JN1+yDDjoo+bzrXe9q2hTTpF2sexLSWKJ9oWq6kX40aUbtvvvuf/H3I1gHUfXESFNWtWEj8pxSzSfdPtUh3HPPPZOP6uHRWkhxN2XKlKatOsmvhp7rqZ7Ts6guGK07pL+pc0xaiartGZHXBtrT0t5UawvV18mTJyeb6kiRZiXVIIXqne7xaD85e/bsZNttt92a9rvf/e7k88ILLySb6u9Rzp5++unJpmst6SKefPLJyabjSfXnnHPOSbYf/ehHTVt1/CJynNHznnHGGcmm+x3Sc6Yzpu5haV9G7y20ftO+ntariy++uGnT+yvap6gmJ2mCzpw5M9n0nSjts6mfuoaRxqqeSc4+++zkQ2foSy655C+2I3gPrZpx+i45gnNN54bWWaotqitJuUA1Xt+9kd4n6TBrP+k6Pb/SnNN86njSHpPeGWr86DoUwVriI0eObNqk90foWaqq21l5N01nvsrfFuicq/2idVxrIL3Ppn7rXFFMk/Zk5f0O1UC10b3p/YOutRQ/VMtUm53e/dM7CZ0/1eOke9H86pkhIutxv5HxfywaY4wxxhhjjDHGGGOMMcYYYzriPywaY4wxxhhjjDHGGGOMMcYYYzriPywaY4wxxhhjjDHGGGOMMcYYYzriPywaY4wxxhhjjDHGGGOMMcYYYzqSVYL/H6iIl5LP3/3d33X0IXFNFRR94oknkg8Jd6po7ODBg5OPCriTYD2Je86dO7dp67NFsCiw3p/GgESIVUxUhXVf7ToVHCeBahIFV2FpElSdMGFCsunckOCvisGTQO6TTz6ZbCqgrKLoESx+rXFAIqsk4K79+s///M/kQ+hcUWxSnHfv3r1pL1u2LPmMHz8+2Xr37t20ly5dmny07z169Eg+PXv2TDYdOxIlpmfRZ/7zn//csU8ROdcpNuj3tJ+Ue3379k02ze0tttiiY58ict+pblRiSkWJI3ItoZimPmktoeuoRigUr5W5UkHnCBZw1rmqxE9Efj6qnVq3yEaxqPNHY0dztWHDhqZN80JxoALcdB2JnmsMU7xW+klr6PXXX59sJK6tjBs3Ltm0Tuy6667JZ86cOcn2uc99ruPv77jjjk374IMPTj6rVq1KthUrVjTtW2+9NflcfvnlyVbJR4pFjX3KIbLp+kQ1l+ZY45yuo3jR56H1Q0XIKdfHjh2bbCrqTmLmFOf6LPR7ul5G5P3GjTfemHwWLFiQbDrHr7zySvKheSebQvOgcT1y5MjSdRs3bmzatD7q2F111VXJh/qtefT2t789+dDeSfeYmmcRXON/9atfNW3KBRoD3cNSjTjooIOSTdeUQYMGJZ/Jkycn25AhQ5q2zkFEngfan9M87Lnnnk2b8nO//fZLNh1Pimna4+l4Ul5RPupY0XpFZ4urr7462RTd10fk/du6deuSz1ZbbZVsWnN32mmn5HPaaac1bT0DRvD68b3vfa9p05lP16aIiE2bNjVt2mcPHDgw2R566KGmffLJJyefYcOGJZvWLtoTUR5rTaCcJZvmzEc/+tHko/Gy8847J5/77rsv2e69996mfcQRRyQfGjtl/fr1yaZ7ooiIXXbZpWl/4AMfSD5nn312smlsXHfddcln7733TrZTTjmlaa9evTr50J5EY/bUU09NPrqmzJ8/P/lcc801yfboo4827enTpycfWh/1bEFx99xzzyWb5vrWW2+dfI466qiONjrz0RzrHoTWD6pvus7ReyHak2yzzTZNm/beGhv0boViWM8RtPbus88+yXbMMcc07WnTpiUfev+g+6unn346+eg4ReRYoFpGfdczAq1XtJfRWBg+fHjy0f2q1tuIiKlTpyabroWLFy9OPvQ+QPcIdBanc6/Ga58+fZLPbrvtlmybbbZZ06b4oZqg6xrVrRkzZiTbXnvt1bTp7D9lypRk0z3Jww8/nHw0h2644YbkQ7HxzDPPNG19bxvBe6cddtihaVOM0XsEheoI7Xd0rOjdJj2f7hdpD011auXKlU37nHPOST5PPfVU06a9OMWw1nhaBypnYdqX0fmxsqeldVXnj/pJY6d7J9oHkq3yXpjqht7r4osvTj7a90996lPJh/aK8+bNa9qXXXZZ8qHzjtZ43eNG8N8MtJ+zZs1KPjQu+o5ba1sE56j60f6R0JpAZyndA1X79LeE/2PRGGOMMcYYY4wxxhhjjDHGGNMR/2HRGGOMMcYYY4wxxhhjjDHGGNMR/2HRGGOMMcYYY4wxxhhjjDHGGNMR/2HRGGOMMcYYY4wxxhhjjDHGGNORrEpahERWVfS0IlRKkPAriSyrYPxLL72UfEgwum/fvk1bRZcjct9JgHPIkCHJtnz58qZNYsaEPsv/9//V/uar4zl79uzk88ILLyTbH//4x6ZN4uI0diqSS0KsKnQfkYVen3322eSjorx07549eyabjrEKM0fw/C1cuLBpqxBsBAsx65iTECvF+R/+8IeOPhoHETnOqE/r1q1LNhWIJhF7jfNu3bolH4oDFfyuxqvm6IYNG5IP3UvHhfpEAsc67xQHhM6VCjNHsJiwxgKJPKu4b0QWllZB94g8fzQGdG+t1VSXqUZoLFCNoHvp71G80nUar+RDuaZjRXlFMVWJWZ0/EgAncW/tA/0WXadQHBA6dvR7WvMjIrbaaqum/f73vz/5UM3Vek5i9CSMrmvvsccem3xWrVqVbCr8riLaERG3335706Z4pX6q0P1JJ52UfEaMGJFsS5YsadpUO7WORET06tUr2RSav8reidDr6D4U11rfKBY1P2hfSDlLfkplDSUBebruxhtvbNq6T4uIOProo5NN951U87/61a8mm84fretk07Gi2kl5rL9XqW20XtLzqR/FAfVJ84Pq3YoVK5JNx0X3nBEcr9tss03TnjZtWvJZvHhxsuk6Ss9Htvnz5zdtrW0REUceeeRf/K2IiJtvvjnZfvOb3zRtOmscfPDByXbooYc2ba3vEbneReT5oxyiWFTbd77zneQza9asjn2gNYb2TmPHjm3aVONpr/a//tf/atqDBw9OPro2UKzQ/kpjmOL1a1/7WrJVzlK6z47I40LnXsp/3U9RDab1SqnWc60bFPu61o8fPz75aF5HRDz11FNNm/KTxk77SXWLnkVjYd99900+tG/5zGc+07QPO+yw5POP//iPyabrE+UjrX06xu973/uSz6ZNm5o21YOVK1cm28CBA//ib0Xwmqb5SOO07bbbJpvuuWidveCCC5Jtt912a9oHHXRQ8tl5552TTedYxymC1x2tLXRuobzq0aNH06a1V/fQVFvo3nrOpXcbtBfWGkRzRbWlT58+TZvykd7r6T7+mmuuST6//e1vk03nhvbndEaYMmVK06Yar/WNxqkyLlRHaL3SNZTyimJD55jild536BzTOFGcDR06tGnrXiMi4qabbko2PbtRftA7WF1rKc51jTn//POTD12n+bDjjjsmn1GjRiXb2rVrmzbFBtVAjRfaS9G8015GoXvp79GYE5dddlnTpjX061//etOeOnVq8nn66aeT7ctf/nLTpr0UofWVxrzyfDQvdJ3WLpoX2gPpXNFaQXtazVuaT8p/jWHqp55fd9hhh+RDXH755U2bzstUW/SZKX6ef/75jr9/6623JhvVzq233rpp0xxTfP7kJz9p2nQOpbnS9WKnnXZKPlrL9O8aEfy+/m8J/8eiMcYYY4wxxhhjjDHGGGOMMaYj/sOiMcYYY4wxxhhjjDHGGGOMMaYj/sOiMcYYY4wxxhhjjDHGGGOMMaYjXdZYpO/dqtaDfhv51dDvB5OeAGlw6Dd+6ZvmpKFA3z5W9Nvd9F3nkSNHJptqoVCfSGNJx470mugb9epH3zSm78Hrd8fHjRuXfEgTR79RT9ov9I3oSZMmJZsyb968pk1jrhorEXns6HvJ9I1qjSnSmiANQB0X1UqIYB0CjSn6vjfljH7b/pFHHkk+V199dcc+0O/169evaZNOEH0LXTUVfv3rX3e8N0Hf26a80pgiPQEaO/3uOWkOEfotfdKxqcTU5MmTkw/NX0X/RrUKCIphzSN9toiICy+8MNm23377pr333nsnH4p9nSvS8iINDq3LFS2xiJpuDs2Vxgv56NjR2kR1qvKdfvrOuq5ppA9BNb5yb8pHzT/SYSX9Ep2rZcuWJR+qG6oZQ9fpd/Mjsm4vaXmo1i7FHWlraUxRbaE5njhxYtOmmH7yySeTTfOD7k0aDhVob6M1kHKI+q75QbVFf4/qJOm1aK6RXgP1U/dOtCei/Fc9PIrpQw45JNl0bkiPhuZP55jGpaKHW9Eyj8jxUtFrIY0civ3rrruuadNcqZZgRI4pqq+kVfahD32oY58oprQ2U70jTaXKmNOeXddRqtW6Pz/ttNOSz4knnphsWpevuOKK5HPppZcmm2qV6X49gmND85jGnPJf9zJ333138iEd3Y9//ONNm2Lj+uuvT7avfOUrTZv2MsOGDUs2zVHa16uuHO0VK3t90lQhHb3Ro0c3bVqzaa60vlEto72+1o2uasMTdFbUeKloe1Z0EclW1QTV9xYVzeWIvBaSlg+Nk/pRzad50HVN9WpfjQkTJjTtPffcs+Pv0TjRfGpe0ZhX4o7OaQceeGCy6RioTnIE76/0LHz//fcnH9W+jMj7TporqlNqo+ejmNL70+9pvaF4raw7dB3poKkfPS+hMUXXUbzomfbHP/5x8jn99NOTTfcbpCH5ve99L9l0XaXaovub6rPo2FFe0ZlE/WgPT3su/b2qJqjuCSg/qCbtscceTZv2j7RPueSSSzr2SetWRF4b6Drd73zyk59MPrRH0HwkH1r/dY2mOklrttYyGl/SWNZ4oZwlbXjd61d0pyMi5syZ07Rp/6jvgSg/Kvtzek9LeaxxsHTp0uRDe32dGxoDyv/KuYXQfWB1f6VzXNW+1uehdxvf+ta3mjadf+id7y233NK06SxO9U3r1O233558Fi1alGyqZ0jn+sMPPzzZdH2k9xgf/OAHk+3ss89u2rfddlvyoT3emDFjmjbVCP2byBFHHJF86Bzxt4T/Y9EYY4wxxhhjjDHGGGOMMcYY0xH/YdEYY4wxxhhjjDHGGGOMMcYY0xH/YdEYY4wxxhhjjDHGGGOMMcYY0xH/YdEYY4wxxhhjjDHGGGOMMcYY05GsPFuExKBV4JOEbUkEVEVkVaw1gkU59f4k/E6i2SqqSoLjaiPRVRK/rfw+jYuKl5LIM4nPqsAo/V7Pnj079oHm86WXXko2FUum60icVX9v/PjxyUfnXcckgsd8xIgRTXufffZJPiSM/sgjj3T0URH0iBzD2223XfLp3r17smnckbgvCeIOGjSoaZMY7PLly5Nt1apVTXvgwIHJZ9SoUU1bhWcjOB8PPvjgpv3oo48mHxK2VVFe8iExYZ13GieKKRX8plyne2kfSFyc4lMh8WsSrX7mmWc69kltY8eOLfVJawmJvFN9rVCpgeRDgthaY6lPlX7SGkMxpTaqywqNL82VPouKWr/a72n9JqFygtYGZffdd082FYP/3ve+l3zuv//+ZFOR9RkzZiSfrbfeOtlGjx7dtG+44YbkM3To0GTTOrVgwYLk06dPn6ZNAtmTJ09ONq15F198cfLRcYqI2GabbZq2ricRnOsf+tCHmjbFRiVnSOSd9im///3vm/bGjRuTD+039P56H+oT7T+on5V4pWfRdY7qOa3Zuk+hebnvvvuSTXObxOj/8Ic/JJvWIF2HIrjvOp4UB7Tn0nykvZs+M8Xdcccdl2y6L7rqqquSD9VF3SNofkZEPPTQQ8k2YMCApj1p0qTko/veiFynaJyoJmm8UJxTfuj+jda0Bx54oGnTXnzatGnJdtRRRzXtxx57LPk899xzybZhw4amTc9CuaexQONLMazj0qtXr+RD+07dU1JNov24Ut3PaY7SHkHnhnKP7q37sJtuuin50Jjr+kRjR2OgNZD2EcuWLUs2zQcaAzqTUP1WKP/VRuOpZxvdB0dEzJ8/P9l0j0D3pufTNYVylt4jaH5ceumlyednP/tZsn3gAx9o2nfffXfy+cpXvpJsZ511VtPu379/8qF8VI4//vhk0zGgdZZiSmOR6ivlh0LzsmbNmmTTdeDLX/5y8lm3bl2y6Z7yq1/9avKZOXNmsukZrxI/ETm3aQ9E8ak2ehb1obpMtVPrPp35Kv2kfQSdwXTeKTZpnzRv3rymTe9NdB8Rkd/50PNRTOl5ivqpz0zvKKjeaT5Q7ab1St+JUA2m9UPXeoLmaurUqU2bzjvap4iI9773vU2bzlf6fjAi4oILLmjaRx99dPKhtW/lypVNW8+AERH33HNP06Z3Y1pHIvIaOnv27OQzcuTIZNM9LK0flDO6t6D4oX7uvPPOTfu73/1u8qGaoPP385//PPmccMIJybbbbrs17R//+MfJR/OKYuXee+9NNh1zPbNEcI3Q2kLjS7VMawLVbsrRtWvXNm3av1Kt1vynmk/7cR0/emdIsaF90DNDRK53L7zwQvKhefjnf/7npr169erkQ+vOAQcc0LRpv7N48eJk03NEv379ko++byGo5lOdOu+885o2jQudj+leitZcek9L8Vp5J/JGwf+xaIwxxhhjjDHGGGOMMcYYY4zpiP+waIwxxhhjjDHGGGOMMcYYY4zpiP+waIwxxhhjjDHGGGOMMcYYY4zpiP+waIwxxhhjjDHGGGOMMcYYY4zpSFboLkLCwSqqSkLMJISs4qwkiEvCqyoqr8K6dO+ILOpKgsokiNvp9yOyYOsf//jHUp9IKFwhgWoVcCXBURIK1j6oeGoEi/Lq3FC/SQx2/fr1Tbtbt27JR0WraZxmzZqVbDrGJHS/bNmyZFMx4f333z/50LhoP/v27dvRJyKPFY0d/Z7OKYkXq/B8RMSQIUOaNgnPagyTiDWN5+jRo5u2ilNH8BioH4kZk6CyQs9y0EEHJZsK6VLOEjvssEPTHjduXPKhZ9a5GTZsWPL54Ac/mGwqjkyxr0LzVCdpzLUmDRo0KPl87GMfSzaNT6rntA7odf/zf+Zlhuq52kjgmHJG6zetMZQfOjckIK91i0S7Kf+32267pk39pufT+2v8RkQccsghyaZi1ySCTjnz4Q9/uGk//vjjyeeBBx5ItlWrVjXt/fbbL/mMHDky2XQeqC6T7cEHH2zaFMPHHHNM0yaxb8qZM888s2lfccUVyYfWnRUrVjRtiunp06cnm659NMe0J9G+Uy2jXNP1n/KYYphqkKL1hmoi7aW0lpHQPcWwrjsahxERI0aMSLbjjz++ad9yyy3J5xvf+EayjRo1qmnTHpP2O/rMNFe0N1QoNjZu3JhsOla6t4mIGDx4cNOmvQbFz1FHHdW0X3zxxeRz2223Jdvw4cOb9uc///nk86//+q/JdtlllzXtq6++OvlQPdXauc8++ySf8ePHJ5uu2bSHJpvmh8Z0RK4bVF9p3dE9F+3Ltt9++2TTMaccorFbu3Zt0648b0TE1ltv3bTpWS6++OJke+SRR5q27n8iIhYsWJBsmleV2hKR84/WAYX22bSGHn744U2b1stzzjkn2fS8QesVxb7uuT7zmc8kn7Fjx3a8jqB6ozWB9ph0Np05c2bTPv/885OPzh+ts0888USyffGLX2zaetaJiFi6dGmyrVmzpmnTukMxpfH54x//OPnoni8i4v3vf3/T1nyJiDjrrLOSTc8bp5xySvI56aSTku1zn/tc0/7yl7+cfDTu6Hkvv/zyZNN99Sc+8YnkQ7mnY661JiLXg4iIm266qWm//e1vTz5Dhw5NNr0/nSfpjKBxTmdTeieiUExRzmi+U3298cYbmzbVSarVugeid0C0Nmge0zjR7+lYVd6bROQ4p73FT3/602SbNm1a09ZaE8G1U8ec9mBaO6n+XHjhhcmmsThx4sTkQ+8R9NxQef8REfGmN72padOaRs+nfaCaP2DAgGTbdtttmzadhXfeeedk++53v9u09d1KBK+1+ny0zum7lGOPPTb5TJ48Odmef/75pk1x/m//9m/JpntoOvPReUDnRs+OEby30OejvT/VeK1ltO+l2NBzko5TRMSjjz7atGnfouteRMTcuXObNtUkque69tE7NVrrNX5OPPHE5EPzoDWe1iu9d0TOI6qBlKNaq3U/GcHnap1jWns/+clPJlun+0TkMwKNAb1H0PWDYpre3ejY0dpLZ2gdT/o9upeu0VTvKu/nKf91faT6Wjl/vJHxfywaY4wxxhhjjDHGGGOMMcYYYzriPywaY4wxxhhjjDHGGGOMMcYYYzriPywaY4wxxhhjjDHGGGOMMcYYYzryd1XNMeXUU09NF6ouEH1Tnb6rrH2gbz/T9271O8f0bXuy6bfsqZ/Kn/70p2QjHaSKlgdpa+l3eauaPHp/+vYz2fS7wzS+9M1/mr9OfaI+kCaHak/RN6pJU2nx4sVNu6qNonpNpNNB31DWsaJ5IS0GvY5iiq7TGCZtBIphik9Fv8tN3+mmHNI5prigcdF70fenCb2O4pX6UNHNoPHUfpGuBPVB70UxTJomOp405grNOdn0Waqashqf1CeaPx0XqiOVb4yT7gHlo/aTvv1OeaV6JTR2WktonGg8K3qqlfWDeK18IvL8VeuIxkL1m/Har2o/K7qd+iw05qRfQuujQjGsfaexo34qlTWGbJX6HpHnivKY+qCxTj56L1o/aFy0LlJdput0zCkf6fl0jqnmq54q3Z+er7LHIx9a/3UNozGge2kskI/WMooxqrl6bxqnyp5k4MCByYfyUbX1Fi1alHxIo1v1W6nmU991/0h6o6StWdHSUE2cZ599NvnMmTOn43WTJk1KPqTppv2kGkF5XIGu03yYP39+8rnhhhuSbcmSJU2bdHpoz/7cc8817ZNPPjn5UEypVplqUUZkbRsaO7JpjacYu+6665Ltrrvuatp0biHNIdXtJV2ryl6Y9udU39SP7k36qaoLSvqCqrtE9Ye0BPfYY4+mTdqeM2bMSLYrr7yyaZPm2Tvf+c5k+9nPfta077vvvuRDsah6hlSXf/SjHyXbvffe27S/+tWvJh/V1Y6IuP7665v2pZdemny0VlPtpnr31re+tWnTvHz6059ONq2TH/jAB5IPaevpuCxcuDD50Dqg69qRRx6ZfEinXOsNjQvlf2X/SO87dFx+8pOfJB+tGxQ/pMmlc/Xwww8nH4rhj3/8402bcpZ0HlWXnDSeTzvttGRTHfZzzz03+WjORuRxIL1hOluolhbFhq4NpDf6u9/9Ltm07p9wwgnJh3RCNQ4oVijGNBar53PVRqS8or2hrkWkJUhrr+556DrVLozI6yGtDTrHpNVKexJ9FtIkphrY1THX+aP3QrSuVt5xVzTkKH7o9yrvnXQMKM+oTmp9JY1Oqm/Vd4T/lVQ0a6nfNC56LxoDGiutG5X3STRXlfmrvOMiG51xicrvVd53VN+J6F6Y4r5ydqLr9N70DojmuKt/i/tr0rt379qACv6PRWOMMcYYY4wxxhhjjDHGGGNMR/yHRWOMMcYYY4wxxhhjjDHGGGNMR/yHRWOMMcYYY4wxxhhjjDHGGGNMR/yHRWOMMcYYY4wxxhhjjDHGGGNMR/6uq4KRH/jABzpeWBX8VJFTEj2lfqpwpgprkg9BfSJxTeXv//7vk037+ec//7n0e2qrPC9BY0e2N7/5zU2b5opEgVVolu5N/VTxYBJ51nvTONF4qmguxYEK3UZEbLHFFn+xjxE1kWXqJ/2e9ouEbTfffPNkUyg26V4aQxSvOn+VGKN7URzQXOkYkLAtjac+SyWmI/K40PNRvGg+VAWkdW6q+a/XUf5rHyheaQx0jCsiz2SjcaoKd1eoCCN369Yt2f74xz/+xftEcF7p3FDOap2qikqrjea8sj5Wf68i0k3XVdberopYk63ST0LHhfqpcU5jrrESkfOInrdSyygfqb5V8qqyR6jmcWWuKmsmXafjUtnf0b3o3nQvXXfIh/YtlNuK7iMiIrbccsumTc+ydu3ajr9XyYWIXKdojitjRbFIz6fo3ob6QHs3yivtE80B7Uk0puj36DpdGyimN27cmGy6D6ScpTHXvQWNuc4n1ZH169cnW/fu3f/ifSI4FnWsaM7pOp13GgOaY70XxStdp/ud3r17Jx+dl4iIl156KdkUirPFixc3bc3riIgePXo0bZoXijsdOxoDijsdu+oc6/PR71HfdU6pTlIM6/PRHvMTn/hEsmnOfP7zn08+Op6UZxQbmzZt6tgnupfGwbhx45LPmjVrkk1rNeXVkCFDkk3zkeZzxYoVyaZ+VMtGjRqVbNqv1atXJx8dK8pPGrthw4Y1bVp7dXwjcvz07ds3+dB4ar/o3Etrr/5ev379kg/lv8ZUdd3RvKq8O4rIcb1s2bLkUzkX0rMMHjy4aT/zzDPJh+6l19H7D4phrW+0zo0YMSLZli9f3rQHDRqUfBYuXJhsGnuUe1TfdP2gsdO+0zpEcaf7Abo31SmN88r7s4hcE2g+aT+nVNZUuldl/xoRsW7duqZN6zPZdP5o7DQfaV4q54jKu9yIHBtUlytn0+raq9fR71Ht1L7Tfo5+j9YCpXIGo9qpeUR1kp6lcnb7r4bGqXIeqLwPqL6DoXHvdO/Kez6i+vvqR/FavX/Fp6vXab+q72l1/qhWa1xX7hNRfxf2X0nv3r279DLX/7FojDHGGGOMMcYYY4wxxhhjjOmI/7BojDHGGGOMMcYYY4wxxhhjjOmI/7BojDHGGGOMMcYYY4wxxhhjjOmI/7BojDHGGGOMMcYYY4wxxhhjjOlIVpAsQgKjCgliqsBpRBblrQrpqoAqXUcCsRUBTr03iW2SmLAKhVaEQ8lG19GYV0RIyabjQvcmIV3tF40BjafOO8WBzh+NAc2DikqTWCqJWKvQNMUPiRDr89HY0fOpjWKz8swkZlwRsaV+0lx1+n2y0djRPFR+j/JDr6P70HWVOK/UsurvKdVapuNJPtpPmhd6lorIM8Wr3p/uQ7WlIhxeES+me6sAON3r5ZdfTj40f5pHlXq+adOm5ENobNDYkU3nrzqfXRV+rsQw/Z5eR79PY14REye68nyVmvj/wl/z/l0VWX8tBcC1dlbqFkHjpGsv+VBNUj+qEbp3i4h405ve1LQp17VPEfn5fv/733fpOno+Gk/1q86nPg+NXeX3K3s+guqUxg/VZVof1Ub7Hdo7rV+/vlM38Vk0hqgmVs4ydJ3Oi8ZhRESvXr24s/8HNC+VfT3V7krO0vhSvKxbt65p03xutdVWHfug94mIeOmll5JNx49ynWy9e/fu2E8dY4ppGgONa5pjimHqg0LPov2k+azsvavPp9fRHmjVqlXJpvenOqk5Q/s7GoO1a9c2barndK9u3bo1bcorin2NYRo7ile19e3bN/kMGDAg2fT+tO5QPVUo9xSKTUJ/j2Jl+PDhyabxs2bNmtLvbbnllk2b8qVyHqB96OrVq5NNY4HymPpQeedD6/HKlSubNsVUnz59Ot6H8lGfr0ePHsmHnk9jn9Y0uk7jjNamyt6CnqVfv37JpvlAMUVzpbFeWdep/vTs2bPj79GzkE1rF8V05f1n5SxOv0d1ufKukWoS1UAdc4qpjRs3JptSeUehNSOC10K9F/Wb6obeq3I2jshzUz3Dax8q74XIr/oeWv2onxUfureOOcXdX/vM/lpBfa/s52ivVnlHSfsbfTdN13X1PFmhEj9VKvHa1b7TdZV38V19l6r3origNZTWmTcq/o9FY4wxxhhjjDHGGGOMMcYYY0xH/IdFY4wxxhhjjDHGGGOMMcYYY0xH/IdFY4wxxhhjjDHGGGOMMcYYY0xHuqyxWKGqsVbRa6Jv7la+d1v5RnxF062iIVPtEz2fXlf5XnMEf1ddobFTGz0ffQdYvy1NPtQnvX9FC7L6rWnVT6R5qXzzm8apf//+yabfya9o1kTk5yHdR3pmjQXVZng1dKwq382n8aU5rmglVr4jX9F4i6h9u7ui6Vb9vcq39ImKhhyNcVdqYEUbMiKPS/Xb6F39PntlzCtaAaSpQFoIFV2QSp2saNZUcoio6qm+lt+Rr/xe5T7VnHmtfq/iV9Eg7Or4VvcaFV3k15Kuaix29Zn1+SprE9FVLQbSN9baSfemflbW6Io2NGnB0DpeidfKGFRzT5+Z5qUydhVdS3peupdqRlRjs6IrQ/Vcr6tq3W3YsKFpk34KPXNF21vHhWKatKdUK4his6t7sK7WZdJ+0vtXNU8rmtmE9p3inPqg+U9jrvciXbKK1mV1j6D7DRoD2itWahn56DxQflT2uZQLe++9d7JpLSF9MR2XSl5H5FiknK2cQ6nekU6Xjh3tFek67WdVf0/3i6oNGcEaZ5VzREUDiOJAx4Dyg/bnOg/Vc6+utRQHqp0akfX2qvsWHePqma+iRU3rTkWHXeOsop0cUdNhpbnSPlXra2XNpnjVnKH1kWJf70/PQvmoWpCVd3FUu2kd0LmiM19Ft5OuozVU57S67ug8kE9X9w3U967qsFfe62k+UH7Q81XObhX9b7qO6lRFR6+yFyaqdapyb30+mruKJnnljFDVBH09Qn3XsaNaRtdpTaB5oXtpTaA9kPJf/Y7itXy/+1qi63FX/75ENUljv1oj/pbwfywaY4wxxhhjjDHGGGOMMcYYYzriPywaY4wxxhhjjDHGGGOMMcYYYzriPywaY4wxxhhjjDHGGGOMMcYYYzriPywaY4wxxhhjjDHGGGOMMcYYYzrSWVX3VaiI9JJAbUWkk4SKCRUKrYigR2RBURIYVXHPirh4RMSb3/zmpk0CwIQ+C4l7kk3HigRHtU8ReR6q4qU6VlWxZBU0JXHfiogt/R7NjUKiwHov6jeJFy9ZsqRpqyB4BAuHaz9pXuj51Eb3JvFynb8tttgi+agIOYmSV0SWyYdiqqtxp3lUFRfX/KgIa0fkeSfRdULnisaFYr8r41Ktr/rMlAtUpyp1siJCTmNO/dR7UV5Rfih0nQrWUz9JIFvjoHv37smHnk/nnWKsItbeVWH2qhi2/l4lZ8lG/SSbPjPdm8alkh+vlQB49d76fNXfr4xB5bqq0H3VT6nEXlf3LQrVFtoHaj7S+ky1Wm20XlJt0XynNZv6WamdRGXM6V5a3yr5WKk/EbkuV9ds9aN+V/pJv0frVWWdoznWfRHNMcWZPg/9XmXeK/emdZbGoLIXJnSM6d6UM/p71X0SjfFrBe1z9Ty3atWq5FM529D+WPcblbpFUC5svvnmyaZxTvNCfdBaVtn3RuRnpv3rUUcd1fFeOgcRtf0c7aF1XKjfXV0/6DrNbTqD0fPpvdauXZt8qEbo823YsCH5UF3U/KM80zij36dapvemeenq+kHX6e9RP19++eVk69GjR8frKnu86vsHHSuqwVQ3Kmda/T2aT4o7PctQv+leNKcVH80jWj9oDHTsaP2gvuvvUc2nGNY+kI/mbGWvQVTX4sr7zsq+kOpy5R1eNY8Vej76PV1n6Drqu/pV3nfQWNL8aZxVz9k6LpVxerU+VOjqWVH9qmfAyvNovFB9pXzU6+idDK3HXX0H8teExkljuFrLNDaq57I+ffo0bdoHVuKlq+9NKjHV1fPIa9kHQvdTtBbS/Ok80B5a57O6x/xrj9V/Ja+/jDXGGGOMMcYYY4wxxhhjjDHGvO7wHxaNMcYYY4wxxhhjjDHGGGOMMR3xHxaNMcYYY4wxxhhjjDHGGGOMMR3xHxaNMcYYY4wxxhhjjDHGGGOMMR3prCj/KpBoZUVstwIJlZLQq9p69+6dfEj4ddOmTU173bp1yadXr15NW4XvI1iMmoQ6FXoWFcAlMVEaz4pQKIl76xhXBc4VFQmP4DHX8av0k4TZ6fdUwJWel8Sh9fkoppcsWZJsM2fObNrDhw9PPiNHjkw2nSsSh608H8UB9XOrrbb6i23qg+ZGBAtPb7755n+xj6/WT4Wu66ogb6Um0b0p9jWGuyogTfeuiLVXhNGpT2TTOK/muvrR2NF12neq55V5oHpONUGfmcZu/fr1yaY1nmqu1qlKfY/Iz0JjTjalGq8KzUulnnd1jsmHYlHvX3kWun9Xn++vee+u/n5XqdZJ9ate15X4pDmn9YNsSs+ePZNNazzlI9l0bqgG07hoTaAx2WKLLZJN11Va18n25z//uWnTs1A/9TpCx46ehe6jftU413tV1lmiul5pTaLraI+g80c+ZKvkh85fNT/0bEFrGu0fuzqe2k/6vcraR2t9pU90lqIx13z805/+1PHeETleKM7Vh85pL730UrLpuFTXD81jqkk0V5XzgO7PI/I80PgS2ocNGzYkH9o7aXyuWrWqow+d0+jeCsUd5YeOXfUMr2NH80nzp350Nq3skyp9isjjUDmf09hRTFXqJN2rkh+VflIcVNZxyvXKelk5gxH0fDR/mleVsz+tFZV5oH7T72l80nU05pW9BaHjQs9HNVdzm3Kd5lifh35P44zGiWJR712JsVfrg0JzrPeneK3s9elZKr9H0LxrjaXnpbpR2Qvrvej3aR2vvDet7LPp9yrvaat76K6eKbv6Dk33DfS+pfKeluKn8h6K5oFi47+bLbfcMtm0TnXr1i350PpfySvap3TlPVB1b9rV9x2V9YqoxOtr5RMR0a9fv6ZN41vZl1XeOdN9qnXjjYr/Y9EYY4wxxhhjjDHGGGOMMcYY0xH/YdEYY4wxxhhjjDHGGGOMMcYY0xH/YdEYY4wxxhhjjDHGGGOMMcYY05EuayxWvj9L30bu6rdlSddh0aJFTZs0uUhrYs2aNU174cKFyUefj7Qfli1blmz6vXL65i99k1q/RTxgwIDkQ5pDCn0Pnr5RX/n2MmnyfcRR4AABAABJREFU6fPRvem7w9ovms8VK1Y0bdK1pPms6KxUNCqo3/SNaO07xTR9t77yTWzSCtDYe/zxx5PP/Pnzk03jheZT7015Td8FVxuNXUWTh+aOxklrCf0e3UvnoapVojbK44p2INW2imZUV78nXtHyq+pFVbTZKt9np5gmKpojNMf6ey+++GLyeeihh5Jtp512atr63fWI/P37ql5kpz6+GpW4q+hhVjVBuzrHXdUc7Uqfqj5dHQPltdQceC11F7uqldhVvY2Khos+H9VXuk41DSjXKdd0T0D1gGy6ztG6TvGi91q6dGmpn8OGDWvapMlRWedo7ipjXFl76d5Uqyv6e5V+kpZIZX0kTaeKLhjtwWj/WNEbr2gcVvKjqimtcU4aIBTnFQ3Sin4ijV1Ff5N8aK50XKrakzp/1X229quiw065UNGeonkhHdZKvHZVN6uiqVbVPNUxr2rW6VjReOq9q/Gqulk05pTrFQ0gqjeVXKccVT/6vYquFOUQ9bMSU0pVq72io1c5B1bmhe5F40RnWo0NGgOah8r+sTIuFIsVnWmaT/WhZyENOY2Xqmauxjn1qfqOQKmcLWjsKrq91Cfae2udopzVflLtJip7t4ouGdVSmmONBRonGk/9vWo+VrS2Kc60n5QLlTNt5fcoDit7xepZsas6xV09Z3fl3lXo9xYvXty0V65cmXx22GGHpk35sXHjxmSrvBuvaDO+HqC6cdtttzXt7bbbLvkMHz482TZt2tS0qcY/99xzyTZjxoym/Y53vCP56NhVzihkq9Qt8qvqY3b1nU9Xr9NxoHilWtKrV6+/eJ+Img7r3zr+j0VjjDHGGGOMMcYYY4wxxhhjTEf8h0VjjDHGGGOMMcYYY4wxxhhjTEf8h0VjjDHGGGOMMcYYY4wxxhhjTEf8h0VjjDHGGGOMMcYYY4wxxhhjTEeyenORiuAnCbGSTUUySUj35ZdfTrYlS5Y0bRJUVbHNiCy4Sdep4CYJKi9atCjZVOy6T58+yeell15KNhVsJeFQFdaOiNiwYUPTnj9/fvIhAd7u3bt3/L1hw4YlW79+/Zq2zkEEz9Xy5cub9rJly5KPPt/o0aOTj/ab7k3jO3DgwGQbMmRI037LW96SfEjgXGOYBGopXrRflAskbN2jR4+mPX78+ORDAsoaUyTSrXFOY0C5vmbNmo73rtSIqog1ieQqJJKrc0PjS3mlkIg9zbE+T1WoXKmI0dPYUSxqnFG/qZ/ah+rv6b1ofGmuVGie8oPyUaHaMnv27GSbMGFC0x4xYkTy0XmnPtGzqI3GriIY/9cWLtd+0bNQP7sqmq33p9/rqkj3X5PXo4A88dccJxoDjQ3yof2V7p0WLlyYfH7/+98n2+GHH960Vfg+gmt17969mzbtd6jv69evb9pPP/108pkzZ06y7bfffk178uTJyYfqotZAgvJRay7dR3Otus5WcrZio/ms7nMVmmNdi2icyKb9pDWNxlPHisauUuNp7HSdo7ymNZv2b8rGjRuTbenSpU2bcnb48OHJttVWW3X8PbqXQs9C12m+076T9jc6nvR7ldyjfXYl7miOKYYVygWNs+p6qddR7lX2j7QHq9QbrcERORZpzrfccstkU+h5qU+V8wChMVWtnXp/2j/SvbSfNFe6NtG9KBa1D9Vn0fGk6yg/9F4U95V9Nb1XoLO+xgLViMrzUZ/oOs0Hyg/qu77LoLqlNqojdJ2uA5V3RwTlUOXMTnNcOdNSrtN1lZiifurcUAzrWFXmhe6l718i+Fl0jGmOKzFMcUf1XO9fXa90PGnsaI9X2c8RWjvpWXS9qNYynb+uvoeiMa+8J6m8G4uovYOpzFUl9yIiLrvssqZNa4y+N9V3whFcO3VtqMbd6xFad+64446mTbVszJgxyaa53bNnz+RD68d1113XtI8//vjkU4kfQmOjq+8/Knv/iNp7qK7c59X47ne/27T1bwgREZMmTUo2PdfTfqfy7q/yHvONjP9j0RhjjDHGGGOMMcYYY4wxxhjTEf9h0RhjjDHGGGOMMcYYY4wxxhjTEf9h0RhjjDHGGGOMMcYYY4wxxhjTEf9h0RhjjDHGGGOMMcYYY4wxxhjTkS4rSJKoswp1qoB0BItdqsAoCSOTALAKmpPAMYmHar9ISFP71KtXr+TTo0ePZNN7bbfddsmHhG1nzpzZtF988cXk07t372TTZ9lmm22Szw477JBsOnbPPPNM8nn66aeTTYV6t9pqq+Rzzz33JNvgwYObdt++fZOPztVdd92VfOj3VOSZ5pyeb88992zaw4cPTz6vvPJKsmmczZkzJ/nMmzev471IEHfXXXdNNo29tWvXln5v6NChTbt///7JRwWG6T40diperPEUwWM3fvz4pj116tTkQ+Ny8803N+0JEyYkH8pHfb5169YlH829iDwPGmN074ict6tWrUo+JMQ8ZcqUpk3PsnHjxqZNwtpUy1REnuoPCc1XBJVJgLvis2HDhmTT+lYVfu7WrVvH62j+tHbSuqPrFc0dXae/R2vhpk2bkk2pCLpHZHF48qExUDH6P/7xjx37FJHjhfpJ8aKxVxG/jsixSOLpGvtUkwidPxonEuBWP5pPWrM1hujeOi8ReY5pzCn29fk0XyI4XrTe0FzpvWgMKnvFq666KvlUavysWbOSz89+9rNke+9739u0aZ1dtmxZsuncPP/888nnueeeS7bTTjutaVO8Us3Veaf5rNQEmk+1aTy9Wp80Z6kGVvKY8pH29VqDaE2j6xSKO7pOx6F79+7Jh/Yy+ny0Zlf2fLR+aLxQHaG9jD4z1bI1a9Yk22c/+9mmfcwxxySfiRMnJps+D9VuqmUaQ/R8FC9aJ+64447k8573vCfZdtlll6a9evXq5KMxTM9CsVjZJ1Fe6bzT81b2FpW1IiLXElrrn3zyyWTbaaedmjblAp1JRo8e3bRpz6d9p2ehPO50nwieB41X2gvT2OmZj3KP9nhqq8aUjnFXY5F8dDypJtG46L3pOho7vRftdyprGu0tPvjBDybbmWee2bT322+/5LNixYpk0xylflbOTlRzaVy0xtNcaVzTfYjf//73TZtynfJKn5nGgOK8Uss+85nPJNuIESOa9kc/+tHkU9kD0bu/Sm7TXOmz0BjQPqJyFqZn0bGi/VXlTEQ+tH7Q/l+hWj1w4MCOPpUYpnmhedAYpufTd2N6ZongONcxqOzvCBpfWos0Nig/aE+i7+wofqjv+nwUw/QuVd9pPfjgg8lH+05jQPmosUHvxugcQWvDX8un6lepNxTnlTWzer7Sdafyfo7uc/fddyfbpZde2rTpHff222+fbF/5yleaNr3n/8IXvpBsjz32WNOmd9VPPfVUsr3rXe9q2oceemjyueiii5JNx5jObuedd16yzZ8/v2l/5CMfST46L7T2Vs7Zb2T8H4vGGGOMMcYYY4wxxhhjjDHGmI74D4vGGGOMMcYYY4wxxhhjjDHGmI74D4vGGGOMMcYYY4wxxhhjjDHGmI50WWORvhGr3/itaEHRdfQdYvoGt96/oqcYkb8JTdfp973pO9b0LPrtdfq+Lmm46LfC6Xvb9B1g/Xb2woULk8/SpUuTTb/nS89S0Wuj8aXvI6vO44ABA5KP6nTQ99JHjRqVbJMnT27aNJ/3339/sqlGHWksEvptedUyjIgYO3Zssum33mfPnp18SNdSx4q+4b5kyZJk0++l03fd9bpHHnkk+VAeDxkypGlrvkSwTujy5cubdkVfJCJrW5Emj2oARORnJk0uemadv6233jr5PPHEE8l27733Nm36bjY93+OPP960p02blnz23Xffjr8/Y8aMZDviiCOats7dq/XpoYceatqkK6P3jshxTpqrixYtSjatU1Tv6JvtJ5xwQtMeNGhQ8qH6pvlBPj/4wQ+a9v777598qG5o3ad14Lbbbku2MWPGNG2aK1ofVS+B8qqif0PaAaTFoGv2ggULks/ixYuTTf1oXacaP27cuI59Ih0JhXJG9wM0TqRLotpTpF9A+h46f1Q7K7pHlb1URF6Pae9GewQdz2effTb5aM0dNmxY8qFx0dw++eSTkw/tIzSmVq5cWfo93XPRdbTf0LWX8qNnz57JpvWUnoXmSvcuVAOplui9SH9XayfFAY1Bnz59Ov4+7VcrOoiUV1q7KIeovmk9p7yi9biig0bodaSjqb+nGuURHIsaL6QJRhrPOqc0BrRn1zpMcU61WuOc5pxsWnNpjun5KppKpPmj9++qNjSNZ0XrplKrK5pnETXdNZorrYtU87/zne8k2z777NO0/+mf/in50L6MasIbAapJOlfkQ+cyPX9Q7SStKV1HKab0LEX3pxqvsUF9opjSNZS0oGjfojbKq8p+p6LVHJHP0LRnp3cwmms0n9RP7ReNC61Xuoet6C5RbaOapPFCexSaY70X1RHqp44B9YniVfc3Fb04orpm6zPTHOvz0dxV4pXyiu6lfadnofcdFe1Z2gvTPkWheq65RrFR1RetoHslGk99Z0h7hop+I+2JCK0RtJ8j7Ul9L0sxPXLkyGTT9yT0LPT+QWvgCy+8kHwoFnWMKe40zilnCc2rwYMHJx86t7weoVjUGKr+HUPHmPKTbHo2pb2izjG9//jd736XbJMmTWratIZedtllyaY6ul//+teTD6HjSbrIH/rQh5JNY+rhhx9OPnT21vcNVDco9nX8KPb1jEJrDOnc057kjYr/Y9EYY4wxxhhjjDHGGGOMMcYY0xH/YdEYY4wxxhhjjDHGGGOMMcYY0xH/YdEYY4wxxhhjjDHGGGOMMcYY0xH/YdEYY4wxxhhjjDHGGGOMMcYY05Gs3lpEBVwjskgliZeSkK+KeZO4N6H3qorYq8hpRfiZ7k3Pp2KpFXHoiCzyTn0iwVgVu501a1by2XzzzTv+nrYjWIBbf49EZSvCtjSeQ4YMado0Bm9605s62mjMSZx506ZNTbs6Bvp8JEKs4rfUTxJiJ/FZ7QP9Hgkx0zgoKg5NYrTTp09PthEjRjRtylkV1o7IorUqCB7BsaE2el7qgwqcU/yQ6LH+Hs0V5Zrea7vttks+w4YNS7a77rqrac+YMSP57LTTTk2b5peEw7faaqumTbWbxIuXL1/etJctW1a6bsGCBU37qaeeSj677LJLsu27775N+7nnnks+ixcvTjaN4Zdffjn5VNYGiqklS5Y07XvvvTf5kIC7QmP3yCOPJJvWQKpbVBc19imm6Tpdw2hNo3jROrF06dLkc/nllyfb+PHjmzbFz/XXX59sOu4nnXRS8tH5vPnmm5PPY489lmw9evRo2lSTaB5UbHvo0KHJh8ZT6zmtz5TbOse0Nmm9i8jjMnfu3OSj9Sci4tFHH23alB9aTymvDz744GRbt25d07711luTz/bbb59sI0eObNpPPvlk8pk5c2ayXXvttU1b1/6IiJ49eybbkUce2bRpfdQ9UUTEhRde2LRpbaJ9ksbi0UcfnXz69u2bbJqjffr06din1atXJx+dl4iIsWPHNm1dhyJ4rrTu0zpAz6L92rhxY/KhXNttt92aNu3naMw1P6h2Ug3U+1PuaX2j36cYfuaZZ5o21ZHRo0cn25QpU5p2//79kw/FvtYgWivmzJmTbFpLqEZU8njFihXJR9feiFxvvvSlLyUfWlP0nLLFFlskH50/WrNp/6G/t8022yQfGhe1VXIhIscGxavu+SLyvpNqGZ2XNc5prmgt0jWT5oX21f/d0F5RoT0C5ZU+s+4ZInj9f+GFF5q2rgsRvF5169ataesePiLvjynuCL2OagTFotau6vsdhWowzYOufRRjtGZr7FMuVN5p0VxR3/XcQs+iczNw4MDkQ/Gj50DKT6qBL774YtOmXKDn0z5QP+nsrXND+2x6Pu0n7VvonD1o0KCO1+lZhs75ep+IPC60h9f3JhG5buizRXCt1rX99ttvTz4Ud29961ubNu1J7rnnnmTTdxKrVq1KPocddliyTZ06tWlrjYqIuOiii5JN4+XQQw9NPpV3x7/4xS+STc/sRxxxRPJ5/vnnk+2CCy5o2lQD9R1XRH4nMnHixOTz0Y9+NNn0fQDl0J133pls5557btNeuHBh8qHc1vvTeqVjTHsNyiutd7TuVN4dvx6o1KTzzjsv+VxxxRXJpvNAawydgXRPS+/+hg8f3rS//e1vJx96f/Wxj32sadO8UGxoHtN7ITqTHHTQQU2b3jlTP3Vd+/GPf5x89D1GRD7L0FmR9vpah+ldo+YQrZf0NyAa4zcq/o9FY4wxxhhjjDHGGGOMMcYYY0xH/IdFY4wxxhhjjDHGGGOMMcYYY0xH/IdFY4wxxhhjjDHGGGOMMcYYY0xH/IdFY4wxxhhjjDHGGGOMMcYYY0xHsqJ8ERKDVtFKEqgkcW+1kQAv2VTklASOSZRT+/4//2ceBhWMpd+n61QsmQScaez0OhIOpz6ogCv93s4775xsKrhLwq8kvFrpE82xPh/9norY0n1INFfFr0mwmsZTBXH1PhEsTKyxQXGwfPnyZFPRahJPJzSvSDSb5kHzT2MlIgsO07MMGDAg2VauXNm0SciX7qU2qhF/+tOfkk1znUSsKV5URJrmk/qgtYTEr1WQOyLn2pQpU5IPiWbvuOOOTfvXv/518nnqqaeaNgmHU/6rjeokxaLWTppjmgcVLycx8169eiXbHnvs0bR1TCJ4HrQmUN2iuqGxSDVip512atp33XVX8tlrr72SbejQoU372WefTT405oMGDfqLfYzgedeaR0LQJAqufaDrSDhcx4pqxBe+8IVk69mzZ8ffu/fee5NNY59q/IoVK5r2HXfckXzOPPPMZNtmm22aNuXHI488kmw6N5TXJDyv+UhzTOhYUZ0k9HlUuDyC16vTTjutaasQfETE9ddf37Rnz56dfPbff/9k23LLLZs25fWkSZOSbdSoUU173LhxyYf6MGHChKZNousamxE5H2k+//CHPySbril07/Xr1yebxrn+fkTEoYcemmyaj5dffnnyefzxx5u21tuIiDFjxiTbY4891rSff/755EOxofl4wQUXJJ9tt9022XQvQ/WOarXuq2mt1z0Y3Z/WUNpfab5TbKjt1ltvTT5XX311smlMUX29++67k2277bZr2u973/uSD50/NBbvueee5HPJJZckm44djdPTTz+dbGeccUbTpr037YE0t0eOHJl8DjvssGTTGrR27drko/H5zDPPJB/aW2gcUH4ed9xxyaa1THM/IuLCCy9MNs1Himna6x9xxBFNm8ZA7x2Ra+V1112XfGgv85GPfKRpT5s2Lfm8HqH8v+qqq5r2DTfckHxoPdY1e/Lkycnnn/7pn5JN8/3nP/958qF9Sr9+/Zr2Qw89lHz0+U444YTko/veiFpdrrzfIZ9Kza3W5VtuuaVpP/zww8nn0UcfTTYdO82XiIj3vve9yaa1i/aKX/va15JNawnl0JAhQ5r2xIkTk8/b3/72ZNN1lc6ql112WbI98MADTVvfD0TkvVtExAEHHNC03//+9ycfWh+1dtJe+IUXXkg2XRto//jud7872aZOndq0Na8jIn7xi180bdob03tFjWtam775zW8mm9bv3/3ud8lH5yUiok+fPk178eLFyYfmSvt1++23J5+bbrop2XSvre9WIiK+8Y1vJNspp5zStE866aTkQ3t2jdnKOYLOkzSeerY48sgjk8+SJUuSTeOF6iTZDjzwwKZN40Q1/rOf/WzTpncptE/S9YPOvYMHD062c845p2lT7Os6R2fxynvTaj1/PULvUrQm6HoSEbH11lsnmz4zjSfVYbXRGWHZsmVNe9GiRcmH9giajzRXtI7rOYKuoz7ou0WKH33nHJH3QLQn2X777ZNNz6u0F9Y1LSJixIgRTZvO51qD6P0ZjcvfEm+MLDbGGGOMMcYYY4wxxhhjjDHG/LfiPywaY4wxxhhjjDHGGGOMMcYYYzriPywaY4wxxhhjjDHGGGOMMcYYYzrSZY1F+saw2ugbvKRDpH6keUbf3B04cGDTpm/b9+7dO9n0u7z0Ddw///nPTZv0Eyr6VzROpI2g96dxom8vV/Sa9FkisoYLfS+dtIP0eei7+TR/aiO9Nu0nfR++R48eHe9d1X7QMa7o9tDvUWySXoJ+S5tiisZcNRzo3pW8oljUeKH5pN97rXSJaK6on9qvqr6g1ojnnnsu+VD+q3ZgZXwjspYW5Trp3+j30Sk/NF4oz2iudDzpOqotOsZUW0gbSXO0f//+yYc0alQbZfTo0cmHdNdUK4DyiuZP853iZ/z48U2b9P/0O/YRWf+CdO3o2+/6zXYaX8or/b4/5RDFa0XTgOZd45zih9Zj1QGhPCabakvQfFbW3lmzZiWb6m+qHl8Ea6XqmNM6S+i6RmsMxaLGNflQbuva/sQTTyQfigPVAKP1WHUkSEPuwQcfTDbVJSUNQhpPXWtJ24+0LTSPac3uqj4trZmHHHJI0yZtJNJ5VC2muXPndrx3RF7DKPf23Xffpn3ssccmHxoXrd8//elPkw/pZuh1NMekm6M6SFR/KF71/lRHKhqZFb3qiJy3pOOraz3pGRGqwUOaxL/85S+T7f7772/aFD+6J4rIz0x74S9+8YvJpjXwtttuSz6XXnppsqkW5Dve8Y7kc/rppyebaj/+4Ac/KF2n6zGNnebMe97znuRDGnmqz/LJT34y+ag2S0SeU9X2imCdp49+9KMd7037DdUhU23hCF77NEdJm3XDhg3Jpvsy2iPQGv3fDemLagyTnpFqiUXkMSc9VYrFD37wg0173rx5yYe0LlUXiLTg6J2IQlplFc1DgvZqne4dkes+1WCquaqpeOqppyYf2s9p/lM+Dhs2LNn222+/pt23b9/kc9RRRyWbro+Ux6qt95vf/Cb50H5H85g0+n71q18l29FHH920qZaS1pXu66n+0LsN1RfUOh0Rce655yab6qcff/zxyWefffZJNl1/r7zyyuSjGoQ/+9nPkg/td1SD+Mknn0w+lAs6BjROtA5oXf7whz+cfCg/NI90zxARcfDBByeb6v3R2ZTWTK2dNC9Dhw5Ntvnz5zdtOidp3dD3Nq9278p7U6qdmv/0vKqnSPci3elnn32243Wk+0z6qR//+MebNuk+0nuSHXfcsWnrO5mIvDekOKDx1LmhOH+jaM+R9qSOC+UQ7XMVqhF33nlnsv34xz9u2rRmq+YhnX9IS1hjmPZ3dCbRXKO1kM4Weq6ms3/l3SbVO6oJqjlK+w96T6vjSTGssX/33XcnH3onqu8o3sj4PxaNMcYYY4wxxhhjjDHGGGOMMR3xHxaNMcYYY4wxxhhjjDHGGGOMMR3xHxaNMcYYY4wxxhhjjDHGGGOMMR3xHxaNMcYYY4wxxhhjjDHGGGOMMR3JCqtFVHA4oibSTeh1//mf/5l8evTokWyjRo1q2lWhUBXcJKFQvReJzJOYqAoM07OQQK32k3z+4z/+o2Mf6HnvvffeZFOR7KqQrs7Vyy+/XOqnCqH27Nkz+VTuo+KpEVmwlcacBIYVijESRtb7071JUF3HYPXq1cmnd+/eyaaxSAK1lHs6NyRiq3FAotLPPfdcso0ePbppr127Nvls3Lgx2VTYlgSHaf4UEtal+VNR3qrQtM4f1YhKv0gAnMTEVbib8vF//+//3bRJfJvqlPadco9EnTVeKrU0Itflo48+OvksXLgw2VSg/oEHHkg+JHCuc0zjQrVE44zqnebjyJEjk8/cuXOTTeeKxK+32WabZKMcVd785jcnW0XEmmqEjgHFD8WGPs8999yTfKiWjB07tmmTiDUJo8+cObNjn8aMGdO0p0+fnnxuu+22ZNO+0xxrvyMi9t1336ZNY0c1UOeGhMMpFnXeKQ7ouhdffLFpz5kzJ/lozkZEfOYzn+nYT91/UJ5RPmodpnpO46n5QXFONUnjfKuttir9nuYMjTmtH1tuuWXTpnWH9o+6hul+8tX6oHO6dOnS5PP888837a9//evJh+JHWbNmTcd7R+QzAq3rNA8DBgxo2jS+FC+vvPJK06a9BcWwxqzW7ojauFB91VqmuRgRccIJJySbjhWN3YEHHphsd955Z9N+4oknkg/tP3Qedtttt+RDNbBy/rj55puT7fHHH2/aBx10UKmfmu90DqX80DX6wQcfTD66L6KaWKnVw4cPTz4zZsxINl2vdEwiIg477LBkO/HEE5s27S2GDBmSbJof3bt3Tz6XXnppsikUG3rviFz3aQ9N8fLfzZNPPplsWoPe//73J5/dd9892bRWUv5fe+21yXbyySc3bXq3QXun0047rWlPmTIl+dA6p1AN1L0M7cGoTlXeC1GN17wiH9ovn3TSSU379NNPTz6UH7rHfN/73pd8tL5GROy9995Nm+blmGOOSTZ9t0D7JD0/0j5Cz00R+d0Cxdg+++yTbBp3WqMi+B3MihUrmjbtoa+++upk0zPCd77zneTz8MMPJ5vu7XXOI3gd0Pmj9xbf+ta3mja9V6D3O7rnmj9/fvIhND/o9yZNmpRsH/jAB5o2rZcUU1deeWXTpvih2NB7UV5//OMfT7Z/+Id/aNrPPPNM8qH3JLo2UN2iOqVQTaqsTbTn0309va+jHF22bFnTpvmkdxv6fLR+EPpugeaKzkla49etW5d8dB4oz2ifpH4Um5V3f68HaN+pMUTnD6qdesajMxGtfVrjKRe0DlOfaB0/+OCDk02hs76uaTSflXW88h4zIp8VJ0yYkHx++MMfJpu+96Z6d9999yWbnj/222+/5KP78V//+tfJR/fwERHjx49Ptjcq/o9FY4wxxhhjjDHGGGOMMcYYY0xH/IdFY4wxxhhjjDHGGGOMMcYYY0xH/IdFY4wxxhhjjDHGGGOMMcYYY0xH/IdFY4wxxhhjjDHGGGOMMcYYY0xHsorm/wMqEEvit2RTUU4SdSWBWBWfJ9FTEq1VYVkSE1UbCZUOGzYs2Sq/T7+nIsBbbLFF8lHhYLJtt912yef5559Ptp49ezZtmhdCRWtpXAYNGtTxPipwGpFFj2nOCR1jFX2O4NhQYVkSKqb502d+8MEHk8+cOXOSTeP6pZdeSj4TJ05MNhXzXrNmTfIhsWTtO4nfqgj57Nmzk88tt9ySbOpH87l48eJk23rrrZOtgj4fCaPPmzcv2ZYvX960VVw4gvNRha1JGLlv374d+9C/f//kQznz5JNPNm2Kfa0RFK+vvPJKsuk8qOBxRMSiRYuSTe9PfaKx0+tUmDki4sADD0w2rUHXXHNN8nn44YeTTeOaaifVt0p+aL2bPHly8rntttuSTeNu3LhxyYdiQ8W9acxpPFUAfLPNNks+FUg0mwS4VYz+7rvvTj7Tpk1Ltre+9a1Nm2Lx0UcfTbbHHnusaVPt1Gc+4IADks/UqVOTTZ+FYuyee+5JttGjRzdtFUqP4HxUaI61/kTU4pXWAb2/7psieC9z8sknN216Fh1zqknbb799sula//LLLycfzb2IiLVr1zZtWneovuo+qVLzI/KYa78jeP7e8pa3JJtCNUnHk3KP5lj3FnRv3QOpgH1EriMRuZ4eeeSRyYfWdb0X5TrNsT4LjTnN8eabb960aexo3vVeVAMr+3jaI2iOko/uwSLy/K1YsaJ0ne4VKQ5ozP/4xz927CeNi+Zjr169ks+oUaOSTcdu48aNyUfPKBE5r6hPhOYM/Z6u2WeffXZHn4j8zDqWr8bq1aubtsZvRMSee+7Z8TrqE+Wajh3VO7LpGNM6QDatG1RbXo/oXjwi13jazy1cuDDZdE6nTJmSfK699tpk0zM7zYueByLynoTQ2K++p6nsKaluUO2sXKdxVzmLR+S40z11BK8pgwcPbtqUQ0uXLk02HSs6n5977rnJ9vjjjzdtyiHdU9K+l9Y0nSvqN70r0nc3tJ8jdE2hvSnNn8b517/+9eRz6KGHJtsZZ5zRtOmdD72X0X3gmDFjko/uk+hZaN1RP5or6mdlH1HhmWeeSTZ6Z6h9oDWbYl/3IPROjc4WOu/0fJW9t+5tCFpjKnvFyp6a7k/PQudzHU+6N6ExRXFHe8O5c+c2bXrXQPsNrYFU87VO0TmCcmblypVNm96f0Xh2NR/+mlC8aF6RD82frh90XeUcSOvHwIEDm/Y73vGO5PPZz3422fSdIdXgGTNmJJvyj//4j8lGa4rGIs05rdm61h988MHJh+71ve99r2nPnDkz+VAM77fffk2bYviFF15o2lQn6T3U3xL+j0VjjDHGGGOMMcYYY4wxxhhjTEf8h0VjjDHGGGOMMcYYY4wxxhhjTEf8h0VjjDHGGGOMMcYYY4wxxhhjTEe6rLFImjj6LWv6PjNpz+i3tEmjovLtfvq+N31zW7+BTd/E1u/y0/eLK5qHlW+H03WkL0LPohpD9C3t8ePHJ5s+c1VbS7/rTGNO32zX56P40W910/jSuOi3l2k+hwwZkmz6TWyaY/qu+4477ti0KV4p9jWG6Xv722yzTbJpvNC8kEaWfl+bvt2tmnE05qTRqbFIeg30bXt9FtU3i4iYMGFCsuk3qe+6667kc8EFFySb5kNVO0jzf+zYscln3333TbZLL720aZP+HtVA1c2heFW9DYpzGvM77rijadM3zklj8YknnmjapKNFMfXII480bdKs2XnnnZOtoitFtVPrzZIlS5JPRYuNxk71L2gMbrrppmRTTZPDDz88+VAcqE4P1XNaB1SzgeaF0DpF2joVPQGqZTRXmn+kC0LxonollMcvvvhisimkYbv//vs3bfom/k9+8pNk03WH1sLKukprIaE6BKStQ2uDrmGksUZzrHob9Hu6XpCuLvVJ45X0PkgbSTUVKA7o9zQ+K/tX6hfpKdJ12i/KK9pvaEyRzgPtKTWPK2u9xn0E60NpXNN+h55FtSZoradnqep9K5prNC807xqLtK7SM2ts0HW6NlGc0zq3xx57NG3Vp4moxTBp+dC9NM4o12ledMxpbaKY0r0+rYWUx9oHmuNKP2lcdE9yyimnJB9a03RvQX2ifb3uEUgrkdYi7Ttp2pOGpM4p5R6hNZfWtIr2G8XU6xGKRa3LtO5U9lwUm7SmVO5N86D9ohqhPhSvZNOzd0U7kaC8Jluln5THWqvpLKVrE/WB6nlF5/Hyyy9PPvfdd1+yfexjH2vaRxxxRPLRHP2nf/qn5ENrk15H63NFu0zPpRGsK6U5U9EWj8hjR7XszjvvTDY9V5MGGGme6xzTubey/6DzTmV/TjmrtYVqN6HPQuePilYq7Qdo3unspNAZTGODtKGffvrpZNMYovzXHKX3VzQPel1lDx+R553mk2Jfc5T29VTLdP7o/QNd9+yzzzZtelelPhH5vRqdwSpxru/BIrKW8Hve857kc9BBByUbaev9d1PRXa3Osa7t1TOtrse0Dujv7bXXXsmH1pQbb7yxaV988cXJh+qBzh/lx8knn5xsGmeV9+cREatWrWrauleNiDjhhBOS7ZBDDmna9H6Q1jmNfaob+v6R3o3THvNvCf/HojHGGGOMMcYYY4wxxhhjjDGmI/7DojHGGGOMMcYYY4wxxhhjjDGmI/7DojHGGGOMMcYYY4wxxhhjjDGmI/7DojHGGGOMMcYYY4wxxhhjjDGmI11WUyfxyd///vdNm8RLSYRUBT5JsJ5EiFUwfu3atcmnW7duHfugAqB0HYk1kzCpinmqiHZETTSbfGjMVYS4IhxM96Lfq4i6k6g83UtF5GnsVASYRIFpjrUPJLDep0+fZFM/itc3v/nNyaZi8BQbNFcadzQvJNyt96JxIVHeCirSTc9L99b8WLp0afK55557kk0F1QcMGJB8aAwmTJjQtEmkV2OMrqPY1LoVkWOBxH1VPDki4ogjjmja8+bNSz4kMD516tSmTfGqDB48ONmOPfbYZFORbhKsHzNmTLKNGzeuaVNdpjFXgXEa87lz5yab1srevXsnn+OOOy7ZKqLZO++8c7JpLenRo0fy0RoxcODA5ENzpffq379/8qH6qrWM6nllTaN1lgSxdeyotlCt1pq3//77J58rr7wy2TQfqN6QwLn2i8ZF5+pnP/tZ8tl1112TTeNl2bJlyWf77bdPthEjRjRtWuuptnTv3r1p0/NSrmke0XVUW3TvtNtuuyUfmisVKqf80Gd+5JFHko8KpUfk2kVrIdl0PKlGUP5fe+21TZvq3YoVK5JNx4rmk9C5on0S9VNjmPYydK9Ro0Y1bXq+yy+/nDv7f6C1OyLigQceaNpPP/108qH6qntTqne0d6K1XaF1R/NB4zeCc1T7SWOuORuR47Oy7x07dmzy0diMiNhvv/2aNq071113XbLpWn/QQQclH6q5Og+0B6N6o+vaBRdckHyef/75ZDvyyCObNsU02SrrHM2x9nPfffdNPrpfpXV22223TbYXX3wx2So+Wpf1XBHBOfvlL3+5adNc0Vqv+z6qnZoLERHr1q1r2n379k0+NOa6b6A1W+OO6gHZdN4pNikfdR9BubDDDjsk22WXXda09d1DBOf2ggULmvZTTz2VfKi26PpI80JjrlAcVK6j3NP6Tesz9VNttMekGNazYfVdg8bGpk2bks+UKVOS7frrr2/ac+bMST5HH310smks0BxPnjw52d75znc2bTpHzJo1q2nT81Isau3Ss1wEn89POumkpr3NNtskn/Xr1yeb1vjhw4cnH9o76X78U5/6VPL5l3/5l2RTPxo7XUMjcu2i9xZac0888cTk88ILLyTb/fff37Sp3lGca/7T3p/GTvcptBZSbRk0aFDTHjZsWPI599xzk+2HP/xh0165cmXyufjii5NN6yn1id6lzJ8/v2lfdNFFyUfXcfr92267Ldne+ta3Nm16X0f7QK15NAZ0VtT5o/mkeqr3p7zSnI2I+OpXv9q0aa9Ia4Pu7SdNmpR8dP2gNZTOoQqt2W8UaE+i794ozunvGFrTqb7qe8yIiLPPPrtpV847tPbrXjwiPwvVLZp3rUEU57QW6VmY1jQ6P+o6RzlEaOzTs9B46t6F3hX169evaVPO0tmf9k4V6P1cV3xeS/wfi8YYY4wxxhhjjDHGGGOMMcaYjvgPi8YYY4wxxhhjjDHGGGOMMcaYjvgPi8YYY4wxxhhjjDHGGGOMMcaYjnRZY5G+MazfpKZv4tO3XvVby6SfQt/A1u/dVvoUkb8DXNEcofvQN3873SeCv69b0S+g7+vTN5sV+nZv9VvEis4fzTH1SW10nX7/uaJFSX0iKtqe1Cf6DrheR79f0bGgmKrkB+VCJaYoDvQb7qrRQ/eJyDoA9K1pyivVgiIdG/rGuMYP6QvS81XijnJU84OuI60Q1UKib82ThoL2gcZc+0TfyKfveetY0fPSvfT3qC5XNHFUPzIiYo899kg2HU/KIRpztZEuCNUS1fKi51OqugCqTUBaolST9Pvs9I16GheNc+onxVQlP+hb86q3p3kdwdoklZpEGk5ap1Srle59/PHHJ5/Zs2d3/D2aK9VTjMjaJBSbNFdaq6t7hIomMM2xzt+0adOSD9Wkhx56qGkvWbIk+WgOkc4U6cPp2kA1YuLEicmmY0xagqSRpdqPDz/8cPIhHSQdz169eiUf2kvpOl7R1ojIeUX6UKQ/oeOpGjIRWctPdcNe7d7ad5pPqrkan6SjQzpEmus0drReKRWNnIicH1STqC5q7NN+R7X9KD9Ih1l19OhZaL3Sedhuu+2SD83xmjVrmvYVV1yRfEhXSvX3tB3Bmq6aa6SdTho1ul+kGkg2nSuqEaol+u1vfzv50Lqja9/NN9+cfCimPvrRjzbt6dOnJx/SRjrrrLOaNmkzUl6pDtqHPvSh5EN6WzNmzGjapKNJa59q6dAeobKfqujo0bmJcrZy7x133DHZtFb++7//e/I55phjkk11e++6667kQzmq54bly5cnH9o/0tmpE9VzttYg2rfQvXSOqXZTjdf6tnr16uRD+x1d62+88cbkQzXiwQcfbNpUf0hLWGs87UluuOGGZDv//PObNu2zn3jiiaZNuohHHHFEsuk7gsMPPzz5fPGLX0y273//+02bNJcXLlyYbLpPOeGEE5IPxYb2k/S3qE597Wtfa9qf+9znkg/lwnHHHde0SRP805/+dNP+yU9+knzoXK/xSZrk9L5DayflAq2P6kc1cMOGDcmm7wNIN5T06VWLrXIWj8ixQHsg2pfpHkE1HiMirrnmmqZd1drUdW7VqlXJh7S+99xzz6ZNe2E6s2vsU82n39P3AVQj6Jyte23aP1J8ak2g93pa92mdPeyww5JNz3NaNyN4Huh953831CfVi6UaQWNVWbPp9/ReVDcoHxV6R6E2imnaf2ic0/6uoiVY1QSkdVyp/K2D9hGV9YpySNcw2ifR+5au/k3m9Yj/Y9EYY4wxxhhjjDHGGGOMMcYY0xH/YdEYY4wxxhhjjDHGGGOMMcYY0xH/YdEYY4wxxhhjjDHGGGOMMcYY0xH/YdEYY4wxxhhjjDHGGGOMMcYY05HOyp7/LzcvCIdGZJFMEpolAUwVKyWxTRLEVNFREhNVUVASISXRUxVUffOb35x8CBUrJfFSer6KyCn56BiTqCzZdE7Jp9p3RceY7lPpZ0VUlqB7k6irxgv1k67TfpHYbqUP1X6qX2U8Kc9IIFfnasCAAcln6NChyTZo0KBkU6huaN8pryrzQD4ULzo3VJOotuj49e3bN/lUhJArfaK5oudTG8XdmjVrko3ur1Bd1PuTKDnNn44xXbdp06Zk07Hq3bt38qGc0fmjHNI+XHHFFcln+fLlyTZ9+vSmTWvM0qVLk61SW+heOnb0LITOMeUe1c7NNtusaQ8ZMiT5kO2ll15q2hSLgwcPTrYVK1Z07KeO1ZQpU5LPjjvumGwkpK1Q/iuU1/R8io5ltQ+Ve0fk+aMa3K9fv2Tbb7/9mnZFZJ5yj3L95ZdfbtoHH3xw8qHauXbt2qbdv3//5PPud7872ZYtW9a0N2zYkHxovdJY1DF5NVTUffXq1R19IiIOP/zwpk1x17Nnz2RbtWpV095rr72Sj47nunXrko/mWUTElltu2bQpP0eNGpVszz//fNM+4YQTks/YsWOTrQKtTboW0dpEdVHHgfKR6s3mm2/etCn/Nfb333//5EPz+cQTT3S898SJE5Ntl112adqjR49OPvPnz0+24447rmlT3GnORuRxmTx5csc+Ub+ottD6r2ttt27dkg+tV9r33XffPflovNx///3J55577ulomzRpUvIZP358R1tlvxUR8cwzzzTtp556KvnQGBxxxBFNe9iwYclH609ExK9//eum/YMf/CD5UE1473vf27RHjBiRfHQNo31vZe2t7HsJuvfWW2+dbP/wD//QtK+66qrk8/nPfz7ZdN9J+XjmmWcmm8YCjS/lh+YjjafWLfKhWNRneeWVVzr6kI32r5V3RbRP23fffZNN8+GHP/xh8lm8eHGybbfddk1ba2IEz5++B6J1jn5PzxK6ztLvHXbYYcmH1tCVK1c27UMOOST50Lxff/31TfuBBx5IPjTHe+65Z9OmvdS0adOSTdeiOXPmJB/as2s+Xnvttcln3rx5yaZ1/33ve1/y2WOPPZr2rFmzks+RRx6ZbHfddVfTvuWWW5LPokWLkq1Xr15Nm9ZL9YnIaybthSlHdU9y8sknJx+qN3Pnzm3alI9Tp05NNq37GpvkExHxox/9qGnPnDkz+ehebdy4ccmH3hloDNNZg+qrnm/oOnovpPVt5513Tj60R9Ac1XNMBJ/njj322KZN5x26TseF4kDXBtqbUj3X/QbtsytnzNcDdLbQvSjVSYpFhdbHyvvACrSu070rfaq8K6Y4oN9TW3U/p2tv9e8t2vfq3yx03un3dH9F96F36n9L+D8WjTHGGGOMMcYYY4wxxhhjjDEd8R8WjTHGGGOMMcYYY4wxxhhjjDEd8R8WjTHGGGOMMcYYY4wxxhhjjDEd8R8WjTHGGGOMMcYYY4wxxhhjjDEd+buKcCdx6qmnpgv/4z/+o2mT4CcJmqoAJ/nQvVRIk4RC6V4q1EnCnSpIWxEXj8hi8CQOT33S+9PvUT/1/iQqq/MSkYVf6fcq40nCpBVxVqIi/FoRlac+EXpdVTBWRYdJRLcizkrPQs+s80dz/Ja3vKV0/073pnmqCPdSnJP4tYpIUy6QiL1CY07jorFAPoQ+M8UBiXTrONDzkei5+lE/dW5UJPjV0H5WRZ91DKiOkHC39pMEjqkP2k+KRZoHvVe1/uh11E/N9RtuuKF078MOO6xpd+/ePfksWLAg2TT/qZbR2Ol1FJs0dlo3KF7p+fT3qNbouk6/R89H99K6UYmfam3ROK/Wc811qpOEjguJ2BM6fzQvVIfVRuNLsa9zTDVJx5jWLxKs19pF/aZ6o7zyyivJ1qtXr2TT56PYrIisV3IhIo/LSy+9lHx69uyZbBp7L7/8csd7R+SxorHT9bhbt27JZ82aNcmmcUexQrbFixc37cGDB5euW7t2bdOmeKU81jmt5oeOJ41vZc9Osaj07t072agur1u3ruO9aE3RPlHuUWxoXaaaRGuK+lEOUV1Uv6VLlyafHj16JNull17atH/3u98ln3POOafjvejeGhuUezR2+nz0vLRX03pK40u1TNdCihXKtdWrVzdtqj/Udx2HFStWlK4bOnRo067s2avnSc0Zyk/6PZ1jujfNsdYpHcuIiPXr1ydb//79mzbFAc0DjbFCa8q4ceOatsZKRK6ntIcndK7oWSgOdDypttCYV94HUN0YPnx406YxWLVqVbLpejhw4MDkQ7Vaayydxfv165dsjz76aNOmtVD7UN0nVfKD5mHlypVNu3L2j8ixSHWLqOQV5YfGAu3PK3FN66PmEOUZ5aeuOxRjX/rSl5Jt2LBhTZvqCK312i/yoTHQZ6a1kPZXlVpNOap9WLZsWfLp06dPsml80v5K+07PS3mle0WKH8pjzSNas6kPmkeUe3Rd5VxfecdE19H7Oa2nlMdaW2he6KyofaJYqZzBCKrLXfH5v/FTtO+0t6DaqbbKu4aI2vlc4676957K30go7rTv9Lw0x7qG0d6tsvbSnoT6WYHGXPteeRdH96m8G6vyWsa+0qtXry5d6P9YNMYYY4wxxhhjjDHGGGOMMcZ0xH9YNMYYY4wxxhhjjDHGGGOMMcZ0xH9YNMYYY4wxxhhjjDHGGGOMMcZ0pCYcWES/ZUvf861ocFR0HiIilixZ0rTpO9303XHtA33TfNGiRU1bv4MewXop+s1d+v50RSes+k1cHePKvSNqOoRd/XZvRSejomdI/a7qw71Wfapqa3a6N/WBvj9d+QY2xTn1Xe9V0cOj3KNv1Ksf5Wclfuh74vT96YomKPWzok9Z+RZ6Ra8yIuc76V9QLOpYVeKVfCpaUPQsr9V35SPyN8Vpriq6OaRDQH1X/QCqudQH1Rige2vO7Lnnnh3vE5HjdePGjcmHNM50XOj77LReqdZEdY4r6w7lY+Wb+DTHmqOk/UDzp9dV9C8o9yoaklWN50qcV3VJlcr6XNGPJr+qrpRquFA+VnRQKH4qGiB0na59Ve1bzT8ag0oeV/up/aJ8rKwNpAFEz6w6RJW6TDWJYlPHgHKdYlH1Weh5aT3WvQTt4SsxTPlY1X1XaMz190jfQ+eBNCxJc1ltVT0j1aek8SXdHB1P0pWicdK5Ir0/OifpGvazn/0s+ZBGlZ7LRo8enXz69u2bbF3RlavocUbkOCA9NZ0Xuj/FAa31GouDBg1KPlSHR40a1bRpfGneNa4nT56cfCjOVC+tco6ovjPoqm6OQr9HeaxxTbpkpMmnWmWU/6S1TTHc6d4ROR8pDnSdo3pO+yuaP6VSlyn3KH70bEjPqzqeETmGaa7ofY7maLUGanzSHoF05TRvacx1DOi8XDmb0rzQmV3HuKqHqWNMtZPW0OXLlzdtqp2UM9oHmmPaz73wwgtN+5e//GXyeeCBB5r2Hnvs0dEnIsfGRz7ykeRD8ar7MNqjUExp3qo+ZgTXMrXRHpP2tJWaW+nnkCFDOvpE5PWp8h6BaktF+7p6VtQaSHFOdUprBPWpou1LezfS+9SaQGso1Td9J0HzotdRXtMYqI20b7ta3/6rqbwrqrzjjsg5Q2thhcraVD37V94LVXQfK+cm+j26N9UynQe6rqLtXZ2rrrxLoft0VU/xjYL/Y9EYY4wxxhhjjDHGGGOMMcYY0xH/YdEYY4wxxhhjjDHGGGOMMcYY0xH/YdEYY4wxxhhjjDHGGGOMMcYY0xH/YdEYY4wxxhhjjDHGGGOMMcYY05GshluEBIZJOFMhscuKKDAJvc6fP79pk2AsCThrP0m8ePHixU27Z8+eyYeEdFUslYRDKyKrJHBK46vjSfemudJnpuuo72RT6F5qIx8VOa3ERUQeA4qxrkL3UoFhmpfKXJFILwlU67hQbJAYtI5fJRZJtJsElbfaaqumTWLG1E/NGRL7rjxLdcx17EhIl4St6Zkr0Pwp69evTzatXXQftVFeU0ypqDTNcUWoWIXEI7jmVvKY+lmJc0Kvoz4RFWFrHWPqE60xy5Yt63id5lBEjjuaY/q9yjpAfdB5oBpB+VGp51QTXn755b/4+692LxXuroh009jRvfVemi8RPAY65vR7lMfdu3dv2pSP9HyVvQXlKPWr8nsKzZXWap3fV7u3xiKNAeXx6tWrm/Zmm22WfGjMaVwUig2dY4oNyuONGzc2bco9suk+k+Kusjek3NN70fNqbJJfV+s5jR3dS+eP5pNqtdqqY/fSSy817R49eiQfih+Nda1RERFvectbOv4+oXlFeyLKD31m8qG6sXLlyqZN80Jxrs9X3SOobdiwYcmH5m/48OFN+9hjj00+AwYMSDY9z1H8dOvWLdkUmj8dK42nCB5zXceplum8ROQxp7ij/dy6deuaNo0BnWl1T7J8+fLkU9mvvpbnsgpd/T3KNR1jGnNaw3S9ovHVmI7I807jS3ml8UnX6VpBeVap53RvOs9p/FBsUt2gNUyp7KspXmmO9XmoT7SGVc47NMZ6He2dtE90b7pOx45ihfZl+l6IahK9L9M9JsUG9VP3O+Sj9S4iPx/FHeV/v379mvauu+6afHSO165dm3ymT5+ebLvttlvT3nHHHZMPPZ/mB8Udvf/UGkT5Ujkj0PhSfugYUw2kNVSvo/WR8lhjj2JK36XQGFC9qbwXondAWhfJh/qgOUP7bHo+XVMo/2n+1K8yvhH5eWgd0LyiftO4aAxTzlben71e0RpI9ZXGRZ+ZYoPiU+em8s6H1iGyaWxUfj8ionfv3k17zZo1yYfGRcdu0aJFyWfQoEHJpjWI6hb1szJXRCU+aTwVqsvVs+EbAf/HojHGGGOMMcYYY4wxxhhjjDGmI/7DojHGGGOMMcYYY4wxxhhjjDGmI/7DojHGGGOMMcYYY4wxxhhjjDGmI/7DojHGGGOMMcYYY4wxxhhjjDGmI11WSiXxSRWtJPFkEgHV60iAkwSjVQyWhLVJLFnFi0kslQRxK/euiF+rcGhEFl6lsSOxW+WvLX6r/SKhUuq7Ph8JzZJNqYisVsSaI3IMV/tUifNKflSFdDVeSQCYhF/1OuqTPh/FJt1bn5nG6emnn062oUOHNu0ePXokHxK/1jyqCGu/Wr+UylzR79G8V9hqq62STUWIqU9VgWFFx5PE4Wns9PcoDsimY06/R3VKbSTyXsltih+qnRs3bmzaJEZPtsq9dY7Jh2JK1zBaY3r27NmxDzQGhM5xpUZE5PyoxA/1k3KIYkPvX1kLK/2OyM9M/a7UkT/84Q+l39MaS/lBeaX3onGiedf8oOej36vUXH3m6nyqH/W7sgej8aV50D7Q89L+UeeK+kQ5qv3aYostkg+NldY8qoG0fuic0jqu80e5TjYdT5qryrxXY0Ohmk8xXMkrio3KddQHHQeaKz3L0HpC99Z6Qz6Ejme1LuuzUD8pZ/T3aD6XL1+ebLqGHXnkkR3vHZH3i7qGR0SsWbOm470ohitrDNUbzW3qE63ZlbWezo/dunVr2noOJp+I3Hd6PhpzrQm0DtB4ap1av3596TqlEvtUW2iuKveurPW0VtDaoGNcPbtV9iSV8yP1SceK5rxyXq7WpMo5lGwa+/S8VN/UtuWWW3a8d0TOv+oeWvO/sq5TH+hZNF6o37Qf0JhavXp18qFx0TijMadapn5UA7t6Pqec0TW7coaPyLF4yCGHJJ/p06c3bcoPyitd6+l5Ca3V9LyV/SOt2ZV3qRTnVJMU2ktR7Gt8UgzTddovWh91jOnetM7p79Gej/qkcUd1uVLLqE+Vd+MEPXMFqkka1xTDmg/VdwY6nhTT1Xei/93QHrNyXqW50rxdt25d8qE87tWrV9OuvBujuaLr9FkoVlauXJls+nyVvI7INe++++5LPhMnTky2nXfeueO9KX4qa3v171edrqP7dPXd8RsF/8eiMcYYY4wxxhhjjDHGGGOMMaYj/sOiMcYYY4wxxhhjjDHGGGOMMaYj/sOiMcYYY4wxxhhjjDHGGGOMMaYjXRbkq+j0VL7TT/eib4yTTb/jPHfu3ORDGm59+/Zt2qRHoc9C31RetWpVsqmeyNSpU5MPfV9Xv01cHTv9BjbNS+Ub9fTNYfo9hb5f3FUtuMq9CR0rGruKdghdR1pFOub0PXG6Tue9oq0Rkb//Tt89p++Vax9IF0THpZILEXmOly1blnzuuOOOZNtxxx3/YjuC50H7QN/8pjmuxPlrqfepc0zf0q9oFVW+Ad5VfdNqruvzVb6b/2o2hfqu40IaIFTLVHuBfLqqpUM6EsratWuTTdeYSi2NyHOzYsWK5EO1ReeK5riidUc+FR07ygXSE9BaRr9HY66xUcl1yjOKqYrGM1HR26Cxq2gH0b00hivxS/evaCpEZJ0c8tF7VXR7InJ80hiQdpiufdSnil4K+ZBNawmtvRTnFT3lSq2uaHJE5L0o7Qf0WahPFZ0XGidCr6MaWNH3oXpOfa/kA63HGkOk5UN90BpPNUn1jGjuqN5pzlT2RBE1rXbKmYoeHo257sMoXivazNQnqhu61lIs0hx37969aXdVm43urTZ6Fvo9zQd6loqWH80V7Y91vaLnq2i6DhkyJPmQjqauH/369Us+lFdKRTuoq5o1Fe3UiJzbtEeoamQqdHbq379/06Y4oPzQOKushZX9ZETX67n2geqd1smIHOf0exQ/+nsVPdWInMfVvYz2geod6SBWNI91bmiNq+iN9+7du3Sdvi+j9170jqDC4MGDk03vX9X21XGoaixWxkrnmOac9qYar7QfoPHUvRrVJKpvOlZVTeA+ffp0vHclHwlaP7QuVmt1Zd+pcU3anpV3G1Vtv4quNtW3yp6ZanxFa5fmSn+Pcpa0/DSGKPf0XjSflT1f9exP69N/N5U9dDXOn3nmmaZ94YUXJp8XXngh2fbYY4+mfeqpp3b8rcpeKiKvTUuWLEk+s2bNSjatb9tuu23yofVY97CPPfZY8qF81PfHNC8Ui7p/q+opah8qZ87q++XX6u8mrwf8H4vGGGOMMcYYY4wxxhhjjDHGmI74D4vGGGOMMcYYY4wxxhhjjDHGmI74D4vGGGOMMcYYY4wxxhhjjDHGmI74D4vGGGOMMcYYY4wxxhhjjDHGmI5k9dQiFUFlEhMmcWaFxD2HDRuWbAsXLmzaKuQZEdG9e/dkU2FSEoxVwWESa1eh64gs+D1jxozkM23atGRTQdx58+YlHxJ+VvFgGl8SL1ZRVRITpd9TIV0SBSdULF3FWiPyPJDgMQkV9+zZs2nT8/bt2zfZVLx47dq1yWfQoEHJpmNQFX5WsWLKD8orHYfNNtuso09ExIoVK5o2CdRqDGtuRLB4sgrb6hxEdF3AmcZFha1VgJx8InLekrgvjaf2ga6j/FABZ7o3jbHGLI2djrHObwSPp9ZTzUX6/YgshE61hWJDa0lFlDwijwsJjtMz671ozGlctJaQWLPWJForaB3Q+NH1JILHU+OM8opEnnUM6HnpOo0F8qHaos9M6yPdS+vw6tWrS7+nsUC5t3z58qa9bNmy5EPxOmnSpKZN80K/p3FOew1dY+g68qF51zGmfup+ICLnle5RIni9qqw7mjO0Pm+xxRbJpnlE+w/6PZ0/+j2qLfp7NAZUp/T3qHaSTe9P+V8Rmqc+0e/pPFCdUh/aZ9O6o/sGijGqnQqN+YYNG5JNY4Gel/ad2gfqJ+W/QvukynpFc9yrV6+mTfWOxkVzhuKA6o32gWoEjac+s/Y7gudK44XqCI2nXlc9t+ic0rpD+wadK1qbdB4opuk6zSPa3+m+MCLPKe176fd07CiP6ff0/hSLdJ3OA8UBjZWuYTQu9HxKJWdpDGht0HvRPolqoPaTYpPGgPJBoXilfFcqdYpyXZ+Zfov2tHpmp3mh31MoDij/dcxpfGneK2t2V88tFC+61tK6TvGi40nPonuwynuFiNqaTejv0bPQXk1rC+35dH9O96eYrsQZzRXtHyv7Mn0+Gju6d+WcTfsWXcMozmmOlep1+sx03qqs2eRD8655TNdR/mt+UD/1WWheaP5070R5RTlbGTu6l44d5RBR2QdSH9RWeVcVkWO2Uu/oeamW6RzTfodyXe9VHQPNB9or0t5b/eje9HyV/Rzt2a+++uqmTXuiT33qUx1/j/Y72k/qN8W5xivFyu23355s++67b9PeYYcdOt6b7k/vP6hGPP30002b9rT0t6ORI0c2bYphff8ZkWvJtddem3zuu+++pk0x/YUvfCHZhgwZ0rSpRlDsV95tVN6zV/aqVfwfi8YYY4wxxhhjjDHGGGOMMcaYjvgPi8YYY4wxxhhjjDHGGGOMMcaYjvgPi8YYY4wxxhhjjDHGGGOMMcaYjvgPi8YYY4wxxhhjjDHGGGOMMcaYjnRWBH4VSIBThUGrYuZqI9FKuk5FTklsk4TRVayUhC379u3btEePHp18BgwYkGwqfnvXXXcln7Vr13bsEwmxknjptttu27RpzJ988slkW7x4cdMeO3Zs8tExiMhzQ0LMJFqrfiQYe++99zZtErqmOFDhVxV9jmAxWO2TiqBGRMyePTvZpk6d2rRHjBiRfEiofO7cuU17w4YNyYeeT8d82rRpyWfo0KHJ1qNHj6a9cuXK5LNgwYKmTWLtFeFwEgUmoWLNx4pgfUQWn64KMeu4kLgv5aPmX79+/Uq/p3G9YsWK5ENivlpLKDZWrVr1F38rgudKa/Wf//zn5NOrV69kGzhwYNOmXKfn0zpFgu4U51o7Fy1alHwopjSGqP7Q72mc0bqjawzNOQkq6++RIDetoXod1XyKV61vVMto7HQMdF2I4HjVfCAfWsM0/yimqAapSLcKnkdEvPzyy02bxo7iXNcZquc0dhpnlI8UL+vWrWvaFHe0jitUIyj2teZSjdDcIz+6d2U/oPMSkWsC5cfSpUuTTeOO1vWKwHl1ndN159e//nXyOeCAA5JNBeMpPyrrKsUU1dPNNtusaVNt0XFRIfoIjkX9PaqlVG+0LtOaTbExePDgpk2xWVn/aY5pX6b7JG1HcC3T8aT9qvrQmYHmQfewtCeiZ9H96tZbb518aC+jY0zrOtVA7Tv1k+JV549+j+ZP70V7GbJpv2jfSXGmUH5QrnUF3W9F5PoTkfeB9PtUh3XtozpJdVih8aU5pvsrWrfo7EZzpc9HZ3+quRpTVEfoOq15NL40drqOU/xQjdfrqL7q2EXkGKY1RvdFVINpHvScTf2me2ndoLGjGNa+V94BReQ5pZpEua73ouvIpvFCawXt1fr3759sij4L7Qsrtup+R+s57fko7nQMqE9UIzSGaC2kMdeaQPeme2lNojVN44zeIVZynfKDYkP3G1R/li1blmwK5RXVG/WjXKDrFJoXqqc6NzQGeiaKyHOj9Yeuo37TdZXxpPzU2KCxo/lTPzqbUn3T8/L999+ffGi/uu+++3a8N63PGsOUV7T+K1QjNDZoTaPzsvrR2FEt09+jfKTc1j5QvNKeRMeOaou+x4jIsThp0qTkQ+8t9JxENUnrN8UBrQ1aO5944onkc9VVVyWbxus999yTfA488MBk22+//Zo25ecDDzyQbMOHD2/alffuERHnnXde06YzPMXGJZdc0rQpht/97nc37Ztvvjn5fOlLX0q2f/u3f+v4+xTnOqc0x5Szlb+7dRX/x6IxxhhjjDHGGGOMMcYYY4wxpiP+w6IxxhhjjDHGGGOMMcYYY4wxpiP+w6IxxhhjjDHGGGOMMcYYY4wxpiNd1lik7/lWNBYrGg7V6/S7/PSNWPretX5vlr41rVoB9O1wuk77QN/3pu/k6jOTFhw9n2r5VPWa9BvGgwYNSj707W79PjtpI9Bc6TPTN6rVZ9y4cclHvytN3H333R19IiJGjhzZtPv06ZN86PvIOjfUJ/o2ud6ffo++W6/fBqdvTZOegH6re968ecln5syZTZvmnNC5etvb3pZ8SAtO+0nxOn/+/GS77777mjbFq36nOyLH+fXXX5986LvcGteku7bzzjsn2/jx45v2kiVLkg89n2q4kuaQ5vGOO+6YfOi65557rmmT1ibpQ+kzb7PNNsmHvomv+V/5ln9E1iGgPlHt1GcmvR36jrzmFX2bXHUW6Fko9/T76FQPqJ/6zXb63j7pnujzkT4d9VOfj/KKNAY0t2mdpWfWvCLdA5oHXY9p/dBv9++www4d7xOR44yel+K8ovNEY6D5QblQ2VvQ2kvzoH2nOKBv8FPOKJqP9Ly0Dui9ad0jXYnK+kHjovlIz0Zjp7/3yCOPJJ9ddtkl2SoaQKSJo/NAe6mu6o2r7klVF03rckWLlvx0rxHBe5k99tijaW+33XYd+xRR01il/Y3WXKpJpKWh8UJ79ssuu6xpk5YH6bfr3FCNuP3225Ptt7/9bdM+8cQTk89ee+2VbBqvtPZS7aTzjVLR5KN8pDFXG60DdJ2OH+V6RQe+ch09C2nG6H58ypQpyYf2O1rPKc5pH6i1jJ6PcltzjWpLRW+LdJe0D5SfNOZ6b4pDqpOqWUl6oxVtSMrH6nhW0JiqaDVH5NgjnSfV6KzoRUVkncDK/i4ijwuNE+1JtF+UV1RbNDaoltH7HI09mjs6K+o40LjQXkbzisZOc4Zik84Dmg+016B9rtYbih9ae9VG+lBUp1TLs6orpX2nmKJ40X1KRXuO6hbtd7S20HWVcy/NMcWU9p3Gifqg8VLVWNQcpbmie+naQDFF+aj1jXz0mSnGqE+qdU01kPJK45XWHVrD9J0L5TqtRcpdd92VbPTMuocmH0LHgTSeK3WL1uy+ffs27Z49eyafilYy1TLSsNXYp7ijM4nqWJIuIuXH8ccf37Tp/QPt2a+44oqmrTrpEfwe+owzzmja22+/fcd+VrWhFaotNH9jxoxp2qNGjUo+tBYqVLvpnHT66ac3bdonnX/++cl26623Nm3SfXzwwQeTTePsmGOOST76zDRO55xzTrJpbaE9dUVftKphresH1cCu4v9YNMYYY4wxxhhjjDHGGGOMMcZ0xH9YNMYYY4wxxhhjjDHGGGOMMcZ0xH9YNMYYY4wxxhhjjDHGGGOMMcZ0xH9YNMYYY4wxxhhjjDHGGGOMMcZ0JCtyFiHRbBVLJaFiEgpVsUkSkSRBShWtpN+jfqrQc+X3SByaxDX1updffjn59OnTJ9lIHFUhwVgVYidB7opQMIntkuixjjFdR/3U8aM4UIFqEocmYeRK/JDIsooJ0+8NHDgw2ZYuXdq0R44cmXxIaFaFZf/whz8kH0Kve+yxx5KPCmtH5PikMde5IsHhQYMGJZuOnQrPRvA8qCjv4sWLkw89n/Zz7NixyYdy6Morr2zaJNZ+xBFHJJvWsnvuuSf5qMhzRM7t5cuXJ58HHngg2XT+KGc1PpctW5Z8Hn/88WTTew0ePDj5LFy4MNlUuJvyY8iQIcmm80BxR3VDryMhZhLSViFmygW6l44L1fijjjqqaZMA+FNPPZVsmg/0vCpOH5Hr1PDhw5MP1fif//znTZtqMMW+5va+++6bfEhUXkXWqS5TDGss/P73v08+VBOeeeaZpr1gwYLko7FIay89i9pofPX3IyLWrl3btGkMJk+enGyaR/R7zz33XMd+ag2O4Jr7xz/+sWmTODwJjPfu3btpU35orlGeqeB5RB47uveIESOSTfcklFck/K5x9uyzzyYfWj+0X1tvvXXyoX2grh/0fLT+6/6K5pPySvORxkXjgND70HX9+/dPPrS/0ut0ziMilixZkmw6V7Tu0FxpnNHzbr755smmY0w1iWKqV69eTfvRRx9NPrrW076F9ly6r6fnXbFiRbLp/NFaT7muc0NrhcZ0RB4X7XcEx7n2i3KIrtO4pvyonKVoT6L3qp4ndawo9376058m29y5c5v2t7/97eQzbNiwZHvhhReaNj0LnTFfeeWVpk3PR2uYzgP50LjoeGq+RORapn18NTRe6Pfp+fSsSGNH516NqWrcaR8oryr7JHr/QTVJz9CE9onymvqkNtpjUm3RmkQ1eMCAAcmmdZjm6sEHH0y2H/3oR037vPPOSz7jxo1LNt2HUUxR7Ktt0aJFyYfGWOeKYl/7RPNLeaWxSGcNmj/KGYXmQeed1l6KKc1jPXNGRAwdOjTZdA9C+xbKGe0DvbfQfTXtX6kuUx8UGl8dK5oryg99FqotNA+aV7Q3pfVD3zeQD9UkPc/RGYHOMnovih8dz5kzZyYfinM9G9IYTJgwIdn0Xd+cOXOSD9m03jzyyCPJh/bCOgb0PonOj1//+tf/4n0iIk444YRk0zw+99xzk4+enejdEf3e5z73uaZ96KGHJh9ae3XeKzUqItdKymN9PxiR84/e79I7vLPOOqtpf/WrX00+06dPT7ZLLrmkab/73e9OPnvuuWey6bmI3nfoukO1m/bZmqP0HoPeFR1wwAFNm+aYaq7WKVrnqObuscceyabQ/FXeP9BZ5mc/+1nT1j083Yuel36vEte0pnUV/b3X8t7+j0VjjDHGGGOMMcYYY4wxxhhjTEf8h0VjjDHGGGOMMcYYY4wxxhhjTEf8h0VjjDHGGGOMMcYYY4wxxhhjTEf8h0VjjDHGGGOMMcYYY4wxxhhjTEc6q9y/CiT0WBF/JIHhyn1IgHerrbZq2iRUvHLlymRTgVoSXVeR00q/I2rCyBVhaxI4JpHuMWPGNG0Sv6WxU2FyEiUn8XIV/FTx7Ve7rhIb6kNizSRQq2NMY0BzrDaduwgWIdbY0Dh8tT489thjTVvFsCNYSPdNb3rTX/z9CBaI1XknAXnNhUGDBnX8/Yg8DzRXm2++ebItXbq0aauIbkTE3//93yfbtttu27SHDBmSfOheq1evbtrTpk1LPoMHD042nXfKx+eeey7ZXnrppaZNYzB8+PBk22+//Tr2SePzxRdfTD5U73bfffemfdxxxyUfEhxXEWt6FhpzFXWn60hwXGNIRd8jIvr3759sRx99dNOmsaP688QTTzTtm266KfnMmzevaR900EHJh0TXdW623nrr5EO51rt376a9Zs2a5HP33Xcnm+bDxIkTkw+NuYrIP/roo8lHYzMi1xKqnbTOaU2idZVqidoo7nTMZ82alXxOPPHEZLv//vub9j333JN8NK8jclyT+Pb8+fOT7fDDD2/aJCp/ww03JNs222zTtKmWUa698MILTfvqq69OPrvuumuyHXjggU17xowZyefSSy9t2rQWUu7pWFEcHHXUUcmmQuy0t3nmmWeS7a677mraVO9ordc4p1wn9HkoP8im40drIY2n7rlo/6HzTs9C+w+dK6otGpsRuQ7TvFDs69wsWLAg+dD6v8suuzRtGjt6Pp132l/R3umVV15p2s8++2zHe69YsSL5aGxG5Fq92267JZ/Ro0cnm66PtC/Umh+Rn6Vv377Jh84fGne0T9I9ZkTeP2qNoj5F5DWT6g3tETQfaW3SswzlEK0DOseU13Sd5tXy5cuTD52l9NxCNX/jxo3Jpn50NqVzkq6r48aNSz609um5gZ5viy226Pj7lHv6e1QjKO66devWtCnGaE1RqAbTGqb9rL7bUKgmUexr3aA9n+YorXsU+7oHo70p7Wl13ul5KR91bgYOHNjRJyLXFnpnoOfQiDx/VANpvdK5obijsdI6QbVT+0DP8tRTTyWbzh/lbPfu3Tv2idYPPRdG5BjScxP1KSLPKcUP7aH1XE99GjVqVLLp3oXOEWqj+XzooYeS7fnnn2/aFJt0Np06dWrTprmic73Wyup7N61JtA5QvP7mN79p2hSv+o4rIs8VvU/SM3xExCmnnNK0aa5+97vfNe3vf//7yYfiTueU6uv06dOT7ZOf/GTTXrRoUfL553/+52QbNmxY06YzLsXwlClTmjbVLVofdS9DNZfm71e/+lXTpv3qmWee2bRp/aB3FBqvlNe6PkfkuaGxo5jSek77c3qHdvzxxzftQw45JPmsWrUq2XRcqC7rvSPyO3xtR0RMmDAh2fR5dC9FUJwTOlc0x7Tf0X0nrR+0P9b4pNik2ND1mGoZ1W+NPVpXaU+7xx57NO2PfOQjyUfHheJ1++23TzYdKzq/0vypja6j/Nd+Vf/GVcH/sWiMMcYYY4wxxhhjjDHGGGOM6Yj/sGiMMcYYY4wxxhhjjDHGGGOM6Yj/sGiMMcYYY4wxxhhjjDHGGGOM6UiXNRaJruonVvR2SHtBtTRIp4O+k6v6LPQNXv0mbfW7tepHv0/X6feRK98TjsjfXld9swgeT/2GOul0VJ6Pvh9MOjZ6L/oWsn4rmHyIyjeN6VkoppSKxgl945z0izQ+6Vv6Y8eOTTYdhwceeKBjnyLyt+VpXnTM6Vmq33pWaB7mzp3btCn3SDtsu+226/h79C19/S4/fVOdNIe0TtG40POpbibpLFA+av6Rdqn2iWKT+vT000837QsuuCD5kAag1kn6hjtpDFT0jEgvoaJrSX1Q3QFaB+hb7zpW9J11jXMac/r+vGojvO1tb0s+VON1rOg7/aRVpt/3p3WA+q51ijQVKtoopDlAca7PR3WLtHQmT57ctKm+HnnkkU17wIAByYfy+LrrrmvalLPHHHNMsmlNuv3225OPaoBEZI3VnXbaKfmQ3obOA63ZlB8a56R1ReOiuhWk26XxonoxEay/p5pjV155ZfIhrctJkyY1bYoxmgdddzQ/I1izTnVzqhqLuu+j9bKr+2WyaR6RtpbGD+lqq8ZrRM7txx9/PPlQTGk+VvSM6F5Ul8mm+UhrKO0DNa6pJlHt1D20avZG5Lij37/vvvuSTdfeESNGJB+KH43XX/7yl8mHnkXrMo3vaaedlmx77rln0x46dGjyefDBB5Ptsssua9qkkUc5o3XjPe95T/LZe++9k61fv35Nm8auogNPe2HNox/+8IfJh/S4de/0hS98oePvR2SNbNpDf+1rX0s2vT+tO7Nnz042fZ6DDz44+Rx66KHJpjXoG9/4RvLRefnXf/3X5EPavhdddFHTnjlzZvKhfYvqsH76059OPpQfCu2z6brKuZfqjcYZ7RX/8R//MdlU+410UVWnmDTXVZc9Io85nUPf+c53JpvqklNtIW00PbfQ3uL6669PNl2jzz777ORD71K0n+94xzuSz80335xsquV9wAEHJJ/zzz8/2XQczjrrrOSjmoDf/e53kw/p/Wl9I828f//3f082ree05/v85z+fbBqvdN6ifNS1nmL6zjvvTDatnfR8tP//2Mc+1rRV7zwi5yzpG37lK19JNh0rWk9oX696W3Rv2qvpOaWq36rnVXpHuXDhwmT7xS9+0bSplpFWompd3nrrrcnn29/+drLpmY/ejan23Gc/+9nkozU/Ij/zt771reRD2teq+05rL42n6ua9//3vTz40x/oOhPQb6cynaz3lI82f7lOoVuv5XOcpgjXkdM2kvKIzra6r9D6JnkX7Tu/+aD+3ww47dOwnvV/RcdD1K4Lfl+kc6zvECH4vo3mlWqYRORZJi5L22Tp2tF5SvdFnoVygMdfYoN+jea/EBmlRaw2kPKYx13pKmsA6L3TWoNhQPeXqOVTnj+a48veWyvmniv9j0RhjjDHGGGOMMcYYY4wxxhjTEf9h0RhjjDHGGGOMMcYYY4wxxhjTEf9h0RhjjDHGGGOMMcYYY4wxxhjTEf9h0RhjjDHGGGOMMcYYY4wxxhjTkawOWYREOV8rMUgSEyUhy/79+zdtErElsUsV8yThzmHDhv3FayIifv/73yebinuSkKYKh5JNBXIjIhYsWJBss2fPbtpvfvObkw+NgYq69+7dO/mQyKrODYlDk7A9jYOic0wxRmLCeh31iUSB9bqXXnop+VBMqTg8CZyTmLj2XcVaIyJGjBiRbCr+SuNLMaWxQM+i41IVflWRXMoFYvDgwU2bcp2EbVXYeujQocmHxItVTHjrrbdOPvvvvz/29f+E6g+JLGtNonEhAWfNNYqfbt26Ne2JEycmHxpPFWKfMWNG8nn22WeTbfjw4U17+vTpyYdqp8YnrQP6LBERm222WdOmMZ87d26y3XDDDU37T3/6U/KhuNYaS3Olcf7KK68kH0J/j2oZ1Q3N9eeff77jvSMifvGLXzRtqhEkOL548eKmTfNCdfEPf/hD06aaW7UpVG90/Cg/tJ9UEyln9bq99947+YwePbpjP6m23HPPPcmmwuRUlwn1o/WZYkNrIFFZPyiGNf+nTJmSfHbYYYdk07GbNWtW8lm5cmWyaX3T2hYR8fjjjyfbjjvu2LSPPfbY5NOvX79k0/3Vfffdl3woXtVGAu5U3zQ/SIyearzOle7vIiKOOuqopk253r1792RTEftLL700+dA86LPsscceyYeeT/tJuUdritZzzbNXQ+eKcohsutZTTGmdJB89a0Tk+j1q1KjkQ2u2xtSkSZOSz6677pps+nzXX3998tF1lu5F80k5ozX33e9+d/IZO3ZssukzX3LJJcmHYl/7SeuQ7hvIR/coETl+jjzyyOQzb968ZNP7v+9970s+FHdjxoxp2nS+o/OO7qtp30tnzIceeqhjnw499NBk09h/4IEHko/mg56tIiL+/d//Pdk0tz/84Q8nHzoPXHPNNU37gx/8YPI555xzkk3XPloLKV4q11Et0xr/+c9/PvnMmTMn2U444YSmTbXloosuatrf/OY3k8/48eOTbbfddmvaV155ZfK58MILk23bbbdt2rSu33XXXck2YcKEpj116tTkozEWEbF+/fqmTbFJa6+eTbUdwe8fLr744qZNub7LLrskm9YSqnfXXntt09ZcjIj4yEc+kmy6F6U6+ZWvfCXZvvvd7/7FPkZEPPfcc8mmc/Wud70r+dA7rSuuuKJjn97xjnck20c/+tGmTevOD37wg2S7+eabm/Z+++2XfDRHf/jDHyYfes/285//vGnr+Tki15+IiHPPPbdpP/roo8mn8o6i8k42Iq9ztPenfaDudygfzzjjjGTTc+dee+2VfD73uc8lm54JqCZpjtIenvYyOgZPPPFE8qE6pft4etdAa+8pp5zStOk9H71/0HcE5EPzvm7duo79pBjeaaedmva3vvWt5KNrDM3LPvvsk2wnnnhi06Z3eKtWrUo2ffdO/aazsO6haewq73x1PYng85z2ndYYegej19G96R3B8uXLm3blvQntUWj/qNfRmJNN12M6Y27atCnZtAZRTaK80vHUuI/g2NdYoNig951aS04//fTko/v/pUuXJh+KqbPOOivZFMp1tdF+kmKqcu+u4v9YNMYYY4wxxhhjjDHGGGOMMcZ0xH9YNMYYY4wxxhhjjDHGGGOMMcZ0xH9YNMYYY4wxxhhjjDHGGGOMMcZ0xH9YNMYYY4wxxhhjjDHGGGOMMcZ0JKuLvoaQiGRXryMhVBUY7t27d/Ih8WIVsiShYhU979GjR/IhwU+9lwqeR7DguIrIkujp7rvvnmwvvvhix3uTEKpCorIk6koi2QoJ6er8vfLKK8lHRVZJrJXGReeYhEpJwFX7RHNM8VMR2x0yZEiy6f2XLFmSfEjUVcVuSaCW8kNjgYSKN2zY0LRJnJ7GXH+Pco/6ue+++zZtGqfzzz8/2VR0/aSTTko+Ki4ekcWKly1blnwmTpyYbCpaT7k+ZsyYZHvTm97UtB966KHkQzmq4szko1Be07NsvfXWTZuE4J988slku++++5o2iXTvuOOOyUZixQrVG41FqiNPP/10smlcv/3tb08+06ZNSzatZSTWrgLgKiT+amifKK80xiJyjaB6S3044ogjmjaJdJPY9nPPPde0e/XqlXzGjh2bbJoPJBxONZdEyJXXSmia1i9ad9auXdu0qY5UxNoHDhyYfGhN0TjXdgTPu44x1QitPxF53mlcSFBd/WjMdb9Da7aOb0REv379/uJ9Inh91DGgNY36cPDBBzdtmpeFCxcmm8YCXac1gmy0j6Ac1ZpHc0WxqONC9Ub3FpdccknyoXVgxIgRTXv16tXJh8ZAc51ic82aNcmmz0J7C4oNjTM6R9CaOWjQoKZNY057Ga3fQ4cOTT66pmy55ZbJh/ZACs05jXnfvn2b9qRJk5LP9OnTk03HfMaMGcnnqaee6njdokWLks+cOXOSTceczjZUEzSuFyxYkHxoj6dzSmuv1lOKV6qBmg+aLxG8Pur8TZ06NfnQGGgfqE+jR49OthUrVjRtWneeeeaZZNPa9fzzzyef+fPnJ5vmKI2n1uUnnngi+VBd1v3/4YcfnnwI3Xd+7WtfSz60R9C+Ux2h/ZXWeFoHaJ+ksXHPPfckn3e9613JdvLJJzdtqje6hn36059OPrfcckuynXLKKU3761//evJ573vfm2yXXXZZ06Z9NsXi6aef3rR33XXX5DNv3rxk0zymdzDbbrttsun6SPFK86fnqy996UvJh/bQWoN0Lx4Rce+99zbtAw44IPnQeUf3pieccELyue6665JN6yntaWmd0zw+9thjk4+uTRG5Js2aNSv5HHroocmmsUD75auuuirZli9f3rRpj6DxSXsiGoPFixc37fvvvz/50H5V+0T1leZdawS9A6IztMYw7RWpBuq9ttlmm+RDa5G+D6BcoHdMGos0V7on+c53vpN8qLaMHDmy470p/xU6z9I86P1p30vnlp49ezZtPTdF8PqhNpoXOn+ceOKJHX9P4/P6669PPl/+8peTTffnH/7wh5MPxWvlHEp7YR1POrvR2XTjxo1Nm84f9I5L92q09tI+UOeBYorO+np/igMdK6qTdN7Rd0yUC7TX//nPf/4Xfz+C9976HpGuo7nScxk9C+WVPg+dafVvQBER3/zmN5v2T3/60+Sj+TBhwoTk8853vjPZdI5pj0nxU/n7R8VGdaur+D8WjTHGGGOMMcYYY4wxxhhjjDEd8R8WjTHGGGOMMcYYY4wxxhhjjDEd8R8WjTHGGGOMMcYYY4wxxhhjjDEd6fJHVem7tfp9W/qGM30LWanqNek3huk7xBW9Jv3OO0Had6SpoN9Qr/x+RP62dOVb0xERAwYM6Ph7ZNNvLdN3yGke9FvhdG/6Vq/60TebSftNIa07/R7zQQcd1NEnIscLjTnpbR1yyCFNm2Ka7qXfpCd9CNIO0m9uU9xVtCcJ/d41xRjZdP5I60a/Dx+Rn5m0YFTLIyLit7/9bdP+xS9+kXxOPfXUZJsyZUrTJr0E/aZ6RJ4ruo6+V77zzjs3bZoXQusbfV9b54H6RDpImleklfjwww8nm+rtURxQzdVv2VOuk9aEaj3QN/ErmmOkC0B1Q/UvKzqINC+kz6DzTs9C46LrzPDhw5PPo48+mmx6f9IzoTV78ODBTbuiQRhR0w6iPui37GnsSH9T9Qtob6GxSH2i2qlxTppOpDWjOgQUY6TNphpc1M+KJgb1qaKfSBqdFU1OWtc1XinO6Vkquhm0t9DraO2lGNa9DMU0oTWB9qGUxxqLtJeieNF5oDGnsdL7k2bEN77xjaZNteXoo49ONl2jZ8+enXxuu+22ZNPcpmeh+dN5p/WZapleV9EOichjRTWCbDp/1E99PuoTrYU6VrSm0R5T41XrewTrqVa0WCju9F50HZ2dXnjhhaatWvERNV0ZPf9EsE6gaoCRjo3mHsUm1UnNPRpfWgtVL7p6XtY4p1xQ3beInKO0Ns2dOzfZPvWpTzXtq6++Ovk8++yzyabjQJpDo0aNatpUWyimdtlll6ZNezfKK9370tp7+eWXJ9uHPvShpk25UHlHUK0tjz/+eNOmZ1HtwoiIO++8s2lX9hYUB7R+aO0iPfezzjor2c4+++ymTTreX/ziF5NNddBIe470TLWftGZTTKkf+dBar7WaalIlXigOVLuU9Grp7KZrEZ3Faf+hulmkRUmxoeciel7VEozIe4SqFj3VWIX2XLqmVM4R1Ccac51P6iO9D9A8orWJYljjrvJulfyoLtO5TNdsGpfKGYH2LXSuVhvVwB/+8IdNm3JPfSLyez3SsNW6FZHfz9H7QTp/aN+1tkXwO1itQTS+qqcakXUB6d6U/xqzpKOpesannXZa8iH9RNUurerHV95jUrxqPaV3jbQvO/PMM5s2xcHFF1+cbBrnpPtM86D1jfbC9E5E37lS3dA5plpaOUdQfp5xxhnJ9oMf/KBpX3jhhcnnmGOOSTZdr97znvckHzoP6PNRfSWNZX1PS+sA1TetL7TfqWigExrnFNOUC7TOVdDr6Pe6iv9j0RhjjDHGGGOMMcYYY4wxxhjTEf9h0RhjjDHGGGOMMcYYY4wxxhjTEf9h0RhjjDHGGGOMMcYYY4wxxhjTEf9h0RhjjDHGGGOMMcYYY4wxxhjTkawEW4SELVX8kcSoSXxWIbFLEnolMV+FhElVtLIirkmisiTurSKg9PuEjl1V2JZERyuoCGhVAFSFn6lPJHCuorEUG0r12Srivl1l8ODByaZxR8K2JIitgs0kyEvjomNHIrY0D927d2/akyZN6lKfKM/UNmzYsOQzffr0ZOvRo0fTJsFhypl99923aa9Zsyb5kO3ggw9u2ltttVXymTNnTrI98sgjTZvGd/z48cnWr1+/jn0iwWi9Pwnp6rhQjFFsXHfddU37iiuuSD50r549ezZtEkancVHxclorSGhex47GYMSIEcn2zDPPNO1LLrkk+fzpT39Ktkqf1LZ27drkQ/VH82PFihXJRwXWI3L+U6yMHTs22S666KKmveeeeyafXr16JdvTTz/dtGlNmzp1arINHz68aVONIFF5Xf8p12nt03GnflbWJq0/ERGbb75503722WeTz/bbb59syv33359s69atS7YhQ4Y0bXoW7VNExLJly5r2okWLko/mbETEpk2bmjbFOe2BVBidckjnk/YRNMdap2jOaa+oewKNw1f7vVmzZjXtww47LPkQeh2Nk9a7iDzGtK5X9oY0VxWR9Xnz5iWbzp+uqRERb3vb25Jt9erVTXv27NnJh/ZAlX022XRPQHVS+xRRGxdajzXXKM4r+2Par6pNc5F+PyLPO9VXGgPNI4of6oPOA9VJQudq8eLFyYf6ftRRRzXtt7/97cnnxRdfTDadG9oPaJ2MyPubDRs2JJ/KmYhqkuYxxTTtr9RG60ClBlL8jBo1Ktkuu+yypn3LLbckH9qr6X6D9h8PP/xwsmkM0XU6VtUzro4V7XupvuqY0/mDzq+6FtK9K7FRred6Hd179913T7YxY8Y07ZkzZyaf0aNHN+299tor+VD86B62W7dupes0PnUsI/IZNyKvmRSbtK7quXfjxo3Jh/Jf6yKdFSkWtV/VvYzux5966qnks379+qb9rne9K/nQOv7CCy807aFDhyYfGjvdP1ZroEI1n8ZOawStO3Sd1gnyofjU56u8+6P93dFHH51s//Iv/9K0Kc4p7rSftIenvb7ODdUkej71q7xHjcj1m56Ffk+vu+aaa5LPypUrk03fr9B4aq7RO4pBgwYlmz7z448/nnxon9S/f/+mTWNONV7XJ/Kh87nOMeWevv+IyO98aAz03V9ExAUXXNC0n3/++eSj7xGpTzR2Op90HdUkXespxuj3dIy33Xbb5EPnnZ/85CdN+7jjjks+9H5lhx12aNoDBw5MPvR8Wktor0jvjzRe6D2U2iju6DrNK6rLe++9d7Jp/tH6Qe+YdY9Ae+HK31toH0h90Bii9aOyNtDeTW2Vc2n1994o+D8WjTHGGGOMMcYYY4wxxhhjjDEd8R8WjTHGGGOMMcYYY4wxxhhjjDEd8R8WjTHGGGOMMcYYY4wxxhhjjDEd8R8WjTHGGGOMMcYYY4wxxhhjjDEd6azC/H+Bik9WxSjVj0RdSdRdxTxJgJOoiGvqvaoCnNr3yr3Jj0RWyaa/R2NHv1e5jkRPVeiVhF9JZFXnj0Rs3/KWtzRtGjv6PfUjn64KsZKQro7nn/70p+RDgt96HYnY6hjQdb17904+q1evTjYVraffI4FhpRJ35DNkyJBk22yzzZo2ie1SbEyZMqVpk3A4jbnef6eddko+W2+9dbKpuDfVHxrPN73pTU2bxMRHjx6dbCrYTs+iqBh2BAuA69xQflJ+9OvXr2nT85Joto4B3fvll19ONo19jd8IFuDW+69bty75kEC9CrGrYHVEzjXqN83nVltt1bRJ5J3E6Cvxc8ABByTbJZdc0rTvvPPO5EPPp783efLk5EPzrs9TneNu3bo1barVNFdamwcPHpx8tFbTvWkedt5556atwvcRWdCdoLyi8dS+Uw2eNGlSss2cObNpP/3008ln0KBBybZ+/fqmTTWX5qoyx7oO0L1pTdP6Rr9P+a/XaTxF5LyOiLjjjjs63pvWncWLFzfttWvXJh9aG9RGeynNPYLWVaLye2pbvnx58pk9e3ayLViwoGlrHEbweqWxQblHe64VK1Y07ZUrV5Z+r2/fvsmm0L0233zzjn2iPbTWU5orvTfVNtpjatzROkDPq/srepYtttgi2XQtouelfa7axowZk3zIprFH995ll12STeed9oFUE3RcKtCZiNYU7TtdR3VjzZo1Tbu6x9T8oHVnu+22S7YePXo07SuvvDL5jBs3Ltl0701149xzz002rW/Tp09PPlpzqXZTbGgNohijdWfOnDlNu3pu0TyivRTVXM0juo7Q3KZ8pJpw+umnN22aK41Pqkk0LppDVEu/8Y1vJJvODdXuCy64INlGjRrVtPU8EhGxZMmSZNMcpXkZNmxYsi1cuLBpU/xQfOr9KR9XrVrV8V7Dhw/v2E+aq4kTJybbHnvs0bR1DxjB86c1orKHj8hrO61pFFO6X6Tno5qr/aQxp7VPf49iUffne+21V/J56KGHku22225r2ocffnjyoX3nrbfe2rSHDh2afKi+6fPR/pzGRWsu1Vdaw3Qvo88bweuO1iC6js7Qegbq3r17x+v0HBzBe1p9T/Lcc88lHxoXXcdpT0tnU91fVfcIGuea1xER99xzT7J95jOfadpjx45NPh/72MeSbbfddmvaTz31VPK58cYbmzbtGY466qhkO+yww5o27cmeffbZZNN6Q/teqhG6NuhYRvC7lKlTpzZt2mPS2qBnb4ofeuez9957J1vlOs1j2j9Wzo9UJ/X3aB9B+ajrFZ2pqcZrXaS8ontVzmB0L91PVf9WpdDYqY3mha6jfr5R8X8sGmOMMcYYY4wxxhhjjDHGGGM64j8sGmOMMcYYY4wxxhhjjDHGGGM64j8sGmOMMcYYY4wxxhhjjDHGGGM60mWNRfqGOn3nuOKj36Cl70/TN3D1e+n0zWb6Lq/+Hn0LWb/BW/1ObuVbvRXtwqq+YOVbwWTT66paPvptafouMH3XXfte+fZ7VddSn6+iYRmR55T0L2hcdP5oPmnMtQ/UT4qzit4eaVvpd7LpG+M6n5Xfisjf16braOxIs0GpaFaRPhXlun67m2oEfS9d70/PQvGi80B6RhUdGZpP7YPq+EXwHCuVmkh+9G101QmKyPoF1E+qZZUaRH1X/YmKpmxE/nY/6V/odTROqkEUkWOK+k05ozlL8TNy5Mhk++xnP9u0X3zxxeRD86d6BRQ/FOc6VpRDlfWRxpxqp/aTtCZUm4T6TX3af//9mzbtbUgjR3OWtDUmTJiQbBpTpFO65557JptqnCxatCj5kD6L6pKSziz1XceP9FtVB4nyg2qZ5j/pBpPWlcYZ1ZH99tsv2e6///6mrZqLEZyj2gfSFyKtCao3SkUTvLpH0LkirZvHH3+8ad9yyy3J56abbko2rQmUsxVNPtKCori7/fbbO/aTtOcOPfTQpl3RMo7I8UlxUNGMonlRXTDSFyJ9H91TvvOd70w+VN90/Vi2bFnyodjQZ6G1gn5PdWz22Wef5LPrrrsm229+85umTRp9pG2lkB6uauRE5PpC+0ddGygOaK+vcU77GKobmjOk20U6aArVGqpJOu+XXnpp8jn22GM73ov2OzSec+fObdof+MAHko+OFemdkx7eo48+2rSnTZuWfOiMeeGFFzZtWpuoTum6Q/eunDtpvaK9k9ZFGvOLLroo2TTODzzwwOTz5JNPNm1aC2kMNK/OO++85PPYY48lm+qe0RnwXe96V7J9//vfb9pnnHFG8iF9L90/Pvjgg8mH9jIaC1TPaR+oz0PzSftq3ffReqX7wN/+9rfJh+qN7o9VlzUi4oorrki2E044oWnT+NK5Rc8btFbQuyIdA8pH0jjT+1NM0bho/q1evTr56Dy84x3vSD5nnnlmsn3qU59q2qQbum7dumTTtZbW+vHjxydbpbaQTWtXVdtLx5N0384+++xk0+ej9ZnGuPJeT+eB8kx1dSOyninp/9LvaV2mdxsf+chHkm3EiBHJplT0m+lsQ1q7um+gmqTnwoi8T9lmm22ST+UdN+Wx5ijlJ9UbzRkaJ3pPov2kmkT30jWUzgyVs1vl3XFE3jvRukPXaf5V9P6o35V3ovQ3EtoDKbRW0H618u6f5orGRaHx7Kqmoqnh/1g0xhhjjDHGGGOMMcYYY4wxxnTEf1g0xhhjjDHGGGOMMcYYY4wxxnTEf1g0xhhjjDHGGGOMMcYYY4wxxnTEf1g0xhhjjDHGGGOMMcYYY4wxxnQkq2gWqYhmktgmQUKdlXutX7++aZOYMInrat8rgqYk/Eo2vXdViFX7QM9L46TXkSgpXad9IPFrQsV9VXT11fqgz0OC0SQQq9Bc6bPQ+FbGk+aKxkWfT8V3X62fGi8k7kvCtgpd171792TTfKDx1TGgmKY51vGs+NDv0dhVcp0Eh7fccstk69evX9Neu3Ztl/pJQsUU5zp/1CcVM6d7UX5oXFfEmiPyvFOM0bjo3NBc0bxrHlXyuuq3YcOGZFOh8M033zz5UN91TmnsKvOiNTEiPwutTZU1lETQSRBbY5j6pKLk1d+jcdE5pusoNjT2KmtaRF7HKa/UpjUjgmv8smXLmvbo0aOTz84775xsmzZtatoUv5RrWr+pn8OGDUs2rWW0NvXq1SvZNB+on9QH7ed2222XfAYMGNC0qd+V2N93332TD82Vjic9y+67755sY8eObdo65xERPXv2TLYxY8Y07UWLFiUfqglvectbmjY9SyX2K/ETkddtnZeIiLe97W1Nm8Zg48aNyTZ06NCmrc8WEbFixYpkUwYPHpxsxxxzTLKtXr26aS9YsCD5DBw4MNk09mkvQ2u2rg1UlynX9P59+vRJPocddljTfuyxx5LPypUrk23QoEFNm+KHcm3vvfdu2jROFUaMGJFsO+ywQ7Jpjad+Tp8+Pdm0Jj3wwAPJ56KLLko2XWcmT56cfLRORuQ8ohzSOkX7Qlp3NB9o/3HooYcm2/PPP9+0zzvvvORD8ao5M27cuORDNUnjZeLEiclHcz0iYt68eU2b8n/HHXdMNo2N/v37Jx9ddyZMmJB8PvzhDyfbL3/5y6b90Y9+NPnQnkTn5hOf+ETyoX7qPNC8kE3zgfKD6tQWW2zRtE8//fTk88UvfjHZvvSlLzXt888/P/noXp/OUlRbtA7feOONyef9739/smndp997xzvekWwPP/xw077jjjuSD+3LtN78/Oc/Tz4//OEPk22fffZp2v/8z/+cfCg/hgwZ0rTpLNWjR49k09in/fl73vOepk3156abbkq2m2++uWlrPEVEbLvttsmmc6XrUEQep4i836DzMs271q5jjz02+VA91zM07aVoHzh+/PimTftlzUe6zznnnJNszz33XNOeP39+8qH9nO6rp0yZknzoLKXxQ+cmWot07KhPlXMgjcuJJ56YbDrvFOeUH5pHa9asST56rw9+8IPJh9Z63fNRn2gvXHkPReOi96K9Ba2resak9YP2+iNHjmzatMbQ/l/HnPqk+yTyofcP+ix0LqR+6v0pDij2tVZSnFPd0NpF/SR0TulvDzRW+nvVd7Aa17Tno/deCp2h9QxE/aZY1DGozCf9HtUf6mfl7FbpJ+3dKKa6sg+s/k3mbwn/x6IxxhhjjDHGGGOMMcYYY4wxpiP+w6IxxhhjjDHGGGOMMcYYY4wxpiP+w6IxxhhjjDHGGGOMMcYYY4wxpiP+w6IxxhhjjDHGGGOMMcY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- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "image/png": {
- "height": 458,
- "width": 907
- },
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "text = data.page()\n",
- "image_show(text)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dcaa8d03-e684-489c-9fc9-17b920f1ebc6",
- "metadata": {},
- "source": [
- "# Background estimation\n",
- "\n",
- "The lower left of the image above is clearly darker than the top right. In this case it is impossible to select a threshold that retains all the text on the right while leaving the background in the lower left clear - see [Manual threshold](#manual). The [histogram](#automatic) methods have the same problem, with varying levels of blackness in the lower left. The [adaptive](#adaptive) methods produce more uniform results, possibly with other artifacts.\n",
- "\n",
- "An alternative to the adaptive approach is to estimate the background intensity and remove it.\n",
- "\n",
- "This approach can work if the following conditions are met:\n",
- "\n",
- "1. The relative brightness of the objects of interest and the background is consistent - in this example the text is always darker than the local bacground.\n",
- "\n",
- "1. The size of the foreground features don't vary too much\n",
- "\n",
- "A classical way to estimate background brightness and remove it is via a morphological _tophat_ filter. A _black tophat_ filter removes bright backgrounds (enhancing dark objects) while a _white tophat_ filter removes dark backgrounds. A black tophat filter is constructed by subtracting the image from a morphologial closing of the image. A white tophat filter subtracts a morphological opening from the image. As described elsewhere, a morphological closing is a dilation (shrinking dark features) followed by an erosion (shrinking bright features).\n",
- "\n",
- "_skimage_ provides tophat filter functions, but we will begin by looking at the _closing_ operation to understand the size of kernel we need and explore speed tradeoffs.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7428c10f-2da0-4fab-b6a1-9ef5ee1a00e4",
- "metadata": {},
- "source": [
- "### Filter size selection\n",
- "\n",
- "Lets start with a tiny filter, defined by the _selem_ argument, and work our way up. We're using rectangle filters because they are much faster, for reasons we'll get to later.\n",
- "\n",
- "We can still see some text below, so this filter is too small."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "629f8049-0592-4aa2-a198-5955ac23aef5",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(, )"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
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- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "image/png": {
- "height": 458,
- "width": 907
- },
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "image_show(morph.closing(text, selem=morph.rectangle(3,3)))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0ce83b00-da66-4e60-a9c8-94cc020bfaea",
- "metadata": {},
- "source": [
- "This time we'll try a much bigger filter"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "30e074d7-47ff-4e7e-ac70-86bd8cbae23d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(, )"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "image/png": {
- "height": 458,
- "width": 907
- },
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "image_show(morph.closing(text, selem=morph.rectangle(31,31)))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6cb59b74-c89e-4b9a-9571-feac929ab084",
- "metadata": {},
- "source": [
- "This looks pretty good - there's no sign of the text and the brightness pattern is what we'd expect. Now for the black tophat version:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "1de3982e-ad41-4300-889c-35b0d0ec4c70",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(, )"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "image/png": {
- "height": 458,
- "width": 907
- },
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "bth = morph.black_tophat(text, selem=morph.rectangle(31,31))\n",
- "image_show(bth)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ad6e69bc-1b65-40b5-8179-ed872d2c2d2b",
- "metadata": {},
- "source": [
- "Some variation in the foreground intensity is visible - text on the right is brighter than the text on the left, but we don't care about this provided we can now select a resonable threshold. Lets see how the automated methods perform:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "e779f131-405d-4c98-8a55-51ace841d7a1",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "image/png": {
- "height": 568,
- "width": 567
- },
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = filters.try_all_threshold(bth, figsize=(10, 8), verbose=False)\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7697ef06-b905-45d5-af05-c3a9f5db2acd",
- "metadata": {},
- "source": [
- "Several of the automated methods now perform in what is probably an acceptable fashion, and possibly just as well as the specialised adaptive methods below.\n",
- "\n",
- "Another idea that you may explore, depending on the data, is matching the shape of the filter to the characteristics of the brightness variation. In many situations the brightness is highest in the middle and drops towards the edges, so there isn't much to be gained by changing the filter aspect ratio. However in the text example, the brightness gradient is stronger left to right than top to bottom, so we might like a structuring element that is higher than it is wide.\n",
- "\n",
- "# Potential problems\n",
- "\n",
- "This approach can fail if the background parts of the image have unusual noise characteristics, such as _salt_ noise consisting of scattered very bright pixels. Such noise, if frequent enough, could lead to over-estimation of background intensity.\n",
- "\n",
- "# Summary\n",
- "\n",
- "Tophat filters are very useful for this class of problems, and quite simple and intuitive to use. Choose a filter that is large enough to remove your largest feature and then proceed with conventional thresholding - no need to write specialised adaptive filters. The process isn't especially sensitive to the filter size, provided it isn't too small, and this usually makes it easy to select something useful - don't be afraid to try a largish structuring element to start with.\n",
- "\n",
- "Tophat filters are fast, simple and useful for this class of problems. Other approaches to estimating nonuniformity are possible. For example, a large median filter might be more appropriate if the objects of interest are both brighter and darker than the background. However this requires background pixes occupy more than 50% of the kernel. Other options, that are more computationally complex, have been developed for MRI where there isn't a useful background to subtract. The N3/N4 family of methods are examples."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "629aea2a-90b6-42f9-bf1f-e110227299dc",
- "metadata": {},
- "source": [
- "# Notes on speed\n",
- "\n",
- "In these examples I chose a rectangular filter. Straight edged filters are often considered undesirable because they can leave visible artifacts in the form of corners and straight lines, and these are visible in the estimated background above. \n",
- "\n",
- "However the gain is speed. The larger filter in the code above runs in the same time as the smaller one, despite the kernel having 100 times as many pixels. Rectangular morphological filters can be decomposed into a pair of lines, and there are fast algorithms that allow erosions and dilations along lines to be computed in constant time.\n",
- "\n",
- "This means we can explore solutions to problems that use morpholgical filters and large rectangular structuring elements without having to worry about speed.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dd9a3173-4b42-40ab-aec7-3b056a2224fc",
- "metadata": {},
- "source": [
- "The two cells below illustrate this with crude timing. The first cell uses a rectangular structuring element and the elapsed time remains constant, while the second cell uses a disk structuring element and the largest time is over 100 times the smallest - i.e complexity is proportional to the number of pixels in the structuring element."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "1a27c934-21b4-404e-a920-1a60c990b510",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[0.005178621970117092, 0.004622356966137886, 0.004363359999842942]\n"
- ]
- }
- ],
- "source": [
- "# time rectanguler structuring elements\n",
- "import time\n",
- "\n",
- "elapsed = list()\n",
- "for sz in [3, 11, 31]:\n",
- " tic=time.perf_counter()\n",
- " a = morph.closing(text, selem=morph.rectangle(sz,sz))\n",
- " elapsed.append(time.perf_counter()-tic)\n",
- "\n",
- "print(elapsed)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "e03c8ecf-faf3-4d73-a7bc-b4306199415d",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[0.009529172093607485, 0.11407211888581514, 1.1505181760294363]\n",
- "120.73642544468744\n"
- ]
- }
- ],
- "source": [
- "# time for circular structuring element\n",
- "elapsed = list()\n",
- "for sz in [3, 11, 31]:\n",
- " tic=time.perf_counter()\n",
- " a = morph.closing(text, selem=morph.disk(sz))\n",
- " elapsed.append(time.perf_counter()-tic)\n",
- "\n",
- "print(elapsed)\n",
- "print(elapsed[2]/elapsed[0])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dd482cdf-9b15-433d-9f44-e47d79eade24",
- "metadata": {},
- "source": [
- "# Review of threshold methods\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2c0c9674-d87f-4d0c-9fee-5d0cb8a45f22",
- "metadata": {},
- "source": [
- "\n",
- "## Manual threshold "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "a1d4e26a-708a-4cf1-abf2-2060ee332e11",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(, )"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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- ""
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- "\n",
- "\n",
- "## Automatic threshold estimation using histograms\n",
- "\n",
- "All of the examples below compute a global threshold by analysing the histogram. The methods make different assumptions about brightness distributions and are therefore function best in different circumstances.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "967f3f36-f038-4a36-a157-dbb2ba91872c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "image/png": {
- "height": 568,
- "width": 567
- },
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = filters.try_all_threshold(text, figsize=(10, 8), verbose=False)\n",
- "plt.show()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1b6af43a-10c2-415c-a4be-58759adefa32",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Adaptive thresholds\n",
- "\n",
- "These methods use information from local neighbourhoods to compute a local threshold."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "75faddc1-358d-4d62-9c87-9e7ae4588296",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(, )"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "image/png": {
- "height": 458,
- "width": 907
- },
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "text_threshold = filters.threshold_sauvola(text, window_size=15, k=0.2)\n",
- "\n",
- "image_show(text < text_threshold);\n",
- "\n",
- "text_threshold = filters.threshold_niblack(text, window_size = 25, k=0.6)\n",
- "image_show(text < text_threshold)\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/lectures/5_tophat_filters.md b/lectures/5_tophat_filters.md
new file mode 100644
index 0000000..c9a2c6e
--- /dev/null
+++ b/lectures/5_tophat_filters.md
@@ -0,0 +1,201 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+# Correcting brightness nonuniformity with tophat filters
+
+Thresholding, usually the first image segmentation technique to be taught, labels individual pixels based on intensity. No information
+from a surrounding region is used.
+
+Basic concepts of segmentation, including thresholding, for skimage are introduced [here](https://scikit-image.org/docs/0.14.x/user_guide/tutorial_segmentation.html) and [here](https://scikit-image.org/docs/stable/auto_examples/segmentation/plot_niblack_sauvola.html) and [here](https://github.com/scikit-image/skimage-tutorials/blob/main/lectures/solutions/4_segmentation.ipynb)
+
+In this tutorial we'll focus on extending the classical thresholding techniques via processes that correct nonuniformity. Brightness nonuniformity is a common problem in many forms of imaging, with varying causes. Having a collection of tools to help deal with nonuniformity can simplify subsequent analysis steps considerably.
+
+## Recap
+
+The _page_ data, shown below, contains some scanned text and the aim is to select a threshold that separates printing from paper (dark from light). The tutorials linked above introduce manual selection, selection based on histograms, and adaptive thresholding, where a different threshold is selected for each pixel based on parameters of the local neighborhood. The results of these methods are shown at the end of the tutorial for reference.
+
+The approach we will introduce is similar to adaptive filtering, but is built using existing filtering procedures to which the manual or histogram approaches can be applied
+
++++
+
+## Set up libraries and display functions
+
+```{code-cell} ipython3
+%matplotlib inline
+%config InlineBackend.figure_format = 'retina'
+import numpy as np
+import matplotlib.pyplot as plt
+
+import skimage.data as data
+import skimage.segmentation as seg
+from skimage import filters
+from skimage import draw
+from skimage import color
+from skimage import exposure
+import skimage.morphology as morph
+
+
+def image_show(image, nrows=1, ncols=1, cmap='gray', **kwargs):
+ fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16))
+ ax.imshow(image, cmap='gray')
+ ax.axis('off')
+ return fig, ax
+```
+
+## Load and display the test data
+
+```{code-cell} ipython3
+text = data.page()
+image_show(text)
+```
+
+## Background estimation
+
+The lower left of the image above is clearly darker than the top right. In this case it is impossible to select a threshold that retains all the text on the right while leaving the background in the lower left clear - see [Manual threshold](#manual). The [histogram](#automatic) methods have the same problem, with varying levels of blackness in the lower left. The [adaptive](#adaptive) methods produce more uniform results, possibly with other artifacts.
+
+An alternative to the adaptive approach is to estimate the background intensity and remove it.
+
+This approach can work if the following conditions are met:
+
+1. The relative brightness of the objects of interest and the background is consistent - in this example the text is always darker than the local bacground.
+
+1. The size of the foreground features don't vary too much
+
+A classical way to estimate background brightness and remove it is via a morphological _tophat_ filter. A _black tophat_ filter removes bright backgrounds (enhancing dark objects) while a _white tophat_ filter removes dark backgrounds. A black tophat filter is constructed by subtracting the image from a morphologial closing of the image. A white tophat filter subtracts a morphological opening from the image. As described elsewhere, a morphological closing is a dilation (shrinking dark features) followed by an erosion (shrinking bright features).
+
+_skimage_ provides tophat filter functions, but we will begin by looking at the _closing_ operation to understand the size of kernel we need and explore speed tradeoffs.
+
+
+
++++
+
+### Filter size selection
+
+Lets start with a tiny filter, defined by the _selem_ argument, and work our way up. We're using rectangle filters because they are much faster, for reasons we'll get to later.
+
+We can still see some text below, so this filter is too small.
+
+```{code-cell} ipython3
+image_show(morph.closing(text, selem=morph.rectangle(3,3)))
+```
+
+This time we'll try a much bigger filter
+
+```{code-cell} ipython3
+image_show(morph.closing(text, selem=morph.rectangle(31,31)))
+```
+
+This looks pretty good - there's no sign of the text and the brightness pattern is what we'd expect. Now for the black tophat version:
+
+```{code-cell} ipython3
+bth = morph.black_tophat(text, selem=morph.rectangle(31,31))
+image_show(bth)
+```
+
+Some variation in the foreground intensity is visible - text on the right is brighter than the text on the left, but we don't care about this provided we can now select a resonable threshold. Lets see how the automated methods perform:
+
+```{code-cell} ipython3
+fig, ax = filters.try_all_threshold(bth, figsize=(10, 8), verbose=False)
+plt.show()
+```
+
+Several of the automated methods now perform in what is probably an acceptable fashion, and possibly just as well as the specialised adaptive methods below.
+
+Another idea that you may explore, depending on the data, is matching the shape of the filter to the characteristics of the brightness variation. In many situations the brightness is highest in the middle and drops towards the edges, so there isn't much to be gained by changing the filter aspect ratio. However in the text example, the brightness gradient is stronger left to right than top to bottom, so we might like a structuring element that is higher than it is wide.
+
+## Potential problems
+
+This approach can fail if the background parts of the image have unusual noise characteristics, such as _salt_ noise consisting of scattered very bright pixels. Such noise, if frequent enough, could lead to over-estimation of background intensity.
+
+## Summary
+
+Tophat filters are very useful for this class of problems, and quite simple and intuitive to use. Choose a filter that is large enough to remove your largest feature and then proceed with conventional thresholding - no need to write specialised adaptive filters. The process isn't especially sensitive to the filter size, provided it isn't too small, and this usually makes it easy to select something useful - don't be afraid to try a largish structuring element to start with.
+
+Tophat filters are fast, simple and useful for this class of problems. Other approaches to estimating nonuniformity are possible. For example, a large median filter might be more appropriate if the objects of interest are both brighter and darker than the background. However this requires background pixes occupy more than 50% of the kernel. Other options, that are more computationally complex, have been developed for MRI where there isn't a useful background to subtract. The N3/N4 family of methods are examples.
+
++++
+
+## Notes on speed
+
+In these examples I chose a rectangular filter. Straight edged filters are often considered undesirable because they can leave visible artifacts in the form of corners and straight lines, and these are visible in the estimated background above.
+
+However the gain is speed. The larger filter in the code above runs in the same time as the smaller one, despite the kernel having 100 times as many pixels. Rectangular morphological filters can be decomposed into a pair of lines, and there are fast algorithms that allow erosions and dilations along lines to be computed in constant time.
+
+This means we can explore solutions to problems that use morpholgical filters and large rectangular structuring elements without having to worry about speed.
+
++++
+
+The two cells below illustrate this with crude timing. The first cell uses a rectangular structuring element and the elapsed time remains constant, while the second cell uses a disk structuring element and the largest time is over 100 times the smallest - i.e complexity is proportional to the number of pixels in the structuring element.
+
+```{code-cell} ipython3
+# time rectanguler structuring elements
+import time
+
+elapsed = list()
+for sz in [3, 11, 31]:
+ tic=time.perf_counter()
+ a = morph.closing(text, selem=morph.rectangle(sz,sz))
+ elapsed.append(time.perf_counter()-tic)
+
+print(elapsed)
+```
+
+```{code-cell} ipython3
+# time for circular structuring element
+elapsed = list()
+for sz in [3, 11, 31]:
+ tic=time.perf_counter()
+ a = morph.closing(text, selem=morph.disk(sz))
+ elapsed.append(time.perf_counter()-tic)
+
+print(elapsed)
+print(elapsed[2]/elapsed[0])
+```
+
+## Review of threshold methods
+
+
++++
+
+
+### Manual threshold
+
+```{code-cell} ipython3
+image_show(text>80)
+```
+
+
+
+### Automatic threshold estimation using histograms
+
+All of the examples below compute a global threshold by analysing the histogram. The methods make different assumptions about brightness distributions and are therefore function best in different circumstances.
+
+```{code-cell} ipython3
+fig, ax = filters.try_all_threshold(text, figsize=(10, 8), verbose=False)
+plt.show()
+```
+
+
+
+### Adaptive thresholds
+
+These methods use information from local neighbourhoods to compute a local threshold.
+
+```{code-cell} ipython3
+text_threshold = filters.threshold_sauvola(text, window_size=15, k=0.2)
+
+image_show(text < text_threshold);
+
+text_threshold = filters.threshold_niblack(text, window_size = 25, k=0.6)
+image_show(text < text_threshold)
+```
diff --git a/lectures/6_watershed_tricks.ipynb b/lectures/6_watershed_tricks.ipynb
deleted file mode 100644
index 0844091..0000000
--- a/lectures/6_watershed_tricks.ipynb
+++ /dev/null
@@ -1,328 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "a7e80ccc-7f74-4594-9143-09cdccc52816",
- "metadata": {},
- "source": [
- "# Useful segmentation tricks with the watershed transform\n",
- "\n",
- "The watershed transform is a powerful and easy to use segmentation algorithm, and very reliable if an appropriate marker generation procedure can be created. In this notebook we introduce a variety of tricks that can be used when creating markers. We illustrate for the coins example, although they are useful in a variety of scenarios.\n",
- "\n",
- "The takeaway points are:\n",
- "\n",
- "1. Develop the markers in stages\n",
- "1. Don't be scared to use large filters - making the image unreconisable usually doesn't matter during marker generation\n",
- "1. Return to the original image when performing segmentation.\n",
- "\n",
- "## Watershed transform background\n",
- "\n",
- "The watershed transform using markers is a long established tool for segmentation that is introduced [here](https://scikit-image.org/docs/0.14.x/user_guide/tutorial_segmentation.html)\n",
- "\n",
- "The important factors to understand about the watershed transform are:\n",
- "\n",
- "1. Regions are grown from a set of \"markers\"\n",
- "1. All image pixels are assigned to exactly one marker (or as a boundary) - that means that at least two marker classes are required to split the image into multiple regions\n",
- "1. Boundaries between regions occur along ridge lines, or peaks, in the image topology (or midpoints of plateaus). There is no threshold step involved.\n",
- "1. Markers lie entirely within the region to be segmented. Markers that cross region boundaries will lead to undesirable results.\n",
- "\n",
- "## Plans\n",
- "\n",
- "We're going to do things a bit differently to the other coin examples floating around. Firstly, we will pretend there isn't a reliable way of creating a background marker and invest our effort in creating reliable foreground markers using big filters. We'll then perform two phases of watershed transform segmentation, the first of which will create our background markers.\n",
- "\n",
- "# The problem\n",
- "\n",
- "As in other examples using this image, the problem is to create an accurate, labelled, mask for each coin.\n",
- "\n",
- "We begin with some setup and a look at the input data.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 86,
- "id": "f545d408-a793-4dd0-a897-555a85b0fb21",
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "def image_show(image, cmap='gray', **kwargs):\n",
- " fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8))\n",
- " ax.imshow(image, cmap=cmap)\n",
- " ax.axis('off')\n",
- " return fig, ax\n",
- "\n",
- "\n",
- "def image_show_multi(imlist, nrows=1, ncols=1, cmap='gray'):\n",
- " fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(8*ncols, 8*nrows))\n",
- " i=0\n",
- " for r in range(nrows):\n",
- " for c in range(ncols):\n",
- " ax[i].imshow(imlist[i])\n",
- " ax[i].axis('off')\n",
- " i=i+1\n",
- " \n",
- " return fig, ax"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 115,
- "id": "9d4d5941-f4be-4f2b-b346-0677d739be39",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import numpy as np\n",
- "import skimage\n",
- "from skimage import data\n",
- "import matplotlib.pyplot as plt\n",
- "coins = data.coins()\n",
- "\n",
- "a=image_show(coins)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b03c28ea-d69a-4f83-8b8e-270e5f68a9ca",
- "metadata": {},
- "source": [
- "# Foreground markers\n",
- "\n",
- "Reliable segmentation using the watershed transform requires a single marker per coin. In addition, the marker must not cross the coin boundary.\n",
- "\n",
- "We begin by making some observations about the scene we want to segment:\n",
- "\n",
- "1. Coins are bright, but textured (see thresholding results in other tutorials)\n",
- "1. Size varies, but not dramatically - the largest coin is probably about twice the diameter of the smallest.\n",
- "\n",
- "\n",
- "Our first attempt at finding markers will be to apply a very large Gaussian smoothing, with a sigma value a substantial proportion of the size of the smallest coin, and find regional maxima (peaks) in that image.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 116,
- "id": "a34d7020-b120-4068-a8d7-1dbf4e79f978",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "from skimage import filters\n",
- "from skimage import morphology\n",
- "from skimage import feature\n",
- "from skimage import color\n",
- "from skimage import measure\n",
- "from skimage import segmentation\n",
- "\n",
- "smoothed = skimage.filters.gaussian(coins, 10)\n",
- "a=image_show(smoothed)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5e4cedea-0c05-46d9-a5d8-f5e753936e16",
- "metadata": {},
- "source": [
- "Now we find the local maxima (peaks) and dilate them."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 94,
- "id": "43c85e11-1265-4631-984b-36b406da9662",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "peak_idx = skimage.feature.peak_local_max(smoothed, min_distance=10)\n",
- "peak_mask = np.zeros_like(smoothed, dtype=bool)\n",
- "peak_mask[tuple(peak_idx.T)] = True\n",
- "# dilate them and label\n",
- "peak_mask = skimage.morphology.dilation(peak_mask, selem=skimage.morphology.square(11))\n",
- "# display\n",
- "peak_overlay = skimage.color.label2rgb(peak_mask, image=smoothed, bg_label=0)\n",
- "a=image_show(peak_overlay)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3dd6fa8b-739a-49c1-98fb-9318c091445f",
- "metadata": {},
- "source": [
- "Now we have a nice marker inside each coin. We've used the min distance parameter of `peak_local_max` in combination with the large smoothing kernel \n",
- "to reduce the chance of having multiple markers in a coin.\n",
- "\n",
- "Now we need to create a marker for the background. We're going to do something a bit different this time - use the watershed to tesselate the scene, then take the boundary lines to use as markers in a second watershed.\n",
- "\n",
- "The steps are:\n",
- "\n",
- "1. Label the peak markers (record the number of peaks for later)\n",
- "1. Invert the smoothed image\n",
- "1. Apply a watershed that produces border regions\n",
- "1. Select the boundary\n",
- "1. Combine with the peak markers"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 105,
- "id": "0a047d09-00ca-4872-a0f2-5270dd5a218c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "labelpeaks, totalpeaks = skimage.measure.label(peak_mask, return_num = True)\n",
- "\n",
- "smoothed_inverted = np.max(smoothed) - smoothed\n",
- "phase1seg = skimage.segmentation.watershed(image = smoothed_inverted, markers = labelpeaks, watershed_line = True) \n",
- "newmarkers = (phase1seg == 0)*(totalpeaks + 1) + labelpeaks\n",
- "\n",
- "newmarkers_overlay = skimage.color.label2rgb(newmarkers, image=smoothed, bg_label=0)\n",
- "newmarkers_overlay_orig = skimage.color.label2rgb(newmarkers, image=coins, bg_label=0)\n",
- "a=image_show_multi([newmarkers_overlay, newmarkers_overlay_orig], nrows=1, ncols=2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ca4dac0f-668b-489d-b31d-2b7aa580758e",
- "metadata": {},
- "source": [
- "Check that it looks OK on the original image - all background markers (green) are between coins and foreground markers are inside coins.\n",
- "\n",
- "Note that the textures inside the coins are likely to cause trouble for our edge operators.\n",
- "\n",
- "Now we create an edge image for the second phase of segmentation. skimage doesn't have derivative of Gaussian functions so we'll smooth then apply Sobel filters instead. A substantial smoothing reduces the chances of holes in the gradient, with the side effect of removing high frequency components. However the outer edges of the coins are smooth so smoothing doesn't matter much."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 112,
- "id": "b5d91323-7e9c-4e03-ab6d-5fb56e1c01ca",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "edges = skimage.filters.gaussian(coins, 3)\n",
- "edges = skimage.filters.sobel(edges)\n",
- "a=image_show(edges)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1348d1d8-e461-4ec4-819d-d88493b108a0",
- "metadata": {},
- "source": [
- "Finally we apply a watershed"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 114,
- "id": "6068c563-e3c8-460e-bfdd-ce6dbb62c8cf",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "finalseg = skimage.segmentation.watershed(edges, newmarkers, watershed_line = False)\n",
- "# remove the background segmentation\n",
- "finalseg[finalseg == (totalpeaks + 1)] = 0\n",
- "# display\n",
- "finalseg_overlay = skimage.color.label2rgb(finalseg, image=coins, bg_label=0)\n",
- "a = image_show(finalseg_overlay)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/lectures/6_watershed_tricks.md b/lectures/6_watershed_tricks.md
new file mode 100644
index 0000000..9402d80
--- /dev/null
+++ b/lectures/6_watershed_tricks.md
@@ -0,0 +1,159 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+# Useful segmentation tricks with the watershed transform
+
+The watershed transform is a powerful and easy to use segmentation algorithm, and very reliable if an appropriate marker generation procedure can be created. In this notebook we introduce a variety of tricks that can be used when creating markers. We illustrate for the coins example, although they are useful in a variety of scenarios.
+
+The takeaway points are:
+
+1. Develop the markers in stages
+1. Don't be scared to use large filters - making the image unreconisable usually doesn't matter during marker generation
+1. Return to the original image when performing segmentation.
+
+## Watershed transform background
+
+The watershed transform using markers is a long established tool for segmentation that is introduced [here](https://scikit-image.org/docs/0.14.x/user_guide/tutorial_segmentation.html)
+
+The important factors to understand about the watershed transform are:
+
+1. Regions are grown from a set of "markers"
+1. All image pixels are assigned to exactly one marker (or as a boundary) - that means that at least two marker classes are required to split the image into multiple regions
+1. Boundaries between regions occur along ridge lines, or peaks, in the image topology (or midpoints of plateaus). There is no threshold step involved.
+1. Markers lie entirely within the region to be segmented. Markers that cross region boundaries will lead to undesirable results.
+
+## Plans
+
+We're going to do things a bit differently to the other coin examples floating around. Firstly, we will pretend there isn't a reliable way of creating a background marker and invest our effort in creating reliable foreground markers using big filters. We'll then perform two phases of watershed transform segmentation, the first of which will create our background markers.
+
+## The problem
+
+As in other examples using this image, the problem is to create an accurate, labelled, mask for each coin.
+
+We begin with some setup and a look at the input data.
+
+```{code-cell} ipython3
+
+def image_show(image, cmap='gray', **kwargs):
+ fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8))
+ ax.imshow(image, cmap=cmap)
+ ax.axis('off')
+ return fig, ax
+
+
+def image_show_multi(imlist, nrows=1, ncols=1, cmap='gray'):
+ fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(8*ncols, 8*nrows))
+ i=0
+ for r in range(nrows):
+ for c in range(ncols):
+ ax[i].imshow(imlist[i])
+ ax[i].axis('off')
+ i=i+1
+
+ return fig, ax
+```
+
+```{code-cell} ipython3
+import numpy as np
+import skimage
+from skimage import data
+import matplotlib.pyplot as plt
+coins = data.coins()
+
+a=image_show(coins)
+```
+
+## Foreground markers
+
+Reliable segmentation using the watershed transform requires a single marker per coin. In addition, the marker must not cross the coin boundary.
+
+We begin by making some observations about the scene we want to segment:
+
+1. Coins are bright, but textured (see thresholding results in other tutorials)
+1. Size varies, but not dramatically - the largest coin is probably about twice the diameter of the smallest.
+
+
+Our first attempt at finding markers will be to apply a very large Gaussian smoothing, with a sigma value a substantial proportion of the size of the smallest coin, and find regional maxima (peaks) in that image.
+
+```{code-cell} ipython3
+from skimage import filters
+from skimage import morphology
+from skimage import feature
+from skimage import color
+from skimage import measure
+from skimage import segmentation
+
+smoothed = skimage.filters.gaussian(coins, 10)
+a=image_show(smoothed)
+```
+
+Now we find the local maxima (peaks) and dilate them.
+
+```{code-cell} ipython3
+peak_idx = skimage.feature.peak_local_max(smoothed, min_distance=10)
+peak_mask = np.zeros_like(smoothed, dtype=bool)
+peak_mask[tuple(peak_idx.T)] = True
+# dilate them and label
+peak_mask = skimage.morphology.dilation(peak_mask, selem=skimage.morphology.square(11))
+# display
+peak_overlay = skimage.color.label2rgb(peak_mask, image=smoothed, bg_label=0)
+a=image_show(peak_overlay)
+```
+
+Now we have a nice marker inside each coin. We've used the min distance parameter of `peak_local_max` in combination with the large smoothing kernel
+to reduce the chance of having multiple markers in a coin.
+
+Now we need to create a marker for the background. We're going to do something a bit different this time - use the watershed to tesselate the scene, then take the boundary lines to use as markers in a second watershed.
+
+The steps are:
+
+1. Label the peak markers (record the number of peaks for later)
+1. Invert the smoothed image
+1. Apply a watershed that produces border regions
+1. Select the boundary
+1. Combine with the peak markers
+
+```{code-cell} ipython3
+labelpeaks, totalpeaks = skimage.measure.label(peak_mask, return_num = True)
+
+smoothed_inverted = np.max(smoothed) - smoothed
+phase1seg = skimage.segmentation.watershed(image = smoothed_inverted, markers = labelpeaks, watershed_line = True)
+newmarkers = (phase1seg == 0)*(totalpeaks + 1) + labelpeaks
+
+newmarkers_overlay = skimage.color.label2rgb(newmarkers, image=smoothed, bg_label=0)
+newmarkers_overlay_orig = skimage.color.label2rgb(newmarkers, image=coins, bg_label=0)
+a=image_show_multi([newmarkers_overlay, newmarkers_overlay_orig], nrows=1, ncols=2)
+```
+
+Check that it looks OK on the original image - all background markers (green) are between coins and foreground markers are inside coins.
+
+Note that the textures inside the coins are likely to cause trouble for our edge operators.
+
+Now we create an edge image for the second phase of segmentation. skimage doesn't have derivative of Gaussian functions so we'll smooth then apply Sobel filters instead. A substantial smoothing reduces the chances of holes in the gradient, with the side effect of removing high frequency components. However the outer edges of the coins are smooth so smoothing doesn't matter much.
+
+```{code-cell} ipython3
+edges = skimage.filters.gaussian(coins, 3)
+edges = skimage.filters.sobel(edges)
+a=image_show(edges)
+```
+
+Finally we apply a watershed
+
+```{code-cell} ipython3
+finalseg = skimage.segmentation.watershed(edges, newmarkers, watershed_line = False)
+# remove the background segmentation
+finalseg[finalseg == (totalpeaks + 1)] = 0
+# display
+finalseg_overlay = skimage.color.label2rgb(finalseg, image=coins, bg_label=0)
+a = image_show(finalseg_overlay)
+```
diff --git a/lectures/adv0_chromosomes.md b/lectures/adv0_chromosomes.md
new file mode 100644
index 0000000..b6aa68f
--- /dev/null
+++ b/lectures/adv0_chromosomes.md
@@ -0,0 +1,94 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+```{code-cell} python
+from __future__ import division, print_function
+%matplotlib inline
+```
+
+# Measuring chromatin fluorescence
+
+Goal: we want to quantify the amount of a particular protein (red fluorescence) localized on the centromeres (green) versus the rest of the chromosome (blue).
+
+
+
+The main challenge here is the uneven illumination, which makes isolating the chromosomes a struggle.
+
+```{code-cell} python
+import numpy as np
+from matplotlib import cm, pyplot as plt
+import skdemo
+plt.rcParams['image.cmap'] = 'cubehelix'
+plt.rcParams['image.interpolation'] = 'none'
+```
+
+```{code-cell} python
+from skimage import io
+image = io.imread('../images/chromosomes.tif')
+skdemo.imshow_with_histogram(image);
+```
+
+Let's separate the channels so we can work on each individually.
+
+```{code-cell} python
+protein, centromeres, chromosomes = image.transpose((2, 0, 1))
+```
+
+Getting the centromeres is easy because the signal is so clean:
+
+```{code-cell} python
+from skimage.filters import threshold_otsu
+centromeres_binary = centromeres > threshold_otsu(centromeres)
+skdemo.imshow_all(centromeres, centromeres_binary)
+```
+
+But getting the chromosomes is not so easy:
+
+```{code-cell} python
+chromosomes_binary = chromosomes > threshold_otsu(chromosomes)
+skdemo.imshow_all(chromosomes, chromosomes_binary, cmap='gray')
+```
+
+Let's try using an adaptive threshold:
+
+```{code-cell} python
+from skimage.filters import threshold_local
+chromosomes_adapt = threshold_local(chromosomes, block_size=51)
+# Question: how did I choose this block size?
+skdemo.imshow_all(chromosomes, chromosomes_adapt)
+```
+
+Not only is the uneven illumination a problem, but there seem to be some artifacts due to the illumination pattern!
+
+**Exercise:** Can you think of a way to fix this?
+
+(Hint: in addition to everything you've learned so far, check out [`skimage.morphology.remove_small_objects`](http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.remove_small_objects))
+
+Now that we have the centromeres and the chromosomes, it's time to do the science: get the distribution of intensities in the red channel using both centromere and chromosome locations.
+
+```python
+# Replace "None" below with the right expressions!
+centromere_intensities = None
+chromosome_intensities = None
+all_intensities = np.concatenate((centromere_intensities,
+ chromosome_intensities))
+minint = np.min(all_intensities)
+maxint = np.max(all_intensities)
+bins = np.linspace(minint, maxint, 100)
+plt.hist(centromere_intensities, bins=bins, color='blue',
+ alpha=0.5, label='centromeres')
+plt.hist(chromosome_intensities, bins=bins, color='orange',
+ alpha=0.5, label='chromosomes')
+plt.legend(loc='upper right')
+plt.show()
+```
diff --git a/lectures/adv1_lesion-quantification.md b/lectures/adv1_lesion-quantification.md
new file mode 100644
index 0000000..3fb7ec9
--- /dev/null
+++ b/lectures/adv1_lesion-quantification.md
@@ -0,0 +1,90 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+```{code-cell} ipython3
+from __future__ import division, print_function
+%matplotlib inline
+```
+
+# Quantifying spinal cord regeneration in zebrafish
+
+We want to quantify the amount of fluorescent cells in a wounded zebrafish embryo spinal column:
+
+
+
+The key steps are:
+
+- estimating the position and width of the cord
+- estimating the average fluorescence along the length of the cord
+
+```{code-cell} ipython3
+from matplotlib import pyplot as plt, cm
+from skimage import io
+image = io.imread('../images/zebrafish-spinal-cord.png')
+```
+
+## SciPy to estimate coordinates
+
+First, we get just the top and bottom rows of pixels, and use a 1D gaussian filter to smooth the signal.
+
+```{code-cell} ipython3
+from scipy import ndimage as nd
+top, bottom = image[[0, -1], :]
+
+fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
+
+top_smooth = nd.gaussian_filter1d(top, sigma=20)
+ax0.plot(top, color='blue', lw=2)
+ax0.plot(top_smooth, color='orange', lw=2)
+ax0.set_title('top')
+
+bottom_smooth = nd.gaussian_filter1d(bottom, sigma=20)
+ax1.plot(bottom, color='blue', lw=2)
+ax1.plot(bottom_smooth, color='orange', lw=2)
+ax1.set_title('bottom')
+```
+
+With smooth curves, we can get the mode (the position of the center) and width of the signal.
+
+```{code-cell} ipython3
+top_mode = top_smooth.argmax()
+top_max = top_smooth[top_mode]
+top_width = (top_smooth > float(top_max) / 2).sum()
+
+bottom_mode = bottom_smooth.argmax()
+bottom_max = bottom_smooth[bottom_mode]
+bottom_width = (bottom_smooth > float(bottom_max) / 2).sum()
+
+width = max(bottom_width, top_width)
+
+print(top_mode, top_width, bottom_mode, bottom_width)
+```
+
+## scikit-image to trace the profile
+
+Now, use `measure.profile_line` to trace from (0, `top_mode`) to (-1, `bottom_mode`).
+
+```python
+from skimage import measure
+trace = measure.profile_line(None) # Replace `None` with correct args
+```
+
+Finally, plot the trace.
+
+```python
+plt.plot(trace, color='black', lw=2)
+plt.xlabel('position along embryo')
+plt.ylabel('mean fluorescence intensity')
+```
+
+From this trace, we can compute various summary statistics (e.g. min/max, gap width, slope, etc), and plot these over time as the wound recovers.
diff --git a/lectures/adv2_microarray.md b/lectures/adv2_microarray.md
new file mode 100644
index 0000000..0722c43
--- /dev/null
+++ b/lectures/adv2_microarray.md
@@ -0,0 +1,203 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+```{code-cell} ipython3
+from __future__ import division, print_function
+%matplotlib inline
+```
+
+# DNA microarray processing
+
+## Data in this example
+
+*Yeast microarrays for genome wide parallel genetic and gene
+expressionโanalysis*
+
+
+
+Two-color fluorescent scan of a yeast microarray containing 2,479 elements
+(ORFs). The center-to-center distance between elements is 345 ฮผm. A probe
+mixture consisting of cDNA from yeast extract/peptone (YEP) galactose (green
+pseudocolor) and YEP glucose (red pseudocolor) grown yeast cultures was
+hybridized to the array. Intensity per element corresponds to ORF expression,
+and pseudocolor per element corresponds to relative ORF expression between the
+two cultures.โ
+
+by Deval A. Lashkari, http://www.pnas.org/content/94/24/13057/F1.expansion
+
+
+
+Learn more about microarrays:
+
+- [Tutorial on how to analyze microarray data](http://www.hhmi.org/biointeractive/how-analyze-dna-microarray-data)
+- [Complementary DNA](http://en.wikipedia.org/wiki/Complementary_DNA)
+
+More example data:
+
+- [MicroArray Genome Imaging & Clustering Tool](http://www.bio.davidson.edu/projects/MAGIC/MAGIC.html) by Laurie Heyer & team, Davidson College
+
+```{code-cell} ipython3
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from skimage import io, img_as_float
+```
+
+```{code-cell} ipython3
+microarray = io.imread('../images/microarray.jpg')
+
+# Scale between zero and one
+microarray = img_as_float(microarray)
+
+plt.figure(figsize=(10, 5))
+plt.imshow(microarray[:500, :1000], cmap='gray', interpolation='nearest');
+```
+
+```{code-cell} ipython3
+from skimage import color
+f, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 10))
+
+red = microarray[..., 0]
+green = microarray[..., 1]
+
+red_rgb = np.zeros_like(microarray)
+red_rgb[..., 0] = red
+
+green_rgb = np.zeros_like(microarray)
+green_rgb[..., 1] = green
+
+ax0.imshow(green_rgb, interpolation='nearest')
+ax1.imshow(red_rgb, interpolation='nearest')
+plt.suptitle('\n\nPseudocolor plots of red and green channels', fontsize=16);
+```
+
+```{code-cell} ipython3
+from skimage import filters
+
+mask = (green > 0.1)
+plt.imshow(mask[:1000, :1000], cmap='gray');
+```
+
+```{code-cell} ipython3
+z = red.copy()
+z /= green
+z[~mask] = 0
+
+print(z.min(), z.max())
+
+plt.imshow(z[:500, :500], cmap=plt.cm.gray, vmin=0, vmax=2);
+```
+
+## Locating the grid
+
+```{code-cell} ipython3
+both = (green + red)
+
+plt.imshow(both, cmap='gray');
+```
+
+```{code-cell} ipython3
+from skimage import feature
+
+sum_down_columns = both.sum(axis=0)
+sum_across_rows = both.sum(axis=1)
+
+dips_columns = feature.peak_local_max(sum_down_columns.max() - sum_down_columns, min_distance=5)
+dips_columns = np.sort(dips_columns.ravel())
+
+M = len(dips_columns)
+column_distance = np.mean(np.diff(dips_columns))
+
+dips_rows = feature.peak_local_max(sum_across_rows.max() - sum_across_rows, min_distance=5)
+dips_rows = np.sort(dips_rows.ravel())
+
+N = len(dips_rows)
+row_distance = np.mean(np.diff(dips_rows))
+
+print('Columns are a mean distance of %.2f apart' % column_distance)
+print('Rows are a mean distance of %.2f apart' % row_distance)
+
+f, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 5))
+
+ax0.plot(sum_down_columns)
+ax0.scatter(dips_columns, sum_down_columns[dips_columns])
+ax0.set_xlim(0, 200)
+ax0.set_title('Column gaps')
+
+ax1.plot(sum_across_rows)
+ax1.scatter(dips_rows, sum_across_rows[dips_rows])
+ax1.set_xlim(0, 200)
+ax0.set_title('Row gaps');
+```
+
+```{code-cell} ipython3
+P, Q = 500, 500
+
+plt.figure(figsize=(15, 10))
+plt.imshow(microarray[:P, :Q])
+
+for i in dips_rows[dips_rows < P]:
+ plt.plot([0, Q], [i, i], 'm')
+
+for j in dips_columns[dips_columns < Q]:
+ plt.plot([j, j], [0, P], 'm')
+
+plt.axis('image');
+```
+
+```{code-cell} ipython3
+out = np.zeros(microarray.shape[:2])
+M, N = len(dips_rows), len(dips_columns)
+
+for i in range(M - 1):
+ for j in range(N - 1):
+ row0, row1 = dips_rows[i], dips_rows[i + 1]
+ col0, col1 = dips_columns[j], dips_columns[j + 1]
+
+ r = microarray[row0:row1, col0:col1, 0]
+ g = microarray[row0:row1, col0:col1, 1]
+
+ ratio = r / g
+ mask = ~np.isinf(ratio)
+
+ mean_ratio = np.mean(ratio[mask])
+ if np.isnan(mean_ratio):
+ mean_ratio = 0
+
+ out[row0:row1, col0:col1] = mean_ratio
+```
+
+```{code-cell} ipython3
+f, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 10))
+
+ax0.imshow(microarray)
+ax0.grid(color='magenta', linewidth=1)
+
+ax1.imshow(out, cmap='gray', interpolation='nearest', vmin=0, vmax=3);
+ax1.grid(color='magenta', linewidth=1)
+```
+
+## Transform the intensity to spot outliers
+
+```{code-cell} ipython3
+from skimage import exposure
+
+f, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 10))
+
+ax0.imshow(microarray)
+ax0.grid(color='magenta', linewidth=1)
+
+ax1.imshow(exposure.adjust_log(out, gain=0.4), cmap='gray', interpolation='nearest', vmin=0, vmax=3);
+ax1.grid(color='magenta', linewidth=1)
+```
diff --git a/lectures/not_yet_booked/adv0_chromosomes.ipynb b/lectures/not_yet_booked/adv0_chromosomes.ipynb
deleted file mode 100644
index 977f0c6..0000000
--- a/lectures/not_yet_booked/adv0_chromosomes.ipynb
+++ /dev/null
@@ -1,332 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "from __future__ import division, print_function\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Measuring chromatin fluorescence\n",
- "\n",
- "Goal: we want to quantify the amount of a particular protein (red fluorescence) localized on the centromeres (green) versus the rest of the chromosome (blue).\n",
- "\n",
- "\n",
- "\n",
- "The main challenge here is the uneven illumination, which makes isolating the chromosomes a struggle."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from matplotlib import cm, pyplot as plt\n",
- "import skdemo\n",
- "plt.rcParams['image.cmap'] = 'cubehelix'\n",
- "plt.rcParams['image.interpolation'] = 'none'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage import io\n",
- "image = io.imread('../images/chromosomes.tif')\n",
- "skdemo.imshow_with_histogram(image);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's separate the channels so we can work on each individually."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "protein, centromeres, chromosomes = image.transpose((2, 0, 1))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Getting the centromeres is easy because the signal is so clean:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage.filter import threshold_otsu\n",
- "centromeres_binary = centromeres > threshold_otsu(centromeres)\n",
- "skdemo.imshow_all(centromeres, centromeres_binary)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "But getting the chromosomes is not so easy:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "chromosomes_binary = chromosomes > threshold_otsu(chromosomes)\n",
- "skdemo.imshow_all(chromosomes, chromosomes_binary, cmap='gray')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's try using an adaptive threshold:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage.filter import threshold_adaptive\n",
- "chromosomes_adapt = threshold_adaptive(chromosomes, block_size=51)\n",
- "# Question: how did I choose this block size?\n",
- "skdemo.imshow_all(chromosomes, chromosomes_adapt)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Not only is the uneven illumination a problem, but there seem to be some artifacts due to the illumination pattern!\n",
- "\n",
- "**Exercise:** Can you think of a way to fix this?\n",
- "\n",
- "(Hint: in addition to everything you've learned so far, check out [`skimage.morphology.remove_small_objects`](http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.remove_small_objects))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now that we have the centromeres and the chromosomes, it's time to do the science: get the distribution of intensities in the red channel using both centromere and chromosome locations."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Replace \"None\" below with the right expressions!\n",
- "centromere_intensities = None\n",
- "chromosome_intensities = None\n",
- "all_intensities = np.concatenate((centromere_intensities,\n",
- " chromosome_intensities))\n",
- "minint = np.min(all_intensities)\n",
- "maxint = np.max(all_intensities)\n",
- "bins = np.linspace(minint, maxint, 100)\n",
- "plt.hist(centromere_intensities, bins=bins, color='blue',\n",
- " alpha=0.5, label='centromeres')\n",
- "plt.hist(chromosome_intensities, bins=bins, color='orange',\n",
- " alpha=0.5, label='chromosomes')\n",
- "plt.legend(loc='upper right')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "%reload_ext load_style\n",
- "%load_style ../themes/tutorial.css"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.4.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/lectures/not_yet_booked/adv1-lesion-quantification.ipynb b/lectures/not_yet_booked/adv1-lesion-quantification.ipynb
deleted file mode 100644
index 0ec1d95..0000000
--- a/lectures/not_yet_booked/adv1-lesion-quantification.ipynb
+++ /dev/null
@@ -1,300 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "from __future__ import division, print_function\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Quantifying spinal cord regeneration in zebrafish\n",
- "\n",
- "We want to quantify the amount of fluorescent cells in a wounded zebrafish embryo spinal column:\n",
- "\n",
- "\n",
- "\n",
- "The key steps are:\n",
- "\n",
- "- estimating the position and width of the cord\n",
- "- estimating the average fluorescence along the length of the cord"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from matplotlib import pyplot as plt, cm\n",
- "from skimage import io\n",
- "image = io.imread('../images/zebrafish-spinal-cord.png')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# SciPy to estimate coordinates\n",
- "\n",
- "First, we get just the top and bottom rows of pixels, and use a 1D gaussian filter to smooth the signal."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from scipy import ndimage as nd\n",
- "top, bottom = image[[0, -1], :]\n",
- "\n",
- "fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n",
- "\n",
- "top_smooth = nd.gaussian_filter1d(top, sigma=20)\n",
- "ax0.plot(top, color='blue', lw=2)\n",
- "ax0.plot(top_smooth, color='orange', lw=2)\n",
- "ax0.set_title('top')\n",
- "\n",
- "bottom_smooth = nd.gaussian_filter1d(bottom, sigma=20)\n",
- "ax1.plot(bottom, color='blue', lw=2)\n",
- "ax1.plot(bottom_smooth, color='orange', lw=2)\n",
- "ax1.set_title('bottom')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "With smooth curves, we can get the mode (the position of the center) and width of the signal."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "top_mode = top_smooth.argmax()\n",
- "top_max = top_smooth[top_mode]\n",
- "top_width = (top_smooth > float(top_max) / 2).sum()\n",
- "\n",
- "bottom_mode = bottom_smooth.argmax()\n",
- "bottom_max = bottom_smooth[bottom_mode]\n",
- "bottom_width = (bottom_smooth > float(bottom_max) / 2).sum()\n",
- "\n",
- "width = max(bottom_width, top_width)\n",
- "\n",
- "print(top_mode, top_width, bottom_mode, bottom_width)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# scikit-image to trace the profile\n",
- "\n",
- "Now, use `measure.profile_line` to trace from (0, `top_mode`) to (-1, `bottom_mode`)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage import measure\n",
- "trace = measure.profile_line(None) # Replace `None` with correct args"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, plot the trace."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "plt.plot(trace, color='black', lw=2)\n",
- "plt.xlabel('position along embryo')\n",
- "plt.ylabel('mean fluorescence intensity')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "From this trace, we can compute various summary statistics (e.g. min/max, gap width, slope, etc), and plot these over time as the wound recovers."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "%reload_ext load_style\n",
- "%load_style ../themes/tutorial.css"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.4.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/lectures/not_yet_booked/adv2_microarray.ipynb b/lectures/not_yet_booked/adv2_microarray.ipynb
deleted file mode 100644
index 3f1330f..0000000
--- a/lectures/not_yet_booked/adv2_microarray.ipynb
+++ /dev/null
@@ -1,439 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "from __future__ import division, print_function\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# DNA microarray processing\n",
- "\n",
- "### Data in this example\n",
- "\n",
- "*Yeast microarrays for genome wide parallel genetic and gene\n",
- "expressionโanalysis*\n",
- "\n",
- "\n",
- "\n",
- "Two-color fluorescent scan of a yeast microarray containing 2,479 elements\n",
- "(ORFs). The center-to-center distance between elements is 345 ฮผm. A probe\n",
- "mixture consisting of cDNA from yeast extract/peptone (YEP) galactose (green\n",
- "pseudocolor) and YEP glucose (red pseudocolor) grown yeast cultures was\n",
- "hybridized to the array. Intensity per element corresponds to ORF expression,\n",
- "and pseudocolor per element corresponds to relative ORF expression between the\n",
- "two cultures.โ\n",
- "\n",
- "by Deval A. Lashkari, http://www.pnas.org/content/94/24/13057/F1.expansion\n",
- "\n",
- "\n",
- " \n",
- "Learn more about microarrays:\n",
- "\n",
- "- [Tutorial on how to analyze microarray data](http://www.hhmi.org/biointeractive/how-analyze-dna-microarray-data)\n",
- "- [Complementary DNA](http://en.wikipedia.org/wiki/Complementary_DNA)\n",
- "\n",
- "More example data:\n",
- "\n",
- "- [MicroArray Genome Imaging & Clustering Tool](http://www.bio.davidson.edu/projects/MAGIC/MAGIC.html) by Laurie Heyer & team, Davidson College\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "import numpy as np\n",
- "\n",
- "from skimage import io, img_as_float"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "microarray = io.imread('../images/microarray.jpg')\n",
- "\n",
- "# Scale between zero and one\n",
- "microarray = img_as_float(microarray)\n",
- "\n",
- "plt.figure(figsize=(10, 5))\n",
- "plt.imshow(microarray[:500, :1000], cmap='gray', interpolation='nearest');"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage import color\n",
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 10))\n",
- "\n",
- "red = microarray[..., 0]\n",
- "green = microarray[..., 1]\n",
- "\n",
- "red_rgb = np.zeros_like(microarray)\n",
- "red_rgb[..., 0] = red\n",
- "\n",
- "green_rgb = np.zeros_like(microarray)\n",
- "green_rgb[..., 1] = green\n",
- "\n",
- "ax0.imshow(green_rgb, interpolation='nearest')\n",
- "ax1.imshow(red_rgb, interpolation='nearest')\n",
- "plt.suptitle('\\n\\nPseudocolor plots of red and green channels', fontsize=16);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage import filter as filters\n",
- "\n",
- "mask = (green > 0.1)\n",
- "plt.imshow(mask[:1000, :1000], cmap='gray');"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "z = red.copy()\n",
- "z /= green\n",
- "z[~mask] = 0\n",
- "\n",
- "print(z.min(), z.max())\n",
- "\n",
- "plt.imshow(z[:500, :500], cmap=plt.cm.gray, vmin=0, vmax=2);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Locating the grid"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "both = (green + red)\n",
- "\n",
- "plt.imshow(both, cmap='gray');"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage import feature\n",
- "\n",
- "sum_down_columns = both.sum(axis=0)\n",
- "sum_across_rows = both.sum(axis=1)\n",
- "\n",
- "dips_columns = feature.peak_local_max(sum_down_columns.max() - sum_down_columns)\n",
- "dips_columns = dips_columns.ravel()\n",
- "\n",
- "M = len(dips_columns)\n",
- "column_distance = np.mean(np.diff(dips_columns))\n",
- "\n",
- "dips_rows = feature.peak_local_max(sum_across_rows.max() - sum_across_rows)\n",
- "dips_rows = dips_rows.ravel()\n",
- "\n",
- "N = len(dips_rows)\n",
- "row_distance = np.mean(np.diff(dips_rows))\n",
- "\n",
- "print('Columns are a mean distance of %.2f apart' % column_distance)\n",
- "print('Rows are a mean distance of %.2f apart' % row_distance)\n",
- "\n",
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 5))\n",
- "\n",
- "ax0.plot(sum_down_columns)\n",
- "ax0.scatter(dips_columns, sum_down_columns[dips_columns])\n",
- "ax0.set_xlim(0, 200)\n",
- "ax0.set_title('Column gaps')\n",
- "\n",
- "ax1.plot(sum_across_rows)\n",
- "ax1.scatter(dips_rows, sum_across_rows[dips_rows])\n",
- "ax1.set_xlim(0, 200)\n",
- "ax0.set_title('Row gaps');"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "P, Q = 500, 500\n",
- "\n",
- "plt.figure(figsize=(15, 10))\n",
- "plt.imshow(microarray[:P, :Q])\n",
- "\n",
- "for i in dips_rows[dips_rows < P]:\n",
- " plt.plot([0, Q], [i, i], 'm')\n",
- "\n",
- "for j in dips_columns[dips_columns < Q]:\n",
- " plt.plot([j, j], [0, P], 'm')\n",
- "\n",
- "plt.axis('image');"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "out = np.zeros(microarray.shape[:2])\n",
- "\n",
- "for i in range(M - 1):\n",
- " for j in range(N - 1):\n",
- " row0, row1 = dips_rows[i], dips_rows[i + 1]\n",
- " col0, col1 = dips_columns[j], dips_columns[j + 1]\n",
- " \n",
- " r = microarray[row0:row1, col0:col1, 0]\n",
- " g = microarray[row0:row1, col0:col1, 1]\n",
- " \n",
- " ratio = r / g\n",
- " mask = ~np.isinf(ratio)\n",
- "\n",
- " mean_ratio = np.mean(ratio[mask])\n",
- " if np.isnan(mean_ratio):\n",
- " mean_ratio = 0\n",
- " \n",
- " out[row0:row1, col0:col1] = mean_ratio"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 10))\n",
- "\n",
- "ax0.imshow(microarray)\n",
- "ax0.grid(color='magenta', linewidth=1)\n",
- "\n",
- "ax1.imshow(out, cmap='gray', interpolation='nearest', vmin=0, vmax=3);\n",
- "ax1.grid(color='magenta', linewidth=1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Transform the intensity to spot outliers"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 10))\n",
- "\n",
- "ax0.imshow(microarray)\n",
- "ax0.grid(color='magenta', linewidth=1)\n",
- "\n",
- "ax1.imshow(np.log(0.5 + out), cmap='gray', interpolation='nearest', vmin=0, vmax=3);\n",
- "ax1.grid(color='magenta', linewidth=1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "%reload_ext load_style\n",
- "%load_style ../themes/tutorial.css"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.4.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/lectures/not_yet_booked/adv3_panorama-stitching.ipynb b/lectures/not_yet_booked/adv3_panorama-stitching.ipynb
deleted file mode 100644
index 274de73..0000000
--- a/lectures/not_yet_booked/adv3_panorama-stitching.ipynb
+++ /dev/null
@@ -1,1394 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from __future__ import division, print_function\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# scikit-image advanced panorama tutorial\n",
- "\n",
- "Enhanced from the original demo as featured in [the scikit-image paper](https://peerj.com/articles/453/).\n",
- "\n",
- "Multiple overlapping images of the same scene, combined into a single image, can yield amazing results. This tutorial will illustrate how to accomplish panorama stitching using scikit-image, from loading the images to cleverly stitching them together."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### First things first\n",
- "\n",
- "Import NumPy and matplotlib, then define a utility function to compare multiple images"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "def compare(*images, **kwargs):\n",
- " \"\"\"\n",
- " Utility function to display images side by side.\n",
- " \n",
- " Parameters\n",
- " ----------\n",
- " image0, image1, image2, ... : ndarrray\n",
- " Images to display.\n",
- " labels : list\n",
- " Labels for the different images.\n",
- " \"\"\"\n",
- " f, axes = plt.subplots(1, len(images), **kwargs)\n",
- " axes = np.array(axes, ndmin=1)\n",
- " \n",
- " labels = kwargs.pop('labels', None)\n",
- " if labels is None:\n",
- " labels = [''] * len(images)\n",
- " \n",
- " for n, (image, label) in enumerate(zip(images, labels)):\n",
- " axes[n].imshow(image, interpolation='nearest', cmap='gray')\n",
- " axes[n].set_title(label)\n",
- " axes[n].axis('off')\n",
- " \n",
- " f.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Load data\n",
- "\n",
- "The ``ImageCollection`` class provides an easy and efficient way to load and represent multiple images. Images in the ``ImageCollection`` are not only read from disk when accessed.\n",
- "\n",
- "Load a series of images into an ``ImageCollection`` with a wildcard, as they share similar names. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage import io\n",
- "\n",
- "pano_imgs = io.ImageCollection('../images/pano/JDW_03*')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Inspect these images using the convenience function `compare()` defined earlier"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# compare(...)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Credit: Images of Private Arch and the trail to Delicate Arch in Arches National Park, USA, taken by Joshua D. Warner. \n",
- "License: CC-BY 4.0\n",
- "\n",
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 0. Pre-processing\n",
- "\n",
- "This stage usually involves one or more of the following:\n",
- "* Resizing, often downscaling with fixed aspect ratio\n",
- "* Conversion to grayscale, as some feature descriptors are not defined for color images\n",
- "* Cropping to region(s) of interest\n",
- "\n",
- "For convenience our example data is already resized smaller, and we won't bother cropping. However, they are presently in color so coversion to grayscale with `skimage.color.rgb2gray` is appropriate."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage.color import rgb2gray\n",
- "\n",
- "# Make grayscale versions of the three color images in pano_imgs\n",
- "# named pano0, pano1, and pano2\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# View the results using compare()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 1. Feature detection and matching\n",
- "\n",
- "We need to estimate a projective transformation that relates these images together. The steps will be\n",
- "\n",
- "1. Define one image as a _target_ or _destination_ image, which will remain anchored while the others are warped\n",
- "2. Detect features in all three images\n",
- "3. Match features from left and right images against the features in the center, anchored image.\n",
- "\n",
- "In this three-shot series, the middle image `pano1` is the logical anchor point.\n",
- "\n",
- "We detect \"Oriented FAST and rotated BRIEF\" (ORB) features in both images. \n",
- "\n",
- "**Note:** For efficiency, in this tutorial we're finding 800 keypoints. The results are good but small variations are expected. If you need a more robust estimate in practice, run multiple times and pick the best result _or_ generate additional keypoints."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage.feature import ORB\n",
- "\n",
- "# Initialize ORB\n",
- "# This number of keypoints is large enough for robust results, \n",
- "# but low enough to run within a few seconds. \n",
- "orb = ORB(n_keypoints=800, fast_threshold=0.05)\n",
- "\n",
- "# Detect keypoints in pano0\n",
- "orb.detect_and_extract(pano0)\n",
- "keypoints0 = orb.keypoints\n",
- "descriptors0 = orb.descriptors\n",
- "\n",
- "# Detect keypoints in pano1 and pano2\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Match features from images 0 <-> 1 and 1 <-> 2."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage.feature import match_descriptors\n",
- "\n",
- "# Match descriptors between left/right images and the center\n",
- "matches01 = match_descriptors(descriptors0, descriptors1, cross_check=True)\n",
- "matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Inspect these matched features side-by-side using the convenience function ``skimage.feature.plot_matches``. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage.feature import plot_matches\n",
- "fig, ax = plt.subplots(1, 1, figsize=(12, 12))\n",
- "\n",
- "# Best match subset for pano0 -> pano1\n",
- "plot_matches(ax, pano0, pano1, keypoints0, keypoints1, matches01)\n",
- "\n",
- "ax.axis('off');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Most of these line up similarly, but it isn't perfect. There are a number of obvious outliers or false matches."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots(1, 1, figsize=(12, 12))\n",
- "\n",
- "# Best match subset for pano2 -> pano1\n",
- "plot_matches(ax, pano1, pano2, keypoints1, keypoints2, matches12)\n",
- "\n",
- "ax.axis('off');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Similar to above, decent signal but numerous false matches.\n",
- "\n",
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 2. Transform estimation\n",
- "\n",
- "To filter out the false matches, we apply RANdom SAmple Consensus (RANSAC), a powerful method of rejecting outliers available in ``skimage.transform.ransac``. The transformation is estimated using an iterative process based on randomly chosen subsets, finally selecting the model which corresponds best with the majority of matches.\n",
- "\n",
- "We need to do this twice, once each for the transforms left -> center and right -> center."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage.transform import ProjectiveTransform\n",
- "from skimage.measure import ransac\n",
- "\n",
- "# Select keypoints from\n",
- "# * source (image to be registered): pano0\n",
- "# * target (reference image): pano1, our middle frame registration target\n",
- "src = keypoints0[matches01[:, 0]][:, ::-1]\n",
- "dst = keypoints1[matches01[:, 1]][:, ::-1]\n",
- "\n",
- "model_robust01, inliers01 = ransac((src, dst), ProjectiveTransform,\n",
- " min_samples=4, residual_threshold=1, max_trials=300)\n",
- "\n",
- "# Select keypoints from\n",
- "# * source (image to be registered): pano2\n",
- "# * target (reference image): pano1, our middle frame registration target\n",
- "src = keypoints2[matches12[:, 1]][:, ::-1]\n",
- "dst = keypoints1[matches12[:, 0]][:, ::-1]\n",
- "\n",
- "model_robust12, inliers12 = ransac((src, dst), ProjectiveTransform,\n",
- " min_samples=4, residual_threshold=1, max_trials=300)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The `inliers` returned from RANSAC select the best subset of matches. How do they look?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Use plot_matches as before, but select only good matches with fancy indexing\n",
- "# e.g., matches01[inliers01]\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Use plot_matches as before, but select only good matches with fancy indexing\n",
- "# e.g., matches12[inliers12]\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Most of the false matches are rejected!\n",
- "\n",
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 3. Warping\n",
- "\n",
- "Next, we produce the panorama itself. We must _warp_, or transform, two of the three images so they will properly align with the stationary image.\n",
- "\n",
- "### Extent of output image\n",
- "The first step is to find the shape of the output image to contain all three transformed images. To do this we consider the extents of all warped images."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage.transform import SimilarityTransform\n",
- "\n",
- "# Shape of middle image, our registration target\n",
- "r, c = pano1.shape[:2]\n",
- "\n",
- "# Note that transformations take coordinates in (x, y) format,\n",
- "# not (row, column), in order to be consistent with most literature\n",
- "corners = np.array([[0, 0],\n",
- " [0, r],\n",
- " [c, 0],\n",
- " [c, r]])\n",
- "\n",
- "# Warp the image corners to their new positions\n",
- "warped_corners01 = model_robust01(corners)\n",
- "warped_corners12 = model_robust12(corners)\n",
- "\n",
- "# Find the extents of both the reference image and the warped\n",
- "# target image\n",
- "all_corners = np.vstack((warped_corners01, warped_corners12, corners))\n",
- "\n",
- "# The overall output shape will be max - min\n",
- "corner_min = np.min(all_corners, axis=0)\n",
- "corner_max = np.max(all_corners, axis=0)\n",
- "output_shape = (corner_max - corner_min)\n",
- "\n",
- "# Ensure integer shape with np.ceil and dtype conversion\n",
- "output_shape = np.ceil(output_shape[::-1]).astype(int)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Apply estimated transforms\n",
- "\n",
- "Warp the images with `skimage.transform.warp` according to the estimated models. A shift, or _translation_ is needed to place as our middle image in the middle - it isn't truly stationary.\n",
- "\n",
- "Values outside the input images are initially set to -1 to distinguish the \"background\", which is identified for later use.\n",
- "\n",
- "**Note:** ``warp`` takes the _inverse_ mapping as an input."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage.transform import warp\n",
- "\n",
- "# This in-plane offset is the only necessary transformation for the middle image\n",
- "offset1 = SimilarityTransform(translation= -corner_min)\n",
- "\n",
- "# Translate pano1 into place\n",
- "pano1_warped = warp(pano1, offset1.inverse, order=3,\n",
- " output_shape=output_shape, cval=-1)\n",
- "\n",
- "# Acquire the image mask for later use\n",
- "pano1_mask = (pano1_warped != -1) # Mask == 1 inside image\n",
- "pano1_warped[~pano1_mask] = 0 # Return background values to 0"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Warp left panel into place"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Warp pano0 to pano1\n",
- "transform01 = (model_robust01 + offset1).inverse\n",
- "pano0_warped = warp(pano0, transform01, order=3,\n",
- " output_shape=output_shape, cval=-1)\n",
- "\n",
- "pano0_mask = (pano0_warped != -1) # Mask == 1 inside image\n",
- "pano0_warped[~pano0_mask] = 0 # Return background values to 0"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Warp right panel into place"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Warp pano2 to pano1\n",
- "transform12 = (model_robust12 + offset1).inverse\n",
- "pano2_warped = warp(pano2, transform12, order=3,\n",
- " output_shape=output_shape, cval=-1)\n",
- "\n",
- "pano2_mask = (pano2_warped != -1) # Mask == 1 inside image\n",
- "pano2_warped[~pano2_mask] = 0 # Return background values to 0"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Inspect the warped images:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "compare(pano0_warped, pano1_warped, pano2_warped, figsize=(12, 10));"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 4. Combining images the easy (and bad) way\n",
- "\n",
- "This method simply \n",
- "\n",
- "1. sums the warped images\n",
- "2. tracks how many images overlapped to create each point\n",
- "3. normalizes the result."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Add the three warped images together. This could create dtype overflows!\n",
- "# We know they are are floating point images after warping, so it's OK.\n",
- "merged = ## Sum warped images\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Track the overlap by adding the masks together\n",
- "overlap = ## Sum masks\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Normalize through division by `overlap` - but ensure the minimum is 1\n",
- "normalized = merged / ## Divisor here\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, view the results!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots(figsize=(12, 12))\n",
- "\n",
- "ax.imshow(normalized, cmap='gray')\n",
- "\n",
- "fig.tight_layout()\n",
- "ax.axis('off');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "---\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**What happened?!** Why are there nasty dark lines at boundaries, and why does the middle look so blurry?\n",
- "\n",
- "\n",
- "The **lines are artifacts (boundary effect) from the warping method**. When the image is warped with interpolation, edge pixels containing part image and part background combine these values. We would have bright lines if we'd chosen `cval=2` in the `warp` calls (try it!), but regardless of choice there will always be discontinuities.\n",
- "\n",
- "...Unless you use `order=0` in `warp`, which is nearest neighbor. Then edges are perfect (try it!). But who wants to be limited to an inferior interpolation method? \n",
- "\n",
- "Even then, it's blurry! Is there a better way?\n",
- "\n",
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 5. Stitching images along a minimum-cost path\n",
- "\n",
- "Let's step back a moment and consider: Is it even reasonable to blend pixels?\n",
- "\n",
- "Take a look at a _difference image_, which is just one image subtracted from the other."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots(figsize=(12, 12))\n",
- "\n",
- "# Generate difference image and inspect it\n",
- "difference_image = pano0_warped - pano1_warped\n",
- "ax.imshow(difference_image, cmap='gray')\n",
- "\n",
- "ax.axis('off');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The surrounding flat gray is zero. _A perfect overlap would show no structure!_ \n",
- "\n",
- "Instead, the overlap region matches fairly well in the middle... but off to the sides where things start to look a little embossed, a simple average blurs the result. This caused the blurring in the previous, method (look again). _Unfortunately, this is almost always the case for panoramas!_\n",
- "\n",
- "How can we fix this?\n",
- "\n",
- "Let's attempt to find a vertical path through this difference image which stays as close to zero as possible. If we use that to build a mask, defining a transition between images, the result should appear _seamless_.\n",
- "\n",
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Seamless image stitching with Minimum-Cost Paths and `skimage.graph`\n",
- "\n",
- "Among other things, `skimage.graph` allows you to\n",
- "* start at any point on an array\n",
- "* find the path to any other point in the array\n",
- "* the path found _minimizes_ the sum of values on the path.\n",
- "\n",
- "\n",
- "The array is called a _cost array_, while the path found is a _minimum-cost path_ or **MCP**.\n",
- "\n",
- "To accomplish this we need\n",
- "\n",
- "* Starting and ending points for the path\n",
- "* A cost array (a modified difference image)\n",
- "\n",
- "This method is so powerful that, with a carefully constructed cost array, the seed points are essentially irrelevant. It just works!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Define seed points"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "rmax = output_shape[0] - 1\n",
- "cmax = output_shape[1] - 1\n",
- "\n",
- "# Start anywhere along the top and bottom, left of center.\n",
- "mask_pts01 = [[0, cmax // 3],\n",
- " [rmax, cmax // 3]]\n",
- "\n",
- "# Start anywhere along the top and bottom, right of center.\n",
- "mask_pts12 = [[0, 2*cmax // 3],\n",
- " [rmax, 2*cmax // 3]]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Construct cost array\n",
- "\n",
- "This utility function exists to give a \"cost break\" for paths from the edge to the overlap region.\n",
- "\n",
- "We will visually explore the results shortly. Examine the code later - for now, just use it."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage.morphology import flood_fill\n",
- "\n",
- "def generate_costs(diff_image, mask, vertical=True, gradient_cutoff=2.):\n",
- " \"\"\"\n",
- " Ensures equal-cost paths from edges to region of interest.\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " diff_image : ndarray of floats\n",
- " Difference of two overlapping images.\n",
- " mask : ndarray of bools\n",
- " Mask representing the region of interest in ``diff_image``.\n",
- " vertical : bool\n",
- " Control operation orientation.\n",
- " gradient_cutoff : float\n",
- " Controls how far out of parallel lines can be to edges before\n",
- " correction is terminated. The default (2.) is good for most cases.\n",
- "\n",
- " Returns\n",
- " -------\n",
- " costs_arr : ndarray of floats\n",
- " Adjusted costs array, ready for use.\n",
- " \"\"\"\n",
- " if vertical is not True:\n",
- " return tweak_costs(diff_image.T, mask.T, vertical=vertical,\n",
- " gradient_cutoff=gradient_cutoff).T\n",
- "\n",
- " # Start with a high-cost array of 1's\n",
- " costs_arr = np.ones_like(diff_image)\n",
- "\n",
- " # Obtain extent of overlap\n",
- " row, col = mask.nonzero()\n",
- " cmin = col.min()\n",
- " cmax = col.max()\n",
- " shape = mask.shape\n",
- "\n",
- " # Label discrete regions\n",
- " labels = mask.copy().astype(np.uint8)\n",
- " cslice = slice(cmin, cmax + 1)\n",
- " submask = np.ascontiguousarray(labels[:, cslice])\n",
- " submask = flood_fill(submask, (0, 0), 2)\n",
- " submask = flood_fill(submask, (shape[0]-1, 0), 3)\n",
- " labels[:, cslice] = submask\n",
- "\n",
- " # Find distance from edge to region\n",
- " upper = (labels == 2).sum(axis=0).astype(np.float64)\n",
- " lower = (labels == 3).sum(axis=0).astype(np.float64)\n",
- "\n",
- " # Reject areas of high change\n",
- " ugood = np.abs(np.gradient(upper[cslice])) < gradient_cutoff\n",
- " lgood = np.abs(np.gradient(lower[cslice])) < gradient_cutoff\n",
- "\n",
- " # Give areas slightly farther from edge a cost break\n",
- " costs_upper = np.ones_like(upper)\n",
- " costs_lower = np.ones_like(lower)\n",
- " costs_upper[cslice][ugood] = upper[cslice].min() / np.maximum(upper[cslice][ugood], 1)\n",
- " costs_lower[cslice][lgood] = lower[cslice].min() / np.maximum(lower[cslice][lgood], 1)\n",
- "\n",
- " # Expand from 1d back to 2d\n",
- " vdist = mask.shape[0]\n",
- " costs_upper = costs_upper[np.newaxis, :].repeat(vdist, axis=0)\n",
- " costs_lower = costs_lower[np.newaxis, :].repeat(vdist, axis=0)\n",
- "\n",
- " # Place these in output array\n",
- " costs_arr[:, cslice] = costs_upper[:, cslice] * (labels[:, cslice] == 2)\n",
- " costs_arr[:, cslice] += costs_lower[:, cslice] * (labels[:, cslice] == 3)\n",
- "\n",
- " # Finally, place the difference image\n",
- " costs_arr[mask] = diff_image[mask]\n",
- "\n",
- " return costs_arr"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Use this function to generate the cost array."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Start with the absolute value of the difference image.\n",
- "# np.abs necessary because we don't want negative costs!\n",
- "costs01 = generate_costs(np.abs(pano0_warped - pano1_warped),\n",
- " pano0_mask & pano1_mask)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Allow the path to \"slide\" along top and bottom edges to the optimal horizontal position by setting top and bottom edges to zero cost."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Set top and bottom edges to zero in `costs01`\n",
- "# Remember (row, col) indexing!\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Our cost array now looks like this"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots(figsize=(15, 12))\n",
- "\n",
- "ax.imshow(costs01, cmap='gray', interpolation='none')\n",
- "\n",
- "ax.axis('off');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The tweak we made with `generate_costs` is subtle but important. Can you see it?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Find the minimum-cost path (MCP)\n",
- "\n",
- "Use `skimage.graph.route_through_array` to find an optimal path through the cost array"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage.graph import route_through_array\n",
- "\n",
- "# Arguments are:\n",
- "# cost array\n",
- "# start pt\n",
- "# end pt\n",
- "# can it traverse diagonally\n",
- "pts, _ = route_through_array(costs01, mask_pts01[0], mask_pts01[1], fully_connected=True)\n",
- "\n",
- "# Convert list of lists to 2d coordinate array for easier indexing\n",
- "pts = np.array(pts)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Did it work?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots(figsize=(12, 12))\n",
- "\n",
- "# Plot the difference image\n",
- "ax.imshow(pano0_warped - pano1_warped, cmap='gray')\n",
- "\n",
- "# Overlay the minimum-cost path\n",
- "ax.plot(pts[:, 1], pts[:, 0]) \n",
- "\n",
- "plt.tight_layout()\n",
- "ax.axis('off');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "That looks like a great seam to stitch these images together - the path looks very close to zero."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Irregularities\n",
- "\n",
- "Due to the random element in the RANSAC transform estimation, everyone will have a slightly different blue path. **Your path will look different from mine, and different from your neighbor's.** That's expected! _The awesome thing about MCP is that everyone just calculated the best possible path to stitch together their unique transforms!_"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Filling the mask\n",
- "\n",
- "Turn that path into a mask, which will be 1 where we want the left image to show through and zero elsewhere. We need to fill the left side of the mask with ones over to our path.\n",
- "\n",
- "**Note**: This is the inverse of NumPy masked array conventions (``numpy.ma``), which specify a negative mask (mask == bad/missing) rather than a positive mask as used here (mask == good/selected).\n",
- "\n",
- "Place the path into a new, empty array."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Start with an array of zeros and place the path\n",
- "mask0 = np.zeros_like(pano0_warped, dtype=np.uint8)\n",
- "mask0[pts[:, 0], pts[:, 1]] = 1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Ensure the path appears as expected"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots(figsize=(12, 12))\n",
- "\n",
- "# View the path in black and white\n",
- "ax.imshow(mask0, cmap='gray')\n",
- "\n",
- "ax.axis('off');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Label the various contiguous regions in the image using `skimage.measure.label`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage.morphology import flood_fill\n",
- "\n",
- "# Labeling starts with one at point (0, 0)\n",
- "mask0 = flood_fill(mask0, (0, 0), 1, connectivity=1)\n",
- "\n",
- "# The result\n",
- "plt.imshow(mask0, cmap='gray');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Looks great!\n",
- "\n",
- "\n",
- "### Rinse and repeat\n",
- "\n",
- "Apply the same principles to images 1 and 2: first, build the cost array"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Start with the absolute value of the difference image.\n",
- "# np.abs is necessary because we don't want negative costs!\n",
- "costs12 = generate_costs(np.abs(pano1_warped - pano2_warped),\n",
- " pano1_mask & pano2_mask)\n",
- "\n",
- "# Allow the path to \"slide\" along top and bottom edges to the optimal \n",
- "# horizontal position by setting top and bottom edges to zero cost\n",
- "costs12[0, :] = 0\n",
- "costs12[-1, :] = 0"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Add an additional constraint this time**, to prevent this path crossing the prior one!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "costs12[mask0 > 0] = 1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Check the result"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots(figsize=(8, 8))\n",
- "ax.imshow(costs12, cmap='gray');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Your results may look slightly different.\n",
- "\n",
- "Compute the minimal cost path"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Arguments are:\n",
- "# cost array\n",
- "# start pt\n",
- "# end pt\n",
- "# can it traverse diagonally\n",
- "pts, _ = route_through_array(costs12, mask_pts12[0], mask_pts12[1], fully_connected=True)\n",
- "\n",
- "# Convert list of lists to 2d coordinate array for easier indexing\n",
- "pts = np.array(pts)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Verify a reasonable result"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots(figsize=(12, 12))\n",
- "\n",
- "# Plot the difference image\n",
- "ax.imshow(pano1_warped - pano2_warped, cmap='gray')\n",
- "\n",
- "# Overlay the minimum-cost path\n",
- "ax.plot(pts[:, 1], pts[:, 0]);\n",
- "\n",
- "ax.axis('off');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Initialize the mask by placing the path in a new array"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mask2 = np.zeros_like(pano0_warped, dtype=np.uint8)\n",
- "mask2[pts[:, 0], pts[:, 1]] = 1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fill the right side this time, again using `skimage.measure.label` - the label of interest is 3"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mask2 = (label(mask2, connectivity=1, background=-1) == 3)\n",
- "\n",
- "# The result\n",
- "plt.imshow(mask2, cmap='gray');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Final mask\n",
- "\n",
- "The last mask for the middle image is one of exclusion - it will be displayed everywhere `mask0` and `mask2` are not."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mask1 = ~(mask0.astype(np.bool) | mask2.astype(np.bool))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Define a convenience function to place masks in alpha channels"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def add_alpha(img, mask=None):\n",
- " \"\"\"\n",
- " Adds a masked alpha channel to an image.\n",
- " \n",
- " Parameters\n",
- " ----------\n",
- " img : (M, N[, 3]) ndarray\n",
- " Image data, should be rank-2 or rank-3 with RGB channels\n",
- " mask : (M, N[, 3]) ndarray, optional\n",
- " Mask to be applied. If None, the alpha channel is added\n",
- " with full opacity assumed (1) at all locations.\n",
- " \"\"\"\n",
- " from skimage.color import gray2rgb\n",
- " if mask is None:\n",
- " mask = np.ones_like(img)\n",
- " \n",
- " if img.ndim == 2:\n",
- " img = gray2rgb(img)\n",
- " \n",
- " return np.dstack((img, mask))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Obtain final, alpha blended individual images and inspect them"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pano0_final = add_alpha(pano0_warped, mask0)\n",
- "pano1_final = add_alpha(pano1_warped, mask1)\n",
- "pano2_final = add_alpha(pano2_warped, mask2)\n",
- "\n",
- "compare(pano0_final, pano1_final, pano2_final, figsize=(15, 15))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What we have here is the world's most complicated and precisely-fitting jigsaw puzzle...\n",
- "\n",
- "Plot all three together and view the results!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots(figsize=(12, 12))\n",
- "\n",
- "# This is a perfect combination, but matplotlib's interpolation\n",
- "# makes it appear to have gaps. So we turn it off.\n",
- "ax.imshow(pano0_final, interpolation='none')\n",
- "ax.imshow(pano1_final, interpolation='none')\n",
- "ax.imshow(pano2_final, interpolation='none')\n",
- "\n",
- "fig.tight_layout()\n",
- "ax.axis('off');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fantastic! Without the black borders, you'd never know this was composed of separate images!\n",
- "\n",
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Bonus round: now, in color!\n",
- "\n",
- "We converted to grayscale for ORB feature detection, back in the initial **preprocessing** steps. Since we stored our transforms and masks, adding color is straightforward!\n",
- "\n",
- "Transform the colored images"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Identical transforms as before, except\n",
- "# * Operating on original color images\n",
- "# * filling with cval=0 as we know the masks\n",
- "pano0_color = warp(pano_imgs[0], (model_robust01 + offset1).inverse, order=3,\n",
- " output_shape=output_shape, cval=0)\n",
- "\n",
- "pano1_color = warp(pano_imgs[1], offset1.inverse, order=3,\n",
- " output_shape=output_shape, cval=0)\n",
- "\n",
- "pano2_color = warp(pano_imgs[2], (model_robust12 + offset1).inverse, order=3,\n",
- " output_shape=output_shape, cval=0)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Apply the custom alpha channel masks"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pano0_final = add_alpha(pano0_color, mask0)\n",
- "pano1_final = add_alpha(pano1_color, mask1)\n",
- "pano2_final = add_alpha(pano2_color, mask2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "View the result!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots(figsize=(12, 12))\n",
- "\n",
- "# Turn off matplotlib's interpolation\n",
- "ax.imshow(pano0_final, interpolation='none')\n",
- "ax.imshow(pano1_final, interpolation='none')\n",
- "ax.imshow(pano2_final, interpolation='none')\n",
- "\n",
- "fig.tight_layout()\n",
- "ax.axis('off');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Save the combined, color panorama locally as `'./pano-advanced-output.png'`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage.color import gray2rgb\n",
- "\n",
- "# Start with empty image\n",
- "pano_combined = np.zeros_like(pano0_color)\n",
- "\n",
- "# Place the masked portion of each image into the array\n",
- "# masks are 2d, they need to be (M, N, 3) to match the color images\n",
- "pano_combined += pano0_color * gray2rgb(mask0)\n",
- "pano_combined += pano1_color * gray2rgb(mask1)\n",
- "pano_combined += pano2_color * gray2rgb(mask2)\n",
- "\n",
- "\n",
- "# Save the output - precision loss warning is expected\n",
- "# moving from floating point -> uint8\n",
- "io.imsave('./pano-advanced-output.png', pano_combined)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "---\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Once more, from the top\n",
- "\n",
- "I hear what you're saying. \"But Josh, those were too easy! The panoramas had too much overlap! Does this still work in the real world?\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Go back to the top. Under \"Load Data\" replace the string `'data/JDW_03*'` with `'data/JDW_9*'`, and re-run all of the cells in order.**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "---\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%reload_ext load_style\n",
- "%load_style ../themes/tutorial.css"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/lectures/not_yet_booked/adv3_panorama-stitching.md b/lectures/not_yet_booked/adv3_panorama-stitching.md
new file mode 100644
index 0000000..f3593b6
--- /dev/null
+++ b/lectures/not_yet_booked/adv3_panorama-stitching.md
@@ -0,0 +1,862 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+```{code-cell} ipython3
+from __future__ import division, print_function
+%matplotlib inline
+```
+
+# scikit-image advanced panorama tutorial
+
+Enhanced from the original demo as featured in [the scikit-image paper](https://peerj.com/articles/453/).
+
+Multiple overlapping images of the same scene, combined into a single image, can yield amazing results. This tutorial will illustrate how to accomplish panorama stitching using scikit-image, from loading the images to cleverly stitching them together.
+
++++
+
+## First things first
+
+Import NumPy and matplotlib, then define a utility function to compare multiple images
+
+```{code-cell} ipython3
+import numpy as np
+import matplotlib.pyplot as plt
+
+def compare(*images, **kwargs):
+ """
+ Utility function to display images side by side.
+
+ Parameters
+ ----------
+ image0, image1, image2, ... : ndarrray
+ Images to display.
+ labels : list
+ Labels for the different images.
+ """
+ f, axes = plt.subplots(1, len(images), **kwargs)
+ axes = np.array(axes, ndmin=1)
+
+ labels = kwargs.pop('labels', None)
+ if labels is None:
+ labels = [''] * len(images)
+
+ for n, (image, label) in enumerate(zip(images, labels)):
+ axes[n].imshow(image, interpolation='nearest', cmap='gray')
+ axes[n].set_title(label)
+ axes[n].axis('off')
+
+ f.tight_layout()
+```
+
+## Load data
+
+The ``ImageCollection`` class provides an easy and efficient way to load and represent multiple images. Images in the ``ImageCollection`` are not only read from disk when accessed.
+
+Load a series of images into an ``ImageCollection`` with a wildcard, as they share similar names.
+
+```{code-cell} ipython3
+from skimage import io
+
+pano_imgs = io.ImageCollection('../images/pano/JDW_03*')
+```
+
+Inspect these images using the convenience function `compare()` defined earlier
+
+```{code-cell} ipython3
+# compare(...)
+```
+
+Credit: Images of Private Arch and the trail to Delicate Arch in Arches National Park, USA, taken by Joshua D. Warner.
+License: CC-BY 4.0
+
+---
+
++++
+
+## 0. Pre-processing
+
+This stage usually involves one or more of the following:
+* Resizing, often downscaling with fixed aspect ratio
+* Conversion to grayscale, as some feature descriptors are not defined for color images
+* Cropping to region(s) of interest
+
+For convenience our example data is already resized smaller, and we won't bother cropping. However, they are presently in color so coversion to grayscale with `skimage.color.rgb2gray` is appropriate.
+
+```{code-cell} ipython3
+from skimage.color import rgb2gray
+
+# Make grayscale versions of the three color images in pano_imgs
+# named pano0, pano1, and pano2
+```
+
+```{code-cell} ipython3
+# View the results using compare()
+```
+
+---
+
++++
+
+## 1. Feature detection and matching
+
+We need to estimate a projective transformation that relates these images together. The steps will be
+
+1. Define one image as a _target_ or _destination_ image, which will remain anchored while the others are warped
+2. Detect features in all three images
+3. Match features from left and right images against the features in the center, anchored image.
+
+In this three-shot series, the middle image `pano1` is the logical anchor point.
+
+We detect "Oriented FAST and rotated BRIEF" (ORB) features in both images.
+
+**Note:** For efficiency, in this tutorial we're finding 800 keypoints. The results are good but small variations are expected. If you need a more robust estimate in practice, run multiple times and pick the best result _or_ generate additional keypoints.
+
+```{code-cell} ipython3
+from skimage.feature import ORB
+
+# Initialize ORB
+# This number of keypoints is large enough for robust results,
+# but low enough to run within a few seconds.
+orb = ORB(n_keypoints=800, fast_threshold=0.05)
+
+# Detect keypoints in pano0
+orb.detect_and_extract(pano0)
+keypoints0 = orb.keypoints
+descriptors0 = orb.descriptors
+
+# Detect keypoints in pano1 and pano2
+```
+
+Match features from images 0 <-> 1 and 1 <-> 2.
+
+```{code-cell} ipython3
+from skimage.feature import match_descriptors
+
+# Match descriptors between left/right images and the center
+matches01 = match_descriptors(descriptors0, descriptors1, cross_check=True)
+matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
+```
+
+Inspect these matched features side-by-side using the convenience function ``skimage.feature.plot_matches``.
+
+```{code-cell} ipython3
+from skimage.feature import plot_matches
+fig, ax = plt.subplots(1, 1, figsize=(12, 12))
+
+# Best match subset for pano0 -> pano1
+plot_matches(ax, pano0, pano1, keypoints0, keypoints1, matches01)
+
+ax.axis('off');
+```
+
+Most of these line up similarly, but it isn't perfect. There are a number of obvious outliers or false matches.
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(1, 1, figsize=(12, 12))
+
+# Best match subset for pano2 -> pano1
+plot_matches(ax, pano1, pano2, keypoints1, keypoints2, matches12)
+
+ax.axis('off');
+```
+
+Similar to above, decent signal but numerous false matches.
+
+---
+
++++
+
+## 2. Transform estimation
+
+To filter out the false matches, we apply RANdom SAmple Consensus (RANSAC), a powerful method of rejecting outliers available in ``skimage.transform.ransac``. The transformation is estimated using an iterative process based on randomly chosen subsets, finally selecting the model which corresponds best with the majority of matches.
+
+We need to do this twice, once each for the transforms left -> center and right -> center.
+
+```{code-cell} ipython3
+from skimage.transform import ProjectiveTransform
+from skimage.measure import ransac
+
+# Select keypoints from
+# * source (image to be registered): pano0
+# * target (reference image): pano1, our middle frame registration target
+src = keypoints0[matches01[:, 0]][:, ::-1]
+dst = keypoints1[matches01[:, 1]][:, ::-1]
+
+model_robust01, inliers01 = ransac((src, dst), ProjectiveTransform,
+ min_samples=4, residual_threshold=1, max_trials=300)
+
+# Select keypoints from
+# * source (image to be registered): pano2
+# * target (reference image): pano1, our middle frame registration target
+src = keypoints2[matches12[:, 1]][:, ::-1]
+dst = keypoints1[matches12[:, 0]][:, ::-1]
+
+model_robust12, inliers12 = ransac((src, dst), ProjectiveTransform,
+ min_samples=4, residual_threshold=1, max_trials=300)
+```
+
+The `inliers` returned from RANSAC select the best subset of matches. How do they look?
+
+```{code-cell} ipython3
+# Use plot_matches as before, but select only good matches with fancy indexing
+# e.g., matches01[inliers01]
+```
+
+```{code-cell} ipython3
+# Use plot_matches as before, but select only good matches with fancy indexing
+# e.g., matches12[inliers12]
+```
+
+Most of the false matches are rejected!
+
+---
+
++++
+
+## 3. Warping
+
+Next, we produce the panorama itself. We must _warp_, or transform, two of the three images so they will properly align with the stationary image.
+
+### Extent of output image
+The first step is to find the shape of the output image to contain all three transformed images. To do this we consider the extents of all warped images.
+
+```{code-cell} ipython3
+from skimage.transform import SimilarityTransform
+
+# Shape of middle image, our registration target
+r, c = pano1.shape[:2]
+
+# Note that transformations take coordinates in (x, y) format,
+# not (row, column), in order to be consistent with most literature
+corners = np.array([[0, 0],
+ [0, r],
+ [c, 0],
+ [c, r]])
+
+# Warp the image corners to their new positions
+warped_corners01 = model_robust01(corners)
+warped_corners12 = model_robust12(corners)
+
+# Find the extents of both the reference image and the warped
+# target image
+all_corners = np.vstack((warped_corners01, warped_corners12, corners))
+
+# The overall output shape will be max - min
+corner_min = np.min(all_corners, axis=0)
+corner_max = np.max(all_corners, axis=0)
+output_shape = (corner_max - corner_min)
+
+# Ensure integer shape with np.ceil and dtype conversion
+output_shape = np.ceil(output_shape[::-1]).astype(int)
+```
+
+### Apply estimated transforms
+
+Warp the images with `skimage.transform.warp` according to the estimated models. A shift, or _translation_ is needed to place as our middle image in the middle - it isn't truly stationary.
+
+Values outside the input images are initially set to -1 to distinguish the "background", which is identified for later use.
+
+**Note:** ``warp`` takes the _inverse_ mapping as an input.
+
+```{code-cell} ipython3
+from skimage.transform import warp
+
+# This in-plane offset is the only necessary transformation for the middle image
+offset1 = SimilarityTransform(translation= -corner_min)
+
+# Translate pano1 into place
+pano1_warped = warp(pano1, offset1.inverse, order=3,
+ output_shape=output_shape, cval=-1)
+
+# Acquire the image mask for later use
+pano1_mask = (pano1_warped != -1) # Mask == 1 inside image
+pano1_warped[~pano1_mask] = 0 # Return background values to 0
+```
+
+Warp left panel into place
+
+```{code-cell} ipython3
+# Warp pano0 to pano1
+transform01 = (model_robust01 + offset1).inverse
+pano0_warped = warp(pano0, transform01, order=3,
+ output_shape=output_shape, cval=-1)
+
+pano0_mask = (pano0_warped != -1) # Mask == 1 inside image
+pano0_warped[~pano0_mask] = 0 # Return background values to 0
+```
+
+Warp right panel into place
+
+```{code-cell} ipython3
+# Warp pano2 to pano1
+transform12 = (model_robust12 + offset1).inverse
+pano2_warped = warp(pano2, transform12, order=3,
+ output_shape=output_shape, cval=-1)
+
+pano2_mask = (pano2_warped != -1) # Mask == 1 inside image
+pano2_warped[~pano2_mask] = 0 # Return background values to 0
+```
+
+Inspect the warped images:
+
+```{code-cell} ipython3
+compare(pano0_warped, pano1_warped, pano2_warped, figsize=(12, 10));
+```
+
+---
+
++++
+
+## 4. Combining images the easy (and bad) way
+
+This method simply
+
+1. sums the warped images
+2. tracks how many images overlapped to create each point
+3. normalizes the result.
+
+```{code-cell} ipython3
+# Add the three warped images together. This could create dtype overflows!
+# We know they are are floating point images after warping, so it's OK.
+merged = ## Sum warped images
+```
+
+```{code-cell} ipython3
+# Track the overlap by adding the masks together
+overlap = ## Sum masks
+```
+
+```{code-cell} ipython3
+# Normalize through division by `overlap` - but ensure the minimum is 1
+normalized = merged / ## Divisor here
+```
+
+Finally, view the results!
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(12, 12))
+
+ax.imshow(normalized, cmap='gray')
+
+fig.tight_layout()
+ax.axis('off');
+```
+
+
+---
+
+
+
++++
+
+**What happened?!** Why are there nasty dark lines at boundaries, and why does the middle look so blurry?
+
+
+The **lines are artifacts (boundary effect) from the warping method**. When the image is warped with interpolation, edge pixels containing part image and part background combine these values. We would have bright lines if we'd chosen `cval=2` in the `warp` calls (try it!), but regardless of choice there will always be discontinuities.
+
+...Unless you use `order=0` in `warp`, which is nearest neighbor. Then edges are perfect (try it!). But who wants to be limited to an inferior interpolation method?
+
+Even then, it's blurry! Is there a better way?
+
+---
+
++++
+
+## 5. Stitching images along a minimum-cost path
+
+Let's step back a moment and consider: Is it even reasonable to blend pixels?
+
+Take a look at a _difference image_, which is just one image subtracted from the other.
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(12, 12))
+
+# Generate difference image and inspect it
+difference_image = pano0_warped - pano1_warped
+ax.imshow(difference_image, cmap='gray')
+
+ax.axis('off');
+```
+
+The surrounding flat gray is zero. _A perfect overlap would show no structure!_
+
+Instead, the overlap region matches fairly well in the middle... but off to the sides where things start to look a little embossed, a simple average blurs the result. This caused the blurring in the previous, method (look again). _Unfortunately, this is almost always the case for panoramas!_
+
+How can we fix this?
+
+Let's attempt to find a vertical path through this difference image which stays as close to zero as possible. If we use that to build a mask, defining a transition between images, the result should appear _seamless_.
+
+---
+
++++
+
+## Seamless image stitching with Minimum-Cost Paths and `skimage.graph`
+
+Among other things, `skimage.graph` allows you to
+* start at any point on an array
+* find the path to any other point in the array
+* the path found _minimizes_ the sum of values on the path.
+
+
+The array is called a _cost array_, while the path found is a _minimum-cost path_ or **MCP**.
+
+To accomplish this we need
+
+* Starting and ending points for the path
+* A cost array (a modified difference image)
+
+This method is so powerful that, with a carefully constructed cost array, the seed points are essentially irrelevant. It just works!
+
++++
+
+### Define seed points
+
+```{code-cell} ipython3
+rmax = output_shape[0] - 1
+cmax = output_shape[1] - 1
+
+# Start anywhere along the top and bottom, left of center.
+mask_pts01 = [[0, cmax // 3],
+ [rmax, cmax // 3]]
+
+# Start anywhere along the top and bottom, right of center.
+mask_pts12 = [[0, 2*cmax // 3],
+ [rmax, 2*cmax // 3]]
+```
+
+### Construct cost array
+
+This utility function exists to give a "cost break" for paths from the edge to the overlap region.
+
+We will visually explore the results shortly. Examine the code later - for now, just use it.
+
+```{code-cell} ipython3
+from skimage.morphology import flood_fill
+
+def generate_costs(diff_image, mask, vertical=True, gradient_cutoff=2.):
+ """
+ Ensures equal-cost paths from edges to region of interest.
+
+ Parameters
+ ----------
+ diff_image : ndarray of floats
+ Difference of two overlapping images.
+ mask : ndarray of bools
+ Mask representing the region of interest in ``diff_image``.
+ vertical : bool
+ Control operation orientation.
+ gradient_cutoff : float
+ Controls how far out of parallel lines can be to edges before
+ correction is terminated. The default (2.) is good for most cases.
+
+ Returns
+ -------
+ costs_arr : ndarray of floats
+ Adjusted costs array, ready for use.
+ """
+ if vertical is not True:
+ return tweak_costs(diff_image.T, mask.T, vertical=vertical,
+ gradient_cutoff=gradient_cutoff).T
+
+ # Start with a high-cost array of 1's
+ costs_arr = np.ones_like(diff_image)
+
+ # Obtain extent of overlap
+ row, col = mask.nonzero()
+ cmin = col.min()
+ cmax = col.max()
+ shape = mask.shape
+
+ # Label discrete regions
+ labels = mask.copy().astype(np.uint8)
+ cslice = slice(cmin, cmax + 1)
+ submask = np.ascontiguousarray(labels[:, cslice])
+ submask = flood_fill(submask, (0, 0), 2)
+ submask = flood_fill(submask, (shape[0]-1, 0), 3)
+ labels[:, cslice] = submask
+
+ # Find distance from edge to region
+ upper = (labels == 2).sum(axis=0).astype(np.float64)
+ lower = (labels == 3).sum(axis=0).astype(np.float64)
+
+ # Reject areas of high change
+ ugood = np.abs(np.gradient(upper[cslice])) < gradient_cutoff
+ lgood = np.abs(np.gradient(lower[cslice])) < gradient_cutoff
+
+ # Give areas slightly farther from edge a cost break
+ costs_upper = np.ones_like(upper)
+ costs_lower = np.ones_like(lower)
+ costs_upper[cslice][ugood] = upper[cslice].min() / np.maximum(upper[cslice][ugood], 1)
+ costs_lower[cslice][lgood] = lower[cslice].min() / np.maximum(lower[cslice][lgood], 1)
+
+ # Expand from 1d back to 2d
+ vdist = mask.shape[0]
+ costs_upper = costs_upper[np.newaxis, :].repeat(vdist, axis=0)
+ costs_lower = costs_lower[np.newaxis, :].repeat(vdist, axis=0)
+
+ # Place these in output array
+ costs_arr[:, cslice] = costs_upper[:, cslice] * (labels[:, cslice] == 2)
+ costs_arr[:, cslice] += costs_lower[:, cslice] * (labels[:, cslice] == 3)
+
+ # Finally, place the difference image
+ costs_arr[mask] = diff_image[mask]
+
+ return costs_arr
+```
+
+Use this function to generate the cost array.
+
+```{code-cell} ipython3
+# Start with the absolute value of the difference image.
+# np.abs necessary because we don't want negative costs!
+costs01 = generate_costs(np.abs(pano0_warped - pano1_warped),
+ pano0_mask & pano1_mask)
+```
+
+Allow the path to "slide" along top and bottom edges to the optimal horizontal position by setting top and bottom edges to zero cost.
+
+```{code-cell} ipython3
+# Set top and bottom edges to zero in `costs01`
+# Remember (row, col) indexing!
+```
+
+Our cost array now looks like this
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(15, 12))
+
+ax.imshow(costs01, cmap='gray', interpolation='none')
+
+ax.axis('off');
+```
+
+The tweak we made with `generate_costs` is subtle but important. Can you see it?
+
++++
+
+### Find the minimum-cost path (MCP)
+
+Use `skimage.graph.route_through_array` to find an optimal path through the cost array
+
+```{code-cell} ipython3
+from skimage.graph import route_through_array
+
+# Arguments are:
+# cost array
+# start pt
+# end pt
+# can it traverse diagonally
+pts, _ = route_through_array(costs01, mask_pts01[0], mask_pts01[1], fully_connected=True)
+
+# Convert list of lists to 2d coordinate array for easier indexing
+pts = np.array(pts)
+```
+
+Did it work?
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(12, 12))
+
+# Plot the difference image
+ax.imshow(pano0_warped - pano1_warped, cmap='gray')
+
+# Overlay the minimum-cost path
+ax.plot(pts[:, 1], pts[:, 0])
+
+plt.tight_layout()
+ax.axis('off');
+```
+
+That looks like a great seam to stitch these images together - the path looks very close to zero.
+
++++
+
+### Irregularities
+
+Due to the random element in the RANSAC transform estimation, everyone will have a slightly different blue path. **Your path will look different from mine, and different from your neighbor's.** That's expected! _The awesome thing about MCP is that everyone just calculated the best possible path to stitch together their unique transforms!_
+
++++
+
+### Filling the mask
+
+Turn that path into a mask, which will be 1 where we want the left image to show through and zero elsewhere. We need to fill the left side of the mask with ones over to our path.
+
+**Note**: This is the inverse of NumPy masked array conventions (``numpy.ma``), which specify a negative mask (mask == bad/missing) rather than a positive mask as used here (mask == good/selected).
+
+Place the path into a new, empty array.
+
+```{code-cell} ipython3
+# Start with an array of zeros and place the path
+mask0 = np.zeros_like(pano0_warped, dtype=np.uint8)
+mask0[pts[:, 0], pts[:, 1]] = 1
+```
+
+Ensure the path appears as expected
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(12, 12))
+
+# View the path in black and white
+ax.imshow(mask0, cmap='gray')
+
+ax.axis('off');
+```
+
+Label the various contiguous regions in the image using `skimage.measure.label`
+
+```{code-cell} ipython3
+from skimage.morphology import flood_fill
+
+# Labeling starts with one at point (0, 0)
+mask0 = flood_fill(mask0, (0, 0), 1, connectivity=1)
+
+# The result
+plt.imshow(mask0, cmap='gray');
+```
+
+Looks great!
+
+
+### Rinse and repeat
+
+Apply the same principles to images 1 and 2: first, build the cost array
+
+```{code-cell} ipython3
+# Start with the absolute value of the difference image.
+# np.abs is necessary because we don't want negative costs!
+costs12 = generate_costs(np.abs(pano1_warped - pano2_warped),
+ pano1_mask & pano2_mask)
+
+# Allow the path to "slide" along top and bottom edges to the optimal
+# horizontal position by setting top and bottom edges to zero cost
+costs12[0, :] = 0
+costs12[-1, :] = 0
+```
+
+**Add an additional constraint this time**, to prevent this path crossing the prior one!
+
+```{code-cell} ipython3
+costs12[mask0 > 0] = 1
+```
+
+Check the result
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(8, 8))
+ax.imshow(costs12, cmap='gray');
+```
+
+Your results may look slightly different.
+
+Compute the minimal cost path
+
+```{code-cell} ipython3
+# Arguments are:
+# cost array
+# start pt
+# end pt
+# can it traverse diagonally
+pts, _ = route_through_array(costs12, mask_pts12[0], mask_pts12[1], fully_connected=True)
+
+# Convert list of lists to 2d coordinate array for easier indexing
+pts = np.array(pts)
+```
+
+Verify a reasonable result
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(12, 12))
+
+# Plot the difference image
+ax.imshow(pano1_warped - pano2_warped, cmap='gray')
+
+# Overlay the minimum-cost path
+ax.plot(pts[:, 1], pts[:, 0]);
+
+ax.axis('off');
+```
+
+Initialize the mask by placing the path in a new array
+
+```{code-cell} ipython3
+mask2 = np.zeros_like(pano0_warped, dtype=np.uint8)
+mask2[pts[:, 0], pts[:, 1]] = 1
+```
+
+Fill the right side this time, again using `skimage.measure.label` - the label of interest is 3
+
+```{code-cell} ipython3
+mask2 = (label(mask2, connectivity=1, background=-1) == 3)
+
+# The result
+plt.imshow(mask2, cmap='gray');
+```
+
+### Final mask
+
+The last mask for the middle image is one of exclusion - it will be displayed everywhere `mask0` and `mask2` are not.
+
+```{code-cell} ipython3
+mask1 = ~(mask0.astype(np.bool) | mask2.astype(np.bool))
+```
+
+Define a convenience function to place masks in alpha channels
+
+```{code-cell} ipython3
+def add_alpha(img, mask=None):
+ """
+ Adds a masked alpha channel to an image.
+
+ Parameters
+ ----------
+ img : (M, N[, 3]) ndarray
+ Image data, should be rank-2 or rank-3 with RGB channels
+ mask : (M, N[, 3]) ndarray, optional
+ Mask to be applied. If None, the alpha channel is added
+ with full opacity assumed (1) at all locations.
+ """
+ from skimage.color import gray2rgb
+ if mask is None:
+ mask = np.ones_like(img)
+
+ if img.ndim == 2:
+ img = gray2rgb(img)
+
+ return np.dstack((img, mask))
+```
+
+Obtain final, alpha blended individual images and inspect them
+
+```{code-cell} ipython3
+pano0_final = add_alpha(pano0_warped, mask0)
+pano1_final = add_alpha(pano1_warped, mask1)
+pano2_final = add_alpha(pano2_warped, mask2)
+
+compare(pano0_final, pano1_final, pano2_final, figsize=(15, 15))
+```
+
+What we have here is the world's most complicated and precisely-fitting jigsaw puzzle...
+
+Plot all three together and view the results!
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(12, 12))
+
+# This is a perfect combination, but matplotlib's interpolation
+# makes it appear to have gaps. So we turn it off.
+ax.imshow(pano0_final, interpolation='none')
+ax.imshow(pano1_final, interpolation='none')
+ax.imshow(pano2_final, interpolation='none')
+
+fig.tight_layout()
+ax.axis('off');
+```
+
+Fantastic! Without the black borders, you'd never know this was composed of separate images!
+
+---
+
++++
+
+## Bonus round: now, in color!
+
+We converted to grayscale for ORB feature detection, back in the initial **preprocessing** steps. Since we stored our transforms and masks, adding color is straightforward!
+
+Transform the colored images
+
+```{code-cell} ipython3
+# Identical transforms as before, except
+# * Operating on original color images
+# * filling with cval=0 as we know the masks
+pano0_color = warp(pano_imgs[0], (model_robust01 + offset1).inverse, order=3,
+ output_shape=output_shape, cval=0)
+
+pano1_color = warp(pano_imgs[1], offset1.inverse, order=3,
+ output_shape=output_shape, cval=0)
+
+pano2_color = warp(pano_imgs[2], (model_robust12 + offset1).inverse, order=3,
+ output_shape=output_shape, cval=0)
+```
+
+Apply the custom alpha channel masks
+
+```{code-cell} ipython3
+pano0_final = add_alpha(pano0_color, mask0)
+pano1_final = add_alpha(pano1_color, mask1)
+pano2_final = add_alpha(pano2_color, mask2)
+```
+
+View the result!
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(12, 12))
+
+# Turn off matplotlib's interpolation
+ax.imshow(pano0_final, interpolation='none')
+ax.imshow(pano1_final, interpolation='none')
+ax.imshow(pano2_final, interpolation='none')
+
+fig.tight_layout()
+ax.axis('off');
+```
+
+Save the combined, color panorama locally as `'./pano-advanced-output.png'`
+
+```{code-cell} ipython3
+from skimage.color import gray2rgb
+
+# Start with empty image
+pano_combined = np.zeros_like(pano0_color)
+
+# Place the masked portion of each image into the array
+# masks are 2d, they need to be (M, N, 3) to match the color images
+pano_combined += pano0_color * gray2rgb(mask0)
+pano_combined += pano1_color * gray2rgb(mask1)
+pano_combined += pano2_color * gray2rgb(mask2)
+
+
+# Save the output - precision loss warning is expected
+# moving from floating point -> uint8
+io.imsave('./pano-advanced-output.png', pano_combined)
+```
+
+
+---
+
+
+
++++
+
+
+
++++
+
+## Once more, from the top
+
+I hear what you're saying. "But Josh, those were too easy! The panoramas had too much overlap! Does this still work in the real world?"
+
++++
+
+**Go back to the top. Under "Load Data" replace the string `'data/JDW_03*'` with `'data/JDW_9*'`, and re-run all of the cells in order.**
+
++++
+
+
+---
+
+
+
+```{code-cell} ipython3
+%reload_ext load_style
+%load_style ../themes/tutorial.css
+```
diff --git a/lectures/not_yet_booked/adv4_warping.ipynb b/lectures/not_yet_booked/adv4_warping.ipynb
deleted file mode 100644
index 1634a70..0000000
--- a/lectures/not_yet_booked/adv4_warping.ipynb
+++ /dev/null
@@ -1,707 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "from __future__ import division, print_function\n",
- "import numpy as np\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Overview\n",
- "\n",
- "- http://scikit-image.org/docs/stable/api/skimage.transform.html\n",
- "- http://scikit-image.org/docs/stable/api/skimage.transform.html#skimage.transform.warp\n",
- "- http://scikit-image.org/docs/stable/api/skimage.transform.html#skimage.transform.AffineTransform (and other similar classes)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Image rotation from scratch\n",
- "\n",
- "The following code shows how to rotate an image using the skimage (scikit-image) library."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "from skimage import transform, data\n",
- "\n",
- "camera = data.camera()\n",
- "rotated = transform.rotate(camera, 30)\n",
- "\n",
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 5))\n",
- "ax0.imshow(camera, cmap='gray')\n",
- "ax1.imshow(rotated, cmap='gray');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Exercise:** Write an algorithm from scratch that will\n",
- "do the same (i.e., take an input image as an ndarray, and rotate it).\n",
- "\n",
- "If you feel creative, you can also write code to magnify (zoom) the image.\n",
- "\n",
- "You may need: http://en.wikipedia.org/wiki/Polar_coordinate_system\n",
- "\n",
- "A (bad) solution is given below--but try it yourself before looking!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### A problematic approach"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage import color"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def rotate(image, theta):\n",
- " theta = np.deg2rad(theta)\n",
- " \n",
- " height, width = image.shape[:2]\n",
- " out = np.zeros_like(image)\n",
- " \n",
- " centre_x, centre_y = width / 2., height / 2.\n",
- " \n",
- " for x in range(width):\n",
- " for y in range(height):\n",
- " \n",
- " x_c = x - centre_x\n",
- " y_c = y - centre_y\n",
- " \n",
- " # Determine polar coordinate of pixel\n",
- " radius = np.sqrt(x_c**2 + y_c**2)\n",
- " angle = np.arctan2(y_c, x_c)\n",
- " \n",
- " new_angle = angle + theta\n",
- " \n",
- " new_x = radius * np.cos(new_angle)\n",
- " new_y = radius * np.sin(new_angle)\n",
- " \n",
- " new_x = new_x + centre_x\n",
- " new_y = new_y + centre_y\n",
- " \n",
- " if (new_x >= width) or (new_x < 0) or\\\n",
- " (new_y >= height) or (new_y < 0):\n",
- " continue\n",
- " else:\n",
- " out[int(new_y), int(new_x)] = image[y, x]\n",
- " \n",
- " return out\n",
- "\n",
- "rotated = rotate(camera, 40)\n",
- " \n",
- "plt.imshow(rotated, cmap='gray', interpolation='nearest');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## And while we can attempt to fix the problem...\n",
- "\n",
- "...this is not an optimal approach"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Attempt at fixing the holes using a median filter\n",
- "# -- it works, sort of, but it's not the best approach.\n",
- "\n",
- "height, width = rotated.shape[:2]\n",
- "\n",
- "out = rotated.copy()\n",
- "\n",
- "for x in range(1, width - 1):\n",
- " for y in range(1, height - 1):\n",
- " if out[y, x] == 0:\n",
- " out[y, x] = np.median([out[y, x-1],\n",
- " out[y, x+1],\n",
- " out[y+1, x],\n",
- " out[y-1, x]])\n",
- " \n",
- "plt.imshow(out, cmap='gray', interpolation='nearest');"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "A = np.array([[4, 2], [1, 6]])\n",
- "print(A)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "plt.imshow(A, cmap='gray', interpolation='nearest');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# For later discussion: interpolation\n",
- "\n",
- "## Bi-linear interpolation\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "Also see [bilinear interpolation on Wikipedia](http://en.wikipedia.org/wiki/Bilinear_interpolation)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Some warping experiments!\n",
- "\n",
- "## Fish-eye"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage import transform, data, io\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "# Load face\n",
- "face = io.imread('../images/stefan.jpg')\n",
- "\n",
- "# Get the eye nicely in the middle\n",
- "face = face[:185, 15:]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "plt.imshow(face)\n",
- "plt.plot([face.shape[1]/2.], [face.shape[0]/2.], 'or', markersize=14, alpha=0.4)\n",
- "plt.axis('image');"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Define a transformation on the x-y coordinates\n",
- "\n",
- "def fisheye(xy):\n",
- " center = np.mean(xy, axis=0)\n",
- " xc, yc = (xy - center).T\n",
- "\n",
- " # Polar coordinates\n",
- " r = np.sqrt(xc**2 + yc**2)\n",
- " theta = np.arctan2(yc, xc)\n",
- "\n",
- " r = 0.8 * np.exp(r**(1/2.1) / 1.8)\n",
- "\n",
- " return np.column_stack((r * np.cos(theta), r * np.sin(theta))) + center"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Warp and display\n",
- "\n",
- "out = transform.warp(face, fisheye)\n",
- "\n",
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 5))\n",
- "ax0.imshow(face)\n",
- "ax0.set_axis_off()\n",
- "\n",
- "ax1.imshow(out)\n",
- "ax1.set_axis_off()\n",
- "\n",
- "plt.title('Knock! Knock!')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Run the following scripts for fun:\n",
- "\n",
- "(Open up the terminal in the \"scripts\" directory first)\n",
- "\n",
- "- **deswirl.py** (run using: ``python deswirl.py``)\n",
- "\n",
- " In the UK, a criminal tried to hide his identity by posting\n",
- " swirled pictures of his face online. Here, we use the\n",
- " Mona Lisa to illustrate what he did. Can you restore\n",
- " her face back to normal? (Note that you can adjust the\n",
- " position of the red dot, as well as move the sliders.)\n",
- " \n",
- " \n",
- "- **clock_deblur.py**\n",
- "\n",
- " I took a picture of a wall clock while moving the camera. Or perhaps the clock moved.\n",
- " Either way, now I cannot read the time! I've implemented a deblurring\n",
- " algorithm--can you adjust its parameters to help me pin-point\n",
- " the time?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Here's code for a swirl transform:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage import transform\n",
- "\n",
- "def swirl(xy, center=[0, 0], strength=1, radius=100, rotation=0):\n",
- " \"\"\"Compute the coordinate mapping for a swirl transformation.\n",
- "\n",
- " \"\"\"\n",
- " x, y = xy.T\n",
- " x0, y0 = center\n",
- " rho = np.sqrt((x - x0)**2 + (y - y0)**2)\n",
- "\n",
- " # Ensure that the transformation decays to approximately 1/1000-th\n",
- " # within the specified radius.\n",
- " radius = radius / 5 * np.log(2)\n",
- "\n",
- " theta = rotation + strength * \\\n",
- " np.exp(-rho / radius) + \\\n",
- " np.arctan2(y - y0, x - x0)\n",
- "\n",
- " xy[..., 0] = x0 + rho * np.cos(theta)\n",
- " xy[..., 1] = y0 + rho * np.sin(theta)\n",
- "\n",
- " return xy\n",
- "\n",
- "\n",
- "h, w = face.shape[:2]\n",
- "\n",
- "parameters = {'center': [w/2., h/2.],\n",
- " 'strength': 8,\n",
- " 'radius': 90,\n",
- " 'rotation': 0}\n",
- "\n",
- "out = transform.warp(face, swirl, parameters)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 4))\n",
- "\n",
- "ax0.imshow(face)\n",
- "ax1.imshow(out);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Can you come up with an even better distortion?\n",
- "\n",
- "## Start with this template:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def my_warp(xy):\n",
- " x = xy[:, 0]\n",
- " y = xy[:, 1]\n",
- " \n",
- " x = x + 1.5 * np.sin(y / 3)\n",
- " \n",
- " return np.hstack((x, y))\n",
- "\n",
- "image = plt.imread('../images/stefan.jpg')\n",
- "out = transform.warp(image, my_warp)\n",
- "\n",
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 4))\n",
- "ax0.imshow(image)\n",
- "ax1.imshow(out);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Composing Transformations\n",
- "\n",
- "scikit-image allows you to compose several transformations. For example:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage import data\n",
- "\n",
- "cat = data.chelsea()\n",
- "horizontal_shift = transform.SimilarityTransform(translation=[20, 0])\n",
- "\n",
- "multiple_shifts = horizontal_shift + horizontal_shift + horizontal_shift\n",
- "\n",
- "f, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(15, 5))\n",
- "ax0.imshow(cat)\n",
- "ax1.imshow(transform.warp(cat, horizontal_shift.inverse)) # Note the inverse!\n",
- "ax2.imshow(transform.warp(cat, multiple_shifts.inverse));"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The `transform` module allows us to rotate images. The inner workings is something like this:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def my_rotate(image, angle):\n",
- " rotation_tf = transform.SimilarityTransform(rotation=np.deg2rad(angle))\n",
- " return transform.warp(image, rotation_tf.inverse)\n",
- "\n",
- "plt.imshow(my_rotate(cat, 30))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note that this rotates the cat around the origin (top-left).\n",
- "\n",
- "**Can you modify `my_rotate` to rotate the image around the center?**\n",
- "\n",
- "*Hint:*\n",
- "\n",
- "1. Shift the image (see above) so that the center of the image lies at (0, 0)\n",
- "2. Rotate the image\n",
- "3. Shift the image back---the opposite of what you did in step 1\n",
- "\n",
- "All of this can be achieved by composing transformations and calling `warp` once."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Advanced challenge: rectifying an image\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We know the above tiles are laid out in a square--can you transform\n",
- "the image so that the tiles are displayed as if you were viewing them from above?\n",
- "\n",
- "The centre-points of the corner circles are, given as (row, column) coordinates:\n",
- "\n",
- "```\n",
- "(72, 129) -- top left\n",
- "(76, 302) -- top right\n",
- "(185, 90) -- bottom left\n",
- "(193, 326) -- bottom right\n",
- "```\n",
- "\n",
- "Hint: there is a linear transformation matrix, $H$, such that\n",
- "\n",
- "$H \\mathbf{x} = \\mathbf{x}'$\n",
- "\n",
- "where $\\mathbf{x}$ is the *homogeneous* coordinate in the original image and\n",
- "$\\mathbf{x}'$ is the *homogeneous* coordinate in the rectified image (with *homogeneous*\n",
- "we simply mean that we add an extra 1 at the end, e.g. (72, 129) becomes (72, 129, 1).\n",
- "The values for $\\mathbf{x}$ and their new values, $\\mathbf{x}'$,\n",
- "are therefore:\n",
- "\n",
- "```\n",
- "x = (72, 129, 1), x' = (0, 0, 1)\n",
- "x = (76, 302, 1), x' = (0, 400, 1)\n",
- "x = (185, 90, 1), x' = (400, 0, 1)\n",
- "x = (193, 326, 1) x' = (400, 400, 1)\n",
- "```\n",
- "\n",
- "(You can choose any output size you like--I chose $400 \\times 400$)\n",
- "\n",
- "Why do we need homogeneous coordinates? It allows us to have *translation* as part of H:\n",
- "\n",
- "$\n",
- "\\left[\\begin{array}{ccc}\n",
- "H_{00} & H_{01} & H_{02}\\\\\n",
- "H_{10} & H_{11} & H_{12}\\\\\n",
- "H_{20} & H_{21} & 1\n",
- "\\end{array}\\right]\\left[\\begin{array}{c}\n",
- "x\\\\\n",
- "y\\\\\n",
- "1\n",
- "\\end{array}\\right]=\\left[\\begin{array}{c}\n",
- "H_{00}x+H_{01}y+H_{02}\\\\\n",
- "H_{10}x+H_{11}y+H_{12}\\\\\n",
- "H_{20}x+H_{21}y+H_{22}\n",
- "\\end{array}\\right]\n",
- "$\n",
- "\n",
- "Note that each element of the output coordinate is of the form $ax + by + c$. Without the 1 in the last position of the coordinate, there would have been no $+ c$ and therefore no translation!\n",
- "\n",
- "The question on how to determine $H$ is left for another day. If you are curious, \n",
- "the [answer can be found here](homography.pdf).\n",
- "\n",
- "In the meantime, I provide some code to calculate $H$:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage.transform import estimate_transform\n",
- "\n",
- "source = np.array([(129, 72),\n",
- " (302, 76),\n",
- " (90, 185),\n",
- " (326, 193)])\n",
- "\n",
- "target = np.array([[0, 0],\n",
- " [400, 0],\n",
- " [0, 400],\n",
- " [400, 400]])\n",
- "\n",
- "tf = estimate_transform('projective', source, target)\n",
- "H = tf.params\n",
- "print(H)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Using the code in the cell above, you can compute the target coordinate of any position in the original image."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Verify that the top left corner maps to (0, 0)\n",
- "\n",
- "x = np.array([[129, 72, 1]])\n",
- "\n",
- "z = np.dot(H, x.T)\n",
- "z /= z[2]\n",
- "\n",
- "print(z)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Here's a template solution:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def rectify(xy):\n",
- " x = xy[:, 0]\n",
- " y = xy[:, 1]\n",
- " \n",
- " # We need to provide the backward mapping, from the target\n",
- " # image to the source image.\n",
- " HH = np.linalg.inv(H)\n",
- " \n",
- " # You must fill in your code here to take\n",
- " # the matrix HH (given above) and to transform\n",
- " # each coordinate to its new position.\n",
- " # \n",
- " # Hint: handy functions are\n",
- " #\n",
- " # - np.dot (matrix multiplication)\n",
- " # - np.ones_like (make an array of ones the same shape as another array)\n",
- " # - np.column_stack\n",
- " # - A.T -- type .T after a matrix to transpose it\n",
- " # - x.reshape -- reshapes the array x\n",
- " \n",
- " # ... your code\n",
- " # ... your code\n",
- " # ... your code\n",
- " # ... your code\n",
- " \n",
- " return ...\n",
- "\n",
- " \n",
- "image = plt.imread('images/chapel_floor.png')\n",
- "out = transform.warp(image, rectify, output_shape=(400, 400))\n",
- "\n",
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 4))\n",
- "ax0.imshow(image)\n",
- "ax1.imshow(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "\n",
- "
\n",
- "The solution to the above problem is provided as [solutions/tile_rectify.py](solutions/tile_rectify.py). Only look at it after you've attempted the problem yourself!\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# For more fun examples see http://scikit-image.org/docs/dev/auto_examples"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.5.1+"
- },
- "widgets": {
- "state": {},
- "version": "1.1.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/lectures/not_yet_booked/adv4_warping.md b/lectures/not_yet_booked/adv4_warping.md
new file mode 100644
index 0000000..54632fd
--- /dev/null
+++ b/lectures/not_yet_booked/adv4_warping.md
@@ -0,0 +1,473 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+```{code-cell} ipython3
+from __future__ import division, print_function
+import numpy as np
+%matplotlib inline
+```
+
+# Warping images
+
+## Overview
+
+- http://scikit-image.org/docs/stable/api/skimage.transform.html
+- http://scikit-image.org/docs/stable/api/skimage.transform.html#skimage.transform.warp
+- http://scikit-image.org/docs/stable/api/skimage.transform.html#skimage.transform.AffineTransform (and other similar classes)
+
++++
+
+## Image rotation from scratch
+
+The following code shows how to rotate an image using the skimage (scikit-image) library.
+
+```{code-cell} ipython3
+import matplotlib.pyplot as plt
+from skimage import transform, data
+
+camera = data.camera()
+rotated = transform.rotate(camera, 30)
+
+f, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 5))
+ax0.imshow(camera, cmap='gray')
+ax1.imshow(rotated, cmap='gray');
+```
+
+**Exercise:** Write an algorithm from scratch that will
+do the same (i.e., take an input image as an ndarray, and rotate it).
+
+If you feel creative, you can also write code to magnify (zoom) the image.
+
+You may need: http://en.wikipedia.org/wiki/Polar_coordinate_system
+
+A (bad) solution is given below--but try it yourself before looking!
+
++++
+
+### A problematic approach
+
+```{code-cell} ipython3
+from skimage import color
+```
+
+```{code-cell} ipython3
+def rotate(image, theta):
+ theta = np.deg2rad(theta)
+
+ height, width = image.shape[:2]
+ out = np.zeros_like(image)
+
+ centre_x, centre_y = width / 2., height / 2.
+
+ for x in range(width):
+ for y in range(height):
+
+ x_c = x - centre_x
+ y_c = y - centre_y
+
+ # Determine polar coordinate of pixel
+ radius = np.sqrt(x_c**2 + y_c**2)
+ angle = np.arctan2(y_c, x_c)
+
+ new_angle = angle + theta
+
+ new_x = radius * np.cos(new_angle)
+ new_y = radius * np.sin(new_angle)
+
+ new_x = new_x + centre_x
+ new_y = new_y + centre_y
+
+ if (new_x >= width) or (new_x < 0) or\
+ (new_y >= height) or (new_y < 0):
+ continue
+ else:
+ out[int(new_y), int(new_x)] = image[y, x]
+
+ return out
+
+rotated = rotate(camera, 40)
+
+plt.imshow(rotated, cmap='gray', interpolation='nearest');
+```
+
+### And while we can attempt to fix the problem...
+
+...this is not an optimal approach
+
+```{code-cell} ipython3
+# Attempt at fixing the holes using a median filter
+# -- it works, sort of, but it's not the best approach.
+
+height, width = rotated.shape[:2]
+
+out = rotated.copy()
+
+for x in range(1, width - 1):
+ for y in range(1, height - 1):
+ if out[y, x] == 0:
+ out[y, x] = np.median([out[y, x-1],
+ out[y, x+1],
+ out[y+1, x],
+ out[y-1, x]])
+
+plt.imshow(out, cmap='gray', interpolation='nearest');
+```
+
+```{code-cell} ipython3
+A = np.array([[4, 2], [1, 6]])
+print(A)
+```
+
+```{code-cell} ipython3
+plt.imshow(A, cmap='gray', interpolation='nearest');
+```
+
+## For later discussion: interpolation
+
+### Bi-linear interpolation
+
+
+
+
+Also see [bilinear interpolation on Wikipedia](http://en.wikipedia.org/wiki/Bilinear_interpolation)
+
++++
+
+## Some warping experiments!
+
+### Fish-eye
+
+```{code-cell} ipython3
+from skimage import transform, data, io
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load face
+face = io.imread('../images/stefan.jpg')
+
+# Get the eye nicely in the middle
+face = face[:185, 15:]
+```
+
+```{code-cell} ipython3
+plt.imshow(face)
+plt.plot([face.shape[1]/2.], [face.shape[0]/2.], 'or', markersize=14, alpha=0.4)
+plt.axis('image');
+```
+
+```{code-cell} ipython3
+# Define a transformation on the x-y coordinates
+
+def fisheye(xy):
+ center = np.mean(xy, axis=0)
+ xc, yc = (xy - center).T
+
+ # Polar coordinates
+ r = np.sqrt(xc**2 + yc**2)
+ theta = np.arctan2(yc, xc)
+
+ r = 0.8 * np.exp(r**(1/2.1) / 1.8)
+
+ return np.column_stack((r * np.cos(theta), r * np.sin(theta))) + center
+```
+
+```{code-cell} ipython3
+# Warp and display
+
+out = transform.warp(face, fisheye)
+
+f, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 5))
+ax0.imshow(face)
+ax0.set_axis_off()
+
+ax1.imshow(out)
+ax1.set_axis_off()
+
+plt.title('Knock! Knock!')
+plt.show()
+```
+
+### Run the following scripts for fun:
+
+(Open up the terminal in the "scripts" directory first)
+
+- **deswirl.py** (run using: ``python deswirl.py``)
+
+ In the UK, a criminal tried to hide his identity by posting
+ swirled pictures of his face online. Here, we use the
+ Mona Lisa to illustrate what he did. Can you restore
+ her face back to normal? (Note that you can adjust the
+ position of the red dot, as well as move the sliders.)
+
+
+- **clock_deblur.py**
+
+ I took a picture of a wall clock while moving the camera. Or perhaps the clock moved.
+ Either way, now I cannot read the time! I've implemented a deblurring
+ algorithm--can you adjust its parameters to help me pin-point
+ the time?
+
++++
+
+### Here's code for a swirl transform:
+
+```{code-cell} ipython3
+from skimage import transform
+
+def swirl(xy, center=[0, 0], strength=1, radius=100, rotation=0):
+ """Compute the coordinate mapping for a swirl transformation.
+
+ """
+ x, y = xy.T
+ x0, y0 = center
+ rho = np.sqrt((x - x0)**2 + (y - y0)**2)
+
+ # Ensure that the transformation decays to approximately 1/1000-th
+ # within the specified radius.
+ radius = radius / 5 * np.log(2)
+
+ theta = rotation + strength * \
+ np.exp(-rho / radius) + \
+ np.arctan2(y - y0, x - x0)
+
+ xy[..., 0] = x0 + rho * np.cos(theta)
+ xy[..., 1] = y0 + rho * np.sin(theta)
+
+ return xy
+
+
+h, w = face.shape[:2]
+
+parameters = {'center': [w/2., h/2.],
+ 'strength': 8,
+ 'radius': 90,
+ 'rotation': 0}
+
+out = transform.warp(face, swirl, parameters)
+```
+
+```{code-cell} ipython3
+f, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 4))
+
+ax0.imshow(face)
+ax1.imshow(out);
+```
+
+## Can you come up with an even better distortion?
+
+### Start with this template:
+
+```{code-cell} ipython3
+def my_warp(xy):
+ x = xy[:, 0]
+ y = xy[:, 1]
+
+ x = x + 1.5 * np.sin(y / 3)
+
+ return np.hstack((x, y))
+
+image = plt.imread('../images/stefan.jpg')
+out = transform.warp(image, my_warp)
+
+f, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 4))
+ax0.imshow(image)
+ax1.imshow(out);
+```
+
+### Composing Transformations
+
+scikit-image allows you to compose several transformations. For example:
+
+```{code-cell} ipython3
+from skimage import data
+
+cat = data.chelsea()
+horizontal_shift = transform.SimilarityTransform(translation=[20, 0])
+
+multiple_shifts = horizontal_shift + horizontal_shift + horizontal_shift
+
+f, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(15, 5))
+ax0.imshow(cat)
+ax1.imshow(transform.warp(cat, horizontal_shift.inverse)) # Note the inverse!
+ax2.imshow(transform.warp(cat, multiple_shifts.inverse));
+```
+
+The `transform` module allows us to rotate images. The inner workings is something like this:
+
+```{code-cell} ipython3
+def my_rotate(image, angle):
+ rotation_tf = transform.SimilarityTransform(rotation=np.deg2rad(angle))
+ return transform.warp(image, rotation_tf.inverse)
+
+plt.imshow(my_rotate(cat, 30))
+```
+
+Note that this rotates the cat around the origin (top-left).
+
+**Can you modify `my_rotate` to rotate the image around the center?**
+
+*Hint:*
+
+1. Shift the image (see above) so that the center of the image lies at (0, 0)
+2. Rotate the image
+3. Shift the image back---the opposite of what you did in step 1
+
+All of this can be achieved by composing transformations and calling `warp` once.
+
++++
+
+## Advanced challenge: rectifying an image
+
+
+
++++
+
+We know the above tiles are laid out in a square--can you transform
+the image so that the tiles are displayed as if you were viewing them from above?
+
+The centre-points of the corner circles are, given as (row, column) coordinates:
+
+```
+(72, 129) -- top left
+(76, 302) -- top right
+(185, 90) -- bottom left
+(193, 326) -- bottom right
+```
+
+Hint: there is a linear transformation matrix, $H$, such that
+
+$H \mathbf{x} = \mathbf{x}'$
+
+where $\mathbf{x}$ is the *homogeneous* coordinate in the original image and
+$\mathbf{x}'$ is the *homogeneous* coordinate in the rectified image (with *homogeneous*
+we simply mean that we add an extra 1 at the end, e.g. (72, 129) becomes (72, 129, 1).
+The values for $\mathbf{x}$ and their new values, $\mathbf{x}'$,
+are therefore:
+
+```
+x = (72, 129, 1), x' = (0, 0, 1)
+x = (76, 302, 1), x' = (0, 400, 1)
+x = (185, 90, 1), x' = (400, 0, 1)
+x = (193, 326, 1) x' = (400, 400, 1)
+```
+
+(You can choose any output size you like--I chose $400 \times 400$)
+
+Why do we need homogeneous coordinates? It allows us to have *translation* as part of H:
+
+$
+\left[\begin{array}{ccc}
+H_{00} & H_{01} & H_{02}\\
+H_{10} & H_{11} & H_{12}\\
+H_{20} & H_{21} & 1
+\end{array}\right]\left[\begin{array}{c}
+x\\
+y\\
+1
+\end{array}\right]=\left[\begin{array}{c}
+H_{00}x+H_{01}y+H_{02}\\
+H_{10}x+H_{11}y+H_{12}\\
+H_{20}x+H_{21}y+H_{22}
+\end{array}\right]
+$
+
+Note that each element of the output coordinate is of the form $ax + by + c$. Without the 1 in the last position of the coordinate, there would have been no $+ c$ and therefore no translation!
+
+The question on how to determine $H$ is left for another day. If you are curious,
+the {download}`answer can be found here `.
+
+In the meantime, I provide some code to calculate $H$:
+
+```{code-cell} ipython3
+from skimage.transform import estimate_transform
+
+source = np.array([(129, 72),
+ (302, 76),
+ (90, 185),
+ (326, 193)])
+
+target = np.array([[0, 0],
+ [400, 0],
+ [0, 400],
+ [400, 400]])
+
+tf = estimate_transform('projective', source, target)
+H = tf.params
+print(H)
+```
+
+Using the code in the cell above, you can compute the target coordinate of any position in the original image.
+
+```{code-cell} ipython3
+# Verify that the top left corner maps to (0, 0)
+
+x = np.array([[129, 72, 1]])
+
+z = np.dot(H, x.T)
+z /= z[2]
+
+print(z)
+```
+
+### Here's a template solution:
+
+```{code-cell} ipython3
+def rectify(xy):
+ x = xy[:, 0]
+ y = xy[:, 1]
+
+ # We need to provide the backward mapping, from the target
+ # image to the source image.
+ HH = np.linalg.inv(H)
+
+ # You must fill in your code here to take
+ # the matrix HH (given above) and to transform
+ # each coordinate to its new position.
+ #
+ # Hint: handy functions are
+ #
+ # - np.dot (matrix multiplication)
+ # - np.ones_like (make an array of ones the same shape as another array)
+ # - np.column_stack
+ # - A.T -- type .T after a matrix to transpose it
+ # - x.reshape -- reshapes the array x
+
+ # ... your code
+ # ... your code
+ # ... your code
+ # ... your code
+
+ return ...
+
+
+image = plt.imread('images/chapel_floor.png')
+out = transform.warp(image, rectify, output_shape=(400, 400))
+
+f, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 4))
+ax0.imshow(image)
+ax1.imshow(out)
+```
+
+
+
+
+The solution to the above problem is provided as [solutions/tile_rectify.py](solutions/tile_rectify.py). Only look at it after you've attempted the problem yourself!
+
+
++++
+
+## For more fun examples see http://scikit-image.org/docs/dev/auto_examples
+
+```{code-cell} ipython3
+
+```
diff --git a/lectures/not_yet_booked/adv5-pores.ipynb b/lectures/not_yet_booked/adv5-pores.ipynb
deleted file mode 100644
index cf9516e..0000000
--- a/lectures/not_yet_booked/adv5-pores.ipynb
+++ /dev/null
@@ -1,522 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Diatom analysis\n",
- "\n",
- "See https://www.nature.com/articles/s41524-019-0202-3:\n",
- "\n",
- "**Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning**, Artem A. Trofimov, Alison A. Pawlicki, Nikolay Borodinov, Shovon Mandal, Teresa J. Mathews, Mark Hildebrand, Maxim A. Ziatdinov, Katherine A. Hausladen, Paulina K. Urbanowicz, Chad A. Steed, Anton V. Ievlev, Alex Belianinov, Joshua K. Michener, Rama Vasudevan, and Olga S. Ovchinnikova."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Set up matplotlib defaults: larger images, gray color map\n",
- "import matplotlib\n",
- "matplotlib.rcParams.update({\n",
- " 'figure.figsize': (10, 10),\n",
- " 'image.cmap': 'gray'\n",
- "})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage import io\n",
- "image = io.imread('../data/diatom-wild-032.jpg')\n",
- "\n",
- "plt.imshow(image);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pores = image[:690, :]\n",
- "\n",
- "plt.imshow(pores);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from scipy import ndimage as ndi\n",
- "from skimage import util\n",
- "\n",
- "denoised = ndi.median_filter(util.img_as_float(pores), size=3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "plt.imshow(denoised);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage import exposure\n",
- "\n",
- "pores_gamma = exposure.adjust_gamma(denoised, 0.7)\n",
- "plt.imshow(pores_gamma);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pores_inv = 1 - pores_gamma\n",
- "plt.imshow(pores_inv);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# This is the problematic part of the manual pipeline: you need\n",
- "# a good segmentation. There are algorithms for automatic thresholding,\n",
- "# such as `filters.otsu` and `filters.li`, but they don't always get the\n",
- "# result you want.\n",
- "\n",
- "t = 0.325\n",
- "thresholded = (pores_gamma <= t)\n",
- "\n",
- "plt.imshow(thresholded);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage import filters\n",
- "\n",
- "filters.try_all_threshold(pores_gamma, figsize=(15, 20));"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage import segmentation, morphology, color"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "distance = ndi.distance_transform_edt(thresholded)\n",
- "\n",
- "plt.imshow(exposure.adjust_gamma(distance, 0.5))\n",
- "plt.title('Distance to background map');"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "local_maxima = morphology.local_maxima(distance)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots(figsize=(20, 20))\n",
- "\n",
- "maxi_coords = np.nonzero(local_maxima)\n",
- "\n",
- "ax.imshow(pores);\n",
- "plt.scatter(maxi_coords[1], maxi_coords[0]);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# This is a utility function that we'll use for display in a while;\n",
- "# you can ignore it for now and come and investigate later.\n",
- "\n",
- "def shuffle_labels(labels):\n",
- " \"\"\"Shuffle the labels so that they are no longer in order.\n",
- " This helps with visualization.\n",
- " \"\"\"\n",
- " indices = np.unique(labels[labels != 0])\n",
- " indices = np.append(\n",
- " [0],\n",
- " np.random.permutation(indices)\n",
- " )\n",
- " return indices[labels]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "markers = ndi.label(local_maxima)[0]\n",
- "labels = segmentation.watershed(denoised, markers)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "f, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(20, 5))\n",
- "ax0.imshow(thresholded)\n",
- "ax1.imshow(np.log(1 + distance))\n",
- "ax2.imshow(shuffle_labels(labels), cmap='magma');"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "labels_masked = segmentation.watershed(thresholded, markers, mask=thresholded, connectivity=2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "f, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(20, 5))\n",
- "ax0.imshow(thresholded)\n",
- "ax1.imshow(np.log(1 + distance))\n",
- "ax2.imshow(shuffle_labels(labels_masked), cmap='magma');"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from skimage import measure\n",
- "\n",
- "contours = measure.find_contours(labels_masked, level=0.5)\n",
- "plt.imshow(pores)\n",
- "for c in contours:\n",
- " plt.plot(c[:, 1], c[:, 0])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "regions = measure.regionprops(labels_masked)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "f, ax = plt.subplots(figsize=(10, 3))\n",
- "ax.hist([r.area for r in regions], bins=100, range=(0, 200));"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from keras import models, layers\n",
- "from keras.layers import Conv2D, MaxPooling2D, UpSampling2D\n",
- "\n",
- "M = 76\n",
- "N = int(23 / 76 * M) * 2\n",
- "\n",
- "model = models.Sequential()\n",
- "model.add(\n",
- " Conv2D(\n",
- " 32,\n",
- " kernel_size=(2, 2),\n",
- " activation='relu',\n",
- " input_shape=(N, N, 1),\n",
- " padding='same'\n",
- " )\n",
- ")\n",
- "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
- "model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
- "model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
- "model.add(UpSampling2D(size=(2, 2)))\n",
- "model.add(\n",
- " Conv2D(\n",
- " 1,\n",
- " kernel_size=(2, 2),\n",
- " activation='sigmoid',\n",
- " padding='same'\n",
- " )\n",
- ")\n",
- "model.compile(loss='mse', optimizer='Adam', metrics=['accuracy'])\n",
- "\n",
- "# Load pre-trained weights from disk\n",
- "model.load_weights('../data/keras_model-diatoms-pores.h5')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "shape = np.array(pores.shape)\n",
- "padded_shape = (np.ceil(shape / 46) * 46).astype(int)\n",
- "delta_shape = padded_shape - shape\n",
- "\n",
- "padded_pores = np.pad(\n",
- " pores,\n",
- " pad_width=[(0, delta_shape[0]), (0, delta_shape[1])],\n",
- " mode='symmetric'\n",
- ")\n",
- "\n",
- "blocks = util.view_as_blocks(padded_pores, (46, 46))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "B_rows, B_cols, _, _ = blocks.shape\n",
- "\n",
- "tiles = blocks.reshape([-1, 46, 46])\n",
- "\n",
- "# `predict` wants input of shape (N, 46, 46, 1)\n",
- "tile_masks = model.predict_classes(tiles[..., np.newaxis])\n",
- "\n",
- "print(tile_masks.shape)\n",
- "tile_masks = tile_masks[..., 0].astype(bool)\n",
- "print(tile_masks.shape)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "nn_mask = util.montage(tile_masks, grid_shape=(B_rows, B_cols))\n",
- "nn_mask = nn_mask[:shape[0], :shape[1]]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "plt.imshow(nn_mask);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "contours = measure.find_contours(nn_mask, level=0.5)\n",
- "plt.imshow(pores)\n",
- "for c in contours:\n",
- " plt.plot(c[:, 1], c[:, 0])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "nn_regions = measure.regionprops(\n",
- " measure.label(nn_mask)\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "f, ax = plt.subplots(figsize=(10, 3))\n",
- "ax.hist([r.area for r in regions], bins='auto', range=(0, 200), alpha=0.4, label='Classic')\n",
- "ax.hist([r.area for r in nn_regions], bins='auto', range=(0, 200), alpha=0.4, label='NN')\n",
- "ax.legend();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Bonus round: region filtering"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def is_circular(regions, eccentricity_threshold=0.1, area_threshold=10):\n",
- " \"\"\"Calculate a boolean mask indicating which regions are circular.\n",
- " \n",
- " Parameters\n",
- " ----------\n",
- " eccentricity_threshold : float, >= 0\n",
- " Regions with an eccentricity less than than this value are\n",
- " considered circular. See `measure.regionprops`.\n",
- " area_threshold : int\n",
- " Only regions with an area greater than this value are considered\n",
- " circular.\n",
- " \"\"\"\n",
- " return np.array([\n",
- " (r.area > area_threshold) and\n",
- " (r.eccentricity <= eccentricity_threshold)\n",
- " for r in regions\n",
- " ])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def filtered_mask(mask, regions, eccentricity_threshold, area_threshold):\n",
- " mask = mask.copy()\n",
- " suppress_regions = np.array(regions)[\n",
- " ~is_circular(\n",
- " regions,\n",
- " eccentricity_threshold=eccentricity_threshold,\n",
- " area_threshold=area_threshold\n",
- " )\n",
- " ]\n",
- " \n",
- " for r in suppress_regions:\n",
- " mask[tuple(r.coords.T)] = 0\n",
- " \n",
- " return mask"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "plt.imshow(filtered_mask(nn_mask, nn_regions,\n",
- " eccentricity_threshold=0.8,\n",
- " area_threshold=20));"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "contours = measure.find_contours(\n",
- " filtered_mask(nn_mask, nn_regions,\n",
- " eccentricity_threshold=0.8,\n",
- " area_threshold=20),\n",
- " level=0.5\n",
- ")\n",
- "plt.imshow(pores)\n",
- "for c in contours:\n",
- " plt.plot(c[:, 1], c[:, 0])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "filtered_regions = np.array(nn_regions)[is_circular(nn_regions, 0.8, 20)]\n",
- "\n",
- "f, ax = plt.subplots(figsize=(10, 3))\n",
- "ax.hist([r.area for r in filtered_regions], bins='auto', range=(0, 200), alpha=0.4);"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/lectures/not_yet_booked/adv5_pores.md b/lectures/not_yet_booked/adv5_pores.md
new file mode 100644
index 0000000..98f9a72
--- /dev/null
+++ b/lectures/not_yet_booked/adv5_pores.md
@@ -0,0 +1,324 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+# Diatom analysis
+
+See https://www.nature.com/articles/s41524-019-0202-3:
+
+**Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning**, Artem A. Trofimov, Alison A. Pawlicki, Nikolay Borodinov, Shovon Mandal, Teresa J. Mathews, Mark Hildebrand, Maxim A. Ziatdinov, Katherine A. Hausladen, Paulina K. Urbanowicz, Chad A. Steed, Anton V. Ievlev, Alex Belianinov, Joshua K. Michener, Rama Vasudevan, and Olga S. Ovchinnikova.
+
+```{code-cell} ipython3
+%matplotlib inline
+%config InlineBackend.figure_format = 'retina'
+import matplotlib.pyplot as plt
+import numpy as np
+```
+
+```{code-cell} ipython3
+# Set up matplotlib defaults: larger images, gray color map
+import matplotlib
+matplotlib.rcParams.update({
+ 'figure.figsize': (10, 10),
+ 'image.cmap': 'gray'
+})
+```
+
+```{code-cell} ipython3
+from skimage import io
+image = io.imread('../data/diatom-wild-032.jpg')
+
+plt.imshow(image);
+```
+
+```{code-cell} ipython3
+pores = image[:690, :]
+
+plt.imshow(pores);
+```
+
+```{code-cell} ipython3
+from scipy import ndimage as ndi
+from skimage import util
+
+denoised = ndi.median_filter(util.img_as_float(pores), size=3)
+```
+
+```{code-cell} ipython3
+plt.imshow(denoised);
+```
+
+```{code-cell} ipython3
+from skimage import exposure
+
+pores_gamma = exposure.adjust_gamma(denoised, 0.7)
+plt.imshow(pores_gamma);
+```
+
+```{code-cell} ipython3
+pores_inv = 1 - pores_gamma
+plt.imshow(pores_inv);
+```
+
+```{code-cell} ipython3
+# This is the problematic part of the manual pipeline: you need
+# a good segmentation. There are algorithms for automatic thresholding,
+# such as `filters.otsu` and `filters.li`, but they don't always get the
+# result you want.
+
+t = 0.325
+thresholded = (pores_gamma <= t)
+
+plt.imshow(thresholded);
+```
+
+```{code-cell} ipython3
+from skimage import filters
+
+filters.try_all_threshold(pores_gamma, figsize=(15, 20));
+```
+
+```{code-cell} ipython3
+from skimage import segmentation, morphology, color
+```
+
+```{code-cell} ipython3
+distance = ndi.distance_transform_edt(thresholded)
+
+plt.imshow(exposure.adjust_gamma(distance, 0.5))
+plt.title('Distance to background map');
+```
+
+```{code-cell} ipython3
+local_maxima = morphology.local_maxima(distance)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(20, 20))
+
+maxi_coords = np.nonzero(local_maxima)
+
+ax.imshow(pores);
+plt.scatter(maxi_coords[1], maxi_coords[0]);
+```
+
+```{code-cell} ipython3
+# This is a utility function that we'll use for display in a while;
+# you can ignore it for now and come and investigate later.
+
+def shuffle_labels(labels):
+ """Shuffle the labels so that they are no longer in order.
+ This helps with visualization.
+ """
+ indices = np.unique(labels[labels != 0])
+ indices = np.append(
+ [0],
+ np.random.permutation(indices)
+ )
+ return indices[labels]
+```
+
+```{code-cell} ipython3
+markers = ndi.label(local_maxima)[0]
+labels = segmentation.watershed(denoised, markers)
+```
+
+```{code-cell} ipython3
+f, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(20, 5))
+ax0.imshow(thresholded)
+ax1.imshow(np.log(1 + distance))
+ax2.imshow(shuffle_labels(labels), cmap='magma');
+```
+
+```{code-cell} ipython3
+labels_masked = segmentation.watershed(thresholded, markers, mask=thresholded, connectivity=2)
+```
+
+```{code-cell} ipython3
+f, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(20, 5))
+ax0.imshow(thresholded)
+ax1.imshow(np.log(1 + distance))
+ax2.imshow(shuffle_labels(labels_masked), cmap='magma');
+```
+
+```{code-cell} ipython3
+from skimage import measure
+
+contours = measure.find_contours(labels_masked, level=0.5)
+plt.imshow(pores)
+for c in contours:
+ plt.plot(c[:, 1], c[:, 0])
+```
+
+```{code-cell} ipython3
+regions = measure.regionprops(labels_masked)
+```
+
+```{code-cell} ipython3
+f, ax = plt.subplots(figsize=(10, 3))
+ax.hist([r.area for r in regions], bins=100, range=(0, 200));
+```
+
+```{code-cell} ipython3
+from keras import models, layers
+from keras.layers import Conv2D, MaxPooling2D, UpSampling2D
+
+M = 76
+N = int(23 / 76 * M) * 2
+
+model = models.Sequential()
+model.add(
+ Conv2D(
+ 32,
+ kernel_size=(2, 2),
+ activation='relu',
+ input_shape=(N, N, 1),
+ padding='same'
+ )
+)
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
+model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
+model.add(UpSampling2D(size=(2, 2)))
+model.add(
+ Conv2D(
+ 1,
+ kernel_size=(2, 2),
+ activation='sigmoid',
+ padding='same'
+ )
+)
+model.compile(loss='mse', optimizer='Adam', metrics=['accuracy'])
+
+# Load pre-trained weights from disk
+model.load_weights('../data/keras_model-diatoms-pores.h5')
+```
+
+```{code-cell} ipython3
+shape = np.array(pores.shape)
+padded_shape = (np.ceil(shape / 46) * 46).astype(int)
+delta_shape = padded_shape - shape
+
+padded_pores = np.pad(
+ pores,
+ pad_width=[(0, delta_shape[0]), (0, delta_shape[1])],
+ mode='symmetric'
+)
+
+blocks = util.view_as_blocks(padded_pores, (46, 46))
+```
+
+```{code-cell} ipython3
+B_rows, B_cols, _, _ = blocks.shape
+
+tiles = blocks.reshape([-1, 46, 46])
+
+# `predict` wants input of shape (N, 46, 46, 1)
+tile_masks = model.predict_classes(tiles[..., np.newaxis])
+
+print(tile_masks.shape)
+tile_masks = tile_masks[..., 0].astype(bool)
+print(tile_masks.shape)
+```
+
+```{code-cell} ipython3
+nn_mask = util.montage(tile_masks, grid_shape=(B_rows, B_cols))
+nn_mask = nn_mask[:shape[0], :shape[1]]
+```
+
+```{code-cell} ipython3
+plt.imshow(nn_mask);
+```
+
+```{code-cell} ipython3
+contours = measure.find_contours(nn_mask, level=0.5)
+plt.imshow(pores)
+for c in contours:
+ plt.plot(c[:, 1], c[:, 0])
+```
+
+```{code-cell} ipython3
+nn_regions = measure.regionprops(
+ measure.label(nn_mask)
+)
+```
+
+```{code-cell} ipython3
+f, ax = plt.subplots(figsize=(10, 3))
+ax.hist([r.area for r in regions], bins='auto', range=(0, 200), alpha=0.4, label='Classic')
+ax.hist([r.area for r in nn_regions], bins='auto', range=(0, 200), alpha=0.4, label='NN')
+ax.legend();
+```
+
+## Bonus round: region filtering
+
+```{code-cell} ipython3
+def is_circular(regions, eccentricity_threshold=0.1, area_threshold=10):
+ """Calculate a boolean mask indicating which regions are circular.
+
+ Parameters
+ ----------
+ eccentricity_threshold : float, >= 0
+ Regions with an eccentricity less than than this value are
+ considered circular. See `measure.regionprops`.
+ area_threshold : int
+ Only regions with an area greater than this value are considered
+ circular.
+ """
+ return np.array([
+ (r.area > area_threshold) and
+ (r.eccentricity <= eccentricity_threshold)
+ for r in regions
+ ])
+```
+
+```{code-cell} ipython3
+def filtered_mask(mask, regions, eccentricity_threshold, area_threshold):
+ mask = mask.copy()
+ suppress_regions = np.array(regions)[
+ ~is_circular(
+ regions,
+ eccentricity_threshold=eccentricity_threshold,
+ area_threshold=area_threshold
+ )
+ ]
+
+ for r in suppress_regions:
+ mask[tuple(r.coords.T)] = 0
+
+ return mask
+```
+
+```{code-cell} ipython3
+plt.imshow(filtered_mask(nn_mask, nn_regions,
+ eccentricity_threshold=0.8,
+ area_threshold=20));
+```
+
+```{code-cell} ipython3
+contours = measure.find_contours(
+ filtered_mask(nn_mask, nn_regions,
+ eccentricity_threshold=0.8,
+ area_threshold=20),
+ level=0.5
+)
+plt.imshow(pores)
+for c in contours:
+ plt.plot(c[:, 1], c[:, 0])
+```
+
+```{code-cell} ipython3
+filtered_regions = np.array(nn_regions)[is_circular(nn_regions, 0.8, 20)]
+
+f, ax = plt.subplots(figsize=(10, 3))
+ax.hist([r.area for r in filtered_regions], bins='auto', range=(0, 200), alpha=0.4);
+```
diff --git a/lectures/skdemo/_skdemo.py b/lectures/skdemo/_skdemo.py
index 7b1a4e9..6e64587 100644
--- a/lectures/skdemo/_skdemo.py
+++ b/lectures/skdemo/_skdemo.py
@@ -74,7 +74,7 @@ def imshow_all(*images, **kwargs):
kwargs.setdefault('vmin', vmin)
kwargs.setdefault('vmax', vmax)
- nrows, ncols = kwargs.get('shape', (1, len(images)))
+ nrows, ncols = kwargs.pop('shape', (1, len(images)))
size = nrows * kwargs.pop('size', 5)
width = size * len(images)
diff --git a/lectures/three_dimensional_image_processing.ipynb b/lectures/three_dimensional_image_processing.ipynb
deleted file mode 100644
index e115a38..0000000
--- a/lectures/three_dimensional_image_processing.ipynb
+++ /dev/null
@@ -1,887 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "hide-cell"
- ]
- },
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "%gui qt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "hide-cell"
- ]
- },
- "outputs": [],
- "source": [
- "import time\n",
- "time.sleep(5)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from matplotlib import pyplot as plt\n",
- "from scipy import ndimage as ndi\n",
- "from skimage import (exposure, feature, filters, io, measure,\n",
- " morphology, restoration, segmentation, transform,\n",
- " util)\n",
- "import napari"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Introduction to three-dimensional image processing\n",
- "\n",
- "Images are represented as `numpy` arrays. A single-channel, or grayscale, image is a 2D matrix of pixel intensities of shape `(row, column)`. We can construct a 3D volume as a series of 2D `planes`, giving 3D images the shape `(plane, row, column)`. Multichannel data adds a `channel` dimension in the final position containing color information. \n",
- "\n",
- "These conventions are summarized in the table below:\n",
- "\n",
- "\n",
- "|Image type|Coordinates|\n",
- "|:---|:---|\n",
- "|2D grayscale|(row, column)|\n",
- "|2D multichannel|(row, column, channel)|\n",
- "|3D grayscale|(plane, row, column)|\n",
- "|3D multichannel|(plane, row, column, channel)|\n",
- "\n",
- "Some 3D images are constructed with equal resolution in each dimension; e.g., a computer generated rendering of a sphere. Most experimental data captures one dimension at a lower resolution than the other two; e.g., photographing thin slices to approximate a 3D structure as a stack of 2D images. The distance between pixels in each dimension, called `spacing`, is encoded in a tuple and is accepted as a parameter by some `skimage` functions and can be used to adjust contributions to filters.\n",
- "\n",
- "## Input/Output and display\n",
- "\n",
- "Three dimensional data can be loaded with `skimage.io.imread`. The data for this tutorial was provided by the Allen Institute for Cell Science. It has been downsampled by a factor of 4 in the `row` and `column` dimensions to reduce computational time."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "nuclei = io.imread('../images/cells.tif')\n",
- "membranes = io.imread('../images/cells_membrane.tif')\n",
- "\n",
- "print(\"shape: {}\".format(nuclei.shape))\n",
- "print(\"dtype: {}\".format(nuclei.dtype))\n",
- "print(\"range: ({}, {})\".format(np.min(nuclei), np.max(nuclei)))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The distance between pixels was reported by the microscope used to image the cells. This `spacing` information will be used to adjust contributions to filters and helps decide when to apply operations planewise. We've chosen to normalize it to `1.0` in the `row` and `column` dimensions."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# The microscope reports the following spacing (in ยตm)\n",
- "original_spacing = np.array([0.2900000, 0.0650000, 0.0650000])\n",
- "\n",
- "# We downsampled each slice 4x to make the data smaller\n",
- "rescaled_spacing = original_spacing * [1, 4, 4]\n",
- "\n",
- "# Normalize the spacing so that pixels are a distance of 1 apart\n",
- "spacing = rescaled_spacing / rescaled_spacing[2]\n",
- "\n",
- "print(f'microscope spacing: {original_spacing}')\n",
- "print(f'after rescaling images: {rescaled_spacing}')\n",
- "print(f'normalized spacing: {spacing}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can view the 3D image using napari."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "viewer = napari.view_image(nuclei, contrast_limits=[0, 1],\n",
- " scale=spacing)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "hide-input"
- ]
- },
- "outputs": [],
- "source": [
- "from napari.utils.notebook_display import nbscreenshot\n",
- "\n",
- "center = nuclei.shape[0] // 2\n",
- "\n",
- "viewer.dims.set_point(0, center)\n",
- "nbscreenshot(viewer)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exposure"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "`skimage.exposure` contains a number of functions for adjusting image contrast. These functions operate on pixel values. Generally, image dimensionality or pixel spacing does not need to be considered.\n",
- "\n",
- "[Gamma correction](https://en.wikipedia.org/wiki/Gamma_correction), also known as Power Law Transform, brightens or darkens an image. The function $O = I^\\gamma$ is applied to each pixel in the image. A `gamma < 1` will brighten an image, while a `gamma > 1` will darken an image.\n",
- "\n",
- "napari has a built-in gamma correction slider for image layers. Try playing with the gamma slider to see its effect on the image."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Helper function for plotting histograms.\n",
- "def plot_hist(ax, data, title=None):\n",
- " ax.hist(data.ravel(), bins=256)\n",
- " ax.ticklabel_format(axis=\"y\", style=\"scientific\", scilimits=(0, 0))\n",
- " \n",
- " if title:\n",
- " ax.set_title(title)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[Histogram equalization](https://en.wikipedia.org/wiki/Histogram_equalization) improves contrast in an image by redistributing pixel intensities. The most common pixel intensities are spread out, allowing areas of lower local contrast to gain a higher contrast. This may enhance background noise."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "equalized = exposure.equalize_hist(nuclei)\n",
- "\n",
- "fig, ((a, b), (c, d)) = plt.subplots(nrows=2, ncols=2)\n",
- "\n",
- "plot_hist(a, nuclei, title=\"Original\")\n",
- "plot_hist(b, equalized, title=\"Histogram equalization\")\n",
- "\n",
- "cdf, bins = exposure.cumulative_distribution(nuclei.ravel())\n",
- "c.plot(bins, cdf, \"r\")\n",
- "c.set_title(\"Original CDF\")\n",
- "\n",
- "cdf, bins = exposure.cumulative_distribution(equalized.ravel())\n",
- "d.plot(bins, cdf, \"r\")\n",
- "d.set_title(\"Histogram equalization CDF\");\n",
- "\n",
- "fig.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can look at the image in our napari viewer:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "viewer.add_image(equalized, contrast_limits=[0, 1], name='histeq')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Most experimental images are affected by salt and pepper noise. A few bright artifacts can decrease the relative intensity of the pixels of interest. A simple way to improve contrast is to clip the pixel values on the lowest and highest extremes. Clipping the darkest and brightest 0.5% of pixels will increase the overall contrast of the image."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "vmin, vmax = np.quantile(nuclei, q=(0.005, 0.995))\n",
- "\n",
- "stretched = exposure.rescale_intensity(\n",
- " nuclei, \n",
- " in_range=(vmin, vmax), \n",
- " out_range=np.float32\n",
- ")\n",
- "\n",
- "viewer.add_image(stretched, contrast_limits=[0, 1], name='stretched')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "hide-input"
- ]
- },
- "outputs": [],
- "source": [
- "viewer.dims.set_point(0, center)\n",
- "nbscreenshot(viewer)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Edge detection\n",
- "\n",
- "[Edge detection](https://en.wikipedia.org/wiki/Edge_detection) highlights regions in the image where a sharp change in contrast occurs. The intensity of an edge corresponds to the steepness of the transition from one intensity to another. A gradual shift from bright to dark intensity results in a dim edge. An abrupt shift results in a bright edge.\n",
- "\n",
- "We saw the [Sobel operator](https://en.wikipedia.org/wiki/Sobel_operator) in the filters lesson. It is an edge detection algorithm that approximates the gradient of the image intensity, and is fast to compute. `skimage.filters.sobel` has not been adapted for 3D images, but it can be readily generalised (see the linked Wikipedia entry). Let's try it!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "hide-cell"
- ]
- },
- "outputs": [],
- "source": [
- "viewer.close()\n",
- "del viewer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "edges = filters.sobel(nuclei)\n",
- "\n",
- "viewer = napari.view_image(nuclei, blending='additive', colormap='green', name='nuclei')\n",
- "viewer.add_image(edges, blending='additive', colormap='magenta', name='edges')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "hide-input"
- ]
- },
- "outputs": [],
- "source": [
- "viewer.dims.set_point(0, center)\n",
- "nbscreenshot(viewer)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "denoised = ndi.median_filter(nuclei, size=3)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[Thresholding](https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29) is used to create binary images. A threshold value determines the intensity value separating foreground pixels from background pixels. Foregound pixels are pixels brighter than the threshold value, background pixels are darker. Thresholding is a form of image segmentation.\n",
- "\n",
- "Different thresholding algorithms produce different results. [Otsu's method](https://en.wikipedia.org/wiki/Otsu%27s_method) and Li's minimum cross entropy threshold are two common algorithms. Below, we use Li. You can use `skimage.filters.threshold_` to find different thresholding methods."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "li_thresholded = denoised > filters.threshold_li(denoised)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "viewer.add_image(li_thresholded, name='thresholded', opacity=0.3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "hide-input"
- ]
- },
- "outputs": [],
- "source": [
- "viewer.dims.set_point(0, center)\n",
- "nbscreenshot(viewer)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can see holes due to variations of the image intensity inside the nuclei. We can actually fill them with `scipy.ndimage.binary_fill_holes`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "filled = ndi.binary_fill_holes(li_thresholded)\n",
- "\n",
- "viewer.add_image(filled, name='filled', opacity=0.3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "hide-input"
- ]
- },
- "outputs": [],
- "source": [
- "viewer.dims.set_point(0, center)\n",
- "nbscreenshot(viewer)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Morphological operations"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[Mathematical morphology](https://en.wikipedia.org/wiki/Mathematical_morphology) operations and structuring elements are defined in `skimage.morphology`. Structuring elements are shapes which define areas over which an operation is applied. The response to the filter indicates how well the neighborhood corresponds to the structuring element's shape.\n",
- "\n",
- "There are a number of two and three dimensional structuring elements defined in `skimage.morphology`. Not all 2D structuring element have a 3D counterpart. The simplest and most commonly used structuring elements are the `disk`/`ball` and `square`/`cube`."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Morphology operations can be chained together to denoise an image. For example, a `closing` applied to an `opening` can remove salt and pepper noise from an image."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Functions operating on [connected components](https://en.wikipedia.org/wiki/Connected_space) can remove small undesired elements while preserving larger shapes.\n",
- "\n",
- "`skimage.morphology.remove_small_holes` fills holes and `skimage.morphology.remove_small_objects` removes bright regions. Both functions accept a `min_size` parameter, which is the minimum size (in pixels) of accepted holes or objects. The `min_size` can be approximated by a cube."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "width = 20\n",
- "\n",
- "remove_holes = morphology.remove_small_holes(\n",
- " filled, \n",
- " area_threshold=width ** 3\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "width = 20\n",
- "\n",
- "remove_objects = morphology.remove_small_objects(\n",
- " remove_holes, \n",
- " min_size=width ** 3\n",
- ")\n",
- "\n",
- "viewer.add_image(remove_objects, name='cleaned', opacity=0.3);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "hide-input"
- ]
- },
- "outputs": [],
- "source": [
- "viewer.dims.set_point(0, center)\n",
- "nbscreenshot(viewer)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Segmentation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[Image segmentation](https://en.wikipedia.org/wiki/Image_segmentation) partitions images into regions of interest. Interger labels are assigned to each region to distinguish regions of interest."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "labels = measure.label(remove_objects)\n",
- "\n",
- "viewer.add_labels(labels, name='labels')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "hide-input"
- ]
- },
- "outputs": [],
- "source": [
- "viewer.dims.set_point(0, center)\n",
- "nbscreenshot(viewer)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Connected components of the binary image are assigned the same label via `skimage.measure.label`. Tightly packed cells connected in the binary image are assigned the same label."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "A better segmentation would assign different labels to disjoint regions in the original image. \n",
- "\n",
- "[Watershed segmentation](https://en.wikipedia.org/wiki/Watershed_%28image_processing%29) can distinguish touching objects. Markers are placed at local minima/maxima and expanded outward until there is a collision with markers from another region, with the image intensity serving as a guide for the marker boundaries."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It can be quite challenging to find markers with the right location. A slight amount of noise in the image can result in very wrong point locations. Here is a common approach: find the distance from the object boundaries, then place points at the maximal distance."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "transformed = ndi.distance_transform_edt(remove_objects, sampling=spacing)\n",
- "\n",
- "maxima = morphology.local_maxima(transformed)\n",
- "viewer.add_points(np.transpose(np.nonzero(maxima)), name='bad points')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "hide-input"
- ]
- },
- "outputs": [],
- "source": [
- "viewer.dims.ndisplay = 3\n",
- "viewer.dims.set_point(0, center)\n",
- "nbscreenshot(viewer)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "viewer.camera.angles"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "With napari, we can combine interactive point selections with the automated watershed algorithm from `skimage.morphology`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "viewer.layers['bad points'].visible = False\n",
- "points = viewer.add_points(name='interactive points', ndim=3)\n",
- "points.mode = 'add'\n",
- "\n",
- "# now, annotate the centers of the nuclei in your image"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "hide-cell"
- ]
- },
- "outputs": [],
- "source": [
- "points.data = np.array(\n",
- " [[ 30. , 14.2598685 , 27.7741219 ],\n",
- " [ 30. , 30.10416663, 81.36513029],\n",
- " [ 30. , 13.32785096, 144.27631406],\n",
- " [ 30. , 46.8804823 , 191.80920846],\n",
- " [ 30. , 43.15241215, 211.84758551],\n",
- " [ 30. , 94.87938547, 160.12061219],\n",
- " [ 30. , 72.97697335, 112.58771779],\n",
- " [ 30. , 138.21820096, 189.01315585],\n",
- " [ 30. , 144.74232372, 242.60416424],\n",
- " [ 30. , 98.14144685, 251.92433962],\n",
- " [ 30. , 153.59649032, 112.58771779],\n",
- " [ 30. , 134.49013081, 40.35635865],\n",
- " [ 30. , 182.95504275, 48.74451649],\n",
- " [ 30. , 216.04166532, 80.89912152],\n",
- " [ 30. , 235.14802483, 130.296051 ],\n",
- " [ 30. , 196.00328827, 169.44078757],\n",
- " [ 30. , 245.86622651, 202.06140137],\n",
- " [ 30. , 213.71162148, 250.52631331],\n",
- " [ 28. , 87.42324517, 52.00657787]],\n",
- " dtype=float,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Once you have marked all the points, you can grab the data back, and make a markers image for `skimage.segmentation.watershed`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "marker_locations = points.data\n",
- "\n",
- "markers = np.zeros(nuclei.shape, dtype=np.uint32)\n",
- "marker_indices = tuple(np.round(marker_locations).astype(int).T)\n",
- "markers[marker_indices] = np.arange(len(marker_locations)) + 1\n",
- "markers_big = morphology.dilation(markers, morphology.ball(5))\n",
- "\n",
- "segmented = segmentation.watershed(\n",
- " edges,\n",
- " markers_big, \n",
- " mask=remove_objects\n",
- ")\n",
- "\n",
- "viewer.add_labels(segmented, name='segmented')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "After watershed, we have better disambiguation between internal cells!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Making measurements"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Once we have defined our objects, we can make measurements on them using `skimage.measure.regionprops` and the new `skimage.measure.regionprops_table`. These measurements include features such as area or volume, bounding boxes, and intensity statistics.\n",
- "\n",
- "Before measuring objects, it helps to clear objects from the image border. Measurements should only be collected for objects entirely contained in the image.\n",
- "\n",
- "Given the layer-like structure of our data, we only want to clear the objects touching the sides of the volume, but not the top and bottom, so we pad and crop the volume along the 0th axis to avoid clearing the mitotic nucleus."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "segmented_padded = np.pad(\n",
- " segmented,\n",
- " ((1, 1), (0, 0), (0, 0)),\n",
- " mode='constant',\n",
- " constant_values=0,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "interior_labels = segmentation.clear_border(segmented_padded)[1:-1]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "After clearing the border, the object labels are no longer sequentially increasing. Optionally, the labels can be renumbered such that there are no jumps in the list of image labels."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "relabeled, fw_map, inv_map = segmentation.relabel_sequential(interior_labels)\n",
- "\n",
- "print(\"relabeled labels: {}\".format(np.unique(relabeled)))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "`skimage.measure.regionprops` automatically measures many labeled image features. Optionally, an `intensity_image` can be supplied and intensity features are extracted per object. It's good practice to make measurements on the original image.\n",
- "\n",
- "Not all properties are supported for 3D data. Below are lists of supported and unsupported 3D measurements."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "regionprops = measure.regionprops(relabeled, intensity_image=nuclei)\n",
- "\n",
- "supported = [] \n",
- "unsupported = []\n",
- "\n",
- "for prop in regionprops[0]:\n",
- " try:\n",
- " regionprops[0][prop]\n",
- " supported.append(prop)\n",
- " except NotImplementedError:\n",
- " unsupported.append(prop)\n",
- "\n",
- "print(\"Supported properties:\")\n",
- "print(\" \" + \"\\n \".join(supported))\n",
- "print()\n",
- "print(\"Unsupported properties:\")\n",
- "print(\" \" + \"\\n \".join(unsupported))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "`skimage.measure.regionprops` ignores the 0 label, which represents the background."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(f'measured regions: {[regionprop.label for regionprop in regionprops]}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "`regionprops_table` returns a dictionary of columns compatible with creating a pandas dataframe of properties of the data:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "\n",
- "\n",
- "info_table = pd.DataFrame(\n",
- " measure.regionprops_table(\n",
- " relabeled, nuclei,\n",
- " properties=['label', 'slice', 'area', 'mean_intensity', 'solidity'],\n",
- " )\n",
- ").set_index('label')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "info_table"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can now use pandas and seaborn for some analysis!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import seaborn as sns\n",
- "\n",
- "sns.scatterplot(x='area', y='solidity', data=info_table, hue='mean_intensity')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can see that the mitotic nucleus is a clear outlier from the others in terms of solidity and intensity."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Challenge problems\n",
- "\n",
- "Put your 3D image processing skills to the test by working through these challenge problems.\n",
- "\n",
- "### Improve the segmentation\n",
- "\n",
- "A few objects were oversegmented in the declumping step. Try to improve the segmentation and assign each object a single, unique label. You can try to use [calibrated denoising](https://scikit-image.org/docs/dev/auto_examples/filters/plot_j_invariant.html) to get smoother nuclei and membrane images.\n",
- "\n",
- "### Segment cell membranes\n",
- "\n",
- "Try segmenting the accompanying membrane channel. In the membrane image, the membrane walls are the bright web-like regions. This channel is difficult due to a high amount of noise in the image. Additionally, it can be hard to determine where the membrane ends in the image (it's not the first and last planes).\n",
- "\n",
- "Below is a 2D segmentation of the membrane:\n",
- "\n",
- "\n",
- "\n",
- "Hint: there should only be one nucleus per membrane.\n",
- "\n",
- "### Measure the area in ยตmยณ of the cells\n",
- "\n",
- "Once you have segmented the cell membranes, use regionprops to measure the distribution of cell areas."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "celltoolbar": "Tags",
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/lectures/three_dimensional_image_processing.md b/lectures/three_dimensional_image_processing.md
new file mode 100644
index 0000000..4028c83
--- /dev/null
+++ b/lectures/three_dimensional_image_processing.md
@@ -0,0 +1,506 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+```{code-cell} ipython3
+:tags: [hide-cell]
+
+%matplotlib inline
+%config InlineBackend.figure_format = 'retina'
+%gui qt
+```
+
+```{code-cell} ipython3
+:tags: [hide-cell]
+
+import time
+time.sleep(5)
+```
+
+```{code-cell} ipython3
+import numpy as np
+from matplotlib import pyplot as plt
+from scipy import ndimage as ndi
+from skimage import (exposure, feature, filters, io, measure,
+ morphology, restoration, segmentation, transform,
+ util)
+import napari
+```
+
+# Introduction to three-dimensional image processing
+
+Images are represented as `numpy` arrays. A single-channel, or grayscale, image is a 2D matrix of pixel intensities of shape `(row, column)`. We can construct a 3D volume as a series of 2D `planes`, giving 3D images the shape `(plane, row, column)`. Multichannel data adds a `channel` dimension in the final position containing color information.
+
+These conventions are summarized in the table below:
+
+
+|Image type|Coordinates|
+|:---|:---|
+|2D grayscale|(row, column)|
+|2D multichannel|(row, column, channel)|
+|3D grayscale|(plane, row, column)|
+|3D multichannel|(plane, row, column, channel)|
+
+Some 3D images are constructed with equal resolution in each dimension; e.g., a computer generated rendering of a sphere. Most experimental data captures one dimension at a lower resolution than the other two; e.g., photographing thin slices to approximate a 3D structure as a stack of 2D images. The distance between pixels in each dimension, called `spacing`, is encoded in a tuple and is accepted as a parameter by some `skimage` functions and can be used to adjust contributions to filters.
+
+## Input/Output and display
+
+Three dimensional data can be loaded with `skimage.io.imread`. The data for this tutorial was provided by the Allen Institute for Cell Science. It has been downsampled by a factor of 4 in the `row` and `column` dimensions to reduce computational time.
+
+```{code-cell} ipython3
+nuclei = io.imread('../images/cells.tif')
+membranes = io.imread('../images/cells_membrane.tif')
+
+print("shape: {}".format(nuclei.shape))
+print("dtype: {}".format(nuclei.dtype))
+print("range: ({}, {})".format(np.min(nuclei), np.max(nuclei)))
+```
+
+The distance between pixels was reported by the microscope used to image the cells. This `spacing` information will be used to adjust contributions to filters and helps decide when to apply operations planewise. We've chosen to normalize it to `1.0` in the `row` and `column` dimensions.
+
+```{code-cell} ipython3
+# The microscope reports the following spacing (in ยตm)
+original_spacing = np.array([0.2900000, 0.0650000, 0.0650000])
+
+# We downsampled each slice 4x to make the data smaller
+rescaled_spacing = original_spacing * [1, 4, 4]
+
+# Normalize the spacing so that pixels are a distance of 1 apart
+spacing = rescaled_spacing / rescaled_spacing[2]
+
+print(f'microscope spacing: {original_spacing}')
+print(f'after rescaling images: {rescaled_spacing}')
+print(f'normalized spacing: {spacing}')
+```
+
+We can view the 3D image using napari.
+
+```{code-cell} ipython3
+viewer = napari.view_image(nuclei, contrast_limits=[0, 1],
+ scale=spacing)
+```
+
+```{code-cell} ipython3
+:tags: [hide-input]
+
+from napari.utils.notebook_display import nbscreenshot
+
+center = nuclei.shape[0] // 2
+
+viewer.dims.set_point(0, center)
+nbscreenshot(viewer)
+```
+
+## Exposure
+
++++
+
+`skimage.exposure` contains a number of functions for adjusting image contrast. These functions operate on pixel values. Generally, image dimensionality or pixel spacing does not need to be considered.
+
+[Gamma correction](https://en.wikipedia.org/wiki/Gamma_correction), also known as Power Law Transform, brightens or darkens an image. The function $O = I^\gamma$ is applied to each pixel in the image. A `gamma < 1` will brighten an image, while a `gamma > 1` will darken an image.
+
+napari has a built-in gamma correction slider for image layers. Try playing with the gamma slider to see its effect on the image.
+
+```{code-cell} ipython3
+# Helper function for plotting histograms.
+def plot_hist(ax, data, title=None):
+ ax.hist(data.ravel(), bins=256)
+ ax.ticklabel_format(axis="y", style="scientific", scilimits=(0, 0))
+
+ if title:
+ ax.set_title(title)
+```
+
+[Histogram equalization](https://en.wikipedia.org/wiki/Histogram_equalization) improves contrast in an image by redistributing pixel intensities. The most common pixel intensities are spread out, allowing areas of lower local contrast to gain a higher contrast. This may enhance background noise.
+
+```{code-cell} ipython3
+equalized = exposure.equalize_hist(nuclei)
+
+fig, ((a, b), (c, d)) = plt.subplots(nrows=2, ncols=2)
+
+plot_hist(a, nuclei, title="Original")
+plot_hist(b, equalized, title="Histogram equalization")
+
+cdf, bins = exposure.cumulative_distribution(nuclei.ravel())
+c.plot(bins, cdf, "r")
+c.set_title("Original CDF")
+
+cdf, bins = exposure.cumulative_distribution(equalized.ravel())
+d.plot(bins, cdf, "r")
+d.set_title("Histogram equalization CDF");
+
+fig.tight_layout()
+```
+
+We can look at the image in our napari viewer:
+
+```{code-cell} ipython3
+viewer.add_image(equalized, contrast_limits=[0, 1], name='histeq')
+```
+
+Most experimental images are affected by salt and pepper noise. A few bright artifacts can decrease the relative intensity of the pixels of interest. A simple way to improve contrast is to clip the pixel values on the lowest and highest extremes. Clipping the darkest and brightest 0.5% of pixels will increase the overall contrast of the image.
+
+```{code-cell} ipython3
+vmin, vmax = np.quantile(nuclei, q=(0.005, 0.995))
+
+stretched = exposure.rescale_intensity(
+ nuclei,
+ in_range=(vmin, vmax),
+ out_range=np.float32
+)
+
+viewer.add_image(stretched, contrast_limits=[0, 1], name='stretched')
+```
+
+```{code-cell} ipython3
+:tags: [hide-input]
+
+viewer.dims.set_point(0, center)
+nbscreenshot(viewer)
+```
+
+## Edge detection
+
+[Edge detection](https://en.wikipedia.org/wiki/Edge_detection) highlights regions in the image where a sharp change in contrast occurs. The intensity of an edge corresponds to the steepness of the transition from one intensity to another. A gradual shift from bright to dark intensity results in a dim edge. An abrupt shift results in a bright edge.
+
+We saw the [Sobel operator](https://en.wikipedia.org/wiki/Sobel_operator) in the filters lesson. It is an edge detection algorithm that approximates the gradient of the image intensity, and is fast to compute. `skimage.filters.sobel` has not been adapted for 3D images, but it can be readily generalised (see the linked Wikipedia entry). Let's try it!
+
+```{code-cell} ipython3
+:tags: [hide-cell]
+
+viewer.close()
+del viewer
+```
+
+```{code-cell} ipython3
+edges = filters.sobel(nuclei)
+
+viewer = napari.view_image(nuclei, blending='additive', colormap='green', name='nuclei')
+viewer.add_image(edges, blending='additive', colormap='magenta', name='edges')
+```
+
+```{code-cell} ipython3
+:tags: [hide-input]
+
+viewer.dims.set_point(0, center)
+nbscreenshot(viewer)
+```
+
+```{code-cell} ipython3
+denoised = ndi.median_filter(nuclei, size=3)
+```
+
+[Thresholding](https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29) is used to create binary images. A threshold value determines the intensity value separating foreground pixels from background pixels. Foregound pixels are pixels brighter than the threshold value, background pixels are darker. Thresholding is a form of image segmentation.
+
+Different thresholding algorithms produce different results. [Otsu's method](https://en.wikipedia.org/wiki/Otsu%27s_method) and Li's minimum cross entropy threshold are two common algorithms. Below, we use Li. You can use `skimage.filters.threshold_` to find different thresholding methods.
+
+```{code-cell} ipython3
+li_thresholded = denoised > filters.threshold_li(denoised)
+```
+
+```{code-cell} ipython3
+viewer.add_image(li_thresholded, name='thresholded', opacity=0.3)
+```
+
+```{code-cell} ipython3
+:tags: [hide-input]
+
+viewer.dims.set_point(0, center)
+nbscreenshot(viewer)
+```
+
+We can see holes due to variations of the image intensity inside the nuclei. We can actually fill them with `scipy.ndimage.binary_fill_holes`.
+
+```{code-cell} ipython3
+filled = ndi.binary_fill_holes(li_thresholded)
+
+viewer.add_image(filled, name='filled', opacity=0.3)
+```
+
+```{code-cell} ipython3
+:tags: [hide-input]
+
+viewer.dims.set_point(0, center)
+nbscreenshot(viewer)
+```
+
+## Morphological operations
+
++++
+
+[Mathematical morphology](https://en.wikipedia.org/wiki/Mathematical_morphology) operations and structuring elements are defined in `skimage.morphology`. Structuring elements are shapes which define areas over which an operation is applied. The response to the filter indicates how well the neighborhood corresponds to the structuring element's shape.
+
+There are a number of two and three dimensional structuring elements defined in `skimage.morphology`. Not all 2D structuring element have a 3D counterpart. The simplest and most commonly used structuring elements are the `disk`/`ball` and `square`/`cube`.
+
++++
+
+Morphology operations can be chained together to denoise an image. For example, a `closing` applied to an `opening` can remove salt and pepper noise from an image.
+
++++
+
+Functions operating on [connected components](https://en.wikipedia.org/wiki/Connected_space) can remove small undesired elements while preserving larger shapes.
+
+`skimage.morphology.remove_small_holes` fills holes and `skimage.morphology.remove_small_objects` removes bright regions. Both functions accept a `min_size` parameter, which is the minimum size (in pixels) of accepted holes or objects. The `min_size` can be approximated by a cube.
+
+```{code-cell} ipython3
+width = 20
+
+remove_holes = morphology.remove_small_holes(
+ filled,
+ area_threshold=width ** 3
+)
+```
+
+```{code-cell} ipython3
+width = 20
+
+remove_objects = morphology.remove_small_objects(
+ remove_holes,
+ min_size=width ** 3
+)
+
+viewer.add_image(remove_objects, name='cleaned', opacity=0.3);
+```
+
+```{code-cell} ipython3
+:tags: [hide-input]
+
+viewer.dims.set_point(0, center)
+nbscreenshot(viewer)
+```
+
+## Segmentation
+
++++
+
+[Image segmentation](https://en.wikipedia.org/wiki/Image_segmentation) partitions images into regions of interest. Interger labels are assigned to each region to distinguish regions of interest.
+
+```{code-cell} ipython3
+labels = measure.label(remove_objects)
+
+viewer.add_labels(labels, name='labels')
+```
+
+```{code-cell} ipython3
+:tags: [hide-input]
+
+viewer.dims.set_point(0, center)
+nbscreenshot(viewer)
+```
+
+Connected components of the binary image are assigned the same label via `skimage.measure.label`. Tightly packed cells connected in the binary image are assigned the same label.
+
++++
+
+A better segmentation would assign different labels to disjoint regions in the original image.
+
+[Watershed segmentation](https://en.wikipedia.org/wiki/Watershed_%28image_processing%29) can distinguish touching objects. Markers are placed at local minima/maxima and expanded outward until there is a collision with markers from another region, with the image intensity serving as a guide for the marker boundaries.
+
++++
+
+It can be quite challenging to find markers with the right location. A slight amount of noise in the image can result in very wrong point locations. Here is a common approach: find the distance from the object boundaries, then place points at the maximal distance.
+
+```{code-cell} ipython3
+transformed = ndi.distance_transform_edt(remove_objects, sampling=spacing)
+
+maxima = morphology.local_maxima(transformed)
+viewer.add_points(np.transpose(np.nonzero(maxima)), name='bad points')
+```
+
+```{code-cell} ipython3
+:tags: [hide-input]
+
+viewer.dims.ndisplay = 3
+viewer.dims.set_point(0, center)
+nbscreenshot(viewer)
+```
+
+```{code-cell} ipython3
+viewer.camera.angles
+```
+
+With napari, we can combine interactive point selections with the automated watershed algorithm from `skimage.morphology`:
+
+```{code-cell} ipython3
+viewer.layers['bad points'].visible = False
+points = viewer.add_points(name='interactive points', ndim=3)
+points.mode = 'add'
+
+# now, annotate the centers of the nuclei in your image
+```
+
+```{code-cell} ipython3
+:tags: [hide-cell]
+
+points.data = np.array(
+ [[ 30. , 14.2598685 , 27.7741219 ],
+ [ 30. , 30.10416663, 81.36513029],
+ [ 30. , 13.32785096, 144.27631406],
+ [ 30. , 46.8804823 , 191.80920846],
+ [ 30. , 43.15241215, 211.84758551],
+ [ 30. , 94.87938547, 160.12061219],
+ [ 30. , 72.97697335, 112.58771779],
+ [ 30. , 138.21820096, 189.01315585],
+ [ 30. , 144.74232372, 242.60416424],
+ [ 30. , 98.14144685, 251.92433962],
+ [ 30. , 153.59649032, 112.58771779],
+ [ 30. , 134.49013081, 40.35635865],
+ [ 30. , 182.95504275, 48.74451649],
+ [ 30. , 216.04166532, 80.89912152],
+ [ 30. , 235.14802483, 130.296051 ],
+ [ 30. , 196.00328827, 169.44078757],
+ [ 30. , 245.86622651, 202.06140137],
+ [ 30. , 213.71162148, 250.52631331],
+ [ 28. , 87.42324517, 52.00657787]],
+ dtype=float,
+)
+```
+
+Once you have marked all the points, you can grab the data back, and make a markers image for `skimage.segmentation.watershed`:
+
+```{code-cell} ipython3
+marker_locations = points.data
+
+markers = np.zeros(nuclei.shape, dtype=np.uint32)
+marker_indices = tuple(np.round(marker_locations).astype(int).T)
+markers[marker_indices] = np.arange(len(marker_locations)) + 1
+markers_big = morphology.dilation(markers, morphology.ball(5))
+
+segmented = segmentation.watershed(
+ edges,
+ markers_big,
+ mask=remove_objects
+)
+
+viewer.add_labels(segmented, name='segmented')
+```
+
+After watershed, we have better disambiguation between internal cells!
+
++++
+
+## Making measurements
+
++++
+
+Once we have defined our objects, we can make measurements on them using `skimage.measure.regionprops` and the new `skimage.measure.regionprops_table`. These measurements include features such as area or volume, bounding boxes, and intensity statistics.
+
+Before measuring objects, it helps to clear objects from the image border. Measurements should only be collected for objects entirely contained in the image.
+
+Given the layer-like structure of our data, we only want to clear the objects touching the sides of the volume, but not the top and bottom, so we pad and crop the volume along the 0th axis to avoid clearing the mitotic nucleus.
+
+```{code-cell} ipython3
+segmented_padded = np.pad(
+ segmented,
+ ((1, 1), (0, 0), (0, 0)),
+ mode='constant',
+ constant_values=0,
+)
+```
+
+```{code-cell} ipython3
+interior_labels = segmentation.clear_border(segmented_padded)[1:-1]
+```
+
+After clearing the border, the object labels are no longer sequentially increasing. Optionally, the labels can be renumbered such that there are no jumps in the list of image labels.
+
+```{code-cell} ipython3
+relabeled, fw_map, inv_map = segmentation.relabel_sequential(interior_labels)
+
+print("relabeled labels: {}".format(np.unique(relabeled)))
+```
+
+`skimage.measure.regionprops` automatically measures many labeled image features. Optionally, an `intensity_image` can be supplied and intensity features are extracted per object. It's good practice to make measurements on the original image.
+
+Not all properties are supported for 3D data. Below are lists of supported and unsupported 3D measurements.
+
+```{code-cell} ipython3
+regionprops = measure.regionprops(relabeled, intensity_image=nuclei)
+
+supported = []
+unsupported = []
+
+for prop in regionprops[0]:
+ try:
+ regionprops[0][prop]
+ supported.append(prop)
+ except NotImplementedError:
+ unsupported.append(prop)
+
+print("Supported properties:")
+print(" " + "\n ".join(supported))
+print()
+print("Unsupported properties:")
+print(" " + "\n ".join(unsupported))
+```
+
+`skimage.measure.regionprops` ignores the 0 label, which represents the background.
+
+```{code-cell} ipython3
+print(f'measured regions: {[regionprop.label for regionprop in regionprops]}')
+```
+
+`regionprops_table` returns a dictionary of columns compatible with creating a pandas dataframe of properties of the data:
+
+```{code-cell} ipython3
+import pandas as pd
+
+
+info_table = pd.DataFrame(
+ measure.regionprops_table(
+ relabeled, nuclei,
+ properties=['label', 'slice', 'area', 'mean_intensity', 'solidity'],
+ )
+).set_index('label')
+```
+
+```{code-cell} ipython3
+info_table
+```
+
+We can now use pandas and seaborn for some analysis!
+
+```{code-cell} ipython3
+import seaborn as sns
+
+sns.scatterplot(x='area', y='solidity', data=info_table, hue='mean_intensity')
+```
+
+We can see that the mitotic nucleus is a clear outlier from the others in terms of solidity and intensity.
+
++++
+
+## Challenge problems
+
+Put your 3D image processing skills to the test by working through these challenge problems.
+
+### Improve the segmentation
+
+A few objects were oversegmented in the declumping step. Try to improve the segmentation and assign each object a single, unique label. You can try to use [calibrated denoising](https://scikit-image.org/docs/dev/auto_examples/filters/plot_j_invariant.html) to get smoother nuclei and membrane images.
+
+### Segment cell membranes
+
+Try segmenting the accompanying membrane channel. In the membrane image, the membrane walls are the bright web-like regions. This channel is difficult due to a high amount of noise in the image. Additionally, it can be hard to determine where the membrane ends in the image (it's not the first and last planes).
+
+Below is a 2D segmentation of the membrane:
+
+
+
+Hint: there should only be one nucleus per membrane.
+
+### Measure the area in ยตmยณ of the cells
+
+Once you have segmented the cell membranes, use regionprops to measure the distribution of cell areas.
+
+```{code-cell} ipython3
+
+```
diff --git a/make.bat b/make.bat
new file mode 100644
index 0000000..153be5e
--- /dev/null
+++ b/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+ set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+ echo.
+ echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+ echo.installed, then set the SPHINXBUILD environment variable to point
+ echo.to the full path of the 'sphinx-build' executable. Alternatively you
+ echo.may add the Sphinx directory to PATH.
+ echo.
+ echo.If you don't have Sphinx installed, grab it from
+ echo.https://www.sphinx-doc.org/
+ exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/objects.inv b/objects.inv
deleted file mode 100644
index 6860670..0000000
Binary files a/objects.inv and /dev/null differ
diff --git a/requirements.txt b/requirements.txt
index ea8af47..0d981bc 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,10 +1,10 @@
scikit-image[data] >= 0.18
+imagecodecs
numpy >= 1.12
scipy >= 1.0
matplotlib >= 2.1
notebook >= 4.0
scikit-learn >= 0.18
-jupyter-book >= 0.10.2
napari[all]
jupytext >=1.10.3
sphinx_autodoc_typehints>=1.11.0
@@ -12,4 +12,11 @@ ghp-import
pytest
pytest-qt
pooch
-furo
\ No newline at end of file
+furo
+pandas
+seaborn
+# For site
+sphinx
+myst-nb
+sphinx-book-theme
+sphinx-copybutton
![](\"../workshops/2014-scipy/images/terminator-vision.png\")
\n",
- "\n",
- "### Segmentation contains two major sub-fields\n",
- "\n",
- "* **Supervised** segmentation: Some prior knowledge, possibly from human input, is used to guide the algorithm. Supervised algorithms currently included in scikit-image include\n",
- " * Thresholding algorithms which require user input (`skimage.filters.threshold_*`)\n",
- " * `skimage.segmentation.random_walker`\n",
- " * `skimage.segmentation.active_contour`\n",
- " * `skimage.segmentation.watershed`\n",
- "* **Unsupervised** segmentation: No prior knowledge. These algorithms attempt to subdivide into meaningful regions automatically. The user may be able to tweak settings like number of regions.\n",
- " * Thresholding algorithms which require no user input.\n",
- " * `skimage.segmentation.slic`\n",
- " * `skimage.segmentation.chan_vese`\n",
- " * `skimage.segmentation.felzenszwalb`\n",
- " * `skimage.segmentation.quickshift`\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "First, some standard imports and a helper function to display our results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "import skimage.data as data\n",
- "import skimage.segmentation as seg\n",
- "from skimage import filters\n",
- "from skimage import draw\n",
- "from skimage import color\n",
- "from skimage import exposure\n",
- "\n",
- "\n",
- "def image_show(image, nrows=1, ncols=1, cmap='gray', **kwargs):\n",
- " fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16))\n",
- " ax.imshow(image, cmap='gray')\n",
- " ax.axis('off')\n",
- " return fig, ax"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Thresholding\n",
- "\n",
- "In some images, global or local contrast may be sufficient to separate regions of interest. Simply choosing all pixels above or below a certain *threshold* may be sufficient to segment such an image.\n",
- "\n",
- "Let's try this on an image of a textbook."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "text = data.page()\n",
- "\n",
- "image_show(text);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Histograms\n",
- "\n",
- "A histogram simply plots the frequency (number of times) values within a certain range appear against the data values themselves. It is a powerful tool to get to know your data - or decide where you would like to threshold."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots(1, 1)\n",
- "ax.hist(text.ravel(), bins=256, range=[0, 255])\n",
- "ax.set_xlim(0, 256);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Experimentation: supervised thresholding\n",
- "\n",
- "Try simple NumPy methods and a few different thresholds on this image. Because *we* are setting the threshold, this is *supervised* segmentation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "text_segmented = ... # Your code here\n",
- "\n",
- "image_show(text_segmented);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Not ideal results! The shadow on the left creates problems; no single global value really fits.\n",
- "\n",
- "What if we don't want to set the threshold every time? There are several published methods which look at the histogram and choose what should be an optimal threshold without user input. These are unsupervised. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Experimentation: unsupervised thresholding\n",
- "\n",
- "Here we will experiment with a number of automatic thresholding methods available in scikit-image. Because these require no input beyond the image itself, this is *unsupervised* segmentation.\n",
- "\n",
- "These functions generally return the threshold value(s), rather than applying it to the image directly.\n",
- "\n",
- "Try `otsu` and `li`, then take a look at `local` or `sauvola`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "text_threshold = filters.threshold_ # Hit tab with the cursor after the underscore, try several methods\n",
- "\n",
- "image_show(text < text_threshold);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Supervised segmentation\n",
- "\n",
- "Thresholding can be useful, but is rather basic and a high-contrast image will often limit its utility. For doing more fun things - like removing part of an image - we need more advanced tools.\n",
- "\n",
- "For this section, we will use the `astronaut` image and attempt to segment Eileen Collins' head using supervised segmentation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Our source image\n",
- "astronaut = data.astronaut()\n",
- "image_show(astronaut);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The contrast is pretty good in this image for her head against the background, so we will simply convert to grayscale with `rgb2gray`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "astronaut_gray = color.rgb2gray(astronaut)\n",
- "image_show(astronaut_gray);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We will use two methods, which segment using very different approaches:\n",
- "\n",
- "* **Active Contour**: Initializes using a user-defined contour or line, which then is attracted to edges and/or brightness. Can be tweaked for many situations, but mixed contrast may be problematic.\n",
- "* **Random walker**: Initialized using any labeled points, fills the image with the label that seems least distant from the origin point (on a path weighted by pixel differences). Tends to respect edges or step-offs, and is surprisingly robust to noise. Only one parameter to tweak."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Active contour segmentation\n",
- "\n",
- "We must have a set of initial parameters to 'seed' our segmentation this. Let's draw a circle around the astronaut's head to initialize the snake.\n",
- "\n",
- "This could be done interactively, with a GUI, but for simplicity we will start at the point [100, 220] and use a radius of 100 pixels. Just a little trigonometry in this helper function..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def circle_points(resolution, center, radius):\n",
- " \"\"\"Generate points defining a circle on an image.\"\"\"\n",
- " radians = np.linspace(0, 2*np.pi, resolution)\n",
- "\n",
- " c = center[1] + radius*np.cos(radians)\n",
- " r = center[0] + radius*np.sin(radians)\n",
- " \n",
- " return np.array([c, r]).T\n",
- "\n",
- "# Exclude last point because a closed path should not have duplicate points\n",
- "points = circle_points(200, [100, 220], 100)[:-1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "snake = seg.active_contour(astronaut_gray, points)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = image_show(astronaut)\n",
- "ax.plot(points[:, 0], points[:, 1], '--r', lw=3)\n",
- "ax.plot(snake[:, 0], snake[:, 1], '-b', lw=3);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Random walker\n",
- "\n",
- "One good analogy for random walker uses graph theory. \n",
- "\n",
- "* The distance from each pixel to its neighbors is weighted by how similar their values are; the more similar, the lower the cost is to step from one to another\n",
- "* The user provides some seed points\n",
- "* The algorithm finds the cheapest paths from each point to each seed value. \n",
- "* Pixels are labeled with the cheapest/lowest path.\n",
- "\n",
- "We will re-use the seed values from our previous example."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "astronaut_labels = np.zeros(astronaut_gray.shape, dtype=np.uint8)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The random walker algorithm expects a label image as input. Any label above zero will be treated as a seed; all zero-valued locations will be filled with labels from the positive integers available.\n",
- "\n",
- "There is also a masking feature where anything labeled -1 will never be labeled or traversed, but we will not use it here."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "indices = draw.circle_perimeter(100, 220, 25)\n",
- "\n",
- "astronaut_labels[indices] = 1\n",
- "astronaut_labels[points[:, 1].astype(np.int), points[:, 0].astype(np.int)] = 2\n",
- "\n",
- "image_show(astronaut_labels);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "astronaut_segmented = seg.random_walker(astronaut_gray, astronaut_labels)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Check our results\n",
- "fig, ax = image_show(astronaut_gray)\n",
- "ax.imshow(astronaut_segmented == 1, alpha=0.3);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Flood fill\n",
- "\n",
- "A basic but effective segmentation technique was recently added to scikit-image: `segmentation.flood` and `segmentation.flood_fill`. These algorithms take a seed point and iteratively find and fill adjacent points which are equal to or within a tolerance of the initial point. `flood` returns the region; `flood_fill` returns a modified image with those points changed to a new value.\n",
- "\n",
- "This approach is most suited for areas which have a relatively uniform color or gray value, and/or high contrast relative to adjacent structures.\n",
- "\n",
- "Can we accomplish the same task with flood fill?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "seed_point = (100, 220) # Experiment with the seed point\n",
- "flood_mask = seg.flood(astronaut_gray, seed_point, tolerance=0.3) # Experiment with tolerance"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = image_show(astronaut_gray)\n",
- "ax.imshow(flood_mask, alpha=0.3);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Not ideal! The flood runs away into the background through the right earlobe.\n",
- "\n",
- "Let's think outside the box. \n",
- "* What if instead of segmenting the head, we segmented the background around it and the collar?\n",
- "* Is there any way to increase the contrast between the background and skin?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "seed_bkgnd = (100, 350) # Background\n",
- "seed_collar = (200, 220) # Collar\n",
- "\n",
- "better_contrast = # Your idea to improve contrast here\n",
- "tol_bkgnd = # Experiment with tolerance for background\n",
- "tol_collar = # Experiment with tolerance for the collar\n",
- "\n",
- "flood_background = seg.flood(better_contrast, seed_bkgnd, tolerance=tol_bkgnd)\n",
- "flood_collar = seg.flood(better_contrast, seed_collar, tolerance=tol_collar)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "fig, ax = image_show(better_contrast)\n",
- "\n",
- "# Combine the two floods with binary OR operator\n",
- "ax.imshow(flood_background | flood_collar, alpha=0.3);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "flood_mask2 = seg.flood(astronaut[..., 2], (200, 220), tolerance=40)\n",
- "fig, ax = image_show(astronaut[..., 2])\n",
- "ax.imshow(flood_mask | flood_mask2, alpha=0.3);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Unsupervised segmentation\n",
- "\n",
- "Sometimes, human input is not possible or feasible - or, perhaps your images are so large that it is not feasible to consider all pixels simultaneously. Unsupervised segmentation can then break the image down into several sub-regions, so instead of millions of pixels you have tens to hundreds of regions.\n",
- "\n",
- "### SLIC\n",
- "\n",
- "There are many analogies to machine learning in unsupervised segmentation. Our first example directly uses a common machine learning algorithm under the hood - K-Means."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# SLIC works in color, so we will use the original astronaut\n",
- "astronaut_slic = seg.slic(astronaut)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# label2rgb replaces each discrete label with the average interior color\n",
- "image_show(color.label2rgb(astronaut_slic, astronaut, kind='avg'));"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We've reduced this image from 512*512 = 262,000 pixels down to 100 regions!\n",
- "\n",
- "And most of these regions make some logical sense."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Chan-Vese\n",
- "\n",
- "This algorithm iterates a level set, which allows it to capture complex and even disconnected features. However, its result is binary - there will only be one region - and it requires a grayscale image.\n",
- "\n",
- "This algorithm takes a few seconds to run."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "chan_vese = seg.chan_vese(astronaut_gray)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = image_show(astronaut_gray)\n",
- "ax.imshow(chan_vese == 0, alpha=0.3);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Chan-Vese has a number of paremeters, which you can try out! In the interest of time, we may move on."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Felzenszwalb\n",
- "\n",
- "This method oversegments an RGB image (requires color, unlike Chan-Vese) using another machine learning technique, a minimum-spanning tree clustering. The number of segments is not guaranteed and can only be indirectly controlled via `scale` parameter."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "astronaut_felzenszwalb = seg.felzenszwalb(astronaut) # Color required"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "image_show(astronaut_felzenszwalb);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Whoa, lots of regions! How many is that?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Find the number of unique labels\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's see if they make sense; label them with the region average (see above with SLIC)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "astronaut_felzenszwalb_colored = # Your code here\n",
- "\n",
- "image_show(astronaut_felzenszwalb_colored);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Actually reasonable small regions. If we wanted fewer regions, we could change the `scale` parameter (try it!) or start here and combine them.\n",
- "\n",
- "This approach is sometimes called **oversegmentation**.\n",
- "\n",
- "But when there are too many regions, they must be combined somehow."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Combining regions with a Region Adjacency Graph (RAG)\n",
- "\n",
- "Remember how the concept behind random walker was functionally looking at the difference between each pixel and its neighbors, then figuring out which were most alike? A RAG is essentially the same, except between regions.\n",
- "\n",
- "We have RAGs now in scikit-image, but we have to import *from the future*; this functionality is exposed in the `future.graph` submodule meaning it is stable and robust enough to ship, but the API may change."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import skimage.future.graph as graph\n",
- "\n",
- "rag = graph.rag_mean_color(astronaut, astronaut_felzenszwalb + 1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we show just one application of a very useful tool - `skimage.measure.regionprops` - to determine the centroid of each labeled region and pass that to the graph. \n",
- "\n",
- "`regionprops` has many, many other uses; see the API documentation for all of the features that can be quantified per-region! \n",
- "http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import skimage.measure as measure\n",
- "\n",
- "# Regionprops ignores zero, but we want to include it, so add one\n",
- "regions = measure.regionprops(astronaut_felzenszwalb + 1) \n",
- "\n",
- "# Pass centroid data into the graph\n",
- "for region in regions:\n",
- " rag.nodes[region['label']]['centroid'] = region['centroid']"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "`display_edges` is a helper function to assist in visualizing the graph."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def display_edges(image, g, threshold):\n",
- " \"\"\"Draw edges of a RAG on its image\n",
- " \n",
- " Returns a modified image with the edges drawn.Edges are drawn in green\n",
- " and nodes are drawn in yellow.\n",
- " \n",
- " Parameters\n",
- " ----------\n",
- " image : ndarray\n",
- " The image to be drawn on.\n",
- " g : RAG\n",
- " The Region Adjacency Graph.\n",
- " threshold : float\n",
- " Only edges in `g` below `threshold` are drawn.\n",
- " \n",
- " Returns:\n",
- " out: ndarray\n",
- " Image with the edges drawn.\n",
- " \"\"\"\n",
- " image = image.copy()\n",
- " for edge in g.edges():\n",
- " n1, n2 = edge\n",
- " \n",
- " r1, c1 = map(int, rag.nodes[n1]['centroid'])\n",
- " r2, c2 = map(int, rag.nodes[n2]['centroid'])\n",
- " \n",
- " line = draw.line(r1, c1, r2, c2)\n",
- " circle = draw.circle(r1,c1,2)\n",
- " \n",
- " if g[n1][n2]['weight'] < threshold :\n",
- " image[line] = 0,255,0\n",
- " image[circle] = 255,255,0\n",
- " \n",
- " return image"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "# All edges are drawn with threshold at infinity\n",
- "edges_drawn_all = display_edges(astronaut_felzenszwalb_colored, rag, np.inf)\n",
- "image_show(edges_drawn_all);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Try a range of thresholds out, see what happens."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "threshold = ... # Experiment\n",
- "\n",
- "edges_drawn_few = display_edges(astronaut_felzenszwalb_colored, rag, threshold)\n",
- "image_show(edges_drawn_few);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Finally, cut the graph\n",
- "\n",
- "Once you are happy with the (dis)connected regions above, the graph can be cut to merge the regions which are still connected."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "final_labels = graph.cut_threshold(astronaut_felzenszwalb + 1, rag, threshold)\n",
- "final_label_rgb = color.label2rgb(final_labels, astronaut, kind='avg')\n",
- "\n",
- "image_show(final_label_rgb);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "How many regions exist now?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": [
- "raises-exception",
- "remove-output"
- ]
- },
- "outputs": [],
- "source": [
- "np.unique(final_labels).size"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise: Cat picture\n",
- "\n",
- "The data directory also has an excellent image of Stรฉfan's cat, Chelsea. With what you've learned, can you segment the cat's nose? How about the eyes? Why is the eye particularly challenging?\n",
- "\n",
- "Hint: the cat's nose is located close to [240, 270] and the right eye center is near [110, 172] in row, column notation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "fig, ax = image_show(data.chelsea())\n",
- "\n",
- "ax.plot(270, 240, marker='o', markersize=15, color=\"g\")\n",
- "ax.plot(172, 110, marker='o', markersize=15, color=\"r\");"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "celltoolbar": "Tags",
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/lectures/4_segmentation.md b/lectures/4_segmentation.md
new file mode 100644
index 0000000..a671635
--- /dev/null
+++ b/lectures/4_segmentation.md
@@ -0,0 +1,494 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+```{code-cell} ipython3
+:tags: [remove-input, remove-output]
+
+%matplotlib inline
+%config InlineBackend.figure_format = 'retina'
+```
+
+# Segmentation
+
++++
+
+## Separating an image into one or more regions of interest.
+
+Everyone has heard or seen Photoshop or a similar graphics editor take a person from one image and place them into another. The first step of doing this is *identifying where that person is in the source image*.
+
+In popular culture, the Terminator's vision segments humans:
+
+
+
+### Segmentation contains two major sub-fields
+
+* **Supervised** segmentation: Some prior knowledge, possibly from human input, is used to guide the algorithm. Supervised algorithms currently included in scikit-image include
+ * Thresholding algorithms which require user input (`skimage.filters.threshold_*`)
+ * `skimage.segmentation.random_walker`
+ * `skimage.segmentation.active_contour`
+ * `skimage.segmentation.watershed`
+* **Unsupervised** segmentation: No prior knowledge. These algorithms attempt to subdivide into meaningful regions automatically. The user may be able to tweak settings like number of regions.
+ * Thresholding algorithms which require no user input.
+ * `skimage.segmentation.slic`
+ * `skimage.segmentation.chan_vese`
+ * `skimage.segmentation.felzenszwalb`
+ * `skimage.segmentation.quickshift`
+
++++
+
+First, some standard imports and a helper function to display our results
+
+```{code-cell} ipython3
+import numpy as np
+import matplotlib.pyplot as plt
+
+import skimage.data as data
+import skimage.segmentation as seg
+from skimage import filters
+from skimage import draw
+from skimage import color
+from skimage import exposure
+
+
+def image_show(image, nrows=1, ncols=1, cmap='gray', **kwargs):
+ fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16))
+ ax.imshow(image, cmap='gray')
+ ax.axis('off')
+ return fig, ax
+```
+
+## Thresholding
+
+In some images, global or local contrast may be sufficient to separate regions of interest. Simply choosing all pixels above or below a certain *threshold* may be sufficient to segment such an image.
+
+Let's try this on an image of a textbook.
+
+```{code-cell} ipython3
+text = data.page()
+
+image_show(text);
+```
+
+### Histograms
+
+A histogram simply plots the frequency (number of times) values within a certain range appear against the data values themselves. It is a powerful tool to get to know your data - or decide where you would like to threshold.
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(1, 1)
+ax.hist(text.ravel(), bins=256, range=[0, 255])
+ax.set_xlim(0, 256);
+```
+
+### Experimentation: supervised thresholding
+
+Try simple NumPy methods and a few different thresholds on this image. Because *we* are setting the threshold, this is *supervised* segmentation.
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+text_segmented = ... # Your code here
+
+image_show(text_segmented);
+```
+
+Not ideal results! The shadow on the left creates problems; no single global value really fits.
+
+What if we don't want to set the threshold every time? There are several published methods which look at the histogram and choose what should be an optimal threshold without user input. These are unsupervised.
+
++++
+
+### Experimentation: unsupervised thresholding
+
+Here we will experiment with a number of automatic thresholding methods available in scikit-image. Because these require no input beyond the image itself, this is *unsupervised* segmentation.
+
+These functions generally return the threshold value(s), rather than applying it to the image directly.
+
+Try `otsu` and `li`, then take a look at `local` or `sauvola`.
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+text_threshold = filters.threshold_ # Hit tab with the cursor after the underscore, try several methods
+
+image_show(text < text_threshold);
+```
+
+```{code-cell} ipython3
+
+```
+
+## Supervised segmentation
+
+Thresholding can be useful, but is rather basic and a high-contrast image will often limit its utility. For doing more fun things - like removing part of an image - we need more advanced tools.
+
+For this section, we will use the `astronaut` image and attempt to segment Eileen Collins' head using supervised segmentation.
+
+```{code-cell} ipython3
+# Our source image
+astronaut = data.astronaut()
+image_show(astronaut);
+```
+
+The contrast is pretty good in this image for her head against the background, so we will simply convert to grayscale with `rgb2gray`.
+
+```{code-cell} ipython3
+astronaut_gray = color.rgb2gray(astronaut)
+image_show(astronaut_gray);
+```
+
+We will use two methods, which segment using very different approaches:
+
+* **Active Contour**: Initializes using a user-defined contour or line, which then is attracted to edges and/or brightness. Can be tweaked for many situations, but mixed contrast may be problematic.
+* **Random walker**: Initialized using any labeled points, fills the image with the label that seems least distant from the origin point (on a path weighted by pixel differences). Tends to respect edges or step-offs, and is surprisingly robust to noise. Only one parameter to tweak.
+
++++
+
+### Active contour segmentation
+
+We must have a set of initial parameters to 'seed' our segmentation this. Let's draw a circle around the astronaut's head to initialize the snake.
+
+This could be done interactively, with a GUI, but for simplicity we will start at the point [100, 220] and use a radius of 100 pixels. Just a little trigonometry in this helper function...
+
+```{code-cell} ipython3
+def circle_points(resolution, center, radius):
+ """Generate points defining a circle on an image."""
+ radians = np.linspace(0, 2*np.pi, resolution)
+
+ c = center[1] + radius*np.cos(radians)
+ r = center[0] + radius*np.sin(radians)
+
+ return np.array([c, r]).T
+
+# Exclude last point because a closed path should not have duplicate points
+points = circle_points(200, [100, 220], 100)[:-1]
+```
+
+```{code-cell} ipython3
+snake = seg.active_contour(astronaut_gray, points)
+```
+
+```{code-cell} ipython3
+fig, ax = image_show(astronaut)
+ax.plot(points[:, 0], points[:, 1], '--r', lw=3)
+ax.plot(snake[:, 0], snake[:, 1], '-b', lw=3);
+```
+
+```{code-cell} ipython3
+
+```
+
+### Random walker
+
+One good analogy for random walker uses graph theory.
+
+* The distance from each pixel to its neighbors is weighted by how similar their values are; the more similar, the lower the cost is to step from one to another
+* The user provides some seed points
+* The algorithm finds the cheapest paths from each point to each seed value.
+* Pixels are labeled with the cheapest/lowest path.
+
+We will re-use the seed values from our previous example.
+
+```{code-cell} ipython3
+astronaut_labels = np.zeros(astronaut_gray.shape, dtype=np.uint8)
+```
+
+The random walker algorithm expects a label image as input. Any label above zero will be treated as a seed; all zero-valued locations will be filled with labels from the positive integers available.
+
+There is also a masking feature where anything labeled -1 will never be labeled or traversed, but we will not use it here.
+
+```{code-cell} ipython3
+indices = draw.circle_perimeter(100, 220, 25)
+
+astronaut_labels[indices] = 1
+astronaut_labels[points[:, 1].astype(np.int), points[:, 0].astype(np.int)] = 2
+
+image_show(astronaut_labels);
+```
+
+```{code-cell} ipython3
+astronaut_segmented = seg.random_walker(astronaut_gray, astronaut_labels)
+```
+
+```{code-cell} ipython3
+# Check our results
+fig, ax = image_show(astronaut_gray)
+ax.imshow(astronaut_segmented == 1, alpha=0.3);
+```
+
+```{code-cell} ipython3
+
+```
+
+## Flood fill
+
+A basic but effective segmentation technique was recently added to scikit-image: `segmentation.flood` and `segmentation.flood_fill`. These algorithms take a seed point and iteratively find and fill adjacent points which are equal to or within a tolerance of the initial point. `flood` returns the region; `flood_fill` returns a modified image with those points changed to a new value.
+
+This approach is most suited for areas which have a relatively uniform color or gray value, and/or high contrast relative to adjacent structures.
+
+Can we accomplish the same task with flood fill?
+
+```{code-cell} ipython3
+seed_point = (100, 220) # Experiment with the seed point
+flood_mask = seg.flood(astronaut_gray, seed_point, tolerance=0.3) # Experiment with tolerance
+```
+
+```{code-cell} ipython3
+fig, ax = image_show(astronaut_gray)
+ax.imshow(flood_mask, alpha=0.3);
+```
+
+Not ideal! The flood runs away into the background through the right earlobe.
+
+Let's think outside the box.
+* What if instead of segmenting the head, we segmented the background around it and the collar?
+* Is there any way to increase the contrast between the background and skin?
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+seed_bkgnd = (100, 350) # Background
+seed_collar = (200, 220) # Collar
+
+better_contrast = # Your idea to improve contrast here
+tol_bkgnd = # Experiment with tolerance for background
+tol_collar = # Experiment with tolerance for the collar
+
+flood_background = seg.flood(better_contrast, seed_bkgnd, tolerance=tol_bkgnd)
+flood_collar = seg.flood(better_contrast, seed_collar, tolerance=tol_collar)
+```
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+fig, ax = image_show(better_contrast)
+
+# Combine the two floods with binary OR operator
+ax.imshow(flood_background | flood_collar, alpha=0.3);
+```
+
+```{code-cell} ipython3
+flood_mask2 = seg.flood(astronaut[..., 2], (200, 220), tolerance=40)
+fig, ax = image_show(astronaut[..., 2])
+ax.imshow(flood_mask | flood_mask2, alpha=0.3);
+```
+
+## Unsupervised segmentation
+
+Sometimes, human input is not possible or feasible - or, perhaps your images are so large that it is not feasible to consider all pixels simultaneously. Unsupervised segmentation can then break the image down into several sub-regions, so instead of millions of pixels you have tens to hundreds of regions.
+
+### SLIC
+
+There are many analogies to machine learning in unsupervised segmentation. Our first example directly uses a common machine learning algorithm under the hood - K-Means.
+
+```{code-cell} ipython3
+# SLIC works in color, so we will use the original astronaut
+astronaut_slic = seg.slic(astronaut)
+```
+
+```{code-cell} ipython3
+# label2rgb replaces each discrete label with the average interior color
+image_show(color.label2rgb(astronaut_slic, astronaut, kind='avg'));
+```
+
+We've reduced this image from 512*512 = 262,000 pixels down to 100 regions!
+
+And most of these regions make some logical sense.
+
++++
+
+### Chan-Vese
+
+This algorithm iterates a level set, which allows it to capture complex and even disconnected features. However, its result is binary - there will only be one region - and it requires a grayscale image.
+
+This algorithm takes a few seconds to run.
+
+```{code-cell} ipython3
+chan_vese = seg.chan_vese(astronaut_gray)
+```
+
+```{code-cell} ipython3
+fig, ax = image_show(astronaut_gray)
+ax.imshow(chan_vese == 0, alpha=0.3);
+```
+
+Chan-Vese has a number of paremeters, which you can try out! In the interest of time, we may move on.
+
+```{code-cell} ipython3
+
+```
+
+### Felzenszwalb
+
+This method oversegments an RGB image (requires color, unlike Chan-Vese) using another machine learning technique, a minimum-spanning tree clustering. The number of segments is not guaranteed and can only be indirectly controlled via `scale` parameter.
+
+```{code-cell} ipython3
+astronaut_felzenszwalb = seg.felzenszwalb(astronaut) # Color required
+```
+
+```{code-cell} ipython3
+image_show(astronaut_felzenszwalb);
+```
+
+Whoa, lots of regions! How many is that?
+
+```{code-cell} ipython3
+# Find the number of unique labels
+```
+
+Let's see if they make sense; label them with the region average (see above with SLIC)
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+astronaut_felzenszwalb_colored = # Your code here
+
+image_show(astronaut_felzenszwalb_colored);
+```
+
+Actually reasonable small regions. If we wanted fewer regions, we could change the `scale` parameter (try it!) or start here and combine them.
+
+This approach is sometimes called **oversegmentation**.
+
+But when there are too many regions, they must be combined somehow.
+
+```{code-cell} ipython3
+
+```
+
+## Combining regions with a Region Adjacency Graph (RAG)
+
+Remember how the concept behind random walker was functionally looking at the difference between each pixel and its neighbors, then figuring out which were most alike? A RAG is essentially the same, except between regions.
+
+We have RAGs now in scikit-image, but we have to import *from the future*; this functionality is exposed in the `future.graph` submodule meaning it is stable and robust enough to ship, but the API may change.
+
+```{code-cell} ipython3
+import skimage.future.graph as graph
+
+rag = graph.rag_mean_color(astronaut, astronaut_felzenszwalb + 1)
+```
+
+Now we show just one application of a very useful tool - `skimage.measure.regionprops` - to determine the centroid of each labeled region and pass that to the graph.
+
+`regionprops` has many, many other uses; see the API documentation for all of the features that can be quantified per-region!
+http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops
+
+```{code-cell} ipython3
+import skimage.measure as measure
+
+# Regionprops ignores zero, but we want to include it, so add one
+regions = measure.regionprops(astronaut_felzenszwalb + 1)
+
+# Pass centroid data into the graph
+for region in regions:
+ rag.nodes[region['label']]['centroid'] = region['centroid']
+```
+
+`display_edges` is a helper function to assist in visualizing the graph.
+
+```{code-cell} ipython3
+def display_edges(image, g, threshold):
+ """Draw edges of a RAG on its image
+
+ Returns a modified image with the edges drawn.Edges are drawn in green
+ and nodes are drawn in yellow.
+
+ Parameters
+ ----------
+ image : ndarray
+ The image to be drawn on.
+ g : RAG
+ The Region Adjacency Graph.
+ threshold : float
+ Only edges in `g` below `threshold` are drawn.
+
+ Returns:
+ out: ndarray
+ Image with the edges drawn.
+ """
+ image = image.copy()
+ for edge in g.edges():
+ n1, n2 = edge
+
+ r1, c1 = map(int, rag.nodes[n1]['centroid'])
+ r2, c2 = map(int, rag.nodes[n2]['centroid'])
+
+ line = draw.line(r1, c1, r2, c2)
+ circle = draw.circle(r1,c1,2)
+
+ if g[n1][n2]['weight'] < threshold :
+ image[line] = 0,255,0
+ image[circle] = 255,255,0
+
+ return image
+```
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+# All edges are drawn with threshold at infinity
+edges_drawn_all = display_edges(astronaut_felzenszwalb_colored, rag, np.inf)
+image_show(edges_drawn_all);
+```
+
+Try a range of thresholds out, see what happens.
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+threshold = ... # Experiment
+
+edges_drawn_few = display_edges(astronaut_felzenszwalb_colored, rag, threshold)
+image_show(edges_drawn_few);
+```
+
+### Finally, cut the graph
+
+Once you are happy with the (dis)connected regions above, the graph can be cut to merge the regions which are still connected.
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+final_labels = graph.cut_threshold(astronaut_felzenszwalb + 1, rag, threshold)
+final_label_rgb = color.label2rgb(final_labels, astronaut, kind='avg')
+
+image_show(final_label_rgb);
+```
+
+How many regions exist now?
+
+```{code-cell} ipython3
+:tags: [raises-exception, remove-output]
+
+np.unique(final_labels).size
+```
+
+## Exercise: Cat picture
+
+The data directory also has an excellent image of Stรฉfan's cat, Chelsea. With what you've learned, can you segment the cat's nose? How about the eyes? Why is the eye particularly challenging?
+
+Hint: the cat's nose is located close to [240, 270] and the right eye center is near [110, 172] in row, column notation.
+
+```{code-cell} ipython3
+fig, ax = image_show(data.chelsea())
+
+ax.plot(270, 240, marker='o', markersize=15, color="g")
+ax.plot(172, 110, marker='o', markersize=15, color="r");
+```
+
+```{code-cell} ipython3
+
+```
+
+```{code-cell} ipython3
+
+```
diff --git a/lectures/5_tophat_filters.ipynb b/lectures/5_tophat_filters.ipynb
deleted file mode 100644
index df0e21e..0000000
--- a/lectures/5_tophat_filters.ipynb
+++ /dev/null
@@ -1,589 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "c04ba556-e6bd-4bbb-b6aa-492b7ad2aa02",
- "metadata": {},
- "source": [
- "# Correcting brightness nonuniformity with tophat filters\n",
- "\n",
- "Thresholding, usually the first image segmentation technique to be taught, labels individual pixels based on intensity. No information\n",
- "from a surrounding region is used.\n",
- "\n",
- "Basic concepts of segmentation, including thresholding, for skimage are introduced [here](https://scikit-image.org/docs/0.14.x/user_guide/tutorial_segmentation.html) and [here](https://scikit-image.org/docs/stable/auto_examples/segmentation/plot_niblack_sauvola.html) and [here](https://github.com/scikit-image/skimage-tutorials/blob/main/lectures/solutions/4_segmentation.ipynb)\n",
- "\n",
- "In this tutorial we'll focus on extending the classical thresholding techniques via processes that correct nonuniformity. Brightness nonuniformity is a common problem in many forms of imaging, with varying causes. Having a collection of tools to help deal with nonuniformity can simplify subsequent analysis steps considerably.\n",
- "\n",
- "## Recap\n",
- "\n",
- "The _page_ data, shown below, contains some scanned text and the aim is to select a threshold that separates printing from paper (dark from light). The tutorials linked above introduce manual selection, selection based on histograms, and adaptive thresholding, where a different threshold is selected for each pixel based on parameters of the local neighborhood. The results of these methods are shown at the end of the tutorial for reference.\n",
- "\n",
- "The approach we will introduce is similar to adaptive filtering, but is built using existing filtering procedures to which the manual or histogram approaches can be applied\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "86586f49-3cba-464d-9ba9-eb889de98d97",
- "metadata": {},
- "source": [
- "# Set up libraries and display functions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "471781bf-4a38-4691-a35f-f9517efa53fb",
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "import skimage.data as data\n",
- "import skimage.segmentation as seg\n",
- "from skimage import filters\n",
- "from skimage import draw\n",
- "from skimage import color\n",
- "from skimage import exposure\n",
- "import skimage.morphology as morph\n",
- "\n",
- "\n",
- "def image_show(image, nrows=1, ncols=1, cmap='gray', **kwargs):\n",
- " fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16))\n",
- " ax.imshow(image, cmap='gray')\n",
- " ax.axis('off')\n",
- " return fig, ax\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b6b126ed-b149-4649-a65b-b53c91fc612f",
- "metadata": {},
- "source": [
- "# Load and display the test data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "8c4b4e8b-469a-447f-b6f9-cbc312026ae6",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(
+
+The main challenge here is the uneven illumination, which makes isolating the chromosomes a struggle.
+
+```{code-cell} python
+import numpy as np
+from matplotlib import cm, pyplot as plt
+import skdemo
+plt.rcParams['image.cmap'] = 'cubehelix'
+plt.rcParams['image.interpolation'] = 'none'
+```
+
+```{code-cell} python
+from skimage import io
+image = io.imread('../images/chromosomes.tif')
+skdemo.imshow_with_histogram(image);
+```
+
+Let's separate the channels so we can work on each individually.
+
+```{code-cell} python
+protein, centromeres, chromosomes = image.transpose((2, 0, 1))
+```
+
+Getting the centromeres is easy because the signal is so clean:
+
+```{code-cell} python
+from skimage.filters import threshold_otsu
+centromeres_binary = centromeres > threshold_otsu(centromeres)
+skdemo.imshow_all(centromeres, centromeres_binary)
+```
+
+But getting the chromosomes is not so easy:
+
+```{code-cell} python
+chromosomes_binary = chromosomes > threshold_otsu(chromosomes)
+skdemo.imshow_all(chromosomes, chromosomes_binary, cmap='gray')
+```
+
+Let's try using an adaptive threshold:
+
+```{code-cell} python
+from skimage.filters import threshold_local
+chromosomes_adapt = threshold_local(chromosomes, block_size=51)
+# Question: how did I choose this block size?
+skdemo.imshow_all(chromosomes, chromosomes_adapt)
+```
+
+Not only is the uneven illumination a problem, but there seem to be some artifacts due to the illumination pattern!
+
+**Exercise:** Can you think of a way to fix this?
+
+(Hint: in addition to everything you've learned so far, check out [`skimage.morphology.remove_small_objects`](http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.remove_small_objects))
+
+Now that we have the centromeres and the chromosomes, it's time to do the science: get the distribution of intensities in the red channel using both centromere and chromosome locations.
+
+```python
+# Replace "None" below with the right expressions!
+centromere_intensities = None
+chromosome_intensities = None
+all_intensities = np.concatenate((centromere_intensities,
+ chromosome_intensities))
+minint = np.min(all_intensities)
+maxint = np.max(all_intensities)
+bins = np.linspace(minint, maxint, 100)
+plt.hist(centromere_intensities, bins=bins, color='blue',
+ alpha=0.5, label='centromeres')
+plt.hist(chromosome_intensities, bins=bins, color='orange',
+ alpha=0.5, label='chromosomes')
+plt.legend(loc='upper right')
+plt.show()
+```
diff --git a/lectures/adv1_lesion-quantification.md b/lectures/adv1_lesion-quantification.md
new file mode 100644
index 0000000..3fb7ec9
--- /dev/null
+++ b/lectures/adv1_lesion-quantification.md
@@ -0,0 +1,90 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+```{code-cell} ipython3
+from __future__ import division, print_function
+%matplotlib inline
+```
+
+# Quantifying spinal cord regeneration in zebrafish
+
+We want to quantify the amount of fluorescent cells in a wounded zebrafish embryo spinal column:
+
+
+
+The key steps are:
+
+- estimating the position and width of the cord
+- estimating the average fluorescence along the length of the cord
+
+```{code-cell} ipython3
+from matplotlib import pyplot as plt, cm
+from skimage import io
+image = io.imread('../images/zebrafish-spinal-cord.png')
+```
+
+## SciPy to estimate coordinates
+
+First, we get just the top and bottom rows of pixels, and use a 1D gaussian filter to smooth the signal.
+
+```{code-cell} ipython3
+from scipy import ndimage as nd
+top, bottom = image[[0, -1], :]
+
+fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
+
+top_smooth = nd.gaussian_filter1d(top, sigma=20)
+ax0.plot(top, color='blue', lw=2)
+ax0.plot(top_smooth, color='orange', lw=2)
+ax0.set_title('top')
+
+bottom_smooth = nd.gaussian_filter1d(bottom, sigma=20)
+ax1.plot(bottom, color='blue', lw=2)
+ax1.plot(bottom_smooth, color='orange', lw=2)
+ax1.set_title('bottom')
+```
+
+With smooth curves, we can get the mode (the position of the center) and width of the signal.
+
+```{code-cell} ipython3
+top_mode = top_smooth.argmax()
+top_max = top_smooth[top_mode]
+top_width = (top_smooth > float(top_max) / 2).sum()
+
+bottom_mode = bottom_smooth.argmax()
+bottom_max = bottom_smooth[bottom_mode]
+bottom_width = (bottom_smooth > float(bottom_max) / 2).sum()
+
+width = max(bottom_width, top_width)
+
+print(top_mode, top_width, bottom_mode, bottom_width)
+```
+
+## scikit-image to trace the profile
+
+Now, use `measure.profile_line` to trace from (0, `top_mode`) to (-1, `bottom_mode`).
+
+```python
+from skimage import measure
+trace = measure.profile_line(None) # Replace `None` with correct args
+```
+
+Finally, plot the trace.
+
+```python
+plt.plot(trace, color='black', lw=2)
+plt.xlabel('position along embryo')
+plt.ylabel('mean fluorescence intensity')
+```
+
+From this trace, we can compute various summary statistics (e.g. min/max, gap width, slope, etc), and plot these over time as the wound recovers.
diff --git a/lectures/adv2_microarray.md b/lectures/adv2_microarray.md
new file mode 100644
index 0000000..0722c43
--- /dev/null
+++ b/lectures/adv2_microarray.md
@@ -0,0 +1,203 @@
+---
+jupytext:
+ text_representation:
+ extension: .md
+ format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.11.2
+kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+```{code-cell} ipython3
+from __future__ import division, print_function
+%matplotlib inline
+```
+
+# DNA microarray processing
+
+## Data in this example
+
+*Yeast microarrays for genome wide parallel genetic and gene
+expressionโanalysis*
+
+
+
+Two-color fluorescent scan of a yeast microarray containing 2,479 elements
+(ORFs). The center-to-center distance between elements is 345 ฮผm. A probe
+mixture consisting of cDNA from yeast extract/peptone (YEP) galactose (green
+pseudocolor) and YEP glucose (red pseudocolor) grown yeast cultures was
+hybridized to the array. Intensity per element corresponds to ORF expression,
+and pseudocolor per element corresponds to relative ORF expression between the
+two cultures.โ
+
+by Deval A. Lashkari, http://www.pnas.org/content/94/24/13057/F1.expansion
+
+
+![](\"../images/chromosomes.jpg\")
\n",
- "\n",
- "The main challenge here is the uneven illumination, which makes isolating the chromosomes a struggle."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from matplotlib import cm, pyplot as plt\n",
- "import skdemo\n",
- "plt.rcParams['image.cmap'] = 'cubehelix'\n",
- "plt.rcParams['image.interpolation'] = 'none'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage import io\n",
- "image = io.imread('../images/chromosomes.tif')\n",
- "skdemo.imshow_with_histogram(image);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's separate the channels so we can work on each individually."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "protein, centromeres, chromosomes = image.transpose((2, 0, 1))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Getting the centromeres is easy because the signal is so clean:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage.filter import threshold_otsu\n",
- "centromeres_binary = centromeres > threshold_otsu(centromeres)\n",
- "skdemo.imshow_all(centromeres, centromeres_binary)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "But getting the chromosomes is not so easy:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "chromosomes_binary = chromosomes > threshold_otsu(chromosomes)\n",
- "skdemo.imshow_all(chromosomes, chromosomes_binary, cmap='gray')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's try using an adaptive threshold:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage.filter import threshold_adaptive\n",
- "chromosomes_adapt = threshold_adaptive(chromosomes, block_size=51)\n",
- "# Question: how did I choose this block size?\n",
- "skdemo.imshow_all(chromosomes, chromosomes_adapt)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Not only is the uneven illumination a problem, but there seem to be some artifacts due to the illumination pattern!\n",
- "\n",
- "**Exercise:** Can you think of a way to fix this?\n",
- "\n",
- "(Hint: in addition to everything you've learned so far, check out [`skimage.morphology.remove_small_objects`](http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.remove_small_objects))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now that we have the centromeres and the chromosomes, it's time to do the science: get the distribution of intensities in the red channel using both centromere and chromosome locations."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Replace \"None\" below with the right expressions!\n",
- "centromere_intensities = None\n",
- "chromosome_intensities = None\n",
- "all_intensities = np.concatenate((centromere_intensities,\n",
- " chromosome_intensities))\n",
- "minint = np.min(all_intensities)\n",
- "maxint = np.max(all_intensities)\n",
- "bins = np.linspace(minint, maxint, 100)\n",
- "plt.hist(centromere_intensities, bins=bins, color='blue',\n",
- " alpha=0.5, label='centromeres')\n",
- "plt.hist(chromosome_intensities, bins=bins, color='orange',\n",
- " alpha=0.5, label='chromosomes')\n",
- "plt.legend(loc='upper right')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- "![\"Zebrafish](\"../images/zebrafish-spinal-cord-color.png\")
\n",
- "\n",
- "The key steps are:\n",
- "\n",
- "- estimating the position and width of the cord\n",
- "- estimating the average fluorescence along the length of the cord"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from matplotlib import pyplot as plt, cm\n",
- "from skimage import io\n",
- "image = io.imread('../images/zebrafish-spinal-cord.png')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# SciPy to estimate coordinates\n",
- "\n",
- "First, we get just the top and bottom rows of pixels, and use a 1D gaussian filter to smooth the signal."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from scipy import ndimage as nd\n",
- "top, bottom = image[[0, -1], :]\n",
- "\n",
- "fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n",
- "\n",
- "top_smooth = nd.gaussian_filter1d(top, sigma=20)\n",
- "ax0.plot(top, color='blue', lw=2)\n",
- "ax0.plot(top_smooth, color='orange', lw=2)\n",
- "ax0.set_title('top')\n",
- "\n",
- "bottom_smooth = nd.gaussian_filter1d(bottom, sigma=20)\n",
- "ax1.plot(bottom, color='blue', lw=2)\n",
- "ax1.plot(bottom_smooth, color='orange', lw=2)\n",
- "ax1.set_title('bottom')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "With smooth curves, we can get the mode (the position of the center) and width of the signal."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "top_mode = top_smooth.argmax()\n",
- "top_max = top_smooth[top_mode]\n",
- "top_width = (top_smooth > float(top_max) / 2).sum()\n",
- "\n",
- "bottom_mode = bottom_smooth.argmax()\n",
- "bottom_max = bottom_smooth[bottom_mode]\n",
- "bottom_width = (bottom_smooth > float(bottom_max) / 2).sum()\n",
- "\n",
- "width = max(bottom_width, top_width)\n",
- "\n",
- "print(top_mode, top_width, bottom_mode, bottom_width)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# scikit-image to trace the profile\n",
- "\n",
- "Now, use `measure.profile_line` to trace from (0, `top_mode`) to (-1, `bottom_mode`)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage import measure\n",
- "trace = measure.profile_line(None) # Replace `None` with correct args"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, plot the trace."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "plt.plot(trace, color='black', lw=2)\n",
- "plt.xlabel('position along embryo')\n",
- "plt.ylabel('mean fluorescence intensity')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "From this trace, we can compute various summary statistics (e.g. min/max, gap width, slope, etc), and plot these over time as the wound recovers."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- "![](\"../images/microarray.jpg\")
\n",
- "\n",
- "Two-color fluorescent scan of a yeast microarray containing 2,479 elements\n",
- "(ORFs). The center-to-center distance between elements is 345 ฮผm. A probe\n",
- "mixture consisting of cDNA from yeast extract/peptone (YEP) galactose (green\n",
- "pseudocolor) and YEP glucose (red pseudocolor) grown yeast cultures was\n",
- "hybridized to the array. Intensity per element corresponds to ORF expression,\n",
- "and pseudocolor per element corresponds to relative ORF expression between the\n",
- "two cultures.โ\n",
- "\n",
- "by Deval A. Lashkari, http://www.pnas.org/content/94/24/13057/F1.expansion\n",
- "\n",
- "\n",
- "![](\"Bilinear_interpolation.png\")
\n",
- "\n",
- "\n",
- "Also see [bilinear interpolation on Wikipedia](http://en.wikipedia.org/wiki/Bilinear_interpolation)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Some warping experiments!\n",
- "\n",
- "## Fish-eye"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage import transform, data, io\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "# Load face\n",
- "face = io.imread('../images/stefan.jpg')\n",
- "\n",
- "# Get the eye nicely in the middle\n",
- "face = face[:185, 15:]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "plt.imshow(face)\n",
- "plt.plot([face.shape[1]/2.], [face.shape[0]/2.], 'or', markersize=14, alpha=0.4)\n",
- "plt.axis('image');"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Define a transformation on the x-y coordinates\n",
- "\n",
- "def fisheye(xy):\n",
- " center = np.mean(xy, axis=0)\n",
- " xc, yc = (xy - center).T\n",
- "\n",
- " # Polar coordinates\n",
- " r = np.sqrt(xc**2 + yc**2)\n",
- " theta = np.arctan2(yc, xc)\n",
- "\n",
- " r = 0.8 * np.exp(r**(1/2.1) / 1.8)\n",
- "\n",
- " return np.column_stack((r * np.cos(theta), r * np.sin(theta))) + center"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Warp and display\n",
- "\n",
- "out = transform.warp(face, fisheye)\n",
- "\n",
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 5))\n",
- "ax0.imshow(face)\n",
- "ax0.set_axis_off()\n",
- "\n",
- "ax1.imshow(out)\n",
- "ax1.set_axis_off()\n",
- "\n",
- "plt.title('Knock! Knock!')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Run the following scripts for fun:\n",
- "\n",
- "(Open up the terminal in the \"scripts\" directory first)\n",
- "\n",
- "- **deswirl.py** (run using: ``python deswirl.py``)\n",
- "\n",
- " In the UK, a criminal tried to hide his identity by posting\n",
- " swirled pictures of his face online. Here, we use the\n",
- " Mona Lisa to illustrate what he did. Can you restore\n",
- " her face back to normal? (Note that you can adjust the\n",
- " position of the red dot, as well as move the sliders.)\n",
- " \n",
- " \n",
- "- **clock_deblur.py**\n",
- "\n",
- " I took a picture of a wall clock while moving the camera. Or perhaps the clock moved.\n",
- " Either way, now I cannot read the time! I've implemented a deblurring\n",
- " algorithm--can you adjust its parameters to help me pin-point\n",
- " the time?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Here's code for a swirl transform:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage import transform\n",
- "\n",
- "def swirl(xy, center=[0, 0], strength=1, radius=100, rotation=0):\n",
- " \"\"\"Compute the coordinate mapping for a swirl transformation.\n",
- "\n",
- " \"\"\"\n",
- " x, y = xy.T\n",
- " x0, y0 = center\n",
- " rho = np.sqrt((x - x0)**2 + (y - y0)**2)\n",
- "\n",
- " # Ensure that the transformation decays to approximately 1/1000-th\n",
- " # within the specified radius.\n",
- " radius = radius / 5 * np.log(2)\n",
- "\n",
- " theta = rotation + strength * \\\n",
- " np.exp(-rho / radius) + \\\n",
- " np.arctan2(y - y0, x - x0)\n",
- "\n",
- " xy[..., 0] = x0 + rho * np.cos(theta)\n",
- " xy[..., 1] = y0 + rho * np.sin(theta)\n",
- "\n",
- " return xy\n",
- "\n",
- "\n",
- "h, w = face.shape[:2]\n",
- "\n",
- "parameters = {'center': [w/2., h/2.],\n",
- " 'strength': 8,\n",
- " 'radius': 90,\n",
- " 'rotation': 0}\n",
- "\n",
- "out = transform.warp(face, swirl, parameters)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 4))\n",
- "\n",
- "ax0.imshow(face)\n",
- "ax1.imshow(out);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Can you come up with an even better distortion?\n",
- "\n",
- "## Start with this template:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def my_warp(xy):\n",
- " x = xy[:, 0]\n",
- " y = xy[:, 1]\n",
- " \n",
- " x = x + 1.5 * np.sin(y / 3)\n",
- " \n",
- " return np.hstack((x, y))\n",
- "\n",
- "image = plt.imread('../images/stefan.jpg')\n",
- "out = transform.warp(image, my_warp)\n",
- "\n",
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 4))\n",
- "ax0.imshow(image)\n",
- "ax1.imshow(out);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Composing Transformations\n",
- "\n",
- "scikit-image allows you to compose several transformations. For example:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage import data\n",
- "\n",
- "cat = data.chelsea()\n",
- "horizontal_shift = transform.SimilarityTransform(translation=[20, 0])\n",
- "\n",
- "multiple_shifts = horizontal_shift + horizontal_shift + horizontal_shift\n",
- "\n",
- "f, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(15, 5))\n",
- "ax0.imshow(cat)\n",
- "ax1.imshow(transform.warp(cat, horizontal_shift.inverse)) # Note the inverse!\n",
- "ax2.imshow(transform.warp(cat, multiple_shifts.inverse));"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The `transform` module allows us to rotate images. The inner workings is something like this:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def my_rotate(image, angle):\n",
- " rotation_tf = transform.SimilarityTransform(rotation=np.deg2rad(angle))\n",
- " return transform.warp(image, rotation_tf.inverse)\n",
- "\n",
- "plt.imshow(my_rotate(cat, 30))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note that this rotates the cat around the origin (top-left).\n",
- "\n",
- "**Can you modify `my_rotate` to rotate the image around the center?**\n",
- "\n",
- "*Hint:*\n",
- "\n",
- "1. Shift the image (see above) so that the center of the image lies at (0, 0)\n",
- "2. Rotate the image\n",
- "3. Shift the image back---the opposite of what you did in step 1\n",
- "\n",
- "All of this can be achieved by composing transformations and calling `warp` once."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Advanced challenge: rectifying an image\n",
- "\n",
- "![](\"../images/chapel_floor.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We know the above tiles are laid out in a square--can you transform\n",
- "the image so that the tiles are displayed as if you were viewing them from above?\n",
- "\n",
- "The centre-points of the corner circles are, given as (row, column) coordinates:\n",
- "\n",
- "```\n",
- "(72, 129) -- top left\n",
- "(76, 302) -- top right\n",
- "(185, 90) -- bottom left\n",
- "(193, 326) -- bottom right\n",
- "```\n",
- "\n",
- "Hint: there is a linear transformation matrix, $H$, such that\n",
- "\n",
- "$H \\mathbf{x} = \\mathbf{x}'$\n",
- "\n",
- "where $\\mathbf{x}$ is the *homogeneous* coordinate in the original image and\n",
- "$\\mathbf{x}'$ is the *homogeneous* coordinate in the rectified image (with *homogeneous*\n",
- "we simply mean that we add an extra 1 at the end, e.g. (72, 129) becomes (72, 129, 1).\n",
- "The values for $\\mathbf{x}$ and their new values, $\\mathbf{x}'$,\n",
- "are therefore:\n",
- "\n",
- "```\n",
- "x = (72, 129, 1), x' = (0, 0, 1)\n",
- "x = (76, 302, 1), x' = (0, 400, 1)\n",
- "x = (185, 90, 1), x' = (400, 0, 1)\n",
- "x = (193, 326, 1) x' = (400, 400, 1)\n",
- "```\n",
- "\n",
- "(You can choose any output size you like--I chose $400 \\times 400$)\n",
- "\n",
- "Why do we need homogeneous coordinates? It allows us to have *translation* as part of H:\n",
- "\n",
- "$\n",
- "\\left[\\begin{array}{ccc}\n",
- "H_{00} & H_{01} & H_{02}\\\\\n",
- "H_{10} & H_{11} & H_{12}\\\\\n",
- "H_{20} & H_{21} & 1\n",
- "\\end{array}\\right]\\left[\\begin{array}{c}\n",
- "x\\\\\n",
- "y\\\\\n",
- "1\n",
- "\\end{array}\\right]=\\left[\\begin{array}{c}\n",
- "H_{00}x+H_{01}y+H_{02}\\\\\n",
- "H_{10}x+H_{11}y+H_{12}\\\\\n",
- "H_{20}x+H_{21}y+H_{22}\n",
- "\\end{array}\\right]\n",
- "$\n",
- "\n",
- "Note that each element of the output coordinate is of the form $ax + by + c$. Without the 1 in the last position of the coordinate, there would have been no $+ c$ and therefore no translation!\n",
- "\n",
- "The question on how to determine $H$ is left for another day. If you are curious, \n",
- "the [answer can be found here](homography.pdf).\n",
- "\n",
- "In the meantime, I provide some code to calculate $H$:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from skimage.transform import estimate_transform\n",
- "\n",
- "source = np.array([(129, 72),\n",
- " (302, 76),\n",
- " (90, 185),\n",
- " (326, 193)])\n",
- "\n",
- "target = np.array([[0, 0],\n",
- " [400, 0],\n",
- " [0, 400],\n",
- " [400, 400]])\n",
- "\n",
- "tf = estimate_transform('projective', source, target)\n",
- "H = tf.params\n",
- "print(H)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Using the code in the cell above, you can compute the target coordinate of any position in the original image."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Verify that the top left corner maps to (0, 0)\n",
- "\n",
- "x = np.array([[129, 72, 1]])\n",
- "\n",
- "z = np.dot(H, x.T)\n",
- "z /= z[2]\n",
- "\n",
- "print(z)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Here's a template solution:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def rectify(xy):\n",
- " x = xy[:, 0]\n",
- " y = xy[:, 1]\n",
- " \n",
- " # We need to provide the backward mapping, from the target\n",
- " # image to the source image.\n",
- " HH = np.linalg.inv(H)\n",
- " \n",
- " # You must fill in your code here to take\n",
- " # the matrix HH (given above) and to transform\n",
- " # each coordinate to its new position.\n",
- " # \n",
- " # Hint: handy functions are\n",
- " #\n",
- " # - np.dot (matrix multiplication)\n",
- " # - np.ones_like (make an array of ones the same shape as another array)\n",
- " # - np.column_stack\n",
- " # - A.T -- type .T after a matrix to transpose it\n",
- " # - x.reshape -- reshapes the array x\n",
- " \n",
- " # ... your code\n",
- " # ... your code\n",
- " # ... your code\n",
- " # ... your code\n",
- " \n",
- " return ...\n",
- "\n",
- " \n",
- "image = plt.imread('images/chapel_floor.png')\n",
- "out = transform.warp(image, rectify, output_shape=(400, 400))\n",
- "\n",
- "f, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 4))\n",
- "ax0.imshow(image)\n",
- "ax1.imshow(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
+
+
+Also see [bilinear interpolation on Wikipedia](http://en.wikipedia.org/wiki/Bilinear_interpolation)
+
++++
+
+## Some warping experiments!
+
+### Fish-eye
+
+```{code-cell} ipython3
+from skimage import transform, data, io
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load face
+face = io.imread('../images/stefan.jpg')
+
+# Get the eye nicely in the middle
+face = face[:185, 15:]
+```
+
+```{code-cell} ipython3
+plt.imshow(face)
+plt.plot([face.shape[1]/2.], [face.shape[0]/2.], 'or', markersize=14, alpha=0.4)
+plt.axis('image');
+```
+
+```{code-cell} ipython3
+# Define a transformation on the x-y coordinates
+
+def fisheye(xy):
+ center = np.mean(xy, axis=0)
+ xc, yc = (xy - center).T
+
+ # Polar coordinates
+ r = np.sqrt(xc**2 + yc**2)
+ theta = np.arctan2(yc, xc)
+
+ r = 0.8 * np.exp(r**(1/2.1) / 1.8)
+
+ return np.column_stack((r * np.cos(theta), r * np.sin(theta))) + center
+```
+
+```{code-cell} ipython3
+# Warp and display
+
+out = transform.warp(face, fisheye)
+
+f, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 5))
+ax0.imshow(face)
+ax0.set_axis_off()
+
+ax1.imshow(out)
+ax1.set_axis_off()
+
+plt.title('Knock! Knock!')
+plt.show()
+```
+
+### Run the following scripts for fun:
+
+(Open up the terminal in the "scripts" directory first)
+
+- **deswirl.py** (run using: ``python deswirl.py``)
+
+ In the UK, a criminal tried to hide his identity by posting
+ swirled pictures of his face online. Here, we use the
+ Mona Lisa to illustrate what he did. Can you restore
+ her face back to normal? (Note that you can adjust the
+ position of the red dot, as well as move the sliders.)
+
+
+- **clock_deblur.py**
+
+ I took a picture of a wall clock while moving the camera. Or perhaps the clock moved.
+ Either way, now I cannot read the time! I've implemented a deblurring
+ algorithm--can you adjust its parameters to help me pin-point
+ the time?
+
++++
+
+### Here's code for a swirl transform:
+
+```{code-cell} ipython3
+from skimage import transform
+
+def swirl(xy, center=[0, 0], strength=1, radius=100, rotation=0):
+ """Compute the coordinate mapping for a swirl transformation.
+
+ """
+ x, y = xy.T
+ x0, y0 = center
+ rho = np.sqrt((x - x0)**2 + (y - y0)**2)
+
+ # Ensure that the transformation decays to approximately 1/1000-th
+ # within the specified radius.
+ radius = radius / 5 * np.log(2)
+
+ theta = rotation + strength * \
+ np.exp(-rho / radius) + \
+ np.arctan2(y - y0, x - x0)
+
+ xy[..., 0] = x0 + rho * np.cos(theta)
+ xy[..., 1] = y0 + rho * np.sin(theta)
+
+ return xy
+
+
+h, w = face.shape[:2]
+
+parameters = {'center': [w/2., h/2.],
+ 'strength': 8,
+ 'radius': 90,
+ 'rotation': 0}
+
+out = transform.warp(face, swirl, parameters)
+```
+
+```{code-cell} ipython3
+f, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 4))
+
+ax0.imshow(face)
+ax1.imshow(out);
+```
+
+## Can you come up with an even better distortion?
+
+### Start with this template:
+
+```{code-cell} ipython3
+def my_warp(xy):
+ x = xy[:, 0]
+ y = xy[:, 1]
+
+ x = x + 1.5 * np.sin(y / 3)
+
+ return np.hstack((x, y))
+
+image = plt.imread('../images/stefan.jpg')
+out = transform.warp(image, my_warp)
+
+f, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 4))
+ax0.imshow(image)
+ax1.imshow(out);
+```
+
+### Composing Transformations
+
+scikit-image allows you to compose several transformations. For example:
+
+```{code-cell} ipython3
+from skimage import data
+
+cat = data.chelsea()
+horizontal_shift = transform.SimilarityTransform(translation=[20, 0])
+
+multiple_shifts = horizontal_shift + horizontal_shift + horizontal_shift
+
+f, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(15, 5))
+ax0.imshow(cat)
+ax1.imshow(transform.warp(cat, horizontal_shift.inverse)) # Note the inverse!
+ax2.imshow(transform.warp(cat, multiple_shifts.inverse));
+```
+
+The `transform` module allows us to rotate images. The inner workings is something like this:
+
+```{code-cell} ipython3
+def my_rotate(image, angle):
+ rotation_tf = transform.SimilarityTransform(rotation=np.deg2rad(angle))
+ return transform.warp(image, rotation_tf.inverse)
+
+plt.imshow(my_rotate(cat, 30))
+```
+
+Note that this rotates the cat around the origin (top-left).
+
+**Can you modify `my_rotate` to rotate the image around the center?**
+
+*Hint:*
+
+1. Shift the image (see above) so that the center of the image lies at (0, 0)
+2. Rotate the image
+3. Shift the image back---the opposite of what you did in step 1
+
+All of this can be achieved by composing transformations and calling `warp` once.
+
++++
+
+## Advanced challenge: rectifying an image
+
+
+
++++
+
+We know the above tiles are laid out in a square--can you transform
+the image so that the tiles are displayed as if you were viewing them from above?
+
+The centre-points of the corner circles are, given as (row, column) coordinates:
+
+```
+(72, 129) -- top left
+(76, 302) -- top right
+(185, 90) -- bottom left
+(193, 326) -- bottom right
+```
+
+Hint: there is a linear transformation matrix, $H$, such that
+
+$H \mathbf{x} = \mathbf{x}'$
+
+where $\mathbf{x}$ is the *homogeneous* coordinate in the original image and
+$\mathbf{x}'$ is the *homogeneous* coordinate in the rectified image (with *homogeneous*
+we simply mean that we add an extra 1 at the end, e.g. (72, 129) becomes (72, 129, 1).
+The values for $\mathbf{x}$ and their new values, $\mathbf{x}'$,
+are therefore:
+
+```
+x = (72, 129, 1), x' = (0, 0, 1)
+x = (76, 302, 1), x' = (0, 400, 1)
+x = (185, 90, 1), x' = (400, 0, 1)
+x = (193, 326, 1) x' = (400, 400, 1)
+```
+
+(You can choose any output size you like--I chose $400 \times 400$)
+
+Why do we need homogeneous coordinates? It allows us to have *translation* as part of H:
+
+$
+\left[\begin{array}{ccc}
+H_{00} & H_{01} & H_{02}\\
+H_{10} & H_{11} & H_{12}\\
+H_{20} & H_{21} & 1
+\end{array}\right]\left[\begin{array}{c}
+x\\
+y\\
+1
+\end{array}\right]=\left[\begin{array}{c}
+H_{00}x+H_{01}y+H_{02}\\
+H_{10}x+H_{11}y+H_{12}\\
+H_{20}x+H_{21}y+H_{22}
+\end{array}\right]
+$
+
+Note that each element of the output coordinate is of the form $ax + by + c$. Without the 1 in the last position of the coordinate, there would have been no $+ c$ and therefore no translation!
+
+The question on how to determine $H$ is left for another day. If you are curious,
+the {download}`answer can be found here