Showcase.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pyMRAW\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pyMRAW functionality showcase\n",
    "\n",
    "`pyMRAW` is an open-source package, enabling the efficient use of the Photron MRAW video files in Python workflows.\n",
    "\n",
    "It's main feature is the use of memory-mapped ([`np.memmap`](https://numpy.org/doc/stable/reference/generated/numpy.memmap.html)) arrays to create memory maps to locally stored raw video files and avoid loading large amounts of data into RAM. \n",
    "\n",
    "**Warning**: to take advantage of pyMRAW's memory-mapping functionality, make sure to save MRAW files either in 8-bit or 16-bit formats, corresponding to standard data types `uint8` and `uint16`! Using pyMRAW to read 12-bit MRAW files is possible, but requires loading the complete image data into RAM to produce standard Numpy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with MRAW files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MRAW files are stored along with `cihx` (or `cih` in legacy applications) metadata files. These contain vital information for interpretation of the binary image data. and must therefore be present alongside the MRAW files.\n",
    "\n",
    "To load `.mraw` - `.cihx` files, use the `pymraw.load_video` function (if the video was cut from its original size during saving, a warning will be shown on first load, and can be safely ignored):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\workspace\\_PY_PACKAGES\\pymraw\\pyMRAW.py:104: UserWarning: Clipped footage! (Total frame: 4, Original total frame: 400)\n",
      "  warnings.warn('Clipped footage! (Total frame: {}, Original total frame: {})'.format(cih['Total Frame'], cih['Original Total Frame'] ))\n"
     ]
    }
   ],
   "source": [
    "images, info = pyMRAW.load_video('data/beam.cihx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case of 16-bit image data, the `video` object is a memory-mapped array of the `uint16` data type and shape `(N_images, height, width)`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(numpy.memmap, dtype('uint16'), (4, 80, 1024))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(images), images.dtype, images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `info` object is a dictionary of metadata, read from the `cihx` file, which can also be easily viewed in Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Date': '2017/7/13',\n",
       " 'Camera Type': 'FASTCAM SA-Z type 2100K-M-64GB',\n",
       " 'Record Rate(fps)': 50000.0,\n",
       " 'Shutter Speed(s)': 200000.0,\n",
       " 'Total Frame': 4,\n",
       " 'Original Total Frame': 400,\n",
       " 'Image Width': 1024,\n",
       " 'Image Height': 80,\n",
       " 'File Format': 'Mraw',\n",
       " 'EffectiveBit Depth': 12,\n",
       " 'EffectiveBit Side': 'Lower',\n",
       " 'Color Bit': 16,\n",
       " 'Comment Text': None}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The images, contained in the `video` memory-mapped array, can be used in the same way as normal Numpy array objects.They will be accepted as input into any workflows where a Numpy array would be accepted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(numpy.memmap, dtype('uint16'), (80, 1024))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_image = images[0]\n",
    "type(first_image), first_image.dtype, first_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(first_image, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some caution is advised when processing the `memmap` arrays to avoid operations that would convert the data into conventional Numpy arrays, loading it into RAM. Especially when using non-Numpy methods, make sure that the output of your processing workflow is still a memory-mapped array before committing to processing large quantities of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving MRAW videos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pyMRAW` also includes the basic functionality of saving your custom image data into the MRAW format using the `pyMRAW.save_mraw()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_images = np.random.randint(low=0, high=255, size=(10, 64, 64), dtype=int)\n",
    "info_dict = {'Record Rate(fps)': 25,\n",
    "            'Total Frame': random_images.shape[0],\n",
    "            'Image Width': random_images.shape[2],\n",
    "            'Image Height': random_images.shape[1],\n",
    "            'Color Type': 'Mono', \n",
    "            'Color Bit': 8,\n",
    "            'Comment Text': 'Randomly generated images.',\n",
    "            }\n",
    "\n",
    "os.makedirs('temp', exist_ok=True)\n",
    "mraw_file, cih_file = pyMRAW.save_mraw(random_images, 'temp/random.mraw', info_dict=info_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The expected image format is an appropriately shaped, `(N_images, height, width)`, integer Numpy array.\n",
    "\n",
    " The `info_dict` argument is a dictionary of metadata you wish to assign to your video. Default values for key metadata entries, required to view the MRAW video using Photron's PFV software, are used if they are not provided via `info_dict`."
   ]
  }
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