Added two bin and updated lbp

Author: arsho

lbp.py

@@ -1,13 +1,7 @@
 import cv2
 import numpy as np
-import matplotlib
 from matplotlib import pyplot as plt
 
-def version_info():
-    print("OpenCV Version = " + cv2.__version__)
-    print("Numpy Version = " + np.__version__)
-    print("Matplotlib Version = " + matplotlib.__version__)
-
 def get_pixel(img, center, x, y):
     new_value = 0
     try:
@@ -56,31 +50,22 @@ def show_output(output_list):
         current_ytick = current_dict["ytick"]
         current_title = current_dict["title"]
         current_type = current_dict["type"]
+        current_plot = figure.add_subplot(1, output_list_len, i+1)
         if current_type == "gray":
-            current_plot = figure.add_subplot(1, output_list_len, i+1)
             current_plot.imshow(current_img, cmap = plt.get_cmap('gray'))
             current_plot.set_title(current_title)
             current_plot.set_xticks(current_xtick)
             current_plot.set_yticks(current_ytick)
             current_plot.set_xlabel(current_xlabel)
             current_plot.set_ylabel(current_ylabel)
-        elif current_type == "color":
-            current_plot = figure.add_subplot(1, output_list_len, i+1)
-            current_plot.imshow(current_img)
-            current_plot.set_title(current_title)
-            current_plot.set_xticks(current_xtick)
-            current_plot.set_yticks(current_ytick)
-            current_plot.set_xlabel(current_xlabel)
-            current_plot.set_ylabel(current_ylabel)        
         elif current_type == "histogram":
-            current_plot = figure.add_subplot(1, output_list_len, i+1)
-            current_plot.hist(current_img.flatten(),256,[0,256], color = 'black')
+            current_plot.plot(current_img, color = "black")
             current_plot.set_xlim([0,260])
             current_plot.set_title(current_title)
             current_plot.set_xlabel(current_xlabel)
             current_plot.set_ylabel(current_ylabel)            
             ytick_list = [int(i) for i in current_plot.get_yticks()]
-            current_plot.set_yticklabels(ytick_list,rotation = 90)            
+            current_plot.set_yticklabels(ytick_list,rotation = 90)
 
     plt.show()
 
@@ -94,7 +79,6 @@ def main():
     for i in range(0, height):
         for j in range(0, width):
              img_lbp[i, j] = lbp_calculated_pixel(img_gray, i, j)
-    hist_gray = cv2.calcHist([img_gray], [0], None, [256], [0, 256])
     hist_lbp = cv2.calcHist([img_lbp], [0], None, [256], [0, 256])
     output_list = []
     output_list.append({
@@ -116,7 +100,7 @@ def main():
         "type": "gray"
     })    
     output_list.append({
-        "img": img_lbp,
+        "img": hist_lbp,
         "xlabel": "Bins",
         "ylabel": "Number of pixels",
         "xtick": None,
@@ -132,5 +116,4 @@ def main():
     print("LBP Program is finished")
 
 if __name__ == '__main__':
-    version_info()
     main()

readme.md

@@ -3,6 +3,8 @@
 `Local Binary Pattern (LBP)` is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. 
 [Click here to learn more.](http://www.scholarpedia.org/article/Local_Binary_Patterns "Local Binary Pattern (LBP)")
 
+Moreover, I also added a pattern code for two bins only to compare the result of LBP and two bin pattern.
+
 ## Packages Used
 The program is coded using Windows 10 (64 bit) operating system with Python version 3.5.2.
 
@@ -15,12 +17,17 @@ Package | Version | Installation
 **Matplotlib** | 1.5.3 | `pip install matplotlib`
 
 ## Image Used
-* [Lena Söderberg](https://raw.githubusercontent.com/arsho/local_binary_patterns/master/lenna.jpg "Lena Söderberg")
+![alt Lena Söderberg](https://raw.githubusercontent.com/arsho/local_binary_patterns/master/lenna.jpg)
+* [Click to download original image of Lena Söderberg](https://raw.githubusercontent.com/arsho/local_binary_patterns/master/lenna.jpg "Lena Söderberg")
 
-## Output of `lbp.py`
+### Output of `lbp.py`
 The output consists of the gray scale image, LBP representation image and LBP histogram.
 ![alt output of lbp](https://raw.githubusercontent.com/arsho/local_binary_patterns/master/screenshot/lbp_output.png)
 
+### Output of `two_bin.py`
+The output consists of the gray scale image, Two Bin representation image and Two Bin histogram.
+![alt output of lbp](https://raw.githubusercontent.com/arsho/local_binary_patterns/master/screenshot/two_bin_output.png)
+
 ### References
 * [Local Binary Patterns with Python & OpenCV](http://www.pyimagesearch.com/2015/12/07/local-binary-patterns-with-python-opencv/ "Local Binary Patterns with Python & OpenCV")
 * [Local Binary Patterns Wiki](https://en.wikipedia.org/wiki/Local_binary_patterns "Local Binary Patterns Wiki")

screenshot/lbp_output.png

@@ -0,0 +1,101 @@
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+def get_pixel(img, center, x, y):
+    new_value = 0
+    try:
+        if img[x][y] >= center:
+            new_value = 1
+    except:
+        pass
+    return new_value
+
+def calculated_pixel(img, x, y):
+    center = img[x][y]
+    left = get_pixel(img, center, x, y-1)    
+    right = get_pixel(img, center, x, y+1)
+    if left>right:
+        return 100
+    else:
+        return 200
+
+def show_output(output_list):
+    output_list_len = len(output_list)
+    figure = plt.figure()
+    for i in range(output_list_len):
+        current_dict = output_list[i]
+        current_img = current_dict["img"]
+        current_xlabel = current_dict["xlabel"]
+        current_ylabel = current_dict["ylabel"]
+        current_xtick = current_dict["xtick"]
+        current_ytick = current_dict["ytick"]
+        current_title = current_dict["title"]
+        current_type = current_dict["type"]
+        current_plot = figure.add_subplot(1, output_list_len, i+1)            
+        if current_type == "gray":
+            current_plot.imshow(current_img, cmap = plt.get_cmap('gray'))
+            current_plot.set_title(current_title)
+            current_plot.set_xticks(current_xtick)
+            current_plot.set_yticks(current_ytick)
+            current_plot.set_xlabel(current_xlabel)
+            current_plot.set_ylabel(current_ylabel)
+        elif current_type == "histogram":
+            current_plot.plot(current_img, color = "black")
+            current_plot.set_xlim([0,260])
+            current_plot.set_title(current_title)
+            current_plot.set_xlabel(current_xlabel)
+            current_plot.set_ylabel(current_ylabel)            
+            ytick_list = [int(i) for i in current_plot.get_yticks()]
+            current_plot.set_yticklabels(ytick_list,rotation = 90)
+
+    plt.show()
+    
+def main():
+    image_file = 'lenna.jpg'
+    img_bgr = cv2.imread(image_file)
+    height, width, channel = img_bgr.shape
+    img_gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
+    
+    img_two_bin = np.zeros((height, width,3), np.uint8)
+    for i in range(0, height):
+        for j in range(0, width):
+             img_two_bin[i, j] = calculated_pixel(img_gray, i, j)
+    hist_two_bin = cv2.calcHist([img_two_bin], [0], None, [256], [0, 256])
+    output_list = []
+    output_list.append({
+        "img": img_gray,
+        "xlabel": "",
+        "ylabel": "",
+        "xtick": [],
+        "ytick": [],
+        "title": "Gray Image",
+        "type": "gray"        
+    })
+    output_list.append({
+        "img": img_two_bin,
+        "xlabel": "",
+        "ylabel": "",
+        "xtick": [],
+        "ytick": [],
+        "title": "Two Bin Image",
+        "type": "gray"
+    })    
+    output_list.append({
+        "img": hist_two_bin,
+        "xlabel": "Bins",
+        "ylabel": "Number of pixels",
+        "xtick": None,
+        "ytick": None,
+        "title": "Histogram(Two Bin)",
+        "type": "histogram"
+    })
+
+    show_output(output_list)
+                             
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    print("Two bin Program is finished")
+
+if __name__ == '__main__':
+    main()