Create channel_functions.py

channel_functions.py

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+def individual_channel_image(img_arr, channel= 'r', ax=None):
+  img_arr = img_arr[:,:,0:3]
+  
+  if channel in ['r','red','Red']:
+    plot_arr = img_arr[:,:,2]
+    channel_name = 'Red'
+    cmap = 'Reds'
+  if channel in ['g','green','Green']:
+    plot_arr = img_arr[:,:,1]
+    channel_name = 'Green'
+    cmap = 'Greens'
+  if channel in ['b','blue','Blue']:
+    plot_arr = img_arr[:,:,0]
+    channel_name = 'Blue'
+    cmap = 'Blues'
+  
+  if channel not in ['r','red','Red','g','green','Green','b','blue','Blue']:
+    plot_arr = img_arr 
+    channel_name = 'Original'
+
+  if ax is None:
+    if channel_name == 'Original':
+      plt.imshow(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))
+    else:
+      plt.imshow(plot_arr)
+      plt.colorbar(orientation= 'vertical', shrink = 0.7, pad = 0.01)
+      plt.title('Image in the {} channel'.format(channel_name))
+      plt.show()
+
+  if ax is not None:
+    if channel_name == 'Original':
+      ax.imshow(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))
+    else:
+      plot = ax.imshow(plot_arr, cmap = cmap)
+      plt.colorbar(plot, orientation= 'vertical', ax = ax)
+      # plt.colorbar(orientiation= 'vertical', shrink = 0.7, pad = 0.1)
+      ax.set_title('Image in the {} channel'.format(channel_name))
+
+
+
+
+def channel_distribution_plotter(img_array):
+
+    img_array = img_array[:,:,:3] #Not considering the A channel, if it's a RGBA image.
+
+    plt.subplot(2,2,1)
+    plt.hist(img_array[:,:,2].ravel(),bins=256,color='red');
+    plt.title("Red Channel")
+
+    plt.subplot(2,2,2)
+    plt.hist(img_array[:,:,1].ravel(),bins=256,color='green');
+    plt.title("Green Channel")
+
+    plt.subplot(2,2,3)
+    plt.hist(img_array[:,:,1].ravel(),bins=256,color='blue');
+    plt.title("Blue Channel")
+
+    plt.subplot(2,2,4)
+    plt.imshow(cv2.cvtColor(img_array,cv2.COLOR_BGR2RGB))
+    plt.title("Original Image")
+
+    plt.suptitle("Pixel values distribution in each channel\nx-axis: pixel values, y-axis: number of pixels")
+    plt.tight_layout()
+
+    plt.show()
+
+
+
+
+def which_channel_dominates(img_arr, original_image_plot = 'yes', original_image_opacity = 0.3, channel_opacity = 0.7):
+    cmap = mcolors.ListedColormap(['red', 'green', 'blue', 'white', 'black', 'gray'])
+    img_arr = img_arr[:,:,:3]
+
+    red_channel = img_arr[:,:,2]
+    green_channel = img_arr[:,:,1]
+    blue_channel = img_arr[:,:,0]
+
+
+    print(np.max(red_channel), np.max(green_channel), np.max(blue_channel))
+
+    if original_image_plot == 'yes':
+        plt.imshow(cv2.cvtColor(img,cv2.COLOR_BGR2RGB), alpha=original_image_opacity)
+
+    which_channel_dominates = np.zeros((img_arr.shape[0],img_arr.shape[1]))
+
+    red_greater_green = np.greater(red_channel,green_channel)
+    red_greater_blue = np.greater(red_channel,blue_channel)
+    green_greater_blue = np.greater(green_channel,blue_channel)
+
+    #Red is greatest if red is greater than green and blue
+
+    which_channel_dominates[(red_greater_green & red_greater_blue)] = 1
+    which_channel_dominates[green_greater_blue & (~red_greater_green)] = 2
+    which_channel_dominates[~green_greater_blue & (~red_greater_blue)] = 3
+
+    which_channel_dominates[(red_channel == green_channel) & (red_channel == blue_channel)] = 6
+    which_channel_dominates[(red_channel == 255) & (blue_channel == 255) & (green_channel == 255)] = 4
+    which_channel_dominates[(red_channel == 0) & (blue_channel == 0) & (green_channel == 0)] = 5
+
+    print(np.unique(which_channel_dominates))
+
+    #Map the color code to the image
+    plot = plt.imshow(which_channel_dominates, cmap=cmap, alpha=channel_opacity)
+
+    #Customize the ticks of the colorbar
+    plt.colorbar(plot, orientation='vertical', 
+                #  ticks=[1,2,3,4,5,6],
+                ticks = [],
+                label='Dominant Color Channel'
+                )
+    
+    text = "Which channel dominates in the image below?\nWhite : R=G=B=255, Black : R=G=B=0\nGray : 0 < R=G=B< 255"
+    plt.title(text)