Update README.md

Author: sichkar-valentyn

README.md

@@ -36,70 +36,14 @@ Experimental results (figures and tables on this page):
 <br/>
 
 ### <a name="Examples of Simple Filtering">Examples of Simple Filtering</a>
+Taking RGB image as input, converting it to GrayScale.
+<br/>Consider following part of the code:
 
 ```py
-# Input RGB image and implementing simple filtering
-
-# Importing needed libraries
-import numpy as np
-from PIL import Image
-import matplotlib.pyplot as plt
-
 # Creating an array from image data
 input_image = Image.open("images/eagle.jpeg")
 image_np = np.array(input_image)
 
-# Checking the type of the array
-print(type(image_np))  # <class 'numpy.ndarray'>
-# Checking the shape of the array
-print(image_np.shape)  # (270, 480, 3)
-
-# Showing image with every channel separately
-channel_0 = image_np[:, :, 0]
-channel_1 = image_np[:, :, 1]
-channel_2 = image_np[:, :, 2]
-
-# Checking if all channels are different
-print(np.array_equal(channel_0, channel_1))  # False
-print(np.array_equal(channel_1, channel_2))  # False
-
-# Creating a figure with subplots
-f, ax = plt.subplots(nrows=2, ncols=2)
-# ax is (2, 2) np array and to make it easier to read we use 'flatten' function
-# Or we can call each time ax[0, 0]
-ax0, ax1, ax2, ax3 = ax.flatten()
-
-# Adjusting first subplot
-ax0.imshow(channel_0, cmap=plt.get_cmap('Reds'))
-ax0.set_xlabel('')
-ax0.set_ylabel('')
-ax0.set_title('First channel')
-
-# Adjusting second subplot
-ax1.imshow(channel_1, cmap=plt.get_cmap('Greens'))
-ax1.set_xlabel('')
-ax1.set_ylabel('')
-ax1.set_title('Second channel')
-
-# Adjusting third subplot
-ax2.imshow(channel_2, cmap=plt.get_cmap('Blues'))
-ax2.set_xlabel('')
-ax2.set_ylabel('')
-ax2.set_title('Third channel')
-
-# Adjusting fourth subplot
-ax3.imshow(image_np)
-ax3.set_xlabel('')
-ax3.set_ylabel('')
-ax3.set_title('Original image')
-
-# Function to make distance between figures
-plt.tight_layout()
-# Giving the name to the window with figure
-f.canvas.set_window_title('GreyScaled image with three identical channels')
-# Showing the plots
-plt.show()
-
 # Preparing image for Edge detection
 # Converting RGB image into GrayScale image
 # Using formula:
@@ -114,7 +58,12 @@ plt.figure('GrayScale image from RGB')
 # Showing the image by using obtained array
 plt.imshow(image_GrayScale, cmap=plt.get_cmap('gray'))
 plt.show()
+```
+
+Setting Hyperparameters and applying Pad frame for input image.
+<br/>Consider following part of the code:
 
+```py
 # Applying to the GrayScale image Pad frame with zero values
 # Using NumPy method 'pad'
 GrayScale_image_with_pad = np.pad(image_GrayScale, (1, 1), mode='constant', constant_values=0)
@@ -130,23 +79,25 @@ print(GrayScale_image_with_pad.shape)  # (272, 482)
 # Width_Out = (Width_In - K_size + 2*Pad)/Step + 1
 # Imagine, that input image is 5x5 spatial size (width and height), then output image:
 # Width_Out = (5 - 3 + 2*1)/1 + 1 = 5, and this is equal to input image
+```
 
-# Preparing zero valued output arrays for filtered images (convolved images)
-# The shape is the same with input image according to the chosen Hyperparameters
-# For three filters for Edge detection and implementing it only for one GrayScale channel
-output_image_1 = np.zeros(image_GrayScale.shape)
-output_image_2 = np.zeros(image_GrayScale.shape)
-output_image_3 = np.zeros(image_GrayScale.shape)
+Declaring filters for **Edge Detection**.
+<br/>Consider following part of the code:
 
+```py
 # Declaring standard filters (kernel) with size 3x3 for edge detection
 filter_1 = np.array([[1, 0, -1], [0, 0, 0], [-1, 0, 1]])
 filter_2 = np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
 filter_3 = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
 # Checking the shape
 print(filter_1.shape, filter_2.shape, filter_3.shape)
 # ((3, 3) (3, 3) (3, 3)
+```
 
+Creating function to delete negative values from resulted image.
+<br/>Consider following part of the code:
 
+```py
 # In order to prevent appearing values that are less than -1
 # Following function is declared
 def relu(array):
@@ -156,8 +107,12 @@ def relu(array):
     result = np.where(array > r, array, r)
     # Returning resulted array
     return result
+```
 
+Creating function to delete values that are more than 255.
+<br/>Consider following part of the code:
 
+```py
 # In order to prevent appearing values that are more than 255
 # The following function is declared
 def image_pixels(array):
@@ -170,8 +125,12 @@ def image_pixels(array):
     result = np.where(array < r, array, r)
     # Returning resulted array
     return result
+```
 
+Implementing filtering for **Edge Detection** also known as **Convolution Operation**.
+<br/>Consider following part of the code:
 
+```py
 # Implementing convolution operation for Edge detection for GrayScale image
 # Going through all input image with pad frame
 for i in range(GrayScale_image_with_pad.shape[0] - 2):
@@ -185,42 +144,10 @@ for i in range(GrayScale_image_with_pad.shape[0] - 2):
         output_image_2[i, j] = np.sum(patch_from_input_image * filter_2)
         # With filter_3
         output_image_3[i, j] = np.sum(patch_from_input_image * filter_3)
-
-# Applying 'relu' and 'image_pixels' function to get rid of negative values and that ones that more than 255
-output_image_1 = image_pixels(relu(output_image_1))
-output_image_2 = image_pixels(relu(output_image_2))
-output_image_3 = image_pixels(relu(output_image_3))
-
-# Showing results on the appropriate figures
-figure_1, ax = plt.subplots(nrows=3, ncols=1)
-
-# Adjusting first subplot
-ax[0].imshow(output_image_1, cmap=plt.get_cmap('gray'))
-ax[0].set_xlabel('')
-ax[0].set_ylabel('')
-ax[0].set_title('Edge #1')
-
-# Adjusting second subplot
-ax[1].imshow(output_image_2, cmap=plt.get_cmap('gray'))
-ax[1].set_xlabel('')
-ax[1].set_ylabel('')
-ax[1].set_title('Edge #2')
-
-# Adjusting third subplot
-ax[2].imshow(output_image_3, cmap=plt.get_cmap('gray'))
-ax[2].set_xlabel('')
-ax[2].set_ylabel('')
-ax[2].set_title('Edge #3')
-
-# Function to make distance between figures
-plt.tight_layout()
-# Giving the name to the window with figure
-figure_1.canvas.set_window_title('Convolution with filters (simple filtering)')
-# Showing the plots
-plt.show() 
-
 ```
 
+Showing resulted images on the figure.
+
 ![Simple_filtering_with_convolution](images/Simple_filtering_with_convolution.png)
 
 Full code is available here: [Simple_Filtering.py](https://github.com/sichkar-valentyn/Image_processing_in_Python/tree/master/Codes/Simple_Filtering.py)