Merge branch 'release/0.1.0'

Author: adamiao

.gitignore

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+#This will include files and rules I want ignored in my repository

.idea/misc.xml

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+The purpose of this repository is to create a tutorial for the application of a Sobel filter.
+This repository is currently under construction.

.idea/modules.xml

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+"""
+There is a lot of good information out there related to this topic. The image used in this example was extracted from:
+https://en.wikipedia.org/wiki/Sobel_operator
+
+The same concepts explained in this code can be used for other types of filters.
+
+I'll not go into much detail in how to transform a colored image (an array of dimension N x M x 3 where N (height) and M
+(width) defines the frame of the image while the 3 dimensions gives us the colors (RedGreenBlue)) but the formulas to do
+such transformation can be found easily in Google.
+
+There are lots of information about colored to grayscale image transformation out there on the web. I use one that takes
+the RGB values of the image and weighs them based on predefined parameters (https://en.wikipedia.org/wiki/Grayscale).
+"""
+
+from matplotlib.image import imread
+import matplotlib.pyplot as plt
+import numpy as np
+
+# #------------------------------------------------------------------------------
+# PART I - Transforming a color image into grayscale
+# #------------------------------------------------------------------------------
+
+# Here we import the image file as a tensor of shape (nx, ny, nz)
+photo_file = 'Images/original_image.PNG'
+input_image = imread(photo_file)
+[nx, ny, nz] = np.shape(input_image)  # nx: height, ny: width, nz: colors (RGB)
+
+# Extracting each one of the RGB components
+# r_img, g_img, b_img = input_image[:, :, 0], input_image[:, :, 1], input_image[:, :, 2]
+r_img, g_img, b_img = input_image[:, :, 0] / 255, input_image[:, :, 1] / 255, input_image[:, :, 2] / 255
+
+# To make it grayscale we can use the following constants to weight
+# r_const, g_const, b_const = 0.2989, 0.5870, 0.1140
+r_const, g_const, b_const = 0.2126, 0.7152, 0.0722
+
+# A weighted average of each color component will give us the grayscale image
+# grayscale = r_const*r_img + g_const*g_img + b_const*b_img
+gamma = 1.0
+grayscale = (r_const*r_img**gamma + g_const*g_img**gamma + b_const*b_img**gamma)*255
+
+# This command will display the image on the screen
+# plt.imshow(grayscale, cmap=plt.get_cmap('gray'))
+# plt.show()
+
+# #------------------------------------------------------------------------------
+# PART II - Applying the kernel Gx and Gy to image
+# #------------------------------------------------------------------------------
+
+"""
+The kernels Gx and Gy can be thought of as a differential operation in the input_image of the input image 
+in the directions x and y respectively. These kernels are given by:
+      _               _                   _                _
+     |                 |                 |                  |
+     | 1.0   0.0  -1.0 |                 |  1.0   2.0   1.0 |
+Gx = | 2.0   0.0  -2.0 |    and     Gy = |  0.0   0.0   0.0 |
+     | 1.0   0.0  -1.0 |                 | -1.0  -2.0  -1.0 |
+     |_               _|                 |_                _|
+"""
+
+# This chunk of code defines the kernel operations and outputs image
+Gx = np.array([[1.0, 0.0, -1.0], [2.0, 0.0, -2.0], [1.0, 0.0, -1.0]])
+Gy = np.array([[1.0, 2.0, 1.0], [0.0, 0.0, 0.0], [-1.0, -2.0, -1.0]])
+[rows, columns] = np.shape(grayscale)
+sobel_filtered_image = np.zeros(shape=(rows, columns))
+for i in range(rows - 2):
+    for j in range(columns - 2):
+        s1 = np.sum(np.multiply(Gx, grayscale[i:i+3, j:j+3]))
+        s2 = np.sum(np.multiply(Gy, grayscale[i:i+3, j:j+3]))
+        sobel_filtered_image[i + 1, j + 1] = np.sqrt(s1 ** 2 + s2 ** 2)
+
+
+# Change the data type for the images we want to display
+sobel_filtered_image = sobel_filtered_image.astype('int32')
+input_image = input_image.astype('int32')
+
+# Display the images
+
+fig = plt.figure()
+# plt.gray()  # show the filtered result in grayscale
+ax1 = fig.add_subplot(121)  # left side
+ax2 = fig.add_subplot(122)  # right side
+
+ax1.imshow(input_image)
+ax2.imshow(sobel_filtered_image, cmap=plt.get_cmap('gray'))
+plt.show()
+
+# Save the filtered image in destination path
+plt.imsave('test_sobel_filtered_image.png', sobel_filtered_image, cmap=plt.get_cmap('gray'))