a2_convolution.py

'''
CODE IS COPIED FROM ASSIGNMENT 1 AND REUSED FOR CONVOLUTION PART IN A2
'''
import cv2
import numpy as np

# Load image and display it.
img = cv2.imread('house.jpg')
cv2.imshow("original", img)
cv2.waitKey(0)
cv2.destroyAllWindows()  # if you press enter on the img you can proceed with the code
# Change image to grayscale and then display it.
img = cv2.cvtColor(src=img, code=cv2.COLOR_BGR2GRAY)
cv2.imshow("gray scale", img)
cv2.waitKey(0)
cv2.destroyAllWindows()  # if you press enter on the img you can proceed with the code

# Filter being use.
filter = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], np.float32)

# calculate the dimention of operation matrix
img_col_num, img_row_num = img.shape
filter_col_num, filter_row_num = filter.shape
y = img_col_num-filter_col_num + 1
x = img_row_num-filter_row_num + 1

# Create a holder for processed image.
filtered_img = np.zeros((y, x))

# iterate pixel by pixel and use convolution to apply the filter
for i in range(y):
    for j in range(x):
        filtered_img[i][j] = np.sum(
            img[i:i+filter_col_num, j:j+filter_row_num]*filter)
        # make sure to check that your pixel values are not above or below the RGB range (0-255)
        if filtered_img[i][j] > 255:
            filtered_img[i][j] = 255
        if filtered_img[i][j] < 0:
            filtered_img[i][j] = 0
    # PROGRAM STATUS INDICATOR
    print("Running!!!")

for i in range(y):
    for j in range(x):
        # Re-writing the original image.
        img[i][j] = img[i][j] + filtered_img[i][j]
    # PROGRAM STATUS INDICATOR
    print("Running!!!")

# PROGRAM STATUS INDICATOR
print("DONE!")

cv2.imshow("test", filtered_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

# Diplay processed image.
cv2.imshow("Convolution", img)
cv2.waitKey(0)
cv2.destroyAllWindows()  # if you press enter on the img you can proceed with the code