import cv2
import numpy as np
from matplotlib import pyplot as plt

img_path = 'inputs/checkerboard.png'
k = 0.05
window_size = 5
threshold = 1000000000.00
input_img = cv2.imread(img_path, 0)

def find_harris_corners(input_img, k, window_size, threshold):
    
    
    corner_list = []
    output_img = cv2.cvtColor(input_img.copy(), cv2.COLOR_GRAY2RGB)
    
    offset = int(window_size/2)
    y_range = input_img.shape[0] - offset
    x_range = input_img.shape[1] - offset
    
    
    dy, dx = np.gradient(input_img)
    Ixx = dx**2
    Ixy = dy*dx
    Iyy = dy**2
    
    
    for y in range(offset, y_range):
        for x in range(offset, x_range):
            
            #Values of sliding window
            start_y = y - offset
            end_y = y + offset + 1
            start_x = x - offset
            end_x = x + offset + 1
            
            #The variable names are representative to 
            #the variable of the Harris corner equation
            windowIxx = Ixx[start_y : end_y, start_x : end_x]
            windowIxy = Ixy[start_y : end_y, start_x : end_x]
            windowIyy = Iyy[start_y : end_y, start_x : end_x]
            
            #Sum of squares of intensities of partial derevatives 
            Sxx = windowIxx.sum()
            Sxy = windowIxy.sum()
            Syy = windowIyy.sum()

            #Calculate determinant and trace of the matrix
            det = (Sxx * Syy) - (Sxy**2)
            trace = Sxx + Syy
            
            #Calculate r for Harris Corner equation
            c = det - k*(trace**2)

            if c > threshold:
                corner_list.append([x, y, c])
                output_img[y,x] = (255, 255, 255)
                
            else:
                grayscale = ((threshold - c)/threshold)*100
                output_img[y,x] = (int(grayscale),int(grayscale),int(grayscale))
                
    return corner_list, output_img 

def main():

    if input_img is not None:
        
        print ("Detecting Corners Started!")
        corner_list, corner_img = find_harris_corners(input_img, k, window_size, threshold)
        
#         cv2.imshow('Detected Corners', corner_img)
#         cv2.waitKey(0)
        
        plt.figure(figsize=(12,8))
        plt.imshow(corner_img)
        plt.show()
        
        if corner_img is not None:
            cv2.imwrite("corners_img.png", corner_img)
    else:
        print ("Error in input image!")
        
    print ("Detecting Corners Complete!")



if __name__ == "__main__":
    main()

Detecting Corners Started!

Detecting Corners Complete!