Computer-Vision/edge_detector.py at master · P-Chandana/Computer-Vision · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as img

def hor_conv(w,h,photo_matrix):

        size_kernel = 3
        kernel = np.array([[1,1,1],[0,0,0],[-1,-1,-1]])

        no_of_square_matrices = ((w-size_kernel)+1)*((h-size_kernel)+1)


        result = []
        r = (h-3)+1
        c = (w-3)+1
        for i in range((h-3)+1 ):
            for j in range((w-3)+1):
                sub_matrix = photo_matrix[i:i+3,j:j+3]
                sum = 0
                for m in range(3):
                    for n in range(3):
                        sum += (sub_matrix[m,n] * kernel[m,n])
                result.append(sum)
        result_matrix = np.asarray(result).reshape(r,c)
        return result_matrix

def vert_conv(w,h,photo_matrix):

        size_kernel = 3
        kernel = np.array([[1,0,-1],[1,0,-1],[1,0,-1]])

        no_of_square_matrices = ((w-size_kernel)+1)*((h-size_kernel)+1)


        result = []
        r = (h-3)+1
        c = (w-3)+1
        for i in range((h-3)+1 ):
            for j in range((w-3)+1):
                sub_matrix = photo_matrix[i:i+3,j:j+3]
                sum = 0
                for m in range(3):
                    for n in range(3):
                        sum += (sub_matrix[m,n] * kernel[m,n])
                result.append(sum)
        result_matrix = np.asarray(result).reshape(r,c)
        return result_matrix


def main():
    import matplotlib.pyplot as plt
    import matplotlib.image as img

    image = img.imread('image.jpg')
    black_n_white = image.mean(axis=2)
    h,w = black_n_white.shape

    vertical = vert_conv(w,h,black_n_white)
    horizontal = hor_conv(w,h,black_n_white)
    image = np.sqrt((vertical**2) + (horizontal**2))
    plt.imshow(image, cmap='gray', interpolation='nearest')
    plt.show()