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- # @Author : lightXu
- # @File : convolve.py
- # @Time : 2019/7/8 0008 下午 16:13
- from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
- from numpy import array, zeros, ravel, pad, dot, uint8
- def im2col(image, block_size):
- rows, cols = image.shape
- dst_height = cols - block_size[1] + 1
- dst_width = rows - block_size[0] + 1
- image_array = zeros((dst_height * dst_width, block_size[1] * block_size[0]))
- row = 0
- for i in range(0, dst_height):
- for j in range(0, dst_width):
- window = ravel(image[i : i + block_size[0], j : j + block_size[1]])
- image_array[row, :] = window
- row += 1
- return image_array
- def img_convolve(image, filter_kernel):
- height, width = image.shape[0], image.shape[1]
- k_size = filter_kernel.shape[0]
- pad_size = k_size // 2
- # Pads image with the edge values of array.
- image_tmp = pad(image, pad_size, mode="edge")
- # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
- image_array = im2col(image_tmp, (k_size, k_size))
- # turn the kernel into shape(k*k, 1)
- kernel_array = ravel(filter_kernel)
- # reshape and get the dst image
- dst = dot(image_array, kernel_array).reshape(height, width)
- return dst
- if __name__ == "__main__":
- # read original image
- img = imread(r"../image_data/lena.jpg")
- # turn image in gray scale value
- gray = cvtColor(img, COLOR_BGR2GRAY)
- # Laplace operator
- Laplace_kernel = array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
- out = img_convolve(gray, Laplace_kernel).astype(uint8)
- imshow("Laplacian", out)
- waitKey(0)
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