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answer_19.py
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answer_19.py
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import cv2
import numpy as np
# Gray scale
def BGR2GRAY(img):
b = img[:, :, 0].copy()
g = img[:, :, 1].copy()
r = img[:, :, 2].copy()
# Gray scale
out = 0.2126 * r + 0.7152 * g + 0.0722 * b
out = out.astype(np.uint8)
return out
# LoG filter
def LoG_filter(img, K_size=5, sigma=3):
if len(img.shape) == 3:
H, W, C = img.shape
else:
H, W = img.shape
# zero padding
pad = K_size // 2
out = np.zeros((H + pad * 2, W + pad * 2), dtype=np.float)
out[pad: pad + H, pad: pad + W] = gray.copy().astype(np.float)
tmp = out.copy()
# LoG Kernel
K = np.zeros((K_size, K_size), dtype=np.float)
for x in range(-pad, -pad + K_size):
for y in range(-pad, -pad + K_size):
K[y + pad, x + pad] = (x ** 2 + y ** 2 - 2 * (sigma ** 2)) * np.exp( - (x ** 2 + y ** 2) / (2 * (sigma ** 2)))
K /= (2 * np.pi * (sigma ** 6))
K /= K.sum()
"""
K = np.array([[0, 0, 1, 0, 0],
[0, 1, 2, 1, 0],
[1, 2, -16, 2, 1],
[0, 1, 2, 1, 0],
[0, 0, 1, 0, 0]])
"""
print(K)
# filtering
for y in range(H):
for x in range(W):
out[pad + y, pad + x] = np.sum(K * tmp[y: y + K_size, x: x + K_size])
out = np.clip(out, 0, 255)
out = out[pad: pad + H, pad: pad + W].astype(np.uint8)
return out
# Read image
img = cv2.imread("imori_noise.jpg")
# grayscale
gray = BGR2GRAY(img)
# LoG filtering
out = LoG_filter(gray, K_size=5, sigma=1)
# Save result
cv2.imwrite("out.jpg", out)
cv2.imshow("result", out)
cv2.waitKey(0)
cv2.destroyAllWindows()