answer_16.py

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


# prewitt filter
def prewitt_filter(img, K_size=3):
	H, W, C = 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()

	out_v = out.copy()
	out_h = out.copy()

	## prewitt vertical kernel
	Kv = [[-1., -1., -1.],[0., 0., 0.], [1., 1., 1.]]
	## prewitt horizontal kernel
	Kh = [[-1., 0., 1.],[-1., 0., 1.],[-1., 0., 1.]]

	# filtering
	for y in range(H):
		for x in range(W):
			out_v[pad + y, pad + x] = np.sum(Kv * (tmp[y: y + K_size, x: x + K_size]))
			out_h[pad + y, pad + x] = np.sum(Kh * (tmp[y: y + K_size, x: x + K_size]))


	out_v = np.clip(out_v, 0, 255)
	out_h = np.clip(out_h, 0, 255)

	out_v = out_v[pad: pad + H, pad: pad + W].astype(np.uint8)
	out_h = out_h[pad: pad + H, pad: pad + W].astype(np.uint8)

	return out_v, out_h

# Read image
img = cv2.imread("imori.jpg").astype(np.float)

# grayscale
gray = BGR2GRAY(img)

# prewitt filtering
out_v, out_h = prewitt_filter(gray, K_size=3)


# Save result
cv2.imwrite("out_v.jpg", out_v)
cv2.imshow("result", out_v)
cv2.waitKey(0)

cv2.imwrite("out_h.jpg", out_h)
cv2.imshow("result", out_h)
cv2.waitKey(0)
cv2.destroyAllWindows()