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


# Canny
def Canny(img):

	# 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


	# Gaussian filter for grayscale
	def gaussian_filter(img, K_size=3, sigma=1.3):

		if len(img.shape) == 3:
			H, W, C = img.shape
			gray = False
		else:
			img = np.expand_dims(img, axis=-1)
			H, W, C = img.shape
			gray = True

		## Zero padding
		pad = K_size // 2
		out = np.zeros([H + pad * 2, W + pad * 2, C], dtype=np.float)
		out[pad : pad + H, pad : pad + W] = img.copy().astype(np.float)

		## prepare 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] = np.exp( - (x ** 2 + y ** 2) / (2 * sigma * sigma))
		#K /= (sigma * np.sqrt(2 * np.pi))
		K /= (2 * np.pi * sigma * sigma)
		K /= K.sum()

		tmp = out.copy()

		# filtering
		for y in range(H):
			for x in range(W):
				for c in range(C):
					out[pad + y, pad + x, c] = np.sum(K * tmp[y : y + K_size, x : x + K_size, c])

		out = np.clip(out, 0, 255)
		out = out[pad : pad + H, pad : pad + W]
		out = out.astype(np.uint8)

		if gray:
			out = out[..., 0]

		return out


	# sobel filter
	def sobel_filter(img, K_size=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] = img.copy().astype(np.float)
		tmp = out.copy()

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

		## Sobel vertical
		Kv = [[1., 2., 1.],[0., 0., 0.], [-1., -2., -1.]]
		## Sobel horizontal
		Kh = [[1., 0., -1.],[2., 0., -2.],[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]
		out_v = out_v.astype(np.uint8)
		out_h = out_h[pad : pad + H, pad : pad + W]
		out_h = out_h.astype(np.uint8)

		return out_v, out_h


	def get_edge_angle(fx, fy):
		# get edge strength
		edge = np.sqrt(np.power(fx.astype(np.float32), 2) + np.power(fy.astype(np.float32), 2))
		edge = np.clip(edge, 0, 255)

		fx = np.maximum(fx, 1e-10)
		#fx[np.abs(fx) <= 1e-5] = 1e-5

		# get edge angle
		angle = np.arctan(fy / fx)

		return edge, angle


	def angle_quantization(angle):
		angle = angle / np.pi * 180
		angle[angle < -22.5] = 180 + angle[angle < -22.5]
		_angle = np.zeros_like(angle, dtype=np.uint8)
		_angle[np.where(angle <= 22.5)] = 0
		_angle[np.where((angle > 22.5) & (angle <= 67.5))] = 45
		_angle[np.where((angle > 67.5) & (angle <= 112.5))] = 90
		_angle[np.where((angle > 112.5) & (angle <= 157.5))] = 135

		return _angle


	def non_maximum_suppression(angle, edge):
		H, W = angle.shape
		_edge = edge.copy()
		
		for y in range(H):
			for x in range(W):
					if angle[y, x] == 0:
							dx1, dy1, dx2, dy2 = -1, 0, 1, 0
					elif angle[y, x] == 45:
							dx1, dy1, dx2, dy2 = -1, 1, 1, -1
					elif angle[y, x] == 90:
							dx1, dy1, dx2, dy2 = 0, -1, 0, 1
					elif angle[y, x] == 135:
							dx1, dy1, dx2, dy2 = -1, -1, 1, 1
					if x == 0:
							dx1 = max(dx1, 0)
							dx2 = max(dx2, 0)
					if x == W-1:
							dx1 = min(dx1, 0)
							dx2 = min(dx2, 0)
					if y == 0:
							dy1 = max(dy1, 0)
							dy2 = max(dy2, 0)
					if y == H-1:
							dy1 = min(dy1, 0)
							dy2 = min(dy2, 0)
					if max(max(edge[y, x], edge[y + dy1, x + dx1]), edge[y + dy2, x + dx2]) != edge[y, x]:
							_edge[y, x] = 0

		return _edge

	def hysterisis(edge, HT=100, LT=30):
		H, W = edge.shape

		# Histeresis threshold
		edge[edge >= HT] = 255
		edge[edge <= LT] = 0

		_edge = np.zeros((H + 2, W + 2), dtype=np.float32)
		_edge[1 : H + 1, 1 : W + 1] = edge

		## 8 - Nearest neighbor
		nn = np.array(((1., 1., 1.), (1., 0., 1.), (1., 1., 1.)), dtype=np.float32)

		for y in range(1, H+2):
				for x in range(1, W+2):
						if _edge[y, x] < LT or _edge[y, x] > HT:
								continue
						if np.max(_edge[y-1:y+2, x-1:x+2] * nn) >= HT:
								_edge[y, x] = 255
						else:
								_edge[y, x] = 0

		edge = _edge[1:H+1, 1:W+1]
								
		return edge

	# grayscale
	gray = BGR2GRAY(img)

	# gaussian filtering
	gaussian = gaussian_filter(gray, K_size=5, sigma=1.4)

	# sobel filtering
	fy, fx = sobel_filter(gaussian, K_size=3)

	# get edge strength, angle
	edge, angle = get_edge_angle(fx, fy)

	# angle quantization
	angle = angle_quantization(angle)

	# non maximum suppression
	edge = non_maximum_suppression(angle, edge)

	# hysterisis threshold
	out = hysterisis(edge, 50, 20)

	return out


# Morphology Dilate
def Morphology_Dilate(img, Erode_time=1):
	H, W = img.shape
	out = img.copy()

	# kernel
	MF = np.array(((0, 1, 0),
				(1, 0, 1),
				(0, 1, 0)), dtype=np.int)

	# each erode
	for i in range(Erode_time):
		tmp = np.pad(out, (1, 1), 'edge')
		# erode
		for y in range(1, H+1):
			for x in range(1, W+1):
				if np.sum(MF * tmp[y-1:y+2, x-1:x+2]) < 255*4:
					out[y-1, x-1] = 0

	return out


# Morphology Erode
def Morphology_Erode(img, Dil_time=1):
	H, W = img.shape

	# kernel
	MF = np.array(((0, 1, 0),
				(1, 0, 1),
				(0, 1, 0)), dtype=np.int)

	# each dilate time
	out = img.copy()
	for i in range(Dil_time):
		tmp = np.pad(out, (1, 1), 'edge')
		for y in range(1, H+1):
			for x in range(1, W+1):
				if np.sum(MF * tmp[y-1:y+2, x-1:x+2]) >= 255:
					out[y-1, x-1] = 255

	return out

# Morphology Closing
def Morphology_Closing(img, time=1):
	out = Morphology_Erode(img, Dil_time=time)
	out = Morphology_Dilate(out, Erode_time=time)
	return out


# Read image
img = cv2.imread("imori.jpg").astype(np.float32)

# Canny
canny = Canny(img)

# Morphology - opening
out = Morphology_Closing(canny, time=1)

# Save result
cv2.imwrite("out.jpg", out)
cv2.imshow("result", out)
cv2.waitKey(0)
cv2.destroyAllWindows()
pythonを用いて、Canny検出した後に、クロージング処理(N=1)を行え。

クロージング処理とは、モルフォロジー処理の膨張をN回行った後に収縮をN回行う処理である。

クロージング処理により、途中で途切れた画素を結合することができる。