September 2017 Update

Task/Canny-edge-detector/Python/canny-edge-detector.py

@@ -0,0 +1,86 @@
+#!/bin/python
+import numpy as np
+from scipy.ndimage.filters import convolve, gaussian_filter
+from scipy.misc import imread, imshow
+	
+def CannyEdgeDetector(im, blur = 1, highThreshold = 91, lowThreshold = 31):
+	im = np.array(im, dtype=float) #Convert to float to prevent clipping values
+
+	#Gaussian blur to reduce noise
+	im2 = gaussian_filter(im, blur)
+
+	#Use sobel filters to get horizontal and vertical gradients
+	im3h = convolve(im2,[[-1,0,1],[-2,0,2],[-1,0,1]])
+	im3v = convolve(im2,[[1,2,1],[0,0,0],[-1,-2,-1]])
+
+	#Get gradient and direction
+	grad = np.power(np.power(im3h, 2.0) + np.power(im3v, 2.0), 0.5)
+	theta = np.arctan2(im3v, im3h)
+	thetaQ = (np.round(theta * (5.0 / np.pi)) + 5) % 5 #Quantize direction
+
+	#Non-maximum suppression
+	gradSup = grad.copy()
+	for r in range(im.shape[0]):
+		for c in range(im.shape[1]):
+			#Suppress pixels at the image edge
+			if r == 0 or r == im.shape[0]-1 or c == 0 or c == im.shape[1] - 1:
+				gradSup[r, c] = 0
+				continue
+			tq = thetaQ[r, c] % 4
+
+			if tq == 0: #0 is E-W (horizontal)
+				if grad[r, c] <= grad[r, c-1] or grad[r, c] <= grad[r, c+1]:
+					gradSup[r, c] = 0
+			if tq == 1: #1 is NE-SW
+				if grad[r, c] <= grad[r-1, c+1] or grad[r, c] <= grad[r+1, c-1]:
+					gradSup[r, c] = 0
+			if tq == 2: #2 is N-S (vertical)
+				if grad[r, c] <= grad[r-1, c] or grad[r, c] <= grad[r+1, c]:
+					gradSup[r, c] = 0
+			if tq == 3: #3 is NW-SE
+				if grad[r, c] <= grad[r-1, c-1] or grad[r, c] <= grad[r+1, c+1]:
+					gradSup[r, c] = 0
+
+	#Double threshold
+	strongEdges = (gradSup > highThreshold)
+
+	#Strong has value 2, weak has value 1
+	thresholdedEdges = np.array(strongEdges, dtype=np.uint8) + (gradSup > lowThreshold)
+
+	#Tracing edges with hysteresis	
+	#Find weak edge pixels near strong edge pixels
+	finalEdges = strongEdges.copy()
+	currentPixels = []
+	for r in range(1, im.shape[0]-1):
+		for c in range(1, im.shape[1]-1):	
+			if thresholdedEdges[r, c] != 1:
+				continue #Not a weak pixel
+			
+			#Get 3x3 patch	
+			localPatch = thresholdedEdges[r-1:r+2,c-1:c+2]
+			patchMax = localPatch.max()
+			if patchMax == 2:
+				currentPixels.append((r, c))
+				finalEdges[r, c] = 1
+
+	#Extend strong edges based on current pixels
+	while len(currentPixels) > 0:
+		newPix = []
+		for r, c in currentPixels:
+			for dr in range(-1, 2):
+				for dc in range(-1, 2):
+					if dr == 0 and dc == 0: continue
+					r2 = r+dr
+					c2 = c+dc
+					if thresholdedEdges[r2, c2] == 1 and finalEdges[r2, c2] == 0:
+						#Copy this weak pixel to final result
+						newPix.append((r2, c2))
+						finalEdges[r2, c2] = 1
+		currentPixels = newPix
+
+	return finalEdges
+
+if __name__=="__main__":
+	im = imread("test.jpg", mode="L") #Open image, convert to greyscale
+	finalEdges = CannyEdgeDetector(im)
+	imshow(finalEdges)