Source code in the python programming language

#!/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)