Canny edge detector: Difference between revisions

=={{Header|Python}}==

In Python, canny edge detection would normally be done using [http://scikit-image.org/docs/dev/auto_examples/plot_canny.html scikit-image] or OpenCV-Python. Here is an approach using numpy/scipy:

<lang python>#!/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, 1)

#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)</python>