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Detection of Edges

By Harris Ripp

The detection of edges in image processing is very important when needing to find straight lines in pictures using a camera. One of the most popular ways to do so is using an algorithm called a Canny Edge Detector. This algorithm was developed by John F. Canny in 1986 and there are 5 main steps to using it. Below are examples of the algorithm in use:

Pre Canny Post Canny

The second image displays the result of using canny edge detection on the first image.

Steps to Edge Detection:

  • Apply Gaussian filter to smooth image
  • Find intensity gradients of image
  • Apply gradient magnitude thresholding or lower bound cut-off suppression to remove false results
  • Track edge by surprisessing weak edges so only strong ones appear

Application of Canny Edge Detection

The below function demonstrates how to use this algorithm:

def img_callback(self, msg):

        # Canny Edge Detection
        # Blurs image and find intensity gradients
        img = self.bridge.imgmsg_to_cv2(msg, 'bgr8')

        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

        kernelSize = 31

First, the image is converting into something usable by cv. It is then grayed and the intensity gradient for the kernel is found

grayBlur = cv2.GaussianBlur(gray, (kernelSize, kernelSize), 0)

The image is then blurred for canny preparation. 

lowEnd = 30
        highEnd = 100
        edges = cv2.Canny(grayBlur, lowEnd, highEnd)

        # Publish for use in finding centroid 
        self.img_pub.publish(self.bridge.cv2_to_imgmsg(edges))

The lower and upper bounds are decided and the Canny algorithm is run on the image. In the case of this function, the new image is then published to a topic called "canny mask" for use by another node.

The above code was created for use in a project completed by myself and fellow student Adam Ring