Image processing in OpenCV 007_Canny edge detection

The main content of this article comes from the image processing part of OpenCV in the OpenCV-Python tutorial . The main content of this part is as follows:

• Change color space

  Learn to change images between different color spaces. Also learn to track colorful objects in videos.

• Geometric transformation of images

  Learn to apply different geometric transformations to images, such as rotation, translation, etc.

• image threshold

  Learn to convert images into binary images using global thresholding, adaptive thresholding, Otsu's binarization, and more.

• smooth image

  Learn to blur images, filter images with custom kernels, and more.

• Morphological transformation

  Understand morphological transformations such as erosion, expansion, opening, closing, etc.

• image gradient

  Learn to find image gradients, edges, and more.

• Canny edge detection

  Learn to find edges with Canny edge detection.

• image pyramid

  Learn about image pyramids and how to use them for image blending.

• Contours in OpenCV

  All about contours in OpenCV.

• Histogram in OpenCV

  All about histograms in OpenCV.

• Image transformation in OpenCV

  Different image transformations are encountered in OpenCV, such as Fourier transform, cosine transform, etc.

• template matching

  Learn to use template matching to search for objects in images.

• Hough line transform

  Learn to detect lines in an image.

• Hough circle transform

  Learn to detect circles in an image.

• Image segmentation using watershed algorithm

  Learn to segment images using the watershed segmentation algorithm.

• Interactive foreground extraction using GrabCut algorithm

  Learn to use GrabCut algorithm to extract foreground

Target

In this chapter we will learn about

• The concept of Canny edge detection
• Function provided for this in OpenCV: cv.Canny()

theory

Canny edge detection is a popular edge detection algorithm. It was developed by John F. Canny

1. This is a multi-stage algorithm and we will go through each stage.
2. Noise Reduction
   Since edge detection is susceptible to noise in the image, the first step is to use a 5x5 Gaussian filter to remove the noise in the image. We have seen this in previous chapters.
3. Find the intensity gradient of the image
   and then filter the smoothed image using the Sobel kernel in the horizontal and vertical directions to obtain the horizontal direction ( $G_x$) and vertical direction ( $G_y$) first derivative. From these two images, we can find the edge gradient and direction of each pixel as follows:
   $$\begin{gathered} Edge\_Gradient(G)=\sqrt{G_x^2+G_y^2} \\ Angle\left(i\right)={\tan }^{-1}\left(\frac{G_y}{G_x}\right) \end{gathered}$$
   gradient direction is always perpendicular to the edge. It is rounded to one of four angles representing vertical, horizontal, and two diagonal directions.
4. Non-maximum suppression
   After obtaining the gradient magnitude and direction, a full scan of the image is performed to remove any unwanted pixels that may not constitute an edge. To do this, at each pixel, it is checked whether the pixel is a local maximum in its neighborhood along the gradient direction. Check the image below:
   
   Point A is on the edge (vertical direction). The gradient direction is perpendicular to the edge. Points B and C are in the gradient direction. So point A is checked together with points B and C to see if it is a local maximum. If it is, it will be considered for the next stage, otherwise, it will be suppressed (zeroed).
   In short, the result we get is a binary image with "thin edges".
5. Hysteresis Threshold
   This stage determines which all edges are real edges and which are not. For this we need two thresholds, minValand maxVal. Any maxValedge with an intensity gradient greater than is definitely an edge, and any minValedge with an intensity gradient below is definitely a non-edge and therefore discarded. Those lying between these two thresholds are classified as edge or non-edge based on their connectivity. If they are connected to "definite edge" pixels, they are considered part of the edge. Otherwise, they are also discarded. See the image below:
   
   Edge A is maxValabove and therefore considered a "definite edge". Even though edge C is maxValbelow , it is connected to edge A and is therefore considered a valid edge, and we get the complete curve. But edge B, even though it is minValabove and in the same area as edge C, is not connected to any "definite edge", so it is discarded. minValTherefore, it is very important that we choose and accordingly maxValto get the correct results.
   This stage also removes small pixel noise under the assumption that edges are long lines.

So what we end up with are strong edges in the image.

Canny edge detection in OpenCV

OpenCV puts all of the above into one function, cv.Canny() . We'll see how to use it. The first parameter is our input image. The second and third parameters are our minValand respectively maxVal. The fourth parameter is aperture_size. It is the size of the Sobel kernel used to find the gradient of the image. Its default is 3. The last parameter is L2gradient, which specifies the equation for finding the gradient magnitude. If it is True, it uses the more precise equation mentioned above, otherwise it uses this function: $Edge\_Gradient(G)=|G_x|+|G_y|$ . Its default value isFalse.

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt


def canary_edge_detection():
    cv.samples.addSamplesDataSearchPath("/media/data/my_multimedia/opencv-4.x/samples/data")
    img = cv.imread(cv.samples.findFile('messi5.jpg'))
    edges = cv.Canny(img, 100, 200)
    plt.subplot(121), plt.imshow(img, cmap='gray')
    plt.title('Original Image'), plt.xticks([]), plt.yticks([])
    plt.subplot(122), plt.imshow(edges, cmap='gray')
    plt.title('Edge Image'), plt.xticks([]), plt.yticks([])
    plt.show()


if __name__ == "__main__":
    canary_edge_detection()

Let’s look at the following results:

Other resources

1. Canny edge detection in Wikipedia

2. Canny edge detection tutorial  by Bill Green, 2002.

practise

Write a small application to find Canny edge detection, the threshold of which can be changed using the two track bars. This way, you can understand the impact of the threshold.

Reference documentation

Canny Edge Detection

Done.