Implement Canny Edge Detector in Python using OpenCV - GeeksforGeeks

22 Feb, 2022
In this article, we will learn the working of the popular Canny edge detection algorithm developed by John F. Canny in 1986. Usually, in Matlab and OpenCV we use the canny edge detection for many popular tasks in edge detection such as lane detection, sketching, border removal, now we will learn the internal working and implementation of this algorithm from scratch. 
The basic steps involved in this algorithm are:  
Noise reduction using Gaussian filter  
Gradient calculation along the horizontal and vertical axis  
Non-Maximum suppression of false edges  
Double thresholding for segregating strong and weak edges  
Edge tracking by hysteresis
Now let us understand these concepts in detail:  
This step is of utmost importance in the Canny edge detection. It uses a Gaussian filter for the removal of noise from the image, it is because this noise can be assumed as edges due to sudden intensity change by the edge detector. The sum of the elements in the Gaussian kernel is 1, so, the kernel should be normalized before applying as convolution to the image. In this tutorial, we will use a kernel of size 5 X 5 and sigma = 1.4, which will blur the image and remove the noise from it. The equation for Gaussian filter kernel is 
 
When the image is smoothed, the derivatives Ix and Iy are calculated w.r.t x and y axis. It can be implemented by using the Sobel-Feldman kernels convolution with image as given: 
Sobel Kernels
after applying these kernel we can use the gradient magnitudes and the angle to further process this step. The magnitude and angle can be calculated as 
Gradient magnitude and angle
 
This step aims at reducing the duplicate merging pixels along the edges to make them uneven. For each pixel find two neighbors in the positive and negative gradient directions, supposing that each neighbor occupies the angle of pi /4, and 0 is the direction straight to the right. If the magnitude of the current pixel is greater than the magnitude of the neighbors, nothing changes, otherwise, the magnitude of the current pixel is set to zero. 
The gradient magnitudes are compared with two specified threshold values, the first one is lower than the second. The gradients that are smaller than the low threshold value are suppressed, the gradients higher than the high threshold value are marked as strong ones and the corresponding pixels are included in the final edge map. All the rest gradients are marked as weak ones and pixels corresponding to these gradients are considered in the next step. 
Since a weak edge pixel caused by true edges will be connected to a strong edge pixel, pixel W with weak gradient is marked as edge and included in the final edge map if and only if it is involved in the same connected component as some pixel S with strong gradient. In other words, there should be a chain of neighbor weak pixels connecting W and S (the neighbors are 8 pixels around the considered one). We will make up and implement an algorithm that finds all the connected components of the gradient map considering each pixel only once. After that, you can decide which pixels will be included in the final edge map.Below is the implementation.  
Python3
import numpy as npimport osimport cv2import matplotlib.pyplot as plt     # defining the canny detector function   # here weak_th and strong_th are thresholds for# double thresholding stepdef Canny_detector(img, weak_th = None, strong_th = None):          # conversion of image to grayscale    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)           # Noise reduction step    img = cv2.GaussianBlur(img, (5, 5), 1.4)           # Calculating the gradients    gx = cv2.Sobel(np.float32(img), cv2.CV_64F, 1, 0, 3)    gy = cv2.Sobel(np.float32(img), cv2.CV_64F, 0, 1, 3)          # Conversion of Cartesian coordinates to polar     mag, ang = cv2.cartToPolar(gx, gy, angleInDegrees = True)           # setting the minimum and maximum thresholds     # for double thresholding    mag_max = np.max(mag)    if not weak_th:weak_th = mag_max * 0.1    if not strong_th:strong_th = mag_max * 0.5          # getting the dimensions of the input image      height, width = img.shape           # Looping through every pixel of the grayscale     # image    for i_x in range(width):        for i_y in range(height):                           grad_ang = ang[i_y, i_x]            grad_ang = abs(grad_ang-180) if abs(grad_ang)>180 else abs(grad_ang)                           # selecting the neighbours of the target pixel            # according to the gradient direction            # In the x axis direction            if grad_ang<= 22.5:                neighb_1_x, neighb_1_y = i_x-1, i_y                neighb_2_x, neighb_2_y = i_x + 1, i_y                          # top right (diagonal-1) direction            elif grad_ang>22.5 and grad_ang<=(22.5 + 45):                neighb_1_x, neighb_1_y = i_x-1, i_y-1                neighb_2_x, neighb_2_y = i_x + 1, i_y + 1                          # In y-axis direction            elif grad_ang>(22.5 + 45) and grad_ang<=(22.5 + 90):                neighb_1_x, neighb_1_y = i_x, i_y-1                neighb_2_x, neighb_2_y = i_x, i_y + 1                          # top left (diagonal-2) direction            elif grad_ang>(22.5 + 90) and grad_ang<=(22.5 + 135):                neighb_1_x, neighb_1_y = i_x-1, i_y + 1                neighb_2_x, neighb_2_y = i_x + 1, i_y-1                          # Now it restarts the cycle            elif grad_ang>(22.5 + 135) and grad_ang<=(22.5 + 180):                neighb_1_x, neighb_1_y = i_x-1, i_y                neighb_2_x, neighb_2_y = i_x + 1, i_y                           # Non-maximum suppression step            if width>neighb_1_x>= 0 and height>neighb_1_y>= 0:                if mag[i_y, i_x]<mag[neighb_1_y, neighb_1_x]:                    mag[i_y, i_x]= 0                    continue               if width>neighb_2_x>= 0 and height>neighb_2_y>= 0:                if mag[i_y, i_x]<mag[neighb_2_y, neighb_2_x]:                    mag[i_y, i_x]= 0       weak_ids = np.zeros_like(img)    strong_ids = np.zeros_like(img)                  ids = np.zeros_like(img)           # double thresholding step    for i_x in range(width):        for i_y in range(height):                          grad_mag = mag[i_y, i_x]                          if grad_mag<weak_th:                mag[i_y, i_x]= 0            elif strong_th>grad_mag>= weak_th:                ids[i_y, i_x]= 1            else:                ids[i_y, i_x]= 2                  # finally returning the magnitude of    # gradients of edges    return mag   frame = cv2.imread('food.jpeg')  # calling the designed function for# finding edgescanny_img = Canny_detector(frame)   # Displaying the input and output image  plt.figure()f, plots = plt.subplots(2, 1) plots[0].imshow(frame)plots[1].imshow(canny_img)
Input image
Output image
adnanirshad158
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