OpenCV Canny Edge Detection

Edge detection is term where identify the boundary of object in image. We will learn about the edge detection using the canny edge detection technique. The syntax is canny edge detection function is given as:

Parameters-

/path/to/img: file path of the image (required)
minVal: Minimum intensity gradient (required)
maxVal: Maximum intensity gradient (required)
aperture: It is optional argument.
L2gradient: Its default value is false, if value is true, Canny () uses a more computationally expensive equation to detect edges, which provides more accuracy at the cost of resources.

Example: 1

import cv2
img = cv2.imread(r'C:\Users\DEVANSH SHARMA\cat_16x9.jpg')
edges = cv2.Canny(img, 100, 200)

cv2.imshow("Edge Detected Image", edges)
cv2.imshow("Original Image", img)
cv2.waitKey(0)  # waits until a key is pressed
cv2.destroyAllWindows()  # destroys the window showing image

Output:

Example: Real Time Edge detection

# import libraries of python OpenCV  
import cv2

# import Numpy by alias name np
import numpy as np

# capture frames from a camera 
cap = cv2.VideoCapture(0)

# loop runs if capturing has been initialized 
while (1):

    # reads frames from a camera 
    ret, frame = cap.read()

    # converting BGR to HSV 
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    # define range of red color in HSV 
    lower_red = np.array([30, 150, 50])
    upper_red = np.array([255, 255, 180])

    # create a red HSV colour boundary and  
    # threshold HSV image 
    mask = cv2.inRange(hsv, lower_red, upper_red)

    # Bitwise-AND mask and original image 
    res = cv2.bitwise_and(frame, frame, mask=mask)

    # Display an original image 
    cv2.imshow('Original', frame)

    # discovers edges in the input image image and 
    # marks them in the output map edges 
    edges = cv2.Canny(frame, 100, 200)

    # Display edges in a frame 
    cv2.imshow('Edges', edges)

    # Wait for Esc key to stop 
    k = cv2.waitKey(5) & 0xFF
    if k == 27:
        break

# Close the window 
cap.release()

# De-allocate any associated memory usage 
cv2.destroyAllWindows()

Output: