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Edge detection is an image processing technique for finding the boundaries of objects within images. It mainly works by detecting discontinuities in brightness. One of the most popular and widely used algorithms is the Canny edge detector.
Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images.
The main stages are:
Learn more here about the theory behind the Canny edge detector.
Read also: Image Transformations using OpenCV in Python.
Alright, let's implement it in Python using OpenCV, installing it:
$ pip install opencv-python matplotlib numpy
Open up a new Python file and follow along:
import cv2 import numpy as np import matplotlib.pyplot as plt
Now let's read the image when want to detect its edges:
# read the image image = cv2.imread("little_flower.jpg")
I have an example image in my current directory, make sure you do too.
Before we pass the image to the Canny edge detector, we need to convert the image to grayscale:
# convert it to grayscale gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
Let's see it:
# show the grayscale image plt.imshow(gray, cmap="gray") plt.show()
All we need to do now is to pass this image to
cv2.Canny() function which finds edges in the input image and marks them in the output map edges using the Canny algorithm:
# perform the canny edge detector to detect image edges edges = cv2.Canny(gray, threshold1=30, threshold2=100)
Learn also: Real-time Object Tracking with OpenCV and YOLOv8 in Python.
The smallest value between
threshold2 is used for edge linking. The largest value is used to find initial segments of strong edges.
Let's see the resulting image:
Interesting, try to fine-tune the threshold values and see if you can make it better.
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If you want to use the live camera, here is the full code for that:
import numpy as np import cv2 cap = cv2.VideoCapture(0) while True: _, frame = cap.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) edges = cv2.Canny(gray, 30, 100) cv2.imshow("edges", edges) cv2.imshow("gray", gray) if cv2.waitKey(1) == ord("q"): break cap.release() cv2.destroyAllWindows()
Alright, we are done!
The purpose of detecting edges is to capture important events and changes in the properties of the world. It is one of the fundamental steps in image processing, image pattern recognition, and computer vision techniques.
Finally, I've collected some useful resources and courses for you for further learning, I highly recommend the following courses:
Learn also: How to Apply HOG Feature Extraction in Python.
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