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Learning OpenCV 5 Computer Vision with Python

You're reading from   Learning OpenCV 5 Computer Vision with Python Tackle computer vision and machine learning with the newest tools, techniques and algorithms

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Product type Paperback
Published in Jul 2025
Publisher Packt
ISBN-13 9781803230221
Length
Edition 4th Edition
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Authors (2):
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Joe Minichino Joe Minichino
Author Profile Icon Joe Minichino
Joe Minichino
Joseph Howse Joseph Howse
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Joseph Howse
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Table of Contents (12) Chapters Close

1. Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning FREE CHAPTER
2. Setting Up OpenCV 3. Handling Files, Cameras, and GUIs 4. Processing Images with OpenCV 5. Detecting and Recognizing Faces 6. Retrieving Images and Searching Using Image Descriptors 7. Building Custom Object Detectors 8. Tracking Objects 9. Camera Models and Augmented Reality 10. Introduction to Neural Networks with OpenCV 11. OpenCV Applications at Scale Appendix A: Bending Color Space with the Curves Filter

Edge detection with Canny

OpenCV offers a handy function called Canny (after the algorithm's inventor, John F. Canny), which is very popular not only because of its effectiveness, but also because of the simplicity of its implementation in an OpenCV program since it is a one-liner:

import cv2
import numpy as np
img = cv2.imread("../images/statue_small.jpg", 0)
cv2.imwrite("canny.jpg", cv2.Canny(img, 200, 300))  # Canny in one line!
cv2.imshow("canny", cv2.imread("canny.jpg"))
cv2.waitKey()
cv2.destroyAllWindows()

The result is a very clear identification of the edges:

Figure 3.3: Results of applying Canny edge detection.

The Canny edge detection algorithm is complex but also quite interesting. It is a five-step process:

  1. Denoise the image with a Gaussian filter.
  2. Calculate the gradients.
  3. Apply non-maximum suppression (NMS) on the edges. Basically, this means that the algorithm selects the best edges from a set of overlapping edges. We&apos...
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