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Raspberry Pi Computer Vision Programming. - Second Edition

You're reading from  Raspberry Pi Computer Vision Programming. - Second Edition

Product type Book
Published in Jun 2020
Publisher Packt
ISBN-13 9781800207219
Pages 306 pages
Edition 2nd Edition
Languages
Author (1):
Ashwin Pajankar Ashwin Pajankar
Profile icon Ashwin Pajankar

Table of Contents (15) Chapters

Preface Chapter 1: Introduction to Computer Vision and the Raspberry Pi Chapter 2: Preparing the Raspberry Pi for Computer Vision Chapter 3: Introduction to Python Programming Chapter 4: Getting Started with Computer Vision Chapter 5: Basics of Image Processing Chapter 6: Colorspaces, Transformations, and Thresholding Chapter 7: Let's Make Some Noise Chapter 8: High-Pass Filters and Feature Detection Chapter 9: Image Restoration, Segmentation, and Depth Maps Chapter 10: Histograms, Contours, and Morphological Transformations Chapter 11: Real-Life Applications of Computer Vision Chapter 12: Working with Mahotas and Jupyter Chapter 13: Appendix Other Books You May Enjoy

Visualizing image contours

A curve that joins all the points that lie continuously along the boundary that have the same value as the color of the pixels is known as a contour. Contours are used for detecting the boundaries in an image. Contours are also used for image segmentation. Contours are usually computed using edges in an image. However, contours are closed curves and that is their main distinction from the edges in an image. It is always a good idea to apply the thresholding operation to an image before we extract contours from an image. It will increase the accuracy of the computation of the contour operation.

The cv2.findContours() function is used to compute the contours in an image. This function accepts an image array, the mode of the retrieval of the contours, and the method for the approximation of contours as arguments. It then returns a list of computer contours in the image. The contour retrieval mode can be any of the following:

  • CV_RETR_CCOMP
  • CV_RETR_TREE...
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