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Mastering OpenCV 4 with Python

You're reading from   Mastering OpenCV 4 with Python A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789344912
Length 532 pages
Edition 1st Edition
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Author (1):
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Alberto Fernández Villán Alberto Fernández Villán
Author Profile Icon Alberto Fernández Villán
Alberto Fernández Villán
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Introduction to OpenCV 4 and Python FREE CHAPTER
2. Setting Up OpenCV 3. Image Basics in OpenCV 4. Handling Files and Images 5. Constructing Basic Shapes in OpenCV 6. Section 2: Image Processing in OpenCV
7. Image Processing Techniques 8. Constructing and Building Histograms 9. Thresholding Techniques 10. Contour Detection, Filtering, and Drawing 11. Augmented Reality 12. Section 3: Machine Learning and Deep Learning in OpenCV
13. Machine Learning with OpenCV 14. Face Detection, Tracking, and Recognition 15. Introduction to Deep Learning 16. Section 4: Mobile and Web Computer Vision
17. Mobile and Web Computer Vision with Python and OpenCV 18. Assessments 19. Other Books You May Enjoy

Chapter 8

  1. The cv2.findContours() function finds contours in a binary image (for example, the resulting image after a thresholding operation).
  2. The four flags OpenCV provides for compressing contours are as follows:
  • cv2.CHAIN_APPROX_NONE
  • cv2.CHAIN_APPROX_SIMPLE
  • cv2.CHAIN_APPROX_TC89_KCOS
  • cv2.CHAIN_APPROX_TC89_L1
  1. The cv2.moments() function calculates all of the moments up to the third order of a polygon or rasterized shape.
  2. The moment, m00, gives the area of the contour.
  3. OpenCV provides the cv2.HuMoments() function to calculate the seven Hu moment invariants.
  4. The cv2.approxPolyDP() function returns a contour approximation of the given contour based on the given precision. This function uses the Douglas-Peucker algorithm. The epsilon parameter specifies the precision for establishing the maximum distance between the original curve and its approximation.
  5. The extreme_points() function...
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