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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
2. Setting Up OpenCV FREE CHAPTER 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

Questions

  1. What is an image histogram?
  2. Calculate the histogram of a grayscale image using 64 bins.
  3. Add 50 to every pixel on a grayscale image (the result will look lighter) and calculate the histogram.
  4. Calculate the red channel histogram of a BGR image without a mask.
  5. What functions do OpenCV, NumPy, and Matplotlib provide for calculating histograms?
  6. Modify the grayscale_histogram.py script to compute the brightness of these three images (gray_image, added_image, and subtracted_image). Rename the script to grayscale_histogram_brightness.py.
  7. Modify the comparing_hist_equalization_clahe.py script to show the execution time of both cv2.equalizeHist() and CLAHE. Rename it to comparing_hist_equalization_clahe_time.py.

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