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Mastering OpenCV 4 with Python

You're reading from  Mastering OpenCV 4 with Python

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789344912
Pages 532 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Alberto Fernández Villán Alberto Fernández Villán
Profile icon Alberto Fernández Villán
LinkedIn
Alberto Fernndez Villn is a software engineer with more than 12 years of experience in developing innovative solutions. In the last couple of years, he has been working in various projects related to monitoring systems for industrial plants, applying both Internet of Things (IoT) and big data technologies. He has a Ph.D. in computer vision (2017), a deep learning certification (2018), and several publications in connection with computer vision and machine learning in journals such as Machine Vision and Applications, IEEE Transactions on Industrial Informatics, Sensors, IEEE Transactions on Industry Applications, IEEE Latin America Transactions, and more. As of 2013, he is a registered and active user (albertofernandez) on the Q&A OpenCV forum.
See other products by Alberto Fernández Villán

Table of Contents (20) Chapters

Preface 1. Section 1: Introduction to OpenCV 4 and Python
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

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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Authors (1)

Profile icon Alberto Fernández Villán
LinkedIn
Alberto Fernndez Villn is a software engineer with more than 12 years of experience in developing innovative solutions. In the last couple of years, he has been working in various projects related to monitoring systems for industrial plants, applying both Internet of Things (IoT) and big data technologies. He has a Ph.D. in computer vision (2017), a deep learning certification (2018), and several publications in connection with computer vision and machine learning in journals such as Machine Vision and Applications, IEEE Transactions on Industrial Informatics, Sensors, IEEE Transactions on Industry Applications, IEEE Latin America Transactions, and more. As of 2013, he is a registered and active user (albertofernandez) on the Q&A OpenCV forum.
Read more
See other products by Alberto Fernández Villán

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