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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
Author (1):
Alberto Fernández Villán Alberto Fernández Villán
Profile icon 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

Adaptive thresholding

In the previous section, we have applied cv2.threshold() using a global threshold value. As we could see, the obtained results were not very good due to the different illumination conditions in the different areas of the image. In these cases, you can try adaptive thresholding. In OpenCV, the adaptive thresholding is performed by the cv2.adapativeThreshold() function. The signature for this method is as follows:

adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst]) -> dst

This function applies an adaptive threshold to the src array (8-bit single-channel image). The maxValue parameter sets the value for the pixels in the dst image for which the condition is satisfied. The adaptiveMethod parameter sets the adaptive thresholding algorithm to use:

  • cv2.ADAPTIVE_THRESH_MEAN_C: The T(x, y) threshold value is calculated as the mean...
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