- Apply a thresholding operation using cv2.threshold() with a threshold value of 100 and using the cv2.THRESH_BINARY thresholding type.
- Apply an adaptive thresholding operation using cv2.adapativeThreshold() ,cv2.ADAPTIVE_THRESH_MEAN_C, C=2 and blockSize=9.
- Apply Otsu's thresholding using the cv2.THRESH_BINARY thresholding type.
- Apply triangle thresholding using the cv2.THRESH_BINARY thresholding type.
- Apply Otsu's thresholding using scikit-image.
- Apply triangle thresholding using scikit-image.
- Apply Niblack's thresholding using scikit-image.
- Apply Sauvola's thresholding using scikit-image and a window size of 25.
- Modify the thresholding_example.py script in order to make use of np.arange(), with the purpose of defining the threshold values to apply to the cv2.threshold() function. Afterwards, call the cv2.threshold() function with the defined threshold...
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You're reading from Mastering OpenCV 4 with Python
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 about Alberto Fernández Villán
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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 about Alberto Fernández Villán