OpenCV Binary Threshold Guide

In OpenCV, the setting of the binarization threshold is determined based on the characteristics and needs of the image. Typically, the binarization threshold can be determined through various methods.

1. Otsu’s Binarization: Otsu’s method is an adaptive binarization technique that automatically determines the optimal threshold value based on the histogram of the image, achieving the best separation between the foreground and background.

ret,thresh = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

1. Fixed threshold: If you have a good understanding of the characteristics of the image, you can determine a suitable fixed threshold based on experience or experimentation.

ret,thresh = cv2.threshold(img,127,255,cv2.THRESH_BINARY)

1. Adaptive thresholding: This method calculates the threshold within a local region, which can better handle images with uneven lighting.

thresh = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2)

Based on the specific application scenario and image characteristics, appropriate binarization methods and parameters can be selected to set the threshold.