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In image processing, thresholding is a commonly used technique to convert an image into a binary image (i.e., black and white image). By setting a threshold, the pixels in the image can be divided into two categories: pixels above the threshold and pixels below the threshold. OpenCV provides various thresholding methods. This article will introduce three common thresholding techniques in detail: simple thresholding, adaptive thresholding, and Otsu's binarization. ## 1. Simple Thresholding (`cv2.threshold()`) Simple thresholding is the most basic thresholding method. It divides pixels in the image into two categories by setting a fixed threshold. OpenCV provides the `cv2.threshold()` function to implement this functionality. ### Function Prototype retval, dst = cv2.threshold(src, thresh, maxval, type) ### Parameter Description * `src`: Input image, usually a grayscale image. * `thresh`: The set threshold value. * `maxval`: The new value assigned when the pixel value exceeds (or is less than, depending on the type) the threshold. * `type`: The type of thresholding operation. Common types include: * `cv2.THRESH_BINARY`: If the pixel value is greater than the threshold, assign `maxval`, otherwise assign `0`. * `cv2.THRESH_BINARY_INV`: Opposite to `cv2.THRESH_BINARY`. If the pixel value is greater than the threshold, assign `0`, otherwise assign `maxval`. * `cv2.THRESH_TRUNC`: If the pixel value is greater than the threshold, assign the threshold value, otherwise keep unchanged. * `cv2.THRESH_TOZERO`: If the pixel value is greater than the threshold, keep unchanged, otherwise assign `0`. * `cv2.THRESH_TOZERO_INV`: Opposite to `cv2.THRESH_TOZERO`. If the pixel value is greater than the threshold, assign `0`, otherwise keep unchanged. ### Return Value * `retval`: The actual threshold used (may differ from the set threshold in some cases). * `dst`: The processed image. ### Example ## Example import cv2 import numpy as np # Read image img = cv2.imread('image.jpg',0) # Simple thresholding ret, thresh1 = cv2.threshold(img,127,255, cv2.THRESH_BINARY) # Display result cv2.imshow('Binary Threshold', thresh1) cv2.waitKey(0) cv2.destroyAllWindows() * * * ## 2. Adaptive Thresholding (`cv2.adaptiveThreshold()`) In some cases, the brightness distribution of an image is uneven, and using a fixed threshold may not yield ideal results. Adaptive thresholding calculates different thresholds for different regions of the image, thus better handling this situation. ### Function Prototype dst = cv2.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C) ### Parameter Description * `src`: Input image, usually a grayscale image. * `maxValue`: The new value assigned when the pixel value exceeds (or is less than, depending on the type) the threshold. * `adaptiveMethod`: The adaptive threshold calculation method. Common types include: * `cv2.ADAPTIVE_THRESH_MEAN_C`: The threshold is the mean value of the neighborhood minus a constant `C`. * `cv2.ADAPTIVE_THRESH_GAUSSIAN_C`: The threshold is the weighted mean value of the neighborhood minus a constant `C`, where the weights are determined by a Gaussian function. * `thresholdType`: The type of thresholding operation, usually `cv2.THRESH_BINARY` or `cv2.THRESH_BINARY_INV`. * `blockSize`: The neighborhood size used to calculate the threshold, must be an odd number. * `C`: The constant subtracted from the mean or weighted mean. ### Return Value * `dst`: The processed image. ### Example ## Example import cv2 import numpy as np # Read image img = cv2.imread('image.jpg',0) # Adaptive thresholding thresh2 = cv2.adaptiveThreshold(img,255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2) # Display result cv2.imshow('Adaptive Threshold', thresh2) cv2.waitKey(0) cv2.destroyAllWindows() ## 3. Otsu's Binarization (`cv2.threshold()` with `cv2.THRESH_OTSU`) Otsu's binarization is a method for automatically determining the threshold. It finds the optimal global threshold by maximizing the inter-class variance, suitable for bimodal images (i.e., images whose histogram has two distinct peaks). ### Function Prototype retval, dst = cv2.threshold(src, thresh, maxval, type) ### Parameter Description * `src`: Input image, usually a grayscale image. * `thresh`: Since Otsu's method automatically determines the threshold, this parameter is usually set to `0`. * `maxval`: The new value assigned when the pixel value exceeds (or is less than, depending on the type) the threshold. * `type`: The type of thresholding operation, usually `cv2.THRESH_BINARY` or `cv2.THRESH_BINARY_INV`, plus `cv2.THRESH_OTSU`. ### Return Value * `retval`: The automatically determined threshold. * `dst`: The processed image. ### Example ## Example import cv2 import numpy as np # Read image img = cv2.imread('image.jpg',0) # Otsu's binarization ret, thresh3 = cv2.threshold(img,0,255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # Display result cv2.imshow('Otsu's Threshold', thresh3) cv2.waitKey(0) cv2.destroyAllWindows()