Python OpenCV Tutorial: Master Image Binarization in 5 Minutes

Image binarization is a process that classifies pixels into black and white categories based on a threshold, simplifying images for easier analysis, and is commonly used in scenarios such as text recognition. The core implementation relies on the `cv2.threshold()` function, which requires inputting a grayscale image, a threshold value, a maximum value, and a type, returning the actual threshold and the binarized image. Common threshold types include: `THRESH_BINARY` (pixels above the threshold turn white), `THRESH_BINARY_INV` (the opposite), and `THRESH_OTSU` (automatically calculates the optimal threshold). For threshold selection: manual selection is suitable for images with uniform brightness, Otsu's method is ideal for high-contrast scenarios, and adaptive thresholds are used for uneven lighting. The key steps are: reading the image and converting it to grayscale → selecting the threshold type → performing binarization → displaying the result. Mastering binarization supports tasks such as edge detection and object segmentation.