Learning Python OpenCV from Scratch: A Step-by-Step Guide to Reading and Displaying Images

This article introduces basic operations of Python OpenCV, including installation, image reading, and displaying. OpenCV is an open-source computer vision library. It can be installed via `pip install opencv-python` (or accelerated by domestic mirror sources). To verify, import the library and print the version number. For reading images, use `cv2.imread()`, specifying the path and parameters (color, grayscale, or original image), and check if the return value is `None` to confirm success. To display images, use `cv2.imshow()`, which should be accompanied by `cv2.waitKey(0)` to wait for a key press and `cv2.destroyAllWindows()` to close windows. Common issues: OpenCV reads images in BGR channels by default; use `cv2.cvtColor()` to convert to RGB to avoid color abnormalities. Path errors may cause reading failure; use absolute paths or confirm the image format. The core steps are installation, reading, and displaying, and hands-on practice can quickly master these operations.