OpenCV, an image recognition engine, is an open-source computer vision library. The name comes from “Open Source Computer Vision Library,” and it is well-known in the fields of image processing and machine vision. With OpenCV, we can easily read, edit, and analyze images. Supported features include image filtering, edge detection, shape recognition, face detection, and object tracking. If you want a computer to “understand” photos, videos, or even detect objects in real-time, OpenCV can help you achieve that. It is written in C++, but has an official Python interface, which means you only need to write a small amount of Python code to call the underlying efficient algorithms. For example, if you want to recognize a person’s face in a camera video, you just need to import OpenCV, open the camera stream, and use OpenCV’s provided face detection classifier, and the program will be able to mark the facial areas appearing in the video in real-time. In addition to image recognition, OpenCV can also perform video processing, such as applying filters to video frames, clipping, and even working with deep learning frameworks (like TensorFlow or PyTorch) to accomplish more advanced image classification and semantic segmentation tasks. OpenCV also provides support for hardware acceleration, allowing certain algorithms to run on the GPU, significantly improving processing speed. In practical applications, OpenCV has been widely used in security monitoring, medical image processing, autonomous driving, intelligent robotics, and various human-computer interaction systems. Due to its open-source nature and large community support, you can find a wealth of examples and tutorials online to help you quickly get started and realize your ideas. If you want your program to have “visual” capabilities, OpenCV will be a great starting point, enabling you to implement seemingly complex image recognition functions with minimal code.
