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This article mainly introduces how to use OpenCV in Python3 to achieve real-time camera face detection, the article is very detailed, has a certain reference value, interested friends must read it!

OpenCV is a C++ library, a popular computer vision programming library, which is used to deal with computer vision problems in real time. It covers many modules in the field of computer vision. OpenCV library is often used to realize image processing in Python.

Equipment preparation:

The USB camera is connected to the USB port of the PC computer, and debug to open the video normally. If the computer has a built-in camera, test whether the camera is working properly.

Download feature Classification Model: XML Model Library:

Github.com/opencv/open...

1. Find the haarcascade_frontalface_default.xml file and click in. If you want to try to detect other feature areas, download the corresponding xml file.

two。 Find Raw and right-click the link (target) to save as.

Implement procedure call model library file

Import the model library file you just downloaded into the program for code implementation:

Turn on the camera.

Turn on your computer's USB camera or bring your own camera.

Code implementation:

Set the size of the display window

Set variables to record face numbers

Face detection

Read video frames and call Opencv feature files for face detection

Code implementation:

Set exit mechanism

Press L key to exit the program * * Code implementation: * *

Program running

All codes

The above is all the contents of the article "how to use OpenCV to achieve real-time camera face detection in Python3". Thank you for reading! Hope to share the content to help you, more related knowledge, welcome to follow the industry information channel!

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