Use Python and OpenCV to achieve real-time facial recognition

pip install opencv-python

import cv2

def main():
    # Load Haar Cascade Classifier    face_cascade = ( + 'haarcascade_frontalface_default.xml')
    '''
    load Haar Cascade Classifier：
    face_cascade = ( + 'haarcascade_frontalface_default.xml')
    ()：This is OpenCV A class in，用于load预先训练好of Haar Cascade Classifier。
     + 'haarcascade_frontalface_default.xml'：This is OpenCV Introduced pre-training Haar Cascade Classifier文件路径，Used to detect positive faces。
    '''
    # Turn on the default camera    cap = (0)
    '''
    Turn on the default camera：
    cap = (0)
    ()：This is OpenCV A class in，Used to capture video。parameter 0 表示Turn on the default camera。
    '''
    while True:
        # Read a frame in the video stream        ret, frame = ()
        '''
        Read a frame in the video stream：
        ret, frame = ()
        ()：Read a frame of image from the camera。Return two values：
        ret：Boolean value，Indicates whether the read is successful。If the read is successful，ret for True；否则for False。
        frame：Read image frame。
        '''
        if not ret:
            break
        '''
         Check if the read is successful:
         if not ret:
             break
         If the read fails (such as the camera disconnects), the loop exits.
         '''
        # Convert frames to grayscale because Haar cascade classifiers require grayscale images        gray = (frame, cv2.COLOR_BGR2GRAY)
        '''
        Will帧转换for灰度：
        gray = (frame, cv2.COLOR_BGR2GRAY)
        ()：This is OpenCV A function in，For color space conversion。
        frame：Enter an image。
        cv2.COLOR_BGR2GRAY：Will BGR 图像转换for灰度图像。
        '''
        # Detect faces        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30),
                                             flags=cv2.CASCADE_SCALE_IMAGE)
        '''
        Detect faces：
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30),
                                             flags=cv2.CASCADE_SCALE_IMAGE)
        face_cascade.detectMultiScale()：This is Haar Cascade Classifierof一个方法，Used to detect human faces in images。
        gray：The input grayscale image。
        scaleFactor=1.1：The ratio of image size reduction each time。
        minNeighbors=5：The number of neighbors that each candidate rectangle should retain。
        minSize=(30, 30)：最小Detect facesof尺寸。
        flags=cv2.CASCADE_SCALE_IMAGE：Markers used to optimize detection process。
        '''
        # Draw a rectangle around the detected face        for (x, y, w, h) in faces:
            (frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
            '''
            Draw a rectangle around the detected face：
            for (x, y, w, h) in faces:
                (frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
            for (x, y, w, h) in faces：Iterate through every face detected。
            ()：This is OpenCV A function in，Used to draw rectangles on images。
            frame：Enter an image。
            (x, y)：Coordinates in the upper left corner of the rectangle。
            (x + w, y + h)：Coordinates at the lower right corner of the rectangle。
            (0, 255, 0)：Rectangle color（green）。
            2：Thickness of rectangular lines。
            '''
        # Show result frames        ('Face Detection', frame)
        '''
        Show result frames：
        ('Face Detection', frame)
        ()：This is OpenCV A function in，Used to display images。
        'Face Detection'：Window title。
        frame：Image to display。
        '''
        # Press 'q' to exit the loop        if (1) & 0xFF == ord('q'):
            break
        '''
         according to'q'Exit the loop：
        if (1) & 0xFF == ord('q'):
            break
        (1)：wait 1 millisecond，wait用户按key。
        & 0xFF：Will按key值转换for ASCII code。
        ord('q')：Get characters 'q' of ASCII code。
        If the user presses 'q' key，则Exit the loop。
        '''
    # Release the camera and close all windows    ()
    ()
    '''
    Release the camera and close all windows：
    ()
    ()
    ()：Release camera resources。
    ()：Close all OpenCV window。
    '''
if __name__ == "__main__":
    main()

python3 face_detection.py