1.项目结构

2.安装依赖

pip install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install numpy -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install cmake -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install dlib -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install face_recognition -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install gradio -i https://pypi.tuna.tsinghua.edu.cn/simple

3.程序实现

import cv2
import numpy as np
import face_recognition
import os
from datetime import datetime
import gradio as gr
from PIL import Image, ImageDraw, ImageFont

path = 'database'  # 人像存储位置
images = []
className = []
myList = os.listdir(path)  # 返回指定文件目录下的列表，这里返回的是人像图片
print(myList)


def cv2AddChineseText(img, text, position, textColor, textSize):
    if (isinstance(img, np.ndarray)):  # 判断是否OpenCV图片类型
        img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    # 创建一个可以在给定图像上绘图的对象
    draw = ImageDraw.Draw(img)
    # 字体的格式
    fontStyle = ImageFont.truetype(
        "simsun.ttc", textSize, encoding="utf-8")  # simsun.ttc语言包放在程序同级目录下
    # 绘制文本
    draw.text(position, text, textColor, font=fontStyle)
    # 转换回OpenCV格式
    return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)

for cl in myList:  # 获取每张人像的名称
    #curImg = cv2.imread(f'{path}/{cl}')
    # 字符流转换字节流，这样可以读取中文文件名
    with open(f'{path}/{cl}', 'rb') as f:
        image_data = f.read()
    curImg = cv2.imdecode(np.frombuffer(image_data, np.uint8), cv2.IMREAD_COLOR)

    images.append(curImg)
    className.append(os.path.splitext(cl)[0])
print(className)


def findEncodings(images):  # 获取所有存储的人像编码
    encodeList = []
    for img in images:
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        encode = face_recognition.face_encodings(img)[0]
        encodeList.append(encode)
    return encodeList


def markAttendance(name):  # 打卡，生成记录
    with open('Attendance.csv', 'r+',encoding='utf-8') as f:
        myDatalist = f.readlines()  # 读取文件中所有的行
        nameList = []
        for line in myDatalist:
            entry = line.split(',')
            nameList.append(entry[0])
        if name not in nameList:
            now = datetime.now()
            dtString = now.strftime('%H:%M:%S')  # 将日期时间格式化成字符串
            f.writelines(f'\n{name},{dtString}')  # 将包含多个字符串的可迭代对象写入文件中，这里是记录人名


encodeListKnown = findEncodings(images)
print('encoding complete')

def get_second(capture):
    if capture.isOpened():
        rate = capture.get(5)  # 帧速率
        FrameNumber = capture.get(7)  # 视频文件的帧数
        duration = FrameNumber / rate  # 帧速率/视频总帧数 是时间，除以60之后单位是分钟
        return int(rate), int(FrameNumber), int(duration)

def snap(cap):
    cap = cv2.VideoCapture(cap)
    fourcc = cv2.VideoWriter_fourcc(*'H264')
    fps = cap.get(cv2.CAP_PROP_FPS)  # 帧数
    print("视频总帧数=>",fps)
    width, height = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))  # 宽高
    out = cv2.VideoWriter('result.mp4', fourcc, fps, ((int)(width/4.0), (int)(height/4)))  # 写入视频
    frame_count = 0
    fps_all = 0
    rate, FrameNumber, duration = get_second(cap)
    print(f"帧速率：{rate},视频文件的帧数:{FrameNumber},时长：{duration}分钟")

    if cap.isOpened():
        while True:
            ret, imgs = cap.read()
            if not ret:
                break

            imgs = cv2.resize(imgs, (0, 0), None, 0.25, 0.25)  # 调整图片大小
            #imgs = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            faceCurFrame = face_recognition.face_locations(imgs)  # 获取人脸位置信息
            encodesCurFrame = face_recognition.face_encodings(imgs, faceCurFrame)  # 获取人脸编码

            for encodeFace, faceLoc in zip(encodesCurFrame, faceCurFrame):  # zip函数，连接成字典
                matches = face_recognition.compare_faces(encodeListKnown, encodeFace)  # 人脸匹配度
                faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)  # 欧式距离
                # print(faceDis)
                matchIndex = np.argmin(faceDis)  # 返回数组中小元素的索引
                if matches[matchIndex]:
                    name = className[matchIndex].upper()
                    print(name)
                    y1, x2, y2, x1 = faceLoc  # 人脸位置
                    y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
                    cv2.rectangle(imgs, (x1, y1), (x2, y2), (0, 255, 0), 1)
                    cv2.rectangle(imgs, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
                    # cv2.putText(imgs, name, (x1 + 6, y2 - 6), cv2.QT_FONT_NORMAL, 1, (255, 255, 255), 2)
                    imgs = cv2AddChineseText(imgs, name, (100, 100), (250, 242, 131), 30)
            out.write(imgs)
    else:
        print("失败")

    cap.release()
    out.release()

    result_vido_file="result.mp4"
    return result_vido_file

webcamrec = gr.Interface(
    fn = snap,
    inputs = [gr.Video(sources='webcam')],
    outputs = gr.Video(),
    live=True,
    title='face_recognition的实时人脸识别系统'
)

tabbed_interface = gr.TabbedInterface([webcamrec], ['摄像头人脸识别'])

if __name__ == "__main__":
    # 定义端口号
    gradio_port = 8080
    gradio_url = f"http://127.0.0.1:{gradio_port}"
    tabbed_interface.launch(
        server_name="127.0.0.1",
        server_port=gradio_port,
        debug=True
    )

运行效果：