近期在研究人脸识别相关的技术方案，由于小企业没有那么大的成本去训练自己的模型，目前选用了OpenCV4，基于Caffe的模型，之所以选择Caffe，是因为参考了业界的大佬们对比后的结论，Caffe的模型更加精准，以下为步骤

1. 首先这里使用的anaconda3进行的环境配置，操作系统使用的Ubuntu20.04，如果环境不一致出现了问题，请自行搜索排查
2. 安装好anaconda之后，需要安装Python 3.7，太高版本可能会导致一些不兼容的问题
3. 安装opencv的依赖库 sudo apt-get install libopencv-*
4. 安装caffe conda install caffe
5. 安装opencv4 conda install -c conda-forge opencv=4.1.0
6. 下载模型 首先需要把openCV大代码clone下来， git clone https://github.com/opencv/opencv.git 因为这里需要用到example中下载模型文件， 然后去目录 opencv/samples/dnn/face_detector中，运行 download_weights.py，会自动下载模型文件
7. 示例代码

#coding=utf-8
import numpy as np
import cv2,os,time

def show_detections(image,detections):
    h,w,c=image.shape
    for i in range(0, detections.shape[2]):
        confidence = detections[0, 0, i, 2]
        if confidence >0.6:
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            text = "{:.2f}%".format(confidence * 100)
            y = startY - 10 if startY - 10 > 10 else startY + 10
            cv2.rectangle(image, (startX, startY), (endX, endY),
                (0, 255,0), 1)
            cv2.putText(image, text, (startX, y),
                cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)
    return image

def detect_img(net,image):
    blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,
(300, 300), (104.0, 177.0, 123.0))
    net.setInput(blob)
    start=time.time()
    detections = net.forward()
    end=time.time()
    print(end-start)
    return show_detections(image,detections)

def test_dir(net,dir="images"):
    files=os.listdir(dir)
    for file in files:
        filepath=dir+"/"+file
        img=cv2.imread(filepath)
        showimg=detect_img(net,img)
        cv2.imshow("img",showimg)
        cv2.waitKey()

def test_camera(net):
    cap=cv2.VideoCapture(0)
    while True:
        ret,img=cap.read()
        if not ret:
            break
        showimg=detect_img(net,img)
        cv2.imshow("img",showimg)
        cv2.waitKey(1)

if __name__=="__main__":
    net = cv2.dnn.readNetFromCaffe("deploy.prototxt","res10_300x300_ssd_iter_140000_fp16.caffemodel")
    #net =cv2.dnn.readNetFromTensorflow("opencv_face_detector_uint8.pb","opencv_face_detector.pbtxt")
    #test_dir(net)
    test_camera(net)