#视频人脸检测

#coding:utf-8

import pytesseract as pyact
import cv2
import dlib
import datetime

# 获取人脸关键点检测器
predictor = dlib.shape_predictor(r"D:modelslydirectxmlfilesshape_predictor_68_face_landmarks.dat")

# 使用默认 人脸识别器模型
detector = dlib.get_frontal_face_detector()

def get_time():

    return datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S-%f')


def ocr_rec():
    path = r"C:UserslyDesktopshortcut.png"
    src=cv2.imread(path)
    text=pyact.image_to_string(src)
    print(text)

#opencv 版本的人脸检测
def face_rec():

    filepath = r"C:UserslyDesktoppictimg.jpg"
    img=cv2.imread(filepath)
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

    classifer = cv2.CascadeClassifier(r"D:modelslydirectxmlfileslbpcascade_frontalface.xml")
    color = (0,255,0)

    faceRects = classifer.detectMultiScale(gray,scaleFactor=1.2,minNeighbors=3,minSize=(32,32))

    if(len(faceRects)):
        for faceRect in faceRects:
            x,y,w,h = faceRect
            cv2.rectangle(img,(x,y),(x+w,y+h),color,2)
    cv2.imshow("image", img)  # 显示图像
    c = cv2.waitKey(0)

#dlib版本的人脸检测 68个特征点的检测 single pic
def dlib_face_recogn():
    # filepath = r"D:modelslydirectpics2008_001322.jpg "
    filepath = r"D:modelslydirectpicstimg.jpg "
    img=cv2.imread(filepath)
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

    # 人脸的识别器模型
    detector = dlib.get_frontal_face_detector()

    # 先通过分类器判断人脸个数
    start = datetime.datetime.now()
    dets = detector(gray,1)
    end = datetime.datetime.now()
    print("识别时间:", end - start)

    for face in dets:
        # 取出一张人脸 跌入检测器找到标记点
        # print(face.left(),face.top(),face.right(),face.bottom())

        pt1 = (face.left(),face.top())
        pt2 = (face.right(),face.bottom())


        cv2.rectangle(img,pt1,pt2,(255,0,0),1)

        start = datetime.datetime.now()
        shape = predictor(img, face)
        end = datetime.datetime.now()
        print("关键点时间:", end - start)


        for pt in shape.parts():
            pt_pos = (pt.x,pt.y)
            cv2.circle(img,pt_pos,1,(0,255,0),1)
        # cv2.imshow("image",img)
        # cv2.waitKey(0)
    pass


#视频版dlib 人脸检测
def video_dlib_face_recogn(img):

    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    dets = detector(gray,1)
    for face in dets:

        left = face.left()
        top = face.top()
        right = face.right()
        bottom = face.bottom()
        if(right-left>100):

            cv2.rectangle(img,(left,top),(right,bottom),(0,255,0),1)

            points=predictor(img,face)

            for point in points.parts():
                per_point = (point.x,point.y)
                cv2.circle(img,per_point,3,(0,0,255),1)
            cv2.imshow("image",img)
            cv2.waitKey(1)
    pass

def start_video_detect():
    video_path = ""
    # cap = cv2.VideoCapture(r"D:modelslydirectpics2020_02_05_19_34_IMG_2936.MP4")
    cap = cv2.VideoCapture(0)
    # video_len = cap.get(7)
    while(1):
        ret,img = cap.read()
        video_dlib_face_recogn(img)

        # if(ret>=video_len):
        #     break

        # if(cv2.waitKey()=='q'):
        #     break

if __name__ == '__main__':
    start=datetime.datetime.now()
    print(start)
    # dlib_face_recogn()
    # 开启检测
    start_video_detect()
    end = datetime.datetime.now()
    print(end)
    print("总体时间:",end-start)

    # face_rec()

效果

最后

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