1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
#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()