FaceRecognition/Project.py at master · derryraja/FaceRecognition · GitHub

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
import os
import cv2
import numpy as np
import face_recognition

path = 'Images'
images = []
classNames = []
mylist = os.listdir(path)
print(mylist)

for name in mylist:
    curImg = cv2.imread(f'{path}/{name}')
    images.append(curImg)
    classNames.append(os.path.splitext(name)[0])
print(classNames)

def findEncoding(images):
    encodeList = []
    for img in images:
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        encode = face_recognition.face_encodings(img)[0]
        encodeList.append(encode)
    return encodeList

encodeListKnown = findEncoding(images)
print('Encoding Completed')

cap = cv2.VideoCapture(0)

while True:
    success, img = cap.read()
    imgS = cv2.resize(img,(0,0), None, 0.25, 0.25)
    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)

    facesCurrentFrame = face_recognition.face_locations(imgS)
    encodeCurrentFrame = face_recognition.face_encodings(imgS, facesCurrentFrame)

    for encodeFace, faceLoc in zip(encodeCurrentFrame, facesCurrentFrame):
        matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
        faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
        matchIndex = np.argmin(faceDis)

        if matches[matchIndex]:
            name = classNames[matchIndex].upper()
            #print(name)
            y1, x2, y2, x1 = faceLoc
            y1, x2, y2, x1 = y1*4, x2*4, y2*4, x1*4
            cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
            cv2.rectangle(img, (x1, y2-35), (x2, y2), (255, 0, 0), cv2.FILLED)
            cv2.putText(img, name, (x1+6, y2-6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)

    cv2.imshow('Cam', img)
    cv2.waitKey(1)