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FaceRecognition.py
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import cv2
import face_recognition
import numpy as np
import os
path = 'learn_images'
images = []
classNames = []
myList = os.listdir(path)
print(myList)
for cl in myList:
curImg = cv2.imread(f'{path}/{cl}')
images.append(curImg)
classNames.append(os.path.splitext(cl)[0])
print('check classNames', classNames)
def findEncodings(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 = findEncodings(images)
print('Encoding completed')
cap = cv2.VideoCapture(0)
print('Check camera', cap)
# getting image from camera
while True:
success, img = cap.read()
imageS = cv2.resize(img, (0,0), None, 0.25, 0.25)
imageS = cv2.cvtColor(imageS, cv2.COLOR_BGR2RGB)
facesCurFrame = face_recognition.face_locations(imageS)
encodesCurFrame = face_recognition.face_encodings(imageS)
for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
print(faceDis)
matchIndex = np.argmin(faceDis)
print('check matchIndex',matchIndex)
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), (0, 255, 0), 2)
cv2.rectangle(img, (x1, y2-35), (x2, y2), (0, 255,0), cv2.FILLED)
cv2.putText(img, name, (x1 + 6, y2 - 6 ), cv2.FONT_HERSHEY_COMPLEX, 1, (255,255,255), 2)
cv2.imshow('Webcam', img)
cv2.waitKey(1)