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29572ad | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | import cv2
import pickle
import numpy as np
import mediapipe as mp
from scipy.spatial.distance import cosine
from src.faceRecognize.facerec.architecture import *
from src.faceRecognize.facerec.train_v2 import normalize,l2_normalizer
from src.faceRecognize.facerec.mobilenet import *
# Initialize MediaPipe Face Detection
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils
face_detection = mp_face_detection.FaceDetection(min_detection_confidence=0.5)
confidence_t=0.99
recognition_t=0.5
required_size = (160,160)
def get_face(img, box):
x1, y1, width, height = box
x1, y1 = abs(x1), abs(y1)
x2, y2 = x1 + width, y1 + height
face = img[y1:y2, x1:x2]
return face, (x1, y1), (x2, y2)
def get_encode(face_encoder, face, size):
face = normalize(face)
face = cv2.resize(face, size)
encode = face_encoder.predict(np.expand_dims(face, axis=0))[0]
return encode
def load_pickle(path):
with open(path, 'rb') as f:
encoding_dict = pickle.load(f)
return encoding_dict
def no_detect(img ,detector,encoder,encoding_dict):
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
results = detector.detect_faces(img_rgb)
try:
for res in results:
face, pt_1, pt_2 = get_face(img_rgb, res['box'])
cv2.rectangle(img, pt_1, pt_2, (0, 0, 255), 2)
except:
pass
return img
def face_detector(image):
#rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Perform face detection
results = face_detection.process(image)
# Draw the face detections and crop the detected faces
if results.detections:
for detection in results.detections:
bboxC = detection.location_data.relative_bounding_box
ih, iw, _ = image.shape
x, y, w, h = int(bboxC.xmin * iw), int(bboxC.ymin * ih), \
int(bboxC.width * iw), int(bboxC.height * ih)
# Draw bounding box
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
cropped_face = image[y:y+h, x:x+w]
return cropped_face, x, y, w, h
else:
return "None",0,0,0,0
def detect(img ,detector,encoder,encoding_dict):
#
face, x, y, w, h = detector(img)
if face is not None:
encode = get_encode(encoder, face, required_size)
encode = l2_normalizer.transform(encode.reshape(1, -1))[0]
name = 'unknown'
distance = float("inf")
for db_name, db_encode in encoding_dict.items():
dist = cosine(db_encode, encode)
if dist < recognition_t and dist < distance:
name = db_name
distance = dist
if name == 'unknown':
#cv2.rectangle(img,(x, y), (x+w, y+h), (0, 0, 255), 2)
cv2.putText(img, name, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 1)
else:
#cv2.rectangle(img,(x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.putText(img, name + f'_{1-distance:.2f}',(x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 200, 200), 2)
return img
else:
return img
if __name__ == "__main__":
required_shape = (160,160)
# face_encoder = InceptionResNetV2()
# path_m = "facenet_keras_weights.h5"
# face_encoder.load_weights(path_m)
face_encoder = build_mobilenetv2(3)
encodings_path = './faceRecognize/facerec/encodings/encodings.pkl'
encoding_dict = load_pickle(encodings_path)
COUNT = 0
cap = cv2.VideoCapture(0)
while cap.isOpened():
ret,frame = cap.read()
if not ret:
print("CAM NOT OPEND")
break
#frame = detect(frame , face_detector , face_encoder , encoding_dict)
print(frame.shape)
try:
frame = detect(frame , face_detector , face_encoder , encoding_dict)
except:
pass
cv2.imshow('camera', frame)
COUNT+=1
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
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