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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | import warnings
warnings.filterwarnings("ignore")
import cv2
import pickle
import numpy as np
import mediapipe as mp
from sklearn.preprocessing import Normalizer
from scipy.spatial.distance import cosine
#from train_v2 import normalize,l2_normalizer
from src.faceRecognize.facerec.mobilenet import *
from src.faceRecognize.facerec.architecture import *
def normalize(img):
mean, std = img.mean(), img.std()
return (img - mean) / std
l2_normalizer = Normalizer('l2')
# 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=0.7
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)
results = face_detection.process(image)
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)
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):
rgb_image = cv2.cvtColor(cv2.flip(img,1), cv2.COLOR_BGR2RGB)
face, x, y, w, h = detector(rgb_image)
face
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)
#print(dist)
if dist < 0.4 :
name = db_name
print(name,dist)
distance = dist
else:
print(name,dist)
if name == 'unknown':
#cv2.rectangle(img,(x, y), (x+w, y+h), (0, 0, 255), 2)
cv2.putText(img, name,(x, y - 5), 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,name
else:
return img,_
def model_selector(model):
if model == "Mobilenet":
face_encoder = build_mobilenetv2(3)
return face_encoder
elif model == "Facenet":
face_encoder = InceptionResNetV2()
path_m = "./src/faceRecognize/facerec/weights/facenet_keras_weights.h5"
face_encoder.load_weights(path_m)
return face_encoder
def run_code():
required_shape = (160,160)
face_encoder = model_selector("Facenet")
encodings_path = './src/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
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()
return frame
#run_code()
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