def genFaceEmbedding(self):
# Grab the paths to the input images in our dataset
print("[INFO] quantifying faces...")
imagePaths = list(paths.list_images(self.args.dataset))
# Initialize the faces embedder
embedding_model = face_model.FaceModel(self.image_size, self.model,
self.threshold, self.det)
# Initialize our lists of extracted facial embeddings and corresponding people names
knownEmbeddings = []
knownNames = []
# Initialize the total number of faces processed
total = 0
# Loop over the imagePaths
for (i, imagePath) in enumerate(imagePaths):
# extract the person name from the image path
print("[INFO] processing image {}/{}".format(
i + 1, len(imagePaths)))
name = imagePath.split(os.path.sep)[-2]
# load the image
image = cv2.imread(imagePath)
# convert face to RGB color
nimg = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
nimg = np.transpose(nimg, (2, 0, 1))
# Get the face embedding vector
face_embedding = embedding_model.get_feature(nimg)
# add the name of the person + corresponding face
# embedding to their respective list
knownNames.append(name)
knownEmbeddings.append(face_embedding)
total += 1
print(total, " faces embedded")
# save to output
data = {"embeddings": knownEmbeddings, "names": knownNames}
f = open(self.args.embeddings, "wb")
f.write(pickle.dumps(data))
f.close()
def __init__(self):
try:
self.image_size = '112,112'
self.model = "./insightface/models/model-y1-test2/model,0"
self.threshold = 1.24
self.det = 0
self.model_filename = 'C:/Sasi/sasi/DLCVNLP/Face Recognition/FaceRecog1/src/model_data/mars-small128.pb'
#self.model_filename = 'C:/Sasi/sasi/DLCVNLP/Face Recognition/FaceRecog1/src/faceEmbeddingModels/my_model.h5'
self.encoder = gdet.create_box_encoder(self.model_filename,
batch_size=1)
# # Initialize detector
self.detector = MTCNN()
# Initialize faces embedding model
self.embedding_model = face_model.FaceModel(
self.image_size, self.model, self.threshold, self.det)
self.embeddings = "./faceEmbeddingModels/embeddings.pickle"
self.le = "./faceEmbeddingModels/le.pickle"
# Load embeddings and labels
self.data = pickle.loads(open(self.embeddings, "rb").read())
self.le = pickle.loads(open(self.le, "rb").read())
self.embeddings = np.array(self.data['embeddings'])
self.labels = self.le.fit_transform(self.data['names'])
# Load the classifier model
self.model = load_model(ConfigurationsPOJO.clssfr_ModelPath)
self.cosine_threshold = 0.8
self.proba_threshold = 0.85
self.comparing_num = 5
# # Tracker params
self.trackers = []
self.texts = []
except Exception as e:
print(e)