class RunFaceID(object):
def __init__(self):
super(RunFaceID, self).__init__()
self.face_detection = FaceDetection()
self.face_recognition = FaceRecognition()
def predict(self, array_embeddings, embeddings_source):
return "unknown"
def processing(self, images, embeddings_source):
frame = copy.deepcopy(images)
faces = self.face_detection.detect_faces(frame)
if faces is None or len(faces) < 1:
return None
data = {}
array_img = []
labels = []
for x, y, w, h in faces:
if w > 0 and h > 0:
img_crop = frame[y:y + h, x:x + w, :]
array_img.append(img_crop)
# labels.append("unknown")
array_img = np.array(array_img)
# data["labels"] = labels
if count >= NUMBER_FRAME:
array_embeddings = self.face_recognition.embedding_image(array_img)
data["labels"] = self.predict_labels(array_embeddings,
embeddings_source)
data["bounding_boxs"] = faces
return data
class RunFaceID(object):
def __init__(self):
super(RunFaceID, self).__init__()
self.face_detection = FaceDetection()
self.face_recognition = FaceRecognition()
self.arr_embeddings = pickle.load(open("data_embeddings", "rb"))
self.labels = pickle.load(open("labels", "rb"))
def predict_labels(self, embeddings):
dis_cs = cs(embeddings, self.arr_embeddings)
index_list = np.argmax(dis_cs, axis=-1)
label_pred = []
for i, index in enumerate(index_list):
if dis_cs[i][index] > 0.6:
label_pred.append(self.labels[index])
else:
label_pred.append("unknown")
return label_pred
def processing(self, images, count):
frame = copy.deepcopy(images)
faces = self.face_detection.detect_faces(frame)
if faces is None or len(faces) < 1:
return None
data = {}
array_img = []
labels = []
for x, y, w, h in faces:
if w > 0 and h > 0:
img_crop = img_crop = frame[y:y + h, x:x + w, :]
array_img.append(img_crop)
labels.append("unknown")
array_img = np.array(array_img)
# data["labels"] = labels
if count >= 5:
array_embeddings = self.face_recognition.embedding_image(array_img)
data["labels"] = self.predict_labels(array_embeddings)
data["bounding_boxs"] = faces
return data
class RunFaceID(object):
def __init__(self):
super(RunFaceID, self).__init__()
self.face_detection = FaceDetection()
self.face_recognition = FaceRecognition()
# self.arr_embeddings = pickle.load(open("data_embeddings", "rb"))
# self.labels = pickle.load(open("labels", "rb"))
def predict_labels(self, embeddings, embeddings_source, labels_index,
labels_name):
dis_cs = cs(embeddings, embeddings_source)
index_list = np.argmax(dis_cs, axis=-1)
label_pred = []
for i, index in enumerate(index_list):
if dis_cs[i][index] > 0.6:
label_index = labels_index[index]
for i, (index_tmp, name_tmp) in enumerate(labels_name):
if label_index == index_tmp:
label_pred.append(labels_name[i])
else:
label_pred.append([-1, "unknown"])
return label_pred
def processing(self, images, count, embeddings_source, labels_index,
labels_name, message_status):
frame = copy.deepcopy(images)
faces = self.face_detection.detect_faces(frame)
if faces is None or len(faces) < 1:
return None
data = {}
array_img = []
labels = []
for x, y, w, h in faces:
if w > 0 and h > 0:
img_crop = frame[y:y + h, x:x + w, :]
array_img.append(img_crop)
# labels.append("unknown")
array_img = np.array(array_img)
# data["labels"] = labels
if count >= NUMBER_FRAME:
array_embeddings = self.face_recognition.embedding_image(array_img)
data["labels"] = self.predict_labels(array_embeddings,
embeddings_source,
labels_index, labels_name)
data["bounding_boxs"] = faces
return data
def get_faces(self, images):
faces = self.face_detection.detect_faces(images)
return list(faces)
def get_bb_embeddings(self, images):
faces = self.get_faces(images)
if len(faces) != 1:
return None
array_img = []
data = {}
for x, y, w, h in faces:
if w > 0 and h > 0:
img_crop = images[y:y + h, x:x + w, :]
array_img.append(img_crop)
embeddings = self.face_recognition.embedding_image(array_img)[0]
data["bounding_box"] = json.dumps(faces[0].tolist())
data['embeddings'] = json.dumps(embeddings.tolist())
return data
