def register_check():
print(">>>>>>>>>>>>>")
check_img = request.json['face_list']
check_np = np.array(check_img)
check_np = np.uint8(check_np)
check_pil = Image.fromarray(check_np)
bboxes, faces = mtcnn.align_multi(check_pil, conf.face_limit,
conf.min_face_size)
bboxes = bboxes[:, :
-1] # shape:[10,4],only keep 10 highest possibiity faces
bboxes = bboxes.astype(int)
bboxes = bboxes + [-1, -1, 1, 1]
for idx, bbox in enumerate(bboxes):
feature = get_face_feature(conf, learner.model, faces[idx])
cos_sim = get_max_cos(feature, register_list)
if cos_sim < 0.9:
check_np[bbox[1]:bbox[3], bbox[0]:bbox[2]] = cv2.blur(
check_np[bbox[1]:bbox[3], bbox[0]:bbox[2]], (23, 23))
tolist_img = check_np.tolist()
check_img = {'check_img': tolist_img}
return jsonify(check_img)
def register():
print("--------------")
register_face = request.json['face_image']
register_np = np.array(register_face)
register_pil = Image.fromarray(register_np, mode='RGB')
feature = get_face_feature(conf, learner.model, register_pil)
register_list.append(feature)
print(register_list)
return "register success!"
def register():
print("--------------")
register_face = request.json['face_image']
register_np = np.array(register_face)
register_np = np.uint8(register_np)
register_pil = Image.fromarray(register_np)
bboxes, faces = mtcnn.align_multi(register_pil, conf.face_limit, conf.min_face_size)
for face in faces:
feature = get_face_feature(conf, learner.model, face)
register_list.append(feature)
return "register success!"
def register():
print("--------------")
register_face = request.json['register_image']
register_np = np.array(register_face)
register_np = np.uint8(register_np)
register_pil = Image.fromarray(register_np)
register_name = request.json['register_name']
bbox, face = mtcnn.align(register_pil, conf.min_face_size)
feature = get_face_feature(conf, learner.model, face)
name_list.append(register_name)
register_list.append(feature)
return register_name + " register success!"
def register_check():
print(">>>>>>>>>>>>>")
face_list = request.json['face_list']
check_list=[]
for face in face_list:
face = np.array(face)
pil_img=Image.fromarray(face,mode='RGB')
feature=get_face_feature(conf,learner.model,pil_img)
cos_sim=get_max_cos(feature,register_list)
if cos_sim>0.9:
check_list.append("known")
else:
check_list.append("unknown")
print(check_list)
check_list = {'check_list':check_list}
return jsonify(check_list)
def register_check():
print(">>>>>>>Register_check<<<<<<<")
face_list = request.json['face_list']
check_list = []
for idx in range(len(face_list)):
face = np.array(face_list[idx])
pil_img = Image.fromarray(face, mode='RGB')
feature = get_face_feature(conf, learner.model, pil_img)
i, cos_sim = get_max_cos(feature, register_list)
if cos_sim > 0.97:
check_list.append(name_list[i])
else:
check_list.append("unknown")
print(check_list)
check_list = {'check_list': check_list}
return jsonify(check_list)
def register():
print("-------Register-------")
register_face = request.json['face_list']
register_np = np.array(register_face)
if register_np.shape[0] > 1:
return "no"
elif register_np.shape[0] == 1:
register_np = np.squeeze(register_np)
register_pil = Image.fromarray(register_np, mode='RGB')
feature = get_face_feature(conf, learner.model, register_pil)
register_list.append(feature)
register_name = request.json['register_name']
name_list.append(register_name)
return register_name + "register success!"
def update():
if request.method == 'GET':
old_name = request.args.get('old_name')
new_name = request.args.get('new_name')
try:
name_idx = name_list.index(old_name)
name_list[name_idx] = new_name
return new_name + ' updated'
except:
return old_name + ' is not a registered face'
else:
name = request.json['name']
new_img = request.json['new_image']
try:
idx = name_list.index(name)
new_np = np.array(new_img)
new_np = np.uint8(new_np)
new_pil = Image.fromarray(new_np)
bbox, face = mtcnn.align(new_pil, conf.min_face_size)
feature = get_face_feature(conf, learner.model, face)
register_list[idx] = feature
return name + ' image updated'
except:
return name + ' is not a registered face'