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_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)