def init_facebank(conf, learner, mtcnn):
if UPDATE:
targets, names = prepare_facebank(conf, learner.model, mtcnn, tta=TTA)
print('facebank updated')
else:
targets, names = load_facebank(conf)
print('facebank loaded')
return targets, names
def update_old_facebank(self, stream_url):
self.stream_url = stream_url
self.cap = cv2.VideoCapture(self.stream_url)
self.cap.set(cv2.CAP_PROP_POS_MSEC, random.randint(3, 1000))
self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 2)
self.targets, self.names = prepare_facebank(self.conf,
self.learner.model,
self.mtcnn,
tta=True)
print('facebank updated')
def train():
conf = get_config(False)
mtcnn = MTCNN()
#print('mtcnn loaded')
learner = face_learner(conf, True)
learner.load_state(conf, 'cpu_final.pth', True, True)
learner.model.eval()
print('learner loaded')
targets, names = prepare_facebank(conf, learner.model, mtcnn)
return {'_result': 'success', '_message': 'Model Is Updated'}
def OnUpdateRegister(self):
self.initDatabase(update=1)
self.targets, self.names = prepare_facebank(conf, learner.model, mtcnn,
True)
for i in range(len(self.targets)):
id_, unit, name, gender = self.names[i + 1].split('_')
self.insertRow([id_, unit, name, gender], 1)
print('人脸数据更新成功')
self.infoText.AppendText('[%s]' % getDateAndTime(hms=1) +
'人脸数据库已更新\r\n')
self.loadDataBase(1)
def fn_face_verify_module():
mtcnn = MTCNN()
print('mtcnn loaded')
learner = face_learner(conf, True)
learner.threshold = args.threshold
if conf.device.type == 'cpu':
learner.load_state(conf, 'cpu_final.pth', True, True)
else:
learner.load_state(conf, 'final.pth', True, True)
learner.model.eval()
print('learner loaded')
if args.update:
targets, names = prepare_facebank(conf,
learner.model,
mtcnn,
tta=args.tta)
print('facebank updated')
else:
targets, names = load_facebank(conf)
print('facebank loaded')
isSuccess, frame = cap.read()
if isSuccess:
try:
image = Image.fromarray(frame)
bboxes, faces = mtcnn.align_multi(image, 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] # personal choice
results, score = learner.infer(conf, faces, targets, args.tta)
for idx, bbox in enumerate(bboxes):
if args.score:
frame = draw_box_name(
bbox,
names[results[idx] + 1] + '_{:.2f}'.format(score[idx]),
frame)
else:
frame = draw_box_name(bbox, names[results[idx] + 1], frame)
except:
print('detect error')
cv2.imshow('face Capture', frame)
if args.save:
video_writer.write(frame)
def register(user_id):
data_path = Path('data')
save_path = data_path / 'facebank' / user_id
fetch_path = data_path / 'dataset' / user_id
images = load_images_from_folder(fetch_path)
print(images)
if not save_path.exists():
save_path.mkdir()
mtcnn = MTCNN()
count = 0
face_id = user_id
count = 0
for img in images:
frame = img
p = Image.fromarray(frame[..., ::-1])
try:
warped_face = np.array(mtcnn.align(p))[..., ::-1]
cv2.imwrite(
"data/facebank/" + str(face_id) + '/' + str(face_id) + '_' +
str(count) + ".jpg", warped_face)
count += 1
#cv2.imwrite(str(save_path/'{}.jpg'.format(str(datetime.now())[:-7].replace(":","-").replace(" ","-"))), warped_face)
except:
result = {
"_result": "Error",
"_message": "Unable to detect the face"
}
if count == len(images):
result = {
"_result": "success",
"_message": "User Registered Successfully"
}
conf = get_config(False)
learner = face_learner(conf, True)
learner.load_state(conf, 'cpu_final.pth', True, True)
learner.model.eval()
#print('learner loaded')
targets, names = prepare_facebank(conf, learner.model, mtcnn, user_id)
#print('facebank updated')
return result
async def update_facebank(self, files, stream_url):
self.stream_url = stream_url
self.cap = cv2.VideoCapture(self.stream_url)
self.cap.set(cv2.CAP_PROP_POS_MSEC, random.randint(3, 1000))
self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 2)
shutil.rmtree(FACEBANK_DIR, ignore_errors=False, onerror=None)
os.makedirs(FACEBANK_DIR)
for file in files:
content = await file.read()
nparr = np.fromstring(content, np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
boxes, faces, face_ids, np_faces = self.detect_yolo_gpu(img)
for idx, face in enumerate(faces):
with open(
os.path.join(FACEBANK_DIR,
str(idx) + "_" + file.filename),
"wb") as buffer:
shutil.copyfileobj(face, buffer)
self.targets, self.names = prepare_facebank(self.conf,
self.learner.model,
self.mtcnn,
tta=True)
print('facebank updated')
def predict():
#img=img_path#注意修改
parser = argparse.ArgumentParser(description='for face verification')
parser.add_argument("-s",
"--save",
help="whether save",
action="store_true")
parser.add_argument('-th',
'--threshold',
help='threshold to decide identical faces',
default=1.54,
type=float)
parser.add_argument("-u",
"--update",
default=True,
help="whether perform update the facebank",
action="store_true")
parser.add_argument("-tta",
"--tta",
default=False,
help="whether testCode time augmentation",
action="store_true")
parser.add_argument("-c",
"--score",
default=True,
help="whether show the confidence score",
action="store_true")
parser.add_argument('--img_path',
'-p',
default='1.jpg',
type=str,
help='input the name of the recording person')
args = parser.parse_args()
mtcnn = MTCNN()
conf = get_config(False)
learner = face_learner(conf, True)
learner.threshold = args.threshold
image = Image.open(
'D:/code/Python/InsightFace-刘悦/InsightFace_Pytorch-master/1.jpg')
if conf.device.type == 'cpu':
learner.load_state(conf, 'ir_se50.pth', True, True)
else:
learner.load_state(conf, 'ir_se50.pth', True, True)
learner.model.eval()
print('learner loaded')
if args.update:
targets, names = prepare_facebank(conf,
learner.model,
mtcnn,
tta=args.tta)
print('facebank updated')
else:
targets, names = load_facebank(conf)
print('facebank loaded')
try:
# image = Image.fromarray(img)
# 利用mtcnn网络，对齐
bboxes, faces = mtcnn.align_multi(image, 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] # personal choice
results, score = learner.infer(conf, faces, targets, args.tta)
print(results, score)
for idx, bbox in enumerate(bboxes):
print(names[results[idx] + 1])
res = "name:" + names[results[idx] + 1]
except:
print('detect')
res = "unknow"
return res
receptive_field_list=cfg.param_receptive_field_list,
receptive_field_stride=cfg.param_receptive_field_stride,
bbox_small_list=cfg.param_bbox_small_list,
bbox_large_list=cfg.param_bbox_large_list,
receptive_field_center_start=cfg.param_receptive_field_center_start,
num_output_scales=cfg.param_num_output_scales)
return face_detector
if __name__ == '__main__':
args = args_parser()
model = face_model.FaceModel(args)
conf = get_config(training=False)
targets, names = prepare_facebank(conf=conf,
model=model,
args=args,
tta=True)
if not os.path.exists(conf.demo):
os.mkdir(conf.demo)
cap = cv2.VideoCapture(str(args.file_name))
cap.set(cv2.CAP_PROP_POS_MSEC, args.begin * 1000)
fps = cap.get(cv2.CAP_PROP_FPS)
video_writer = cv2.VideoWriter(
str(conf.facebank_path / '{}.avi'.format(args.save_name)),
cv2.VideoWriter_fourcc(*'XVID'), int(fps), (1280, 720))
# for file in glob.glob(args.folder + "/*.{}".format(args.extension)):
while cap.isOpened():
print('READING----------------------------')
parser.add_argument("-f",
"--facebank",
type=str,
help="dir of facebank picture",
default='facebank')
args = parser.parse_args()
conf = get_config(False)
mtcnn = load_MTCNN()
learner = load_Learner(conf, args)
conf.facebank_path = conf.data_path / args.facebank
start = time.time()
if args.update:
targets, names = prepare_facebank(conf, learner.model, mtcnn, False)
print('facebank updated')
else:
targets, names = load_facebank(conf)
print('facebank loaded')
print(f'WorkingTime[Load facebank]: {time.time()-start}')
print("Names")
# list image directory
img_list = glob(args.image_dir + '**/*.jpg')
acc = 0
detect_err = 0
fails = []
print(f"{'Found':^15}{'Name':^20}{'Result':^15}{'Score':^15}")
conf,
'2019-03-19-20-18_accuracy:0.9307142857142857_step:300234_None.pth',
False, True)
learner.model.eval()
print('learner loaded')
'''vgg2_fp, vgg2_fp_issame = get_val_pair(conf.emore_folder, 'vgg2_fp')
accuracy, best_threshold, roc_curve_tensor = learner.evaluate(conf, vgg2_fp, vgg2_fp_issame, nrof_folds=10, tta=True)
print('vgg2_fp - accuray:{}, threshold:{}'.format(accuracy, best_threshold))
exit(0)'''
df = pd.read_csv(args.csv)
imlst = df.groupby('classnm')['imgfile'].apply(
lambda x: x.tolist()).to_dict()
if args.update:
targets, ftoid, idinfo = prepare_facebank(conf,
imlst,
learner.model,
mtcnn,
tta=args.tta,
save=True)
print('facebank updated')
else:
targets, ftoid, idinfo = load_facebank(conf)
print('facebank loaded')
faces = []
predfns = []
with open(args.file) as f:
imgfiles = list(map(str.strip, f.readlines()))
for imgfn in imgfiles:
try:
face = Image.open(imgfn)
except:
print('cannot open query image file {}'.format(imgfn))
def face_detection(video_path, result_csv_path):
conf = get_config(False)
mtcnn = MTCNN(select_largest=False, keep_all=True)
print('mtcnn loaded')
learner = face_learner(conf, True)
learner.threshold = 0.5
if conf.device.type == 'cpu':
learner.load_state(conf, 'cpu_final.pth', True, True)
else:
learner.load_state(conf, 'final.pth', True, True)
learner.model.eval()
print('learner loaded')
targets, names = prepare_facebank(conf, learner.model, mtcnn, tta = True)
print('facebank updated')
cap = cv2.VideoCapture(video_path)
cap.set(cv2.CAP_PROP_POS_MSEC, 0)
fps = cap.get(cv2.CAP_PROP_FPS)
#video writer
#video_writer = cv2.VideoWriter(str(conf.facebank_path/'{}.avi'.format(args.save_name)),
# cv2.VideoWriter_fourcc(*'XVID'), int(fps), (int(cap.get(3)),int(cap.get(4))))
#video_writer = cv2.VideoWriter(str(conf.facebank_path/'{}.avi'.format(args.save_name)),
# cv2.CAP_OPENCV_MJPEG, int(fps), (int(cap.get(3)),int(cap.get(4))))
#### csv
df = pd.DataFrame(columns=['frame_number', 'ID', 'LT', 'RB', 'score'])
framecounter = 0
while cap.isOpened():
isSuccess,frame = cap.read()
if isSuccess:
# image = Image.fromarray(frame[...,::-1]) #bgr to rgb
image = Image.fromarray(frame)
try:
bboxes, faces = mtcnn.align_multi(image, conf.face_limit, 16)
# mtcnn 에서 검출된 얼굴 BB 와 5point landmark
except:
bboxes = []
faces = []
if len(bboxes) == 0:
print('no face')
# continue
else:
bboxes = bboxes[:,:-1] #shape:[10,4],only keep 10 highest possibiity faces
bboxes = bboxes.astype(int)
bboxes = bboxes + [-1,-1,1,1] # personal choice
# 사람 identification
results, score = learner.infer(conf, faces, targets, True)
for idx,bbox in enumerate(bboxes):
#frame = draw_box_name(bbox, names[results[idx] + 1] + '_{:.2f}'.format(score[idx]), frame)
df = df.append({'frame_number':framecounter, 'ID':names[results[idx] + 1], 'LT':(bbox[0],bbox[1]), 'RB':(bbox[2],bbox[3]), 'score':'{:.2f}'.format(score[idx])}, ignore_index=True)
#video_writer.write(frame)
#print('{0:.2f}' .format(framecounter/duration*100))
framecounter +=1
else:
break
cap.release()
#video_writer.release()
df.to_csv(result_csv_path, index=False)
def test(self,conf,img_dir,update=False,view_score=False,view_error=False):
#Load models
mtcnn = MTCNN()
learner = face_learner(conf, True)
if conf.device.type == 'cpu':
learner.load_state(conf,'cpu_final.pth',True,True)
else:
learner.load_state(conf,'final.pth',True,True)
learner.model.eval()
#Load Facebank
if update:
targets, names = prepare_facebank(conf, learner.model, mtcnn, False)
print('facebank updated')
else:
targets, names = load_facebank(conf)
print('facebank loaded')
#Load Image list
img_list = glob(img_dir + '**/*.jpg')
acc = 0
detect_err=0
fails = []
print(f"{'Found':^15}{'Name':^20}{'Result':^15}{'Score':^15}")
pbar = enumerate(img_list)
pbar = tqdm(pbar, total = len(img_list))
for i, x in pbar:
preds = []
label = str(os.path.dirname(x))
label = os.path.basename(label)
image = Image.open(x)
frame = cv2.imread(x,cv2.IMREAD_COLOR)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
try:
bboxes, faces = mtcnn.align_multi(image, 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] # personal choice
results, score = learner.infer(conf, faces, targets, False)
for idx,bbox in enumerate(bboxes):
print(f'{Label}: {score[idx]}')
if view_score:
frame = draw_box_name(bbox, names[results[idx] + 1] + '_{:.2f}'.format(score[idx]), frame)
else:
frame = draw_box_name(bbox, names[results[idx] + 1], frame)
preds.append(names[results[idx]+1])
if label in preds:
acc += 1
else:
fails.append([label,preds])
# Image.fromarray(frame,'RGB').show()
except Exception as ex:
fails.append([label,ex])
detect_err += 1
f = len(bboxes)
tf = str(True if label in preds else False)
t = f'{f:^15}{label:^20}{tf:^15}{acc/(i+1):^15.4}'
pbar.set_description(t)
if detect_err>0:
print(f'Detect Error: {detect_err}')
if view_error:
pp(fails)
else:
print(f'If you want to see details, make veiw_error True.')
print(f'Accuracy: {acc/len(img_list)}')
if conf.device.type == 'cpu':
learner.load_state(conf, 'cpu_final.pth', True, True)
else:
learner.load_state(conf, 'final.pth', True, True)
learner.model.eval()
print('learner loaded')
# todo, modify this
filepath = './input.txt'
# read files
train_dict, test_dict, output_dir = get_inputs(filepath)
if args.update:
targets, names = prepare_facebank(conf,
learner.model,
mtcnn,
tta=args.tta,
load_from_custom_dir=True,
dir_name_dict=train_dict)
print('facebank updated')
else:
targets, names = load_facebank(conf)
print('facebank loaded')
id_results = []
random_output_idx = 0
for tc_id in test_dict:
tc_path = test_dict[tc_id]
result = str(tc_id)
image = cv2.imread(tc_path, cv2.IMREAD_COLOR) # queryImage
try:
# image = Image.fromarray(frame[...,::-1]) #bgr to rgb
conf = get_config(False)
update = False
learner = face_learner(conf, True)
if conf.device.type == 'cpu':
learner.load_state(conf, '2019-11-11-08-11.pth', True, True)
else:
learner.load_state(conf, 'model_2019-11-11-08-11.pth', True, True)
learner.model.eval()
print('learner loaded')
if update:
targets, names = prepare_facebank(conf, learner.model)
print('facebank updated')
else:
targets, names = load_facebank(conf)
print('facebank loaded')
imgs = []
names = []
testlist = []
learner.threshold = 0.25
with open(conf.testlist_path, 'r') as f:
testlist = f.readlines()
f.close()
# print(testlist)
acc = 0
total = 0
def updateDatabase():
return prepare_facebank(conf, learner.model, mtcnn, tta="store_true")
mtcnn = MTCNN()
print('mtcnn loaded')
learner = face_learner(conf, True)
learner.threshold = args.threshold
if conf.device.type == 'cpu':
learner.load_state(conf, 'cpu_final.pth', True, True)
else:
learner.load_state(conf, 'final.pth', True, True)
learner.model.eval()
print('learner loaded')
if args.update:
targets, names = prepare_facebank(conf,
learner.model,
mtcnn,
tta=args.tta)
print('facebank updated')
else:
targets, names = load_facebank(conf)
print('facebank loaded')
#cap = cv2.VideoCapture(str(conf.facebank_path/args.file_name))
cap = cv2.VideoCapture("1.mp4")
cap.set(cv2.CAP_PROP_POS_MSEC, args.begin * 1000)
fps = cap.get(cv2.CAP_PROP_FPS)
video_writer = cv2.VideoWriter(
str(conf.facebank_path / '{}.avi'.format(args.save_name)),
cv2.VideoWriter_fourcc(*'XVID'), int(fps), (1280, 720))
def update_facebank(self,conf):
mtcnn = MTCNN()
learner = face_learner(conf,True)
learner.load_state(conf,'final.pth',True,True)
learner.model.eval()
_, _ = prepare_facebank(conf,learner.model,mtcnn,False)