def inference(self,conf,img):
mtcnn = MTCNN()
learner = face_learner(conf,True)
learner.load_state(conf,'final.pth',True,True)
learner.model.eval()
targets, names = load_facebank(conf)
image = Image.open(img)
frame = cv2.imread(img,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)
name = names[results[0]+1]
frame = draw_box_name(bboxes[0], name, frame)
except Exception as ex:
name = "Can't detect face."
h, w, c = frame.shape
bbox = [int(h*0.5),int(w*0.5),int(h*0.5),int(w*0.5)]
frame = draw_box_name(bbox, name, frame)
return name, frame
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 recognize(path):
conf = get_config(False)
mtcnn = MTCNN()
print('mtcnn loaded')
learner = face_learner(conf, True)
learner.threshold = 1.35
learner.load_state(conf, 'cpu_final.pth', True, True)
learner.model.eval()
print('learner loaded')
targets, names = load_facebank(conf)
print(names)
print('facebank loaded')
count = 0
while True:
frame = cv2.imread(path)
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)
for idx, bbox in enumerate(bboxes):
frame = draw_box_name(
bbox, names[results[idx] + 1] +
'_{:.2f}'.format(100 - score[idx]), frame)
result = {
"_result": "success",
"User Verified with": {
"confidence": '{:.2f}%'.format(100 - score[idx]),
"userid": names[results[idx] + 1],
"error": "Success"
}
}
accuracy.append('{:.2f}'.format(100 - score[idx]))
user.append(names[results[idx] + 1])
print(names[results[idx] + 1],
'{:.2f}'.format(100 - score[idx]))
count = 1
except:
print('detect error')
if count > 0:
break
return result
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 __init__(self):
self.stream_url = "https://demo.bahien.com/live/stream/playlist.m3u8"
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.conf = get_config(False)
self.mtcnn = MTCNN()
self.learner = face_learner(self.conf, True)
self.learner.threshold = THRESHOLD
self.learner.load_state(self.conf, 'final.pth', True, True)
self.learner.model.eval()
print('learner loaded')
self.targets, self.names = load_facebank(self.conf)
print('facebank loaded')
self.net, self.ln = yolo_detection.init_net()
print('yolo model loaded')
self.model = Darknet(config_file)
self.model.load_weights(weight_file)
self.model.cuda()
def __init__(self, register=False):
self.conf = get_config()
self.conf.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
self.mtcnn = My_Function_lib.MTCNN()
print('mtcnn loaded')
self.learner = face_learner(self.conf, True)
if self.conf.device.type == 'cpu':
self.learner.load_state(self.conf, 'best.pth', True)
else:
self.learner.load_state(self.conf, 'final.pth', True)
self.learner.model.eval()
print('learner loaded')
if register == False:
self.targets, self.names = load_facebank(self.conf)
print('facebank updated')
self.process_this_frame = True
self.Img = None
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))
if args.duration != 0:
i = 0
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
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))
continue
try:
face = mtcnn.align(face)
except:
print('mtcnn failed for {}'.format(imgfn))
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)}')
