def main():
args = get_args()
torch.set_grad_enabled(False)
cfg = None
if args.network == "mobile0.25":
cfg = cfg_mnet
elif args.network == "resnet50":
cfg = cfg_re50
# net and model
net = RetinaFace(cfg=cfg, phase="test")
net = load_model(net, args.trained_model, args.cpu)
net.eval()
print("Finished loading model!")
print(net)
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
# ------------------------ export -----------------------------
output_onnx = "FaceDetector.onnx"
print("==> Exporting model to ONNX format at '{}'".format(output_onnx))
input_names = ["input0"]
output_names = ["output0"]
inputs = torch.randn(1, 3, args.long_side, args.long_side).to(device)
torch.onnx._export(net,
inputs,
output_onnx,
export_params=True,
verbose=False,
input_names=input_names,
output_names=output_names)
def validate(model_path, network='mobile0.25'):
cfg = None
if network == "mobile0.25":
cfg = cfg_mnet
elif network == "resnet50":
cfg = cfg_re50
else:
raise ValueError(network)
net = RetinaFace(cfg=cfg, phase='test')
net = load_model(net, model_path, args.cpu)
#net.eval()
print('Finished loading model!')
#print(net)
cudnn.benchmark = True
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
net.eval()
#net.phase = 'eval'
with torch.no_grad():
preds = predict(net, cfg)
#net.phase = 'train'
#net.train()
del net
aps = evaluation(preds, './widerface_evaluate/ground_truth/')
avg = np.mean(aps)
return [avg] + aps
def __init__(self, on_gpu=False):
self.on_gpu = on_gpu
# from classifier by Sizykh Ivan
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
# parser = argparse.ArgumentParser(description='Retinaface')
# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#
# parser.add_argument('-m', '--trained_model', default='./weights/Resnet50_Final.pth',
# type=str, help='Trained state_dict file path to open')
# parser.add_argument('--network', default='resnet50', help='Backbone network mobile0.25 or resnet50')
# # parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference')
# parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference')
# parser.add_argument('--confidence_threshold', default=0.02, type=float, help='confidence_threshold')
# parser.add_argument('--top_k', default=5000, type=int, help='top_k')
# parser.add_argument('--nms_threshold', default=0.4, type=float, help='nms_threshold')
# parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k')
# parser.add_argument('-s', '--save_image', action="store_true", default=True, help='show detection results')
# parser.add_argument('--vis_thres', default=0.6, type=float, help='visualization_threshold')
#
# parser.add_argument('-v', '--video', default='vid.mp4', type=str)
#
# args = parser.parse_args()
self.trained_model = './weights/Resnet50_Final.pth'
self.network = 'resnet50'
self.cpu = False
self.confidence_threshold = 0.02
self.top_k = 5000
self.nms_threshold = 0.4
self.keep_top_k = 750
self.vis_thres = 0.6
self.resize = 1
torch.set_grad_enabled(False)
cfg = None
if self.network == "mobile0.25":
cfg = cfg_mnet
elif self.network == "resnet50":
cfg = cfg_re50
# cfg = cfg_re50
# net and model
detector = RetinaFace(cfg=cfg, phase='test')
detector = self.load_model(model=detector,
pretrained_path=self.trained_model,
load_to_cpu=self.cpu)
detector.eval()
print('Finished loading model!')
# print(detector)
if self.on_gpu:
cudnn.benchmark = True
self.detector = detector.to(self.device)
else:
self.detector = detector
self.cfg = cfg
def model_cfg(trained_model, cfg, device, cpu):
net = RetinaFace(cfg=cfg, phase='test')
net = load_model(net, trained_model, load_to_cpu=cpu)
net.eval()
print('Finished loading model!')
cudnn.benchmark = True
net = net.to(device)
return net
def __init__(self,
on_gpu=False,
confidence_threshold=0.02,
top_k=5000,
nms_threshold=0.4,
keep_top_k=750,
vis_thres=0.6,
network='resnet50'):
self.on_gpu = on_gpu
# from classifier by Sizykh Ivan
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
self.network = network
self.cpu = False
self.confidence_threshold = confidence_threshold
self.top_k = top_k
self.nms_threshold = nms_threshold
self.keep_top_k = keep_top_k
self.vis_thres = vis_thres
if network == 'resnet50':
self.trained_model = './weights/Resnet50_Final.pth'
else:
self.trained_model = './weights/mobilenet0.25_Final.pth'
self.resize = 1
torch.set_grad_enabled(False)
cfg = None
if self.network == "mobile0.25":
cfg = cfg_mnet
elif self.network == "resnet50":
cfg = cfg_re50
# cfg = cfg_re50
# net and model
detector = RetinaFace(cfg=cfg, phase='test')
detector = self.load_model(model=detector,
pretrained_path=self.trained_model,
load_to_cpu=self.cpu)
detector.eval()
print('Finished loading model!')
# print(detector)
if self.on_gpu:
cudnn.benchmark = True
self.detector = detector.to(self.device)
else:
self.detector = detector
self.cfg = cfg
def load_net():
torch.set_grad_enabled(False)
cfg = None
if args.network == "mobile0.25":
cfg = cfg_mnet
elif args.network == "resnet50":
cfg = cfg_re50
# net and model
net = RetinaFace(cfg=cfg, phase='test')
net = load_model(net, args.trained_model, args.cpu)
net.eval()
print('Finished loading model!')
cudnn.benchmark = True
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
return net, device, cfg
def initFaceDetector():
global args, net, device, cfg
init_args()
torch.set_grad_enabled(False)
cfg = None
if args.network == "mobile0.25":
cfg = cfg_mnet
elif args.network == "resnet50":
cfg = cfg_re50
net = RetinaFace(cfg=cfg, phase='test')
net, forest = load_model(net, args.trained_model, args.cpu,
args.forest_path)
net.eval()
cudnn.benchmark = True
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
def __init__(self,
threshold=0.5,
network="mobile0.25",
device=torch.device("cuda")):
torch.set_grad_enabled(False)
cfg = None
if network == "mobile0.25":
cfg = cfg_mnet
elif network == "resnet50":
cfg = cfg_re50
# net and model
net = RetinaFace(cfg=cfg, phase='test')
net = load_model(net, "./weights/mobilenet0.25_Final.pth", False)
net.eval()
# print('Finished loading model!')
# print(net)
cudnn.benchmark = True
self.device = device
net = net.to(self.device)
self.net = net
torch.set_grad_enabled(False)
self._t = {'forward_pass': Timer(), 'misc': Timer()}
self.cfg = cfg
self.threshold = threshold
def main():
torch.set_grad_enabled(False)
cfg = None
if args.network == "mobile0.25":
cfg = cfg_mnet
elif args.network == "resnet50":
cfg = cfg_re50
# net and model
net = RetinaFace(cfg=cfg, phase='test')
net = load_model(net, args.trained_model, args.cpu)
net.eval()
print('Finished loading model!')
print(net)
cudnn.benchmark = True
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
# data_dir = '../face_dataset/masked_whn'
# target_dir = '../face_dataset/masked_whn_crop'
# data_dir = '../face_dataset/CASIA-maxpy-clean'
# target_dir = '../face_dataset/CASIA-maxpy-clean_crop'
# data_dir = '../frvtTestbed/pnas/images'
# target_dir = '../frvtTestbed/pnas_crop'
#
# crop_face(net, device, cfg, data_dir, target_dir)
#
# data_dir = '../frvtTestbed/common/images'
# target_dir = '../frvtTestbed/mugshot_crop'
#
# crop_face(net, device, cfg, data_dir, target_dir)
# data_dir = '../face_dataset/calfw/aligned_images'
# target_dir = '../face_dataset/calfw/aligned_images_crop'
#
# crop_face(net, device, cfg, data_dir, target_dir)
#
# data_dir = '../face_dataset/cplfw/aligned_images'
# target_dir = '../face_dataset/cplfw/aligned_images_crop'
#
# crop_face(net, device, cfg, data_dir, target_dir)
# data_dir = '../face_dataset/Celeba/img_align_celeba'
# target_dir = '../face_dataset/Celeba/img_align_celeba_crop'
#
# crop_face(net, device, cfg, data_dir, target_dir)
# data_dir = '../face_dataset/GEO_enroll'
# target_dir = '../face_dataset/GEO_enroll_crop'
# crop_face(net, device, cfg, data_dir, target_dir)
#
# data_dir = '../face_dataset/GEO_enroll'
# target_dir = '../face_dataset/GEO_enroll_large_crop'
# crop_face(net, device, cfg, data_dir, target_dir, left_scale=0.1, right_scale=0.1, up_scale=0.1, low_scale=0.1)
#
# data_dir = '../face_dataset/GEO_Mask_Testing_Dataset'
# target_dir = '../face_dataset/GEO_Mask_Testing_Dataset_large_crop'
# crop_face(net, device, cfg, data_dir, target_dir, left_scale=0.1, right_scale=0.1, up_scale=0.1, low_scale=0.1)
#
# data_dir = '../face_dataset/GEO_Mask_Testing_Dataset'
# target_dir = '../face_dataset/GEO_Mask_Testing_Dataset_crop'
# crop_face(net, device, cfg, data_dir, target_dir)
#
# data_dir = '../face_dataset/GEO_env_dataset'
# target_dir = '../face_dataset/GEO_env_dataset_crop'
# crop_face(net, device, cfg, data_dir, target_dir)
#
# data_dir = '../face_dataset/GEO_identity'
# target_dir = '../face_dataset/GEO_identity_crop'
# crop_face(net, device, cfg, data_dir, target_dir)
# data_dir = '../face_dataset/MEDS_II'
# target_dir = '../face_dataset/MEDS_II_crop'
# crop_face(net, device, cfg, data_dir, target_dir)
#
# data_dir = '../face_dataset/MEDS_II_mask'
# target_dir = '../face_dataset/MEDS_II_mask_crop'
# crop_face(net, device, cfg, data_dir, target_dir)
# data_dir = '/media/bossun/Bossun_TX2/face_dataset/CACD_VS'
# target_dir = '/media/bossun/Bossun_TX2/face_dataset/CACD_VS_crop'
# crop_face(net, device, cfg, data_dir, target_dir)
# data_dir = '../face_dataset/CASIA-maxpy-clean'
# target_dir = '../face_dataset/CASIA-maxpy-clean_large_crop'
# crop_face(net, device, cfg, data_dir, target_dir, left_scale=0.05, right_scale=0.05, up_scale=0.05, low_scale=0.05)
data_dir = '/workspace/data/public/FR/ms1m_database_100k_final/base'
target_dir = '/workspace/data/public/FR/ms1m_large_range_crop'
crop_face(net,
device,
cfg,
data_dir,
target_dir,
left_scale=0.05,
right_scale=0.05,
up_scale=0.05,
low_scale=0.05)
data_dir = '/workspace/data/public/FR/VGGFACE2_Cleandata/train'
target_dir = '/workspace/data/public/FR/VGGFACE2_range_crop'
crop_face(net,
device,
cfg,
data_dir,
target_dir,
left_scale=0.05,
right_scale=0.05,
up_scale=0.05,
low_scale=0.05)
# 加载测试模型
if os.access(ops.landmarks_model, os.F_OK): # checkpoint
chkpt = torch.load(ops.landmarks_model, map_location=device)
landmarks_model.load_state_dict(chkpt)
print('load landmarks model : {}'.format(ops.landmarks_model))
#--------------------------------------------------------------------------- 构建人脸检测模型
cfg = None
if ops.detect_network == "mobile0.25":
cfg = cfg_mnet
elif ops.detect_network == "resnet50":
cfg = cfg_re50
# net and model
detect_model = RetinaFace(cfg=cfg, phase='test')
detect_model = detect_model.to(device)
if os.access(ops.detect_model, os.F_OK): # checkpoint
chkpt = torch.load(ops.detect_model, map_location=device)
detect_model.load_state_dict(chkpt)
print('load detect model : {}'.format(ops.detect_model))
detect_model.eval()
if use_cuda:
cudnn.benchmark = True
print('loading model done ~')
#-------------------------------------------------------------------------- run vedio
video_capture = cv2.VideoCapture(ops.test_path)
with torch.no_grad():
idx = 0
def main():
cfg = None
if args.network == "mobile0.25":
cfg = cfg_mnet
elif args.network == "resnet18":
cfg = cfg_re18
elif args.network == "resnet34":
cfg = cfg_re34
elif args.network == "resnet50":
cfg = cfg_re50
elif args.network == "Efficientnet-b0":
cfg = cfg_eff_b0
elif args.network == "Efficientnet-b4":
cfg = cfg_eff_b4
# net and model
net = RetinaFace(cfg=cfg, phase='test')
net = load_model(net, args.trained_model, args.cpu)
net.eval()
print('Finished loading model!')
print(net)
cudnn.benchmark = True
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
# testing dataset
testset_folder = args.dataset_folder
# testset_list = args.dataset_folder[:-7] + "wider_val.txt"
# with open(testset_list, 'r') as fr:
# test_dataset = fr.read().split()
test_dataset = []
for event in os.listdir(testset_folder):
subdir = os.path.join(testset_folder, event)
img_names = os.listdir(subdir)
for img_name in img_names:
test_dataset.append([event, os.path.join(subdir, img_name)])
num_images = len(test_dataset)
_t = {'forward_pass': Timer(), 'misc': Timer()}
# testing begin
for i, (event, img_name) in enumerate(test_dataset):
if i % 100 == 0:
torch.cuda.empty_cache()
# image_path = testset_folder + img_name
img_raw = cv2.imread(img_name, cv2.IMREAD_COLOR)
img = np.float32(img_raw)
# testing scale
target_size = 480
max_size = 2150
im_shape = img.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
resize = float(target_size) / float(im_size_min)
# prevent bigger axis from being more than max_size:
if np.round(resize * im_size_max) > max_size:
resize = float(max_size) / float(im_size_max)
if args.origin_size:
resize = 1
if resize != 1:
img = cv2.resize(img,
None,
None,
fx=resize,
fy=resize,
interpolation=cv2.INTER_LINEAR)
im_height, im_width, _ = img.shape
scale = torch.Tensor(
[img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img = (img - 127.5) / 128.0
# img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(device)
scale = scale.to(device)
_t['forward_pass'].tic()
loc, conf, landms = net(img) # forward pass
_t['forward_pass'].toc()
_t['misc'].tic()
priorbox = PriorBox(cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data,
cfg['variance'])
scale1 = torch.Tensor([
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2]
])
scale1 = scale1.to(device)
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > args.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1]
# order = scores.argsort()[::-1][:args.top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32,
copy=False)
keep = py_cpu_nms(dets, args.nms_threshold)
# keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
# dets = dets[:args.keep_top_k, :]
# landms = landms[:args.keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
_t['misc'].toc()
# --------------------------------------------------------------------
# save_name = args.save_folder + img_name[:-4] + ".txt"
save_name = os.path.join(
args.save_folder, event,
img_name.split('/')[-1].split('.')[0] + ".txt")
dirname = os.path.dirname(save_name)
if not os.path.isdir(dirname):
os.makedirs(dirname)
with open(save_name, "w") as fd:
bboxs = dets
file_name = os.path.basename(save_name)[:-4] + "\n"
bboxs_num = str(len(bboxs)) + "\n"
fd.write(file_name)
fd.write(bboxs_num)
for box in bboxs:
x = int(box[0])
y = int(box[1])
w = int(box[2]) - int(box[0])
h = int(box[3]) - int(box[1])
confidence = str(box[4])
line = str(x) + " " + str(y) + " " + str(w) + " " + str(
h) + " " + confidence + " \n"
fd.write(line)
print('im_detect: {:d}/{:d} forward_pass_time: {:.4f}s misc: {:.4f}s'.
format(i + 1, num_images, _t['forward_pass'].average_time,
_t['misc'].average_time))
# save image
if args.save_image:
for b in dets:
if b[4] < args.vis_thres:
continue
text = "{:.4f}".format(b[4])
b = list(map(int, b))
cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255),
2)
cx = b[0]
cy = b[1] + 12
cv2.putText(img_raw, text, (cx, cy), cv2.FONT_HERSHEY_DUPLEX,
0.5, (255, 255, 255))
# landms
cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4)
cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4)
cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4)
cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4)
cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4)
# save image
if not os.path.exists("./results/"):
os.makedirs("./results/")
name = "./results/" + str(i) + ".jpg"
cv2.imwrite(name, img_raw)
class Retina_Detector:
def __init__(self):
torch.set_grad_enabled(False)
cudnn.benchmark = True
self.opt=get_config()
if self.opt.network == "mobile0.25":
self.cfg = cfg_mnet
elif self.opt.network == "resnet50":
self.cfg = cfg_re50
# net and model
self.net = RetinaFace(cfg=self.cfg, phase = 'test')
self.net = self.load_model(self.net, self.opt.trained_model, self.opt.cpu)
self.net.eval()
self.net = self.net.to(self.opt.device)
def check_keys(self,model, pretrained_state_dict):
ckpt_keys = set(pretrained_state_dict.keys())
model_keys = set(model.state_dict().keys())
used_pretrained_keys = model_keys & ckpt_keys
unused_pretrained_keys = ckpt_keys - model_keys
missing_keys = model_keys - ckpt_keys
print('Missing keys:{}'.format(len(missing_keys)))
print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
print('Used keys:{}'.format(len(used_pretrained_keys)))
assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
return True
def remove_prefix(self,state_dict, prefix):
''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
print('remove prefix \'{}\''.format(prefix))
f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
return {f(key): value for key, value in state_dict.items()}
def load_model(self,model, pretrained_path, load_to_cpu):
print('Loading pretrained model from {}'.format(pretrained_path))
if load_to_cpu:
pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
else:
device = torch.cuda.current_device()
pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
if "state_dict" in pretrained_dict.keys():
pretrained_dict = self.emove_prefix(pretrained_dict['state_dict'], 'module.')
else:
pretrained_dict = self.remove_prefix(pretrained_dict, 'module.')
self.check_keys(model, pretrained_dict)
model.load_state_dict(pretrained_dict, strict=False)
return model
def img_process(self, img):
target_size = self.cfg["image_size"]
max_size = 1080
im_shape = img.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
im_scale = float(target_size) / float(im_size_min)
if np.round(im_scale * im_size_max) > max_size:
im_scale = float(max_size) / float(im_size_max)
im = cv2.resize(img, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR)
return im, im_scale
def detect(self,img):
img,imscale=self.img_process(img)
resize=1
img_raw = img
img = np.float32(img_raw)
im_height, im_width, _ = img.shape
scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(self.opt.device)
scale = scale.to(self.opt.device)
tic = time.time()
loc, conf, landms = self.net(img) # forward pass
print('net forward time: {:.4f}'.format(time.time() - tic))
t1=time.time()
priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(self.opt.device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance'])
scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2]])
scale1 = scale1.to(self.opt.device)
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > self.opt.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:self.opt.top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
keep = py_cpu_nms(dets, self.opt.nms_threshold)
# keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
dets = dets[keep, :]
print("len ",len(dets))
landms = landms[keep]
dets/=imscale
landms /=imscale
# keep top-K faster NMS
dets = dets[:self.opt.keep_top_k, :]
boxes=[list(map(int, x)) for x in dets]
landms = landms[:self.opt.keep_top_k, :]
lands=[list(map(int, x)) for x in landms]
# dets = np.concatenate((dets, landms), axis=1)
return boxes,lands
def process_video_files(
network: str,
trained_model: str,
decode_gpu: bool,
is_fp16: bool,
file_paths: list,
num_gpu: Optional[int],
gpu_id: int,
output_path: Path,
is_save_boxes: bool,
is_save_crops: bool,
num_frames: int,
resize_coeff: Optional[Tuple],
confidence_threshold: float,
num_workers: int,
nms_threshold: float,
batch_size: int,
resize_scale: float,
min_size: int,
keep_top_k: int,
) -> None:
torch.set_grad_enabled(False)
if network == "mobile0.25":
cfg = cfg_mnet_test
elif network == "resnet50":
cfg = cfg_re50_test
else:
raise NotImplementedError(
f"Only mobile0.25 and resnet50 are suppoted, but we got {network}")
if min_size < 0:
raise ValueError(
f"Min size should be positive, but we got {min_size}.")
# net and model
net = RetinaFace(cfg=cfg, phase="test")
net = load_model(net, trained_model, load_to_cpu=False)
net.eval()
if is_fp16:
net = net.half()
device = torch.device("cuda")
net.to(device)
print("Finished loading model!")
cudnn.benchmark = True
transform = albu.Compose([
albu.Normalize(
p=1, mean=(104, 117, 123), std=(1.0, 1.0, 1.0), max_pixel_value=1)
],
p=1)
if num_gpu is not None:
start, end = split_array(len(file_paths), num_gpu, gpu_id)
file_paths = file_paths[start:end]
with torch.no_grad():
func = partial(get_frames,
num_frames=num_frames,
resize_coeff=resize_coeff,
transform=transform,
decode_gpu=decode_gpu)
with torch.no_grad():
with concurrent.futures.ProcessPoolExecutor(
num_workers) as executor:
for result in tqdm(executor.map(func, file_paths),
total=len(file_paths),
leave=False,
desc="Loading data files"):
if len(result) != 0:
result["is_fp16"] = is_fp16
result["device"] = device
result["batch_size"] = batch_size
result["cfg"] = cfg
result["nms_threshold"] = nms_threshold
result["confidence_threshold"] = confidence_threshold
result["is_save_crops"] = is_save_crops
result["is_save_boxes"] = is_save_boxes
result["output_path"] = output_path
result["net"] = net
result["min_size"] = min_size
result["resize_scale"] = resize_scale
result["keep_top_k"] = keep_top_k
process_frames(**result)
def __init__(self,
input_mode=0,
output_mode=0,
record_video=False,
email_to_share=None,
channel=0,
on_gpu=False,
display=False,
only_headcount=False,
send_to_nvr=False,
parallel=False):
self.save_into_sheet = True
self.on_gpu = on_gpu
self.send_to_nvr = send_to_nvr
if email_to_share == None:
self.save_into_sheet = False
if self.save_into_sheet or self.send_to_nvr:
self.api = API(email_to_share)
uri = 'rtsp://' + secrets.ip_camera_login + ':' + secrets.ip_camera_password + \
'@{}:554/cam/realmonitor?channel=1&subtype=0&unicast=true&proto=Onvif'
self.input_mode = input_mode
self.output_mode = output_mode # 0 - pretty display, 1 - separate graph, 2 - graph with black background
self.record_video = record_video
self.display = display
self.only_headcount = only_headcount
if input_mode == 0:
self.channel = 0 # webcam
elif input_mode == 1: # ip camera
self.channel = uri.format(channel)
self.ip = channel
elif input_mode == 2: # video
self.channel = channel
if parallel and not on_gpu:
self.parallel = True
else:
self.parallel = False
# from classifier by Sizykh Ivan
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
self.class_labels = [
'ANGRY', 'DISGUST', 'FEAR', 'HAPPY', 'SAD', 'SURPRISE', 'NEUTRAL'
]
# PATH = "./check_points_4/net_714.pth"
PATH = "./net_714.pth"
if self.on_gpu:
self.classifier = Classifier().to(self.device)
self.classifier.load_state_dict(torch.load(PATH))
else:
self.classifier = Classifier()
self.classifier.load_state_dict(
torch.load(PATH, map_location={'cuda:0': 'cpu'}))
# from detector by Belyakova Katerina
self.parser = argparse.ArgumentParser(description='Retinaface')
self.parser.add_argument('-m',
'--trained_model',
default='./weights/Resnet50_Final.pth',
type=str,
help='Trained state_dict file path to open')
self.parser.add_argument(
'--network',
default='resnet50',
help='Backbone network mobile0.25 or resnet50')
self.parser.add_argument('--cpu',
action="store_true",
default=False,
help='Use cpu inference')
self.parser.add_argument('--confidence_threshold',
default=0.02,
type=float,
help='confidence_threshold')
self.parser.add_argument('--top_k',
default=5000,
type=int,
help='top_k')
self.parser.add_argument('--nms_threshold',
default=0.4,
type=float,
help='nms_threshold')
self.parser.add_argument('--keep_top_k',
default=750,
type=int,
help='keep_top_k')
self.parser.add_argument('-s',
'--save_image',
action="store_true",
default=True,
help='show detection results')
self.parser.add_argument('--vis_thres',
default=0.6,
type=float,
help='visualization_threshold')
self.parser.add_argument('-v', '--video', default='vid.mp4', type=str)
self.parser_args = self.parser.parse_args()
self.resize = 1
"""sets parameters for RetinaFace, prerun() is used once while first usege of run()"""
torch.set_grad_enabled(False)
cfg = None
if self.parser_args.network == "mobile0.25":
cfg = cfg_mnet
elif self.parser_args.network == "resnet50":
cfg = cfg_re50
# net and model
detector = RetinaFace(cfg=cfg, phase='test')
detector = self.load_model(
model=detector,
pretrained_path=self.parser_args.trained_model,
load_to_cpu=self.parser_args.cpu)
detector.eval()
print('Finished loading model!')
print(detector)
if self.on_gpu:
cudnn.benchmark = True
self.detector = detector.to(self.device)
else:
self.detector = detector
self.cfg = cfg
def main():
args = get_args()
torch.set_grad_enabled(False)
cfg = None
if args.network == "mobile0.25":
cfg = cfg_mnet
elif args.network == "resnet50":
cfg = cfg_re50
# net and model
net = RetinaFace(cfg=cfg, phase="test")
net = load_model(net, args.trained_model, args.cpu)
net.eval()
print("Finished loading model!")
print(net)
cudnn.benchmark = True
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
args.save_folder.mkdir(exist_ok=True)
fw = open(os.path.join(args.save_folder, args.dataset + "_dets.txt"), "w")
# testing dataset
testset_folder = os.path.join("data", args.dataset, "images/")
testset_list = os.path.join("data", args.dataset, "img_list.txt")
with open(testset_list, "r") as fr:
test_dataset = fr.read().split()
num_images = len(test_dataset)
# testing scale
resize = 1
_t = {"forward_pass": Timer(), "misc": Timer()}
# testing begin
for i, img_name in enumerate(test_dataset):
image_path = testset_folder + img_name + ".jpg"
img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
img = np.float32(img_raw)
if resize != 1:
img = cv2.resize(img,
None,
None,
fx=resize,
fy=resize,
interpolation=cv2.INTER_LINEAR)
im_height, im_width, _ = img.shape
scale = torch.Tensor(
[img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(device)
scale = scale.to(device)
_t["forward_pass"].tic()
loc, conf, landms = net(img) # forward pass
_t["forward_pass"].toc()
_t["misc"].tic()
priorbox = PriorBox(cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, cfg["variance"])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data,
cfg["variance"])
scale1 = torch.Tensor([
img.shape[3],
img.shape[2],
img.shape[3],
img.shape[2],
img.shape[3],
img.shape[2],
img.shape[3],
img.shape[2],
img.shape[3],
img.shape[2],
])
scale1 = scale1.to(device)
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > args.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
# order = scores.argsort()[::-1][:args.top_k]
order = scores.argsort()[::-1]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32,
copy=False)
keep = py_cpu_nms(dets, args.nms_threshold)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
# dets = dets[:args.keep_top_k, :]
# landms = landms[:args.keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
_t["misc"].toc()
# save dets
if args.dataset == "FDDB":
fw.write("{:s}\n".format(img_name))
fw.write("{:.1f}\n".format(dets.shape[0]))
for k in range(dets.shape[0]):
xmin = dets[k, 0]
ymin = dets[k, 1]
xmax = dets[k, 2]
ymax = dets[k, 3]
score = dets[k, 4]
w = xmax - xmin + 1
h = ymax - ymin + 1
# fw.write('{:.3f} {:.3f} {:.3f} {:.3f} {:.10f}\n'.format(xmin, ymin, w, h, score))
fw.write("{:d} {:d} {:d} {:d} {:.10f}\n".format(
int(xmin), int(ymin), int(w), int(h), score))
print("im_detect: {:d}/{:d} forward_pass_time: {:.4f}s misc: {:.4f}s".
format(i + 1, num_images, _t["forward_pass"].average_time,
_t["misc"].average_time))
# show image
if args.save_image:
for b in dets:
if b[4] < args.vis_thres:
continue
text = "{:.4f}".format(b[4])
b = list(map(int, b))
cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255),
2)
cx = b[0]
cy = b[1] + 12
cv2.putText(img_raw, text, (cx, cy), cv2.FONT_HERSHEY_DUPLEX,
0.5, (255, 255, 255))
# landms
cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4)
cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4)
cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4)
cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4)
cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4)
# save image
if not os.path.exists("./results/"):
os.makedirs("./results/")
name = "./results/" + str(i) + ".jpg"
cv2.imwrite(name, img_raw)
fw.close()
else:
pretrained_dict = remove_prefix(pretrained_dict, 'module.')
check_keys(model, pretrained_dict)
model.load_state_dict(pretrained_dict, strict=False)
return model
torch.set_grad_enabled(False)
cfg = None
if args.network == "mobile0.25":
cfg = cfg_mnetv1
elif args.network == "mobilenetv2":
cfg = cfg_mnetv2
elif args.network == "mobilenetv3":
cfg = cfg_mnetv3
elif args.network == "efficientnetb0":
cfg = cfg_efnetb0
# net and model
model = RetinaFace(cfg=cfg, phase='test')
model = load_model(model, args.trained_model, args.cpu)
model.eval()
print('Finished loading model!')
print(model)
#cudnn.benchmark = True
device = torch.device("cpu")
model = model.to(device)
example = torch.rand(1, 3, 640, 640)
traced_script_module = torch.jit.trace(model, example)
traced_script_module.save("face.pt")
def main():
args = get_args()
torch.set_grad_enabled(False)
if args.network == "mobile0.25":
cfg = cfg_mnet
elif args.network == "resnet50":
cfg = cfg_re50
else:
raise NotImplementedError(f"Only mobile0.25 and resnet50 are suppoted.")
# net and model
net = RetinaFace(cfg=cfg, phase="test")
net = load_model(net, args.trained_model, args.cpu)
net.eval()
if args.fp16:
net = net.half()
print("Finished loading model!")
cudnn.benchmark = True
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
file_paths = sorted(args.input_path.rglob("*.mp4"))[: args.num_videos]
if args.num_gpu is not None:
start, end = split_array(len(file_paths), args.num_gpu, args.gpu_id)
file_paths = file_paths[start:end]
output_path = args.output_path
if args.save_boxes:
output_label_path = output_path / "labels"
output_label_path.mkdir(exist_ok=True, parents=True)
if args.save_crops:
output_image_path = output_path / "images"
output_image_path.mkdir(exist_ok=True, parents=True)
if args.video_decoder == "cpu":
decode_device = cpu(0)
elif args.video_decoder == "gpu":
decode_device = gpu(0)
else:
raise NotImplementedError(f"Only CPU and GPU devices are supported by decard, but got {args.video_decoder}")
transform = albu.Compose([albu.Normalize(p=1, mean=(104, 117, 123), std=(1.0, 1.0, 1.0), max_pixel_value=1)], p=1)
with torch.no_grad():
for video_path in tqdm(file_paths):
labels = []
video_id = video_path.stem
with video_reader(str(video_path), ctx=decode_device) as video:
len_video = len(video)
if args.num_frames is None or args.num_frames == 1:
frame_ids = list(range(args.num_frames))
elif args.num_frames > 1:
if len_video < args.num_frames:
step = 1
else:
step = int(len_video / args.num_frames)
frame_ids = list(range(0, len_video, step))[: args.num_frames]
else:
raise ValueError(f"Expect None or integer > 1 for args.num_frames, but got {args.num_frames}")
frames = video.get_batch(frame_ids)
if args.video_decoder == "cpu":
frames = frames.asnumpy()
elif args.video_decoder == "gpu":
frames = dlpack.from_dlpack(frames.to_dlpack())
if args.video_decoder == "gpu":
del video
torch.cuda.empty_cache()
gc.collect()
num_frames = len(frames)
image_height = frames.shape[1]
image_width = frames.shape[2]
scale1 = torch.Tensor(
[
image_width,
image_height,
image_width,
image_height,
image_width,
image_height,
image_width,
image_height,
image_width,
image_height,
]
)
scale1 = scale1.to(device)
scale = torch.Tensor([image_width, image_height, image_width, image_height])
scale = scale.to(device)
priorbox = PriorBox(cfg, image_size=(image_height, image_width))
priors = priorbox.forward()
priors = priors.to(device)
prior_data = priors.data
if args.resize_coeff is not None:
target_size = min(args.resize_coeff)
max_size = max(args.resize_coeff)
image_height = frames.shape[1]
image_width = frames.shape[2]
image_size_min = min([image_width, image_height])
image_size_max = max([image_width, image_height])
resize = float(target_size) / float(image_size_min)
if np.round(resize * image_size_max) > max_size:
resize = float(max_size) / float(image_size_max)
else:
resize = 1
for pred_id in range(num_frames):
frame = frames[pred_id]
torched_image = prepare_image(frame, transform, args.video_decoder).to(device)
if args.fp16:
torched_image = torched_image.half()
loc, conf, land = net(torched_image) # forward pass
frame_id = frame_ids[pred_id]
boxes = decode(loc.data[0], prior_data, cfg["variance"])
boxes *= scale / resize
boxes = boxes.cpu().numpy()
scores = conf[0].data.cpu().numpy()[:, 1]
landmarks = decode_landm(land.data[0], prior_data, cfg["variance"])
landmarks *= scale1 / resize
landmarks = landmarks.cpu().numpy()
# ignore low scores
valid_index = np.where(scores > args.confidence_threshold)[0]
boxes = boxes[valid_index]
landmarks = landmarks[valid_index]
scores = scores[valid_index]
# keep top-K before NMS
order = scores.argsort()[::-1]
# order = scores.argsort()[::-1][:args.top_k]
boxes = boxes[order]
landmarks = landmarks[order]
scores = scores[order]
# do NMS
detection = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
keep = py_cpu_nms(detection, args.nms_threshold)
# keep = nms(detection, args.nms_threshold,force_cpu=args.cpu)
# x_min, y_min, x_max, y_max, score
detection = detection[keep, :]
landmarks = landmarks[keep].astype(int)
if detection.shape[0] == 0:
continue
bboxes = detection[:, :4].astype(int)
confidence = detection[:, 4].astype(np.float64)
for crop_id in range(len(detection)):
bbox = bboxes[crop_id]
labels += [
{
"frame_id": int(frame_id),
"crop_id": crop_id,
"bbox": bbox.tolist(),
"score": confidence[crop_id],
"landmarks": landmarks[crop_id].tolist(),
}
]
if args.save_crops:
x_min, y_min, x_max, y_max = bbox
x_min = max(0, x_min)
y_min = max(0, y_min)
crop = frame[y_min:y_max, x_min:x_max]
target_folder = output_image_path / f"{video_id}"
target_folder.mkdir(exist_ok=True, parents=True)
crop_file_path = target_folder / f"{frame_id}_{crop_id}.jpg"
if crop_file_path.exists():
continue
cv2.imwrite(
str(crop_file_path),
cv2.cvtColor(crop, cv2.COLOR_BGR2RGB),
[int(cv2.IMWRITE_JPEG_QUALITY), 90],
)
if args.save_boxes:
result = {
"file_path": str(video_path),
"file_id": video_id,
"bboxes": labels,
}
with open(output_label_path / f"{video_id}.json", "w") as f:
json.dump(result, f, indent=2)
cfg = cfg_mnet
elif args.network == "resnet50":
cfg = cfg_re50
elif args.network == "mobile0.25_highway":
cfg = cfg_mnet_highway
# load pre-trained model
if args.network == "mobile0.25":
from models.retinaface import RetinaFace
model = RetinaFace(cfg=cfg, phase='test')
elif args.network == "mobile0.25_highway":
from models.retinaface_highway import RetinaFaceHighway
model = RetinaFaceHighway(cfg=cfg, phase='test')
model = load_model(model, args.trained_model, args.cpu)
model.to(device)
def fine_tuner(masked_model, epochs=5):
train(masked_model, cfg, resume_epoch=cfg['epoch'] - epochs)
def evaluator(masked_model, level='average'):
evaluate(masked_model, cfg)
cmd = 'cd ./widerface_evaluate \
&& python3 setup.py build_ext --inplace \
&& python3 evaluation.py -e {} \
&& cd ..'.format(args.experiment_data_dir)
os.system(cmd)
with open(
from models.retinaface import RetinaFace
from utils.net_utils import load_model, image_process, process_face_data
# import torch2trt.converters.cat
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.set_grad_enabled(False)
cfg = cfg_mnet
retina_trained_model = "./weights/mobilenet0.25_Final.pth"
use_cpu = False
# cfg = cfg_re50
retina_net = RetinaFace(cfg=cfg, phase='test')
retina_net = load_model(retina_net, retina_trained_model, use_cpu)
retina_net.eval()
cudnn.benchmark = True
retina_net = retina_net.to(device)
def main(img_path):
test_img = cv2.imread(img_path)
resize = 1
im, im_width, im_height, scale = image_process(test_img, device)
print(im.shape)
model = torch2trt(retina_net, [im],
fp16_mode=True,
max_workspace_size=100000)
tic = time.time()
loc, conf, landms = model(im)
print('net forward time: {:.4f}'.format(time.time() - tic))
result_data = process_face_data(cfg, im, im_height, im_width, loc, scale,
conf, landms, resize)
def main():
args = get_args()
torch.set_grad_enabled(False)
cfg = None
if args.network == "mobile0.25":
cfg = cfg_mnet
elif args.network == "resnet50":
cfg = cfg_re50
# net and model
net = RetinaFace(cfg=cfg, phase="test")
net = load_model(net, args.trained_model, args.cpu)
net.eval()
print("Finished loading model!")
print(net)
cudnn.benchmark = True
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
resize = 1
# testing begin
for _ in range(100):
image_path = "./curve/test.jpg"
img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
img = np.float32(img_raw)
im_height, im_width = img.shape[:2]
scale = torch.Tensor(
[img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(device)
scale = scale.to(device)
tic = time.time()
loc, conf, landms = net(img) # forward pass
print("net forward time: {:.4f}".format(time.time() - tic))
priorbox = PriorBox(cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, cfg["variance"])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data,
cfg["variance"])
scale1 = torch.Tensor([
img.shape[3],
img.shape[2],
img.shape[3],
img.shape[2],
img.shape[3],
img.shape[2],
img.shape[3],
img.shape[2],
img.shape[3],
img.shape[2],
])
scale1 = scale1.to(device)
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > args.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:args.top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32,
copy=False)
keep = py_cpu_nms(dets, args.nms_threshold)
# keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
dets = dets[:args.keep_top_k, :]
landms = landms[:args.keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
# show image
if args.save_image:
for b in dets:
if b[4] < args.vis_thres:
continue
text = "{:.4f}".format(b[4])
b = list(map(int, b))
cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255),
2)
cx = b[0]
cy = b[1] + 12
cv2.putText(img_raw, text, (cx, cy), cv2.FONT_HERSHEY_DUPLEX,
0.5, (255, 255, 255))
# landms
cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4)
cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4)
cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4)
cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4)
cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4)
# save image
name = "test.jpg"
cv2.imwrite(name, img_raw)
class FaceDetector():
def __init__(self):
# TODO: add initialization logic
torch.set_grad_enabled(False)
self.cfg = None
if args.network == "mobile0.25":
self.cfg = cfg_mnet
elif args.network == "resnet50":
self.cfg = cfg_re50
elif args.network == "resnet18":
self.cfg = cfg_re18
elif args.network == "resnet34":
self.cfg = cfg_re34
# net and model
self.net = RetinaFace(cfg=self.cfg, phase='test')
# self.net = load_model(self.net, args.trained_model, args.cpu)
self.net.eval()
print('Finished loading model!')
print(self.net)
cudnn.benchmark = True
self.device = torch.device("cpu" if args.cpu else "cuda")
self.net = self.net.to(self.device)
self.resize = 1
def detect_image(self, img) -> List[FaceDetection]:
# TODO: add detect logic for single image
print(np.shape(img))
tic = time.time()
img = np.float32(img)
im_height, im_width, _ = img.shape
scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(self.device)
scale = scale.to(self.device)
loc, conf, landms = self.net(img) # forward pass
priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(self.device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
boxes = boxes * scale / self.resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
# ignore low scores
inds = np.where(scores > args.confidence_threshold)[0]
boxes = boxes[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:args.top_k]
boxes = boxes[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
keep = py_cpu_nms(dets, args.nms_threshold)
dets = dets[keep, :]
dets = dets[:args.keep_top_k, :]
# show image
box_list = []
for b in dets:
if b[4] < args.vis_thres:
continue
score = b[4]
b = list(map(int, b))
box_list.append(FaceDetection(b[0], b[1], b[2], b[3], 0, score))
print('net forward time: {:.4f}'.format(time.time() - tic))
return box_list
def detect_images(self, imgs) -> List[List[FaceDetection]]:
boxes_list = []
for img in imgs:
boxes = self.detect_image(img)
boxes_list.append(boxes)
return boxes_list
def visualize(self, image, detection_list: List[FaceDetection], color=(0,0,255), thickness=5):
img = image.copy()
for detection in detection_list:
bbox = detection.bbox
p1 = bbox.left, bbox.top
p2 = bbox.right, bbox.bottom
cv2.rectangle(img, p1, p2, color, thickness=thickness, lineType=cv2.LINE_AA)
return img
def run(args):
# net and load
cfg = cfg_mnet
net = RetinaFace(cfg=cfg, phase='test')
new_state_dict = load_normal(args.trained_model)
net.load_state_dict(new_state_dict)
print('Finished loading model!')
print(net)
torch.set_grad_enabled(False)
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
input = torch.randn(1, 3, 270, 480).cuda()
flops, params = profile(net, inputs=(input, ))
print('flops:', flops, 'params:', params)
# testing dataset
with open(args.test_list_dir, 'r') as fr:
test_dataset = fr.read().split()
test_dataset.sort()
_t = {'forward_pass': Timer(), 'misc': Timer()}
# testing begin
if not os.path.isdir(args.save_folder):
os.makedirs(args.save_folder)
f_ = open(os.path.join(args.save_folder, 'vis_bbox.txt'), 'w')
net.eval()
for i, image_path in enumerate(test_dataset):
#img_name = os.path.split(image_path)[-1]
img_name = image_path[image_path.find('datasets') + 9:]
img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
# img_raw = cv2.resize(img_raw, None, fx=1./3, fy=1.0/3, interpolation=cv2.INTER_AREA)
img = np.float32(img_raw)
# testing scale
target_size = 1600
max_size = 2150
im_shape = img.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
resize = float(target_size) / float(im_size_min)
# prevent bigger axis from being more than max_size:
if np.round(resize * im_size_max) > max_size:
resize = float(max_size) / float(im_size_max)
if args.origin_size:
resize = 1
if resize != 1:
img = cv2.resize(img,
None,
None,
fx=resize,
fy=resize,
interpolation=cv2.INTER_LINEAR)
im_height, im_width, _ = img.shape
scale = torch.Tensor(
[img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(device)
scale = scale.to(device)
_t['forward_pass'].tic()
loc, conf, landms = net(img) # forward pass
_t['forward_pass'].toc()
_t['misc'].tic()
priorbox = PriorBox(cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data,
cfg['variance'])
scale1 = torch.Tensor([
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2]
])
scale1 = scale1.to(device)
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > args.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1]
order = scores.argsort()[::-1][:args.top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32,
copy=False)
keep = py_cpu_nms(dets, args.nms_threshold)
# keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
dets = dets[:args.keep_top_k, :]
landms = landms[:args.keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
_t['misc'].toc()
# --------------------------------------------------------------------
save_name = os.path.join(args.save_folder, 'txt',
img_name)[:-4] + '.txt'
dirname = os.path.dirname(save_name)
if not os.path.isdir(dirname):
os.makedirs(dirname)
with open(save_name, "w") as fd:
bboxs = dets
file_name = os.path.basename(save_name)[:-4] + "\n"
bboxs_num = str(len(bboxs)) + "\n"
fd.write(file_name)
fd.write(bboxs_num)
for box in bboxs:
x = int(box[0])
y = int(box[1])
w = int(box[2]) - int(box[0])
h = int(box[3]) - int(box[1])
confidence = str(box[4])
line = str(x) + " " + str(y) + " " + str(w) + " " + str(
h) + " " + confidence + " \n"
fd.write(line)
print('im_detect: {:d}/{:d}'
' forward_pass_time: {:.4f}s'
' misc: {:.4f}s'
' img_shape:{:}'.format(i + 1, len(test_dataset),
_t['forward_pass'].average_time,
_t['misc'].average_time, img.shape))
# save bbox-image
line_write = save_image(dets,
args.vis_thres,
img_raw,
args.save_folder,
img_name,
save_all=args.save_image_all)
f_.write(line_write)
f_.flush()
f_.close()
class RetinaDetector:
def __init__(self, network, confidence=0.02, top_k=5000, nms_thresh=0.4, keep_top_k=750, vis_thresh=0.6):
torch.set_grad_enabled(False)
self.confidence = confidence
self.top_k = top_k
self.nms_thresh = nms_thresh
self.keep_top_k = keep_top_k
self.vis_thresh = vis_thresh
self.device = "cuda" if torch.cuda.is_available() else "cpu"
if network == "resnet":
self.cfg = cfg_re50
model_path = "weights/Resnet50_Final.pth"
else:
self.cfg = cfg_mnet
model_path = "weights/mobilenet0.25_Final.pth"
self.net = RetinaFace(cfg=self.cfg, phase='test')
self.net = load_model(self.net, model_path,
True if self.device == "cpu" else False)
self.net.eval()
print('Finished loading model!')
print(self.net)
cudnn.benchmark = True
self.device = torch.device(self.device)
self.net = self.net.to(self.device)
def detect(self, frame):
resize = 1
img = np.float32(frame)
im_height, im_width, _ = img.shape
scale = torch.Tensor([im_width, im_height, im_width, im_height])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(self.device)
scale = scale.to(self.device)
loc, conf, landms = self.net(img)
priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(self.device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(
0), prior_data, self.cfg['variance'])
scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2]])
scale1 = scale1.to(self.device)
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > self.confidence)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:self.top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(
np.float32, copy=False)
keep = py_cpu_nms(dets, self.nms_thresh)
# keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
dets = dets[:self.keep_top_k, :]
landms = landms[:self.keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
results = []
for det in dets:
r = {}
r["point"] = {}
r["point"]["x1"] = int(det[0])
r["point"]["y1"] = int(det[1])
r["point"]["x2"] = int(det[2])
r["point"]["y2"] = int(det[3])
r["confidence"] = det[4]
r["landmark"] = {}
r["landmark"]["p1_x"] = int(det[5])
r["landmark"]["p1_y"] = int(det[6])
r["landmark"]["p2_x"] = int(det[7])
r["landmark"]["p2_y"] = int(det[8])
r["landmark"]["p3_x"] = int(det[9])
r["landmark"]["p3_y"] = int(det[10])
r["landmark"]["p4_x"] = int(det[11])
r["landmark"]["p4_y"] = int(det[12])
r["landmark"]["p5_x"] = int(det[13])
r["landmark"]["p5_y"] = int(det[14])
results.append(r)
return results
def write_bbox(self, frame, results, confidence=True, landmark=True):
frame_copy = np.copy(frame)
for r in results:
if r["confidence"] < self.vis_thresh:
continue
text = "{:.4f}".format(r["confidence"])
cv2.rectangle(frame_copy, (r["point"]["x1"], r["point"]["y1"]),
(r["point"]["x2"], r["point"]["y2"]), (0, 0, 255), 2)
if confidence:
cx = r["point"]["x1"]
cy = r["point"]["y1"] + 12
cv2.putText(frame_copy, text, (cx, cy),
cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
if landmark:
cv2.circle(frame_copy, (r["landmark"]["p1_x"], r["landmark"]["p1_y"]), 1, (0, 0, 255), 4)
cv2.circle(frame_copy, (r["landmark"]["p2_x"], r["landmark"]["p2_y"]), 1, (0, 0, 255), 4)
cv2.circle(frame_copy, (r["landmark"]["p3_x"], r["landmark"]["p3_y"]), 1, (0, 0, 255), 4)
cv2.circle(frame_copy, (r["landmark"]["p4_x"], r["landmark"]["p4_y"]), 1, (0, 0, 255), 4)
cv2.circle(frame_copy, (r["landmark"]["p5_x"], r["landmark"]["p5_y"]), 1, (0, 0, 255), 4)
return frame_copy
class RetinaFaceDetector:
def __init__(self, device, pretrained_model):
self.device = device
self.cfg = {
'name': 'Resnet50',
'min_sizes': [[16, 32], [64, 128], [256, 512]],
'steps': [8, 16, 32],
'variance': [0.1, 0.2],
'clip': False,
'loc_weight': 2.0,
'gpu_train': True,
'batch_size': 24,
'ngpu': 4,
'epoch': 100,
'decay1': 70,
'decay2': 90,
'image_size': 840,
'pretrain': True,
'return_layers': {
'layer2': 1,
'layer3': 2,
'layer4': 3
},
'in_channel': 256,
'out_channel': 256
}
self.net = RetinaFace(cfg=self.cfg, phase='test')
self.net = load_model(self.net, pretrained_model, device)
self.net.eval()
self.net = self.net.to(device)
def predict(self,
image,
confidence_threshold=0.02,
top_k=5000,
nms_threshold=0.4,
keep_top_k=750):
torch.set_grad_enabled(False)
img = np.float32(image)
resize = 1
im_height, im_width, _ = img.shape
scale = torch.Tensor(
[img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(self.device)
scale = scale.to(self.device)
loc, conf, landms = self.net(img) # forward pass
# print('net forward time: {:.4f}'.format(time.time() - tic))
priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(self.device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data,
self.cfg['variance'])
scale1 = torch.Tensor([
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2]
])
scale1 = scale1.to(self.device)
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32,
copy=False)
keep = py_cpu_nms(dets, nms_threshold)
# keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
dets = dets[:keep_top_k, :]
landms = landms[:keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
bboxes, landmarks, confident_scores = dets[:, :4], dets[:, 5:], dets[:,
4]
boxes = []
if bboxes is None:
boxes = None
else:
for box in bboxes:
x0, y0, x1, y1 = tuple(box.astype(int))
height, width = y1 - y0, x1 - x0
distance = max(height, width)
if height < distance:
gap = distance - height
y0 -= gap / 2
y1 += gap / 2
elif width < distance:
gap = distance - width
x0 -= gap / 2
x1 += gap / 2
if y0 < 0:
y1 -= y0
y0 = 0
if x0 < 0:
x1 -= x0
x0 = 0
boxes.append([x0, y0, x1, y1])
boxes = np.array(boxes).astype(int)
return boxes, landmarks.reshape(-1, 5, 2), confident_scores
class Inference(object):
def __init__(self, weight_path, network, use_cpu=False):
self.weight_path = weight_path
self.network = network
self.use_cpu = use_cpu
self.resize = 1
self.confidence_threshold = 0.02
self.nms_threshold = 0.4
self.vis_thres = 0.5
self.input_height = 720
self.input_width = 1280
self._initialize_weight()
self.scale = torch.Tensor([1280, 720, 1280, 720]).to(self.device)
self.prior_data = self._initialize_priorbox(self.cfg,
self.input_height,
self.input_width)
def _initialize_weight(self):
self.cfg = None
if self.network == "mobile0.25":
self.cfg = cfg_mnet
elif self.network == "resnet50":
self.cfg = cfg_re50
self.net = RetinaFace(cfg=self.cfg, phase='test')
self.net = self._load_model(self.net, self.weight_path, self.use_cpu)
self.net.eval()
print('Finished loading model!')
print(self.net)
cudnn.benchmark = True
self.device = torch.device("cpu" if self.use_cpu else "cuda")
print("self. device : ", self.device)
self.net = self.net.to(self.device)
def _initialize_priorbox(self, cfg, im_height, im_width):
priorbox = PriorBox(cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(self.device)
prior_data = priors.data
return prior_data
def _remove_prefix(self, state_dict, prefix):
''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
print('remove prefix \'{}\''.format(prefix))
f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
return {f(key): value for key, value in state_dict.items()}
def _check_keys(self, model, pretrained_state_dict):
ckpt_keys = set(pretrained_state_dict.keys())
model_keys = set(model.state_dict().keys())
used_pretrained_keys = model_keys & ckpt_keys
unused_pretrained_keys = ckpt_keys - model_keys
missing_keys = model_keys - ckpt_keys
print('Missing keys:{}'.format(len(missing_keys)))
print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
print('Used keys:{}'.format(len(used_pretrained_keys)))
assert len(
used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
return True
def _load_model(self, model, pretrained_path, load_to_cpu):
print('Loading pretrained model from {}'.format(pretrained_path))
if load_to_cpu:
pretrained_dict = torch.load(
pretrained_path, map_location=lambda storage, loc: storage)
else:
device = torch.cuda.current_device()
pretrained_dict = torch.load(
pretrained_path,
map_location=lambda storage, loc: storage.cuda(device))
if "state_dict" in pretrained_dict.keys():
pretrained_dict = self._remove_prefix(
pretrained_dict['state_dict'], 'module.')
else:
pretrained_dict = self._remove_prefix(pretrained_dict, 'module.')
self._check_keys(model, pretrained_dict)
model.load_state_dict(pretrained_dict, strict=False)
return model
def _forward(self, img_raw):
# img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
if img_raw is None:
print("img is None")
return None, None, None
img = np.float32(img_raw)
if self.resize != 1:
img = cv2.resize(img,
None,
None,
fx=self.resize,
fy=self.resize,
interpolation=cv2.INTER_LINEAR)
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(self.device)
loc, conf, landms = self.net(img) # forward pass
# decode boxes
boxes = decode(loc.data.squeeze(0), self.prior_data,
self.cfg['variance'])
boxes = boxes * self.scale / self.resize
boxes = boxes.cpu().numpy()
# scores
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
# landmarks
landms = decode_landm(landms.data.squeeze(0), self.prior_data,
self.cfg['variance'])
scale1 = torch.Tensor([
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2]
])
scale1 = scale1.to(self.device)
landms = landms * scale1 / self.resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > self.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32,
copy=False)
keep = py_cpu_nms(dets, self.nms_threshold)
dets = dets[keep, :]
landms = landms[keep]
dets = np.concatenate((dets, landms), axis=1)
boxes_list = []
socres_list = []
landmarks_list = []
for b in dets:
if b[4] < self.vis_thres:
continue
s = b[4]
b = list(map(int, b))
boxes_list.append([b[0], b[1], b[2], b[3]])
socres_list.append(s)
landmarks_list.append([
b[5], b[6], b[7], b[8], b[9], b[10], b[11], b[12], b[13], b[14]
])
return boxes_list, socres_list, landmarks_list
def __call__(self, img_raw):
return self._forward(img_raw)
def wxf(img):
cap = cv2.VideoCapture(img)
cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))
torch.set_grad_enabled(False)
cfg = None
if args.network == "mobile0.25":
cfg = cfg_mnet
elif args.network == "resnet50":
cfg = cfg_re50
# net and model
net = RetinaFace(cfg=cfg, phase='test')
net = load_model(net, args.trained_model, args.cpu)
net.eval()
# print('Finished loading model!')
# print(net)
cudnn.benchmark = True
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
while (1):
ret, imgre = cap.read()
if not ret:
print('Video open error.')
break
img = np.float32(imgre)
target_size = 1600
max_size = 2150
im_shape = img.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
resize = float(target_size) / float(im_size_min)
# prevent bigger axis from being more than max_size:
if np.round(resize * im_size_max) > max_size:
resize = float(max_size) / float(im_size_max)
if args.origin_size:
resize = 1
if resize != 1:
img = cv2.resize(img,
None,
None,
fx=resize,
fy=resize,
interpolation=cv2.INTER_LINEAR)
im_height, im_width, _ = img.shape
scale = torch.Tensor(
[img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(device)
scale = scale.to(device)
loc, conf, landms = net(img) # forward pass
priorbox = PriorBox(cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data,
cfg['variance'])
scale1 = torch.Tensor([
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2]
])
scale1 = scale1.to(device)
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > args.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1]
# order = scores.argsort()[::-1][:args.top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32,
copy=False)
keep = py_cpu_nms(dets, args.nms_threshold)
# keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
# dets = dets[:args.keep_top_k, :]
# landms = landms[:args.keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
for b in dets:
if b[4] < args.vis_thres:
continue
text = "{:.4f}".format(b[4])
b = list(map(int, b))
cv2.rectangle(imgre, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
cx = b[0]
cy = b[1] + 12
# cv2.putText(imgre, text, (cx, cy),
# cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
#
# landms
# cv2.circle(imgre, (b[5], b[6]), 1, (0, 0, 255), 4)
# cv2.circle(imgre, (b[7], b[8]), 1, (0, 255, 255), 4)
# cv2.circle(imgre, (b[9], b[10]), 1, (255, 0, 255), 4)
# cv2.circle(imgre, (b[11], b[12]), 1, (0, 255, 0), 4)
# cv2.circle(imgre, (b[13], b[14]), 1, (255, 0, 0), 4)
#img = numpy.array(img)
cv2.imshow('wyfRetinaface', imgre)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
# if args.cpu:
# pretrained_dict = torch.load(
# args.trained_model, map_location=lambda storage, loc: storage)
# else:
# device = torch.cuda.current_device()
# pretrained_dict = torch.load(
# args.trained_model, map_location=lambda storage, loc: storage.cuda(device))
# net.load_state_dict(pretrained_dict)
# net.eval()
net = load_model(net, args.trained_model, args.cpu)
net.eval()
print('Finished loading model!')
cudnn.benchmark = True
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)
resize = 1 # Hyperparameter
img_raw = cv.imread("demo.jpg", cv.IMREAD_COLOR)
img = np.float32(img_raw)
im_height, im_width, _ = img.shape
scale = torch.Tensor(
[img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(device)
scale = scale.to(device)
def main():
args = get_args()
torch.set_grad_enabled(False)
if args.network == "mobile0.25":
cfg = cfg_mnet
elif args.network == "resnet50":
cfg = cfg_re50
else:
raise NotImplementedError(
f"Only mobile0.25 and resnet50 are suppoted.")
# net and model
net = RetinaFace(cfg=cfg, phase="test")
net = load_model(net, args.trained_model, args.cpu)
net.eval()
if args.fp16:
net = net.half()
print("Finished loading model!")
cudnn.benchmark = True
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
file_paths = sorted(args.input_path.rglob("*.jpg"))
if args.num_gpu is not None:
start, end = split_array(len(file_paths), args.num_gpu, args.gpu_id)
file_paths = file_paths[start:end]
output_path = args.output_path
if args.save_boxes:
output_label_path = output_path / "labels"
output_label_path.mkdir(exist_ok=True, parents=True)
if args.save_crops:
output_image_path = output_path / "images"
output_image_path.mkdir(exist_ok=True, parents=True)
transform = albu.Compose([
albu.Normalize(
p=1, mean=(104, 117, 123), std=(1.0, 1.0, 1.0), max_pixel_value=1)
],
p=1)
test_loader = DataLoader(
InferenceDataset(file_paths, args.origin_size, transform=transform),
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False,
)
with torch.no_grad():
for raw_input in tqdm(test_loader):
torched_images = raw_input["torched_image"]
if args.fp16:
torched_images = torched_images.half()
resizes = raw_input["resize"]
image_paths = Path(raw_input["image_path"])
raw_images = raw_input["raw_image"]
labels = []
if (args.batch_size == 1 and args.save_boxes
and (output_label_path /
f"{Path(image_paths[0]).stem}.json").exists()):
continue
loc, conf, land = net(torched_images.to(device)) # forward pass
batch_size = torched_images.shape[0]
image_height, image_width = torched_images.shape[2:]
scale1 = torch.Tensor([
image_width,
image_height,
image_width,
image_height,
image_width,
image_height,
image_width,
image_height,
image_width,
image_height,
])
scale1 = scale1.to(device)
scale = torch.Tensor(
[image_width, image_height, image_width, image_height])
scale = scale.to(device)
priorbox = PriorBox(cfg, image_size=(image_height, image_width))
priors = priorbox.forward()
priors = priors.to(device)
prior_data = priors.data
for batch_id in range(batch_size):
image_path = image_paths[batch_id]
file_id = Path(image_path).stem
raw_image = raw_images[batch_id]
resize = resizes[batch_id].float()
boxes = decode(loc.data[batch_id], prior_data, cfg["variance"])
boxes *= scale / resize
scores = conf[batch_id][:, 1]
landmarks = decode_landm(land.data[batch_id], prior_data,
cfg["variance"])
landmarks *= scale1 / resize
# ignore low scores
valid_index = torch.where(
scores > args.confidence_threshold)[0]
boxes = boxes[valid_index]
landmarks = landmarks[valid_index]
scores = scores[valid_index]
order = scores.argsort(descending=True)
boxes = boxes[order]
landmarks = landmarks[order]
scores = scores[order]
# do NMS
keep = nms(boxes, scores, args.nms_threshold)
boxes = boxes[keep, :].int()
landmarks = landmarks[keep].int()
if boxes.shape[0] == 0:
continue
scores = scores[keep].cpu().numpy().astype(np.float64)
for crop_id, bbox in enumerate(boxes):
bbox = bbox.cpu().numpy()
labels += [{
"crop_id": crop_id,
"bbox": bbox.tolist(),
"score": scores[crop_id],
"landmarks": landmarks[crop_id].tolist(),
}]
if args.save_crops:
x_min, y_min, x_max, y_max = bbox
x_min = max(0, x_min)
y_min = max(0, y_min)
crop = raw_image[y_min:y_max,
x_min:x_max].cpu().numpy()
target_folder = output_image_path / f"{file_id}"
target_folder.mkdir(exist_ok=True, parents=True)
crop_file_path = target_folder / f"{file_id}_{crop_id}.jpg"
if crop_file_path.exists():
continue
cv2.imwrite(
str(crop_file_path),
cv2.cvtColor(crop, cv2.COLOR_BGR2RGB),
[int(cv2.IMWRITE_JPEG_QUALITY), 90],
)
if args.save_boxes:
result = {
"file_path": image_path,
"file_id": file_id,
"bboxes": labels,
}
with open(output_label_path / f"{file_id}.json", "w") as f:
json.dump(result, f, indent=2)
if __name__ == '__main__':
torch.set_grad_enabled(False)
cfg = None
if args.network == "mobile0.25":
cfg = cfg_mnet
elif args.network == "resnet50":
cfg = cfg_re50
# net and model
net = RetinaFace(cfg=cfg, phase='test')
net = load_model(net, args.trained_model, args.cpu)
net.eval()
print('Finished loading model!')
print(net)
cudnn.benchmark = True
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
resize = 1
for i in range(1):
image_path = "nano.jpg"
img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
print("The original image shape is ", img_raw.shape)
img = np.float32(img_raw)
im_height, im_width, _ = img.shape
print("The image shape is ", img.shape)
scale = torch.Tensor(
[img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
def wxf(imgpath):
print(imgpath)
torch.set_grad_enabled(False)
cfg = None
if args.network == "mobile0.25":
cfg = cfg_mnet
elif args.network == "resnet50":
cfg = cfg_re50
# net and model
net = RetinaFace(cfg=cfg, phase='test')
net = load_model(net, args.trained_model, args.cpu)
net.eval()
#print('Finished loading model!')
print(net)
cudnn.benchmark = True
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
image_path = imgpath
img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
img = np.float32(img_raw)
target_size = 1600
max_size = 2150
im_shape = img.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
resize = float(target_size) / float(im_size_min)
# prevent bigger axis from being more than max_size:
if np.round(resize * im_size_max) > max_size:
resize = float(max_size) / float(im_size_max)
if args.origin_size:
resize = 1
if resize != 1:
img = cv2.resize(img,
None,
None,
fx=resize,
fy=resize,
interpolation=cv2.INTER_LINEAR)
im_height, im_width, _ = img.shape
scale = torch.Tensor(
[img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(device)
scale = scale.to(device)
loc, conf, landms = net(img) # forward pass
priorbox = PriorBox(cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance'])
scale1 = torch.Tensor([
img.shape[3], img.shape[2], img.shape[3], img.shape[2], img.shape[3],
img.shape[2], img.shape[3], img.shape[2], img.shape[3], img.shape[2]
])
scale1 = scale1.to(device)
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > args.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1]
# order = scores.argsort()[::-1][:args.top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32,
copy=False)
keep = py_cpu_nms(dets, args.nms_threshold)
# keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
# dets = dets[:args.keep_top_k, :]
# landms = landms[:args.keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
# name = rlsb.sb(imgpath)
# save image
if args.save_image:
for b in dets:
if b[4] < args.vis_thres:
continue
# text = "{:.4f}".format(b[4])
b = list(map(int, b))
cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
# cx = b[0]
# cy = b[1] + 12
#
#
# cv2.putText(img_raw, text, (cx, cy),
# cv2.FONT_HERSHEY_SCRIPT_COMPLEX, 0.5, (255, 255, 255))
#
# landms
# cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4)
# cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4)
# cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4)
# cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4)
# cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4)
# save image
if not os.path.exists("./results/"):
os.makedirs("./results/")
name = "./results/" + "wxf" + ".jpg"
cv2.imwrite(name, img_raw)
return name
