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
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
class FaceDetectorRetinaFace(object):
"""
Class to support the face detection via RetinaFace
Based on the code found at https://github.com/biubug6/Pytorch_Retinaface/blob/master/test_fddb.py
"""
def __init__(
self,
enable_cuda=settings.CUDA_ENABLED,
face_rect_expand_factor=FACE_RECT_EXPAND_FACTOR,
trained_model=settings.FACE_DETECTION_MODEL,
network=settings.FACE_DETECTION_NETWORK,
):
"""
Initializes the RetinaFace in PyTorch
Arguments:
enable_cuda: boolean indicating whether CUDA must be used for the extraction of the features
face_rect_expand_factor: Expansion factor for the detection face rectangle
trained_model: Path to a pretrained model file with weights
network: Name of the network used for the detection. The options are 'mobile0.25' or 'resnet50'.
"""
torch.set_grad_enabled(False)
cudnn.benchmark = True
self.is_cuda_enable = enable_cuda
self.face_rect_expand_factor = face_rect_expand_factor
self.trained_model = trained_model
self.cfg = None
if network == 'mobile0.25':
self.cfg = cfg_mnet
elif network == 'resnet50':
self.cfg = cfg_re50
assert self.cfg != None, "Network name can only be 'resnet50' or 'mobile0.25' !"
self.net = RetinaFace(cfg=self.cfg, phase='test')
self.net = self.load_model(self.net, self.trained_model,
not self.is_cuda_enable)
self.net.eval()
self.device = torch.device(
'cpu' if not self.is_cuda_enable else 'cuda')
self.net = self.net.to(self.device)
def check_keys(self, model, pretrained_state_dict):
"""
Checks missing dictionary keys in the pretrained model.
Extracted 'as is' from https://github.com/biubug6/Pytorch_Retinaface/blob/master/test_fddb.py
"""
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.'
Extracted 'as is' from https://github.com/biubug6/Pytorch_Retinaface/blob/master/test_fddb.py
"""
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):
"""
Loads the specified trained model
Arguments:
load_to_cpu: boolean indicating whether to load the model on the CPU or the GPU
model: RetinaFace model object
pretrained_path: Path to a pretrained model file with weights
Extracted 'as is' from https://github.com/biubug6/Pytorch_Retinaface/blob/master/test_fddb.py
"""
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 detect_faces(self, img, return_best=False):
"""
Computes a list of faces detected in the input image in the form of a list of bounding-boxes, one per each detected face.
Arguments:
img: The image to be input to the RetinaFace model
return_best: boolean indicating whether to return just to best detection or the complete list of detections
Returns:
A list of arrays. Each array contains the image coordinates of the corners of a bounding-box and the score of the detection
in the form [x1,y1,x2,y2,score], where (x1,y1) are the integer coordinates of the top-left corner of the box and (x2,y2) are
the coordinates of the bottom-right corner of the box. The score is a floating-point number.
When return_best is True, the returned list will contain only one bounding-box
"""
if numpy.all(img != None):
try:
im_height, im_width, _ = img.shape
scale = torch.Tensor(
[img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img = numpy.float32(img)
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)
# note below that the landmarks (3rd returned value) are ignored
loc, conf, _ = 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
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
# ignore low scores
inds = numpy.where(scores > CONF_THRESH)[0]
boxes = boxes[inds]
scores = scores[inds]
# keep top-K before NMS
# order = scores.argsort()[::-1][:args.top_k]
order = scores.argsort()[::-1]
boxes = boxes[order]
scores = scores[order]
# do NMS
dets = numpy.hstack(
(boxes, scores[:, numpy.newaxis])).astype(numpy.float32,
copy=False)
keep = py_cpu_nms(dets, NMS_THRESH)
# keep top-K faster NMS
detections = dets[keep, :]
if len(detections) > 0:
if return_best:
# detections is ordered by confidence so the first one is the best
det = numpy.squeeze(detections[0, 0:5])
bounding_box = numpy.zeros(5, dtype=numpy.float32)
# extend detection
extend_factor = self.face_rect_expand_factor
width = round(det[2] - det[0] + 1)
height = round(det[3] - det[1] + 1)
length = (width + height) / 2.0
centrepoint = [
round(det[0]) + width / 2.0,
round(det[1]) + height / 2.0
]
bounding_box[0] = centrepoint[0] - round(
(1 + extend_factor) * length / 2.0)
bounding_box[1] = centrepoint[1] - round(
(1 + extend_factor) * length / 2.0)
bounding_box[2] = centrepoint[0] + round(
(1 + extend_factor) * length / 2.0)
bounding_box[3] = centrepoint[1] + round(
(1 + extend_factor) * length / 2.0)
# prevent going off image
bounding_box[0] = int(max(bounding_box[0], 0))
bounding_box[1] = int(max(bounding_box[1], 0))
bounding_box[2] = int(
min(bounding_box[2], img.shape[3]))
bounding_box[3] = int(
min(bounding_box[3], img.shape[2]))
bounding_box[4] = det[4]
return [bounding_box]
else:
det_list = []
for j in range(len(detections)):
det = numpy.squeeze(detections[j, 0:5])
bounding_box = numpy.zeros(5, dtype=numpy.float32)
# extend detection
extend_factor = self.face_rect_expand_factor
width = round(det[2] - det[0] + 1)
height = round(det[3] - det[1] + 1)
length = (width + height) / 2.0
centrepoint = [
round(det[0]) + width / 2.0,
round(det[1]) + height / 2.0
]
bounding_box[0] = centrepoint[0] - round(
(1 + extend_factor) * length / 2.0)
bounding_box[1] = centrepoint[1] - round(
(1 + extend_factor) * length / 2.0)
bounding_box[2] = centrepoint[0] + round(
(1 + extend_factor) * length / 2.0)
bounding_box[3] = centrepoint[1] + round(
(1 + extend_factor) * length / 2.0)
# prevent going off image
bounding_box[0] = int(max(bounding_box[0], 0))
bounding_box[1] = int(max(bounding_box[1], 0))
bounding_box[2] = int(
min(bounding_box[2], img.shape[3]))
bounding_box[3] = int(
min(bounding_box[3], img.shape[2]))
bounding_box[4] = det[4]
det_list.append(bounding_box)
return det_list
else:
return None
except Exception as e:
print('Exception in FaceDetectorRetinaFace: ' + str(e))
pass
return None
def wxf():
cap = cv2.VideoCapture(0)
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)
images = os.listdir('./images')
known_face_names = []
known_face_encodings = []
for i in images:
picture_newname = i
someone_img = face_recognition.load_image_file("images/" +i)
someone_face_encoding = face_recognition.face_encodings(someone_img)[0]
known_face_names.append(picture_newname)
known_face_encodings.append(someone_face_encoding)
someone_img = []
someone_face_encoding = []
name = "Unknown"
while (1):
ret, imgre = cap.read()
small_frame = cv2.resize(imgre, (0, 0), fx=0.25, fy=0.25)
if not ret:
print('Video open error.')
break
rgb_small_frame = small_frame[:, :, ::-1]
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
for i in face_encodings:
match = face_recognition.compare_faces(known_face_encodings, i, tolerance=0.39)
if True in match:
match_index = match.index(True)
name = known_face_names[match_index].split('.')[0]
# To print name and time
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, name, (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()
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
while True:
ret, img_raw = video_capture.read()
if ret:
if idx == 0:
print('video shape : {}'.format(img_raw.shape))
idx += 1
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=0.05, right_scale=0.05, up_scale=0.05, low_scale=0.05)
# 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=0.1, right_scale=0.1, up_scale=0.1, low_scale=0.1)
data_dir = '../face_dataset/1N_test_dataset_origin/GEO_Mask_Testing_Dataset_1N/identity'
target_dir = '../face_dataset/1N_test_dataset/GEO_Mask_Testing_Dataset_large_crop_1N/identity'
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)
class FaceDetector:
def __init__(self,
trained_mode='./weights/mobilenet0.25_Final.pth',
network='mobile0.25',
cpu=True,
confidence_threshold=0.02,
top_k=5000,
nms_threshold=0.4,
keep_top_k=750,
vis_thres=0.6):
self.trained_model = trained_mode
self.network = network
self.network = network
self.cpu = cpu
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
torch.set_grad_enabled(False)
self.cfg = None
if self.network == "mobile0.25":
setattr(self, 'cfg', cfg_mnet)
elif self.network == "resnet50":
setattr(self, 'cfg', cfg_re50)
else:
raise (Exception("Invalid NetWork"))
# build net and load model
self.net = RetinaFace(self.cfg, phase='test')
self.net = load_model(self.net, self.trained_model, self.cpu)
self.net = self.net.eval()
self.device = torch.device("cpu" if self.cpu else "cuda")
self.net = self.net.to(self.device)
self.resize = 1
def detect(self, image_raw):
"""
Detect face from single image
:param image_raw: ndarray of image
:return:
"""
img = np.float32(image_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.device)
scale = scale.to(self.device)
tic = time.time()
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 / self.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 / self.resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > self.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
print("shape of landms: ", landms.shape)
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_threshold)
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)
return dets
from data import cfg_mnet
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))
model.load_state_dict(pretrained_dict, strict=False)
return model
if __name__ == '__main__':
torch.set_grad_enabled(False) #test的标志
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() #test ，训练时 写作net.train()
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[:-8] + "test_val.txt"
with open(testset_list, 'r') as fr:
test_dataset = fr.read().split('\n')
num_images = len(test_dataset)
_t = {'forward_pass': Timer(), 'misc': Timer()}
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
elif args.network == "resnet34_hsfd":
cfg = cfg_re34_hsfd_finetune
elif args.network == "resnet34_hsfd_not_finetune":
cfg = cfg_re34_hsfd_not_finetune
# 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)
used_channels = cfg['used_channels']
img_dim = cfg['image_size']
test_dataset = EcustHsfdDetection(args.dataset_file,
used_channels,
preproc=valid_preproc(img_dim, None),
mode='valid')
num_images = len(test_dataset)
datadir = '/'.join(args.dataset_file.split('/')[:-1])
pred_file = os.path.join(f'{args.save_folder:s}_pred.txt')
gt_file = os.path.join(f'{args.save_folder:s}_gt.txt')
fp1 = open(pred_file, 'w')
fp2 = open(gt_file, 'w')
_t = {'forward_pass': Timer(), 'misc': Timer()}
# testing begin
for i, img_name in enumerate(test_dataset.imgs_path):
if i % 100 == 0:
torch.cuda.empty_cache()
# image_path = testset_folder + img_name
img_raw = load_datacube(img_name)[..., used_channels]
# img_raw = cv2.imread(img_name, cv2.IMREAD_COLOR)
img = np.float32(img_raw)
# testing scale
target_size = img_dim
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 = np.stack([
cv2.resize(img[..., i], None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR) \
for i in range(img.shape[-1])
], axis=-1)
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, :]
prediction = np.concatenate((dets, landms), axis=1)
_t['misc'].toc()
# --------------------------------------------------------------------
# save_name = os.path.join(args.save_folder, 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)
fp1.write(f"# {img_name.lstrip(datadir).lstrip('/')}\n")
if dets.shape[0] > 0:
dets = prediction[0][:4].astype(np.int).tolist()
dets[2] -= dets[0]
dets[3] -= dets[1]
landms = prediction[0][4:14]
scores = prediction[0][14]
label = [0. for _ in range(20)]
label[-1] = scores
label[:4] = dets
label[4:-1] = landms
label = ' '.join(list(map(str, label)))
fp1.write(f'{label}\n')
gt_label = ' '.join(list(map(str, test_dataset.words[i][0])))
fp2.write(f"# {img_name.lstrip(datadir).lstrip('/')}\n")
fp2.write(f'{gt_label}\n')
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)
fp1.close()
def train():
net = RetinaFace(cfg=cfg)
logger.info("Printing net...")
logger.info(net)
if args.resume_net is not None:
logger.info('Loading resume network...')
state_dict = torch.load(args.resume_net)
# create new OrderedDict that does not contain `module.`
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
head = k[:7]
if head == 'module.':
name = k[7:] # remove `module.`
else:
name = k
new_state_dict[name] = v
net.load_state_dict(new_state_dict)
if num_gpu > 1 and gpu_train:
net = torch.nn.DataParallel(net).cuda()
else:
net = net.cuda()
cudnn.benchmark = True
priorbox = PriorBox(cfg, image_size=(img_dim, img_dim))
with torch.no_grad():
priors = priorbox.forward()
priors = priors.cuda()
net.train()
epoch = 0 + args.resume_epoch
logger.info('Loading Dataset...')
trainset = WiderFaceDetection(training_dataset, preproc=train_preproc(img_dim, rgb_mean), mode='train')
validset = WiderFaceDetection(training_dataset, preproc=valid_preproc(img_dim, rgb_mean), mode='valid')
# trainset = WiderFaceDetection(training_dataset, transformers=train_transformers(img_dim), mode='train')
# validset = WiderFaceDetection(training_dataset, transformers=valid_transformers(img_dim), mode='valid')
trainloader = data.DataLoader(trainset, batch_size, shuffle=True, num_workers=num_workers, collate_fn=detection_collate)
validloader = data.DataLoader(validset, batch_size, shuffle=True, num_workers=num_workers, collate_fn=detection_collate)
logger.info(f'Totally {len(trainset)} training samples and {len(validset)} validating samples.')
epoch_size = math.ceil(len(trainset) / batch_size)
max_iter = max_epoch * epoch_size
logger.info(f'max_epoch: {max_epoch:d} epoch_size: {epoch_size:d}, max_iter: {max_iter:d}')
# optimizer = optim.SGD(net.parameters(), lr=initial_lr, momentum=momentum, weight_decay=weight_decay)
optimizer = optim.Adam(net.parameters(), lr=initial_lr, weight_decay=weight_decay)
scheduler = _utils.get_linear_schedule_with_warmup(optimizer, int(0.1 * max_iter), max_iter)
criterion = MultiBoxLoss(num_classes, 0.35, True, 0, True, 7, 0.35, False)
stepvalues = (cfg['decay1'] * epoch_size, cfg['decay2'] * epoch_size)
step_index = 0
if args.resume_epoch > 0:
start_iter = args.resume_epoch * epoch_size
else:
start_iter = 0
best_loss_val = float('inf')
for iteration in range(start_iter, max_iter):
if iteration % epoch_size == 0:
# create batch iterator
# batch_iterator = iter(tqdm(trainloader, total=len(trainloader)))
batch_iterator = iter(trainloader)
# if (epoch % 10 == 0 and epoch > 0) or (epoch % 5 == 0 and epoch > cfg['decay1']):
# torch.save(net.state_dict(), save_folder + cfg['name']+ '_epoch_' + str(epoch) + '.pth')
epoch += 1
torch.cuda.empty_cache()
if (valid_steps > 0) and (iteration > 0) and (iteration % valid_steps == 0):
net.eval()
# validation
loss_l_val = 0.
loss_c_val = 0.
loss_landm_val = 0.
loss_val = 0.
# for val_no, (images, targets) in tqdm(enumerate(validloader), total=len(validloader)):
for val_no, (images, targets) in enumerate(validloader):
# load data
images = images.cuda()
targets = [anno.cuda() for anno in targets]
# forward
with torch.no_grad():
out = net(images)
loss_l, loss_c, loss_landm = criterion(out, priors, targets)
loss = cfg['loc_weight'] * loss_l + loss_c + loss_landm
loss_l_val += loss_l.item()
loss_c_val += loss_c.item()
loss_landm_val += loss_landm.item()
loss_val += loss.item()
loss_l_val /= len(validloader)
loss_c_val /= len(validloader)
loss_landm_val /= len(validloader)
loss_val /= len(validloader)
logger.info('[Validating] Epoch:{}/{} || Epochiter: {}/{} || Iter: {}/{} || Total: {:.4f} Loc: {:.4f} Cla: {:.4f} Landm: {:.4f}'
.format(epoch, max_epoch, (iteration % epoch_size) + 1,
epoch_size, iteration + 1, max_iter,
loss_val, loss_l_val, loss_c_val, loss_landm_val))
if loss_val < best_loss_val:
best_loss_val = loss_val
pth = os.path.join(save_folder, cfg['name'] + '_iter_' + str(iteration) + f'_{loss_val:.4f}_' + '.pth')
torch.save(net.state_dict(), pth)
logger.info(f'Best validating loss: {best_loss_val:.4f}, model saved as {pth:s})')
net.train()
load_t0 = time.time()
# if iteration in stepvalues:
# step_index += 1
# lr = adjust_learning_rate(optimizer, gamma, epoch, step_index, iteration, epoch_size)
# load train data
images, targets = next(batch_iterator)
images = images.cuda()
targets = [anno.cuda() for anno in targets]
# forward
out = net(images)
# backprop
optimizer.zero_grad()
loss_l, loss_c, loss_landm = criterion(out, priors, targets)
loss = cfg['loc_weight'] * loss_l + loss_c + loss_landm
loss.backward()
optimizer.step()
scheduler.step()
load_t1 = time.time()
batch_time = load_t1 - load_t0
eta = int(batch_time * (max_iter - iteration))
if iteration % verbose_steps == 0:
logger.info('[Training] Epoch:{}/{} || Epochiter: {}/{} || Iter: {}/{} || Total: {:.4f} Loc: {:.4f} Cla: {:.4f} Landm: {:.4f} || LR: {:.8f} || Batchtime: {:.4f} s || ETA: {}'
.format(epoch, max_epoch, (iteration % epoch_size) + 1,
epoch_size, iteration + 1, max_iter,
loss.item(), loss_l.item(), loss_c.item(), loss_landm.item(),
scheduler.get_last_lr()[-1], batch_time, str(datetime.timedelta(seconds=eta))))
class RetinaFaceDet(object):
def __init__(self,model_type="mobile0.25",model_path="./weights/mobilenet0.25_Final.pth",
backbone_location="./weights/mobilenetV1X0.25_pretrain.tar",use_cpu=True,loading=True):
self.cfg = None
self.use_cpu = use_cpu
self.model_path = model_path
if model_type == "mobile0.25":
self.cfg = cfg_mnet
elif model_type == "resnet50":
self.cfg = cfg_re50
self.device = torch.device("cpu" if use_cpu else "cuda")
self.net = RetinaFace(cfg=self.cfg,phase="test",backbone_location=backbone_location)
if loading:
print('No model path exist!') if not os.path.exists(model_path) else None
self.loading()
self._priors = None
self.im_width = 0
self.im_height = 0
self.im_nch = 0
def _get_model(self):
self.net.eval()
return self.net
def loading(self):
self.net = load_model(self.net, self.model_path, self.use_cpu)
self.net.eval()
self.net = self.net.to(self.device)
def set_default_size(self,imgshape=[640,480,3]):#[H,W,nCh]
im_height, im_width, im_nch = imgshape
if im_height == self.im_height and im_width == self.im_width and self._priors is not None:
pass
else:
self.im_height,self.im_width,self.im_nch = imgshape
"""
priorbox shape [-1,4]; dim0: number of predicted bbox from network; dim1:[x_center,y_center,w,h]
priorbox存储的内容分别是bbox中心点的位置以及人脸预设的最小尺寸，长宽比例通过variance解决
这里的数值都是相对图像尺寸而言的相对值，取值在(0,1)之间
"""
priorbox = PriorBox(self.cfg,image_size=(self.im_height,self.im_width))
self._priors = priorbox.forward()
@torch.no_grad()
def execute_batch_mlu(self,net_output,batch_shape,threshold=0.8,topk=5000,keep_topk=750,nms_threshold=0.2):
locs,confs,landmss = net_output
nB, nCh, im_height, im_width = batch_shape
scale = torch.Tensor([im_width, im_height]*2)
scale1 = torch.Tensor([im_width, im_height] * 5)
detss = []
if im_height == self.im_height and im_width == self.im_width and self._priors is not None:
pass
else:
self.set_default_size([im_height, im_width, nCh])
priors = self._priors.unsqueeze(dim=0)
boxes = batch_decode(locs, priors, self.cfg['variance'])
boxes = boxes * scale
scores = confs[:, :, 1]
landms = batch_decode_landm(landmss, priors, self.cfg['variance'])
landms = landms * scale1
landms = landms.data.cpu().numpy()
scores = scores.data.cpu().numpy()
boxes = boxes.data.cpu().numpy()
for n in range(nB):
_landms = landms[n]
_scores = scores[n]
_boxes = boxes[n]
# ignore low scores
inds = np.where(_scores > threshold)[0]
_boxes = _boxes[inds]
_landms = _landms[inds]
_scores = _scores[inds]
# keep top-K before NMS
order = _scores.argsort()[::-1][:topk]
_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)
dets = dets[keep, :]
_landms = _landms[keep]
# keep top-K faster NMS
dets = dets[:keep_topk, :]
_landms = _landms[:keep_topk, :]
# x0,y0,x1,y1,score,landmarks...
dets = np.concatenate((dets, _landms), axis=1)
detss.append(dets)
return detss
def execute_batch(self,img_batch,threshold=0.8,topk=5000,keep_topk=750,nms_threshold=0.2):
resize = 1
with torch.no_grad():
img_batch = img_batch.to(self.device)
locs,confs,landmss = self.net(img_batch)
nB,nCh,im_height, im_width = img_batch.shape
scale = torch.Tensor([im_width, im_height, im_width, im_height])
scale = scale.to(self.device)
if im_height == self.im_height and im_width == self.im_width and self._priors is not None:
pass
else:
self.set_default_size([im_height,im_width,nCh])
priors = self._priors
priors = priors.to(self.device)
prior_data = priors.data
detss = []
"""
以bbox的location为例子，最终要得到的是：
bbox_center_x
bbox_center_y
bbox_w
bbox_h
但是，直接预测这些数值是困难的，所以需要脱离图像的尺寸，压缩到0-1的范围，所以我们改为预测：_bbox_center_x，_bbox_center_y，_bbox_w，_bbox_w，他们的关系如下：
bbox_center_x = (_bbox_center_x)*imgW
bbox_center_y = (_bbox_center_y)*imgH
bbox_w = (_bbox_w)*imgW
bbox_h = (_bbox_w)*imgH
进一步，引入anchor的概念，即预先设定多个bbox的中心和最小的人脸长宽。我们只预测真实值与预设值之间的比例、偏移关系，
模型预测结果为[x_offset,y_offset,w_scale,h_scale]
预设bbox为[x_center,y_center,face_w,face_h] 即prior_data
vx,vy控制人脸的长宽比
他们之间相互关系为：
_bbox_center_x = x_center + x_offset*face_w*vx
_bbox_center_y = y_center + y_offset*face_h*vy
_bbox_w = face_w*exp(w_scale*vx)
_bbox_h = face_h*exp(h_scale*vy)
最终得到：
bbox_center_x = (x_center + x_offset*face_w*vx)*imgW
bbox_center_y = (y_center + y_offset*face_h*vy)*imgH
bbox_w = (face_w*exp(w_scale*vx))*imgW
bbox_h = (face_h*exp(h_scale*vy))*imgH
"""
for idx in range(nB):
loc = locs[idx]
conf = confs[idx]
landms = landmss[idx]
"""
对loc而言，网络输出的是shape为[-1,4]的矩阵，dim1是[x_offset,y_offset,w_scale,h_scale],需要通过decode进行恢复到正常的bbox
loc: [-1,4]; dim1: [x_offset,y_offset,w_scale,h_scale]
prior_data: [-1,4]; dim1:[x_center,y_center,face_w,face_h]
虽然这里的face_w!= face_h,但本质上是相等的，因为是face_w/face_h相对图像尺寸的值。所以，本质上这里是正方形的anchor，需要variance来控制长宽比。
variance: [vx,vy] 控制长宽比例
_bbox_center_x = x_center + x_offset*face_w*vx
_bbox_center_y = y_center + y_offset*face_h*vy
_bbox_w = face_w*exp(w_scale*vx)
_bbox_h = face_h*exp(h_scale*vy)
进一步，转为left top corner x, left top corner y, right bottom corner x, right bottom corner y的形式
"""
boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
"""
之前的结果都是normalize的结果，即(0,1)，因此，需要重新rescale回去。
这个scale即图像的大小。
"""
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
"""
基本原理同上
"""
landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance'])
scale1 = torch.Tensor([im_width, im_height, im_width, im_height,
im_width, im_height, im_width, im_height,
im_width, im_height])
scale1 = scale1.to(self.device)
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:topk]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
"""
py_cpu_nms 非极大抑制
基本原理，假设dets是一个队列，每次取队列第一个元素(pop)，并加入到keep的list中，将该元素与dets队列中其它元素比较，剔除bbox交集大于nms_threshold的元素。
然后不断循环，直到dets为空。
"""
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_topk, :]
landms = landms[:keep_topk, :]
# x0,y0,x1,y1,score,landmarks...
dets = np.concatenate((dets, landms), axis=1)
detss.append(dets)
return detss
def execute(self,img_cv,threshold=0.6,topk=5000,keep_topk=750,nms_threshold=0.7):
resize = 1
with torch.no_grad():
img = np.float32(img_cv)
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
if im_height == self.im_height and im_width == self.im_width and self._priors is not None:
pass
else:
self.set_default_size([im_height,im_width,3])
priors = self._priors
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 > threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:topk]
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_topk, :]
landms = landms[:keep_topk, :]
# x0,y0,x1,y1,score,landmarks...
dets = np.concatenate((dets, landms), axis=1)
return dets
def execute_debug(self,img_cv,threshold=0.6,topk=5000,keep_topk=750,nms_threshold=0.7):
resize = 1
dtime = {'detection': [],
'nms': [],
'decode': [],
}
tob = timer(display=False)
with torch.no_grad():
img = np.float32(img_cv)
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)
data = img.reshape(-1).tolist()
jsd = {
"data":[
{
"INPUT":{
"content":data,
"shape":[460,640]
}
}
]
}
# jsdata = json.dumps( jsd )
store(jsd)
tob.start()
loc, conf, landms = self.net(img) # forward pass
utm = tob.eclapse()
dtime['detection'].append(utm)
if im_height == self.im_height and im_width == self.im_width and self._priors is not None:
pass
else:
self.set_default_size([im_height,im_width,3])
priors = self._priors
tob.start()
priors = priors.to(self.device)
prior_data = priors.data
print('nin=', prior_data.shape[0])
print('loc.data',loc.data.squeeze(0)[:2,:])
boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
print('boxes',boxes[:2,:])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
print('landms.data',landms.data.squeeze(0)[:2,:])
landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance'])
print('landms', landms[:2, :])
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 > threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
print('nout=', scores.shape[0])
# keep top-K before NMS
order = scores.argsort()[::-1][:topk]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
utm = tob.eclapse()
dtime['decode'].append(utm)
tob.start()
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
print('nms in=', dets.shape[0])
keep = py_cpu_nms(dets, nms_threshold)
# keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
dets = dets[keep, :]
print('nms out=', dets.shape[0])
landms = landms[keep]
# keep top-K faster NMS
dets = dets[:keep_topk, :]
landms = landms[:keep_topk, :]
# x0,y0,x1,y1,score,landmarks...
dets = np.concatenate((dets, landms), axis=1)
utm = tob.eclapse()
dtime['nms'].append(utm)
return dets,dtime
class RetinaFaceDetector:
def __init__(self,
trained_model='weights/Resnet50_Final.pth',
network='resnet50',
cpu=False,
confidence_threshold=0.02,
top_k=5000,
nms_threshold=0.4,
keep_top_k=750,
show_image=False,
vis_thres=0.6
):
self.cpu = cpu
self.confidence_threshold = confidence_threshold
self.top_k = top_k
self.nms_threshold = nms_threshold
self.keep_top_k = keep_top_k
self.show_image = show_image
self.vis_thres = vis_thres
torch.set_grad_enabled(False)
self.cfg = None
if network == "mobile0.25":
self.cfg = cfg_mnet
elif network == "resnet50":
self.cfg = cfg_re50
self.cfg['pretrain'] = False
# net and model
self.net = RetinaFace(cfg=self.cfg, phase = 'test')
self.net = load_model(self.net, os.path.join(os.path.dirname(inspect.getabsfile(RetinaFace)), '../' + trained_model), cpu)
self.net.eval()
print('Finished loading model!')
print(self.net)
cudnn.benchmark = True
self.device = torch.device("cpu" if cpu else "cuda")
self.net = self.net.to(self.device)
self.resize = 1
def detect_faces(self, img_raw, mean=(104, 117, 123)):
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 -= mean
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(self.device)
scale = scale.to(self.device)
tic = time.time()
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 / self.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 / 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][: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_threshold)
# keep = nms(dets, self.nms_threshold,force_cpu=self.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)
# show image
if self.show_image:
for b in dets:
if b[4] < self.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)
# Show image
cv2.imshow('result', img_raw)
cv2.waitKey(100)
results = []
for det in dets:
box = det[:4]
score = det[4]
keypoints = det[5:]
if score < self.vis_thres:
continue
results.append({'box':box.tolist(), 'score':score.tolist(), 'keypoints':keypoints.tolist()})
return results
def detect_faces_batch(self, img_raws, mean=(104, 117, 123)):
imgs = []
for img_raw in img_raws:
imgs.append(np.float32(img_raw))
imgs = np.stack(imgs, 0)
batch_size, im_height, im_width, _ = imgs.shape
scale = torch.Tensor([imgs.shape[2], imgs.shape[1], imgs.shape[2], imgs.shape[1]])
imgs -= mean
imgs = imgs.transpose(0, 3, 1, 2)
imgs = torch.from_numpy(imgs)
imgs = imgs.to(self.device)
scale = scale.to(self.device)
tic = time.time()
loc, conf, landms = self.net(imgs) # 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
list_results = []
for idx in range(batch_size):
boxes = decode(loc.data[idx], prior_data, self.cfg['variance'])
boxes = boxes * scale / self.resize
boxes = boxes.cpu().numpy()
scores = conf.data[idx].cpu().numpy()[:, 1]
keypoints = decode_landm(landms[idx].data, prior_data, self.cfg['variance'])
scale1 = torch.Tensor([
imgs.shape[3], imgs.shape[2], imgs.shape[3], imgs.shape[2],
imgs.shape[3], imgs.shape[2], imgs.shape[3], imgs.shape[2],
imgs.shape[3], imgs.shape[2]])
scale1 = scale1.to(self.device)
keypoints = keypoints * scale1 / self.resize
keypoints = keypoints.cpu().numpy()
# ignore low scores
inds = np.where(scores > self.confidence_threshold)[0]
boxes = boxes[inds]
keypoints = keypoints[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:self.top_k]
boxes = boxes[order]
keypoints = keypoints[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)
# keep = nms(dets, self.nms_threshold,force_cpu=self.cpu)
dets = dets[keep, :]
keypoints = keypoints[keep]
# keep top-K faster NMS
dets = dets[:self.keep_top_k, :]
keypoints = keypoints[:self.keep_top_k, :]
dets = np.concatenate((dets, keypoints), axis=1)
# show image
if self.show_image:
for b in dets:
if b[4] < self.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))
# keypoints
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)
# Show image
cv2.imshow('result', img_raw)
cv2.waitKey(100)
results = []
for det in dets:
box = det[:4]
score = det[4]
keypoints = det[5:]
if score < self.vis_thres:
continue
results.append({'box':box.tolist(), 'score':score.tolist(), 'keypoints':keypoints.tolist()})
list_results.append(results)
return list_results
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 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
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)
# 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()
num_images = len(test_dataset)
_t = {"forward_pass": Timer(), "misc": Timer()}
# testing begin
for i, img_name in enumerate(test_dataset):
image_path = testset_folder + img_name
img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
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 = args.save_folder + 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".
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)
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)
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)
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)
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 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
name="",
op_dict=None,
producer_op_list=None)
return graph
if __name__ == '__main__':
torch.set_grad_enabled(False)
cfg = cfg_mnet
net = RetinaFace(cfg=cfg, phase='test')
net = load_model(net,
"./converted_models/mobilenet/mobilenet0.25_Final.pth",
True)
net.eval()
print('Finish loading model!')
#print(net)
#cudnn.benchmark = True
device = torch.device("cpu")
net = net.to(device)
img_raw = cv2.imread("./Face_Detector_ncnn/sample.jpg")
#img = np.ones((3,240,320), dtype=np.float32)
img = np.float32(img_raw)
long_side = 320
im_shape = img.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
resize = float(long_side) / float(im_size_min)
if np.round(resize * im_size_max) > long_side:
def detect(img_path):
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 = FaceBoxes(phase='test', size=None, num_classes=2)
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)
_t = {'forward_pass': Timer(), 'misc': Timer()}
resize = 1
# 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)
if type(img_path) is not np.ndarray:
img = Image.open(img_path)
if img.mode == 'L':
img = img.convert('RGB')
img_raw = np.array(img)
else:
img_raw = img_path
#img_raw = img_path
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))
#priorbox = PriorBox1(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('forward_pass_time: {:.4f}s misc: {:.4f}s'.format(
_t['forward_pass'].average_time, _t['misc'].average_time))
# if type(img_path) is not np.ndarray:
# img_raw = cv2.imread(img_path, cv2.IMREAD_COLOR)
# else:
# img_raw = img_path
# # 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)
return dets, img_path