def get_FPS(self, image, test_interval):
image = np.array(image,np.float32)
im_height, im_width, _ = np.shape(image)
scale = [np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0]]
scale_for_landmarks = [np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0]]
if self.letterbox_image:
image = np.array(letterbox_image(image,[self.input_shape[1], self.input_shape[0]]), np.float32)
else:
self.anchors = Anchors(self.cfg, image_size=(im_height, im_width)).get_anchors()
with torch.no_grad():
image = torch.from_numpy(preprocess_input(image).transpose(2, 0, 1)).unsqueeze(0)
if self.cuda:
self.anchors = self.anchors.cuda()
image = image.cuda()
loc, conf, landms = self.net(image)
boxes = decode(loc.data.squeeze(0), self.anchors, self.cfg['variance'])
boxes = boxes.cpu().numpy()
conf = conf.data.squeeze(0)[:,1:2].cpu().numpy()
landms = decode_landm(landms.data.squeeze(0), self.anchors, self.cfg['variance'])
landms = landms.cpu().numpy()
boxes_conf_landms = np.concatenate([boxes, conf, landms],-1)
boxes_conf_landms = non_max_suppression(boxes_conf_landms, self.confidence)
if len(boxes_conf_landms)>0:
if self.letterbox_image:
boxes_conf_landms = retinaface_correct_boxes(boxes_conf_landms, np.array([self.input_shape[0], self.input_shape[1]]), np.array([im_height, im_width]))
boxes_conf_landms[:,:4] = boxes_conf_landms[:,:4]*scale
boxes_conf_landms[:,5:] = boxes_conf_landms[:,5:]*scale_for_landmarks
t1 = time.time()
for _ in range(test_interval):
with torch.no_grad():
loc, conf, landms = self.net(image)
boxes = decode(loc.data.squeeze(0), self.anchors, self.cfg['variance'])
boxes = boxes.cpu().numpy()
conf = conf.data.squeeze(0)[:,1:2].cpu().numpy()
landms = decode_landm(landms.data.squeeze(0), self.anchors, self.cfg['variance'])
landms = landms.cpu().numpy()
boxes_conf_landms = np.concatenate([boxes, conf, landms],-1)
boxes_conf_landms = non_max_suppression(boxes_conf_landms, self.confidence)
if len(boxes_conf_landms)>0:
if self.letterbox_image:
boxes_conf_landms = retinaface_correct_boxes(boxes_conf_landms, np.array([self.input_shape[0], self.input_shape[1]]), np.array([im_height, im_width]))
boxes_conf_landms[:,:4] = boxes_conf_landms[:,:4]*scale
boxes_conf_landms[:,5:] = boxes_conf_landms[:,5:]*scale_for_landmarks
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
def box_handle(img, conf, im_height, im_width, scale, loc, landms):
priorbox = PriorBox(cfg_mnet, 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_mnet['variance'])
boxes = boxes * scale
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data,
cfg_mnet['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
landms = landms.cpu().numpy()
inds = np.where(scores > confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
order = scores.argsort()[::-1]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
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]
dets = np.concatenate((dets, landms), axis=1)
return dets
def _postprocess(loc, conf, landms, priors, cfg, img):
"""Postprocess TensorRT outputs.
# Args
loc: [x, y, w, h]
conf: [not object confidence, object confidence]
landms: [eye_left.x, eye_left.y,
eye_right.x, eye_right.y,
nose.x, nose.y
mouth_left.x, mouth_right.y
mouth_left.x, mouth_right.y]
priors: priors boxes with retinaface model
cfg: retinaface model parameter configure
img: input image
# Returns
facePositions, landmarks (after NMS)
"""
long_side = max(img.shape)
img_size = cfg['image_size']
variance = cfg['variance']
scale = np.ones(4) * img_size
scale1 = np.ones(10) * img_size
confidence_threshold = 0.2
top_k = 50
nms_threshold = 0.5
# decode boxes
boxes = decode(np.squeeze(loc, axis=0), priors, variance)
boxes = boxes * scale
# decode landmarks
landms = decode_landm(np.squeeze(landms, axis=0), priors, variance)
landms = landms * scale1
# ignore low scores
scores = np.squeeze(conf, axis=0)[:, 1]
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)
dets = dets[keep, :]
landms = landms[keep]
# resize
res = long_side / img_size
facePositions = (dets[:, :4] * res).astype(int).tolist()
landmarks = (landms * res).astype(int).tolist()
return facePositions, landmarks
def detect_image(self, image):
# 绘制人脸框
old_image = image.copy()
image = np.array(image, np.float32)
im_height, im_width, _ = np.shape(image)
# 它的作用是将归一化后的框坐标转换成原图的大小
scale = torch.Tensor([np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0]])
scale_for_landmarks = torch.Tensor([np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0]])
# pytorch
image = preprocess_input(image).transpose(2, 0, 1)
# 增加batch_size维度
image = torch.from_numpy(image).unsqueeze(0)
# 计算先验框
anchors = Anchors(self.cfg, image_size=(im_height, im_width)).get_anchors()
with torch.no_grad():
if self.cuda:
scale = scale.cuda()
scale_for_landmarks = scale_for_landmarks.cuda()
image = image.cuda()
anchors = anchors.cuda()
loc, conf, landms = self.net(image) # forward pass
boxes = decode(loc.data.squeeze(0), anchors, self.cfg['variance'])
boxes = boxes * scale
boxes = boxes.cpu().numpy()
conf = conf.data.squeeze(0)[:,1:2].cpu().numpy()
landms = decode_landm(landms.data.squeeze(0), anchors, self.cfg['variance'])
landms = landms * scale_for_landmarks
landms = landms.cpu().numpy()
boxes_conf_landms = np.concatenate([boxes,conf,landms],-1)
boxes_conf_landms = non_max_suppression(boxes_conf_landms, self.confidence)
for b in boxes_conf_landms:
text = "{:.4f}".format(b[4])
b = list(map(int, b))
cv2.rectangle(old_image, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
cx = b[0]
cy = b[1] + 12
cv2.putText(old_image, text, (cx, cy),
cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
# landms
cv2.circle(old_image, (b[5], b[6]), 1, (0, 0, 255), 4)
cv2.circle(old_image, (b[7], b[8]), 1, (0, 255, 255), 4)
cv2.circle(old_image, (b[9], b[10]), 1, (255, 0, 255), 4)
cv2.circle(old_image, (b[11], b[12]), 1, (0, 255, 0), 4)
cv2.circle(old_image, (b[13], b[14]), 1, (255, 0, 0), 4)
pnum = len(boxes_conf_landms)
return old_image , pnum
def detect(self, img):
device = self.device
prior_data, scale, scale1 = self.decode_params(*img.shape[:2])
# REF: test_fddb.py
img = np.float32(img)
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(device, dtype=torch.float32)
loc, conf, landms = self.net(img)
loc = loc.cpu()
conf = conf.cpu()
landms = landms.cpu()
# Decode results
boxes = decode(loc.squeeze(0), prior_data, self.variance)
boxes = boxes * scale
scores = conf.squeeze(0)[:, 1]
landms = decode_landm(landms.squeeze(0), prior_data, self.variance)
landms = landms * scale1
inds = scores > self.confidence_threshold
boxes = boxes[inds]
landms = landms[inds]
return boxes, landms
def do_detect(img_raw, net, device, cfg):
resize = 1
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)
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)
return dets, landms
def pipeline(net, frame, args, device, resize, cfg):
img = np.float32(frame)
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][: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)
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)
objects_to_draw = dict(draw_box=True, draw_text=True, draw_landmarks=True)
frame = draw(frame, dets, args.vis_thres, **objects_to_draw)
return frame
def process_face_data(cfg,
im,
im_height,
im_width,
loc,
scale,
conf,
landms,
resize,
top_k=5000,
nms_threshold=0.4,
keep_top_k=750):
priorbox = PriorBox(cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.cuda()
priors_data = priors.data
boxes = decode(loc.data.squeeze(0), priors_data, cfg['variance'])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).cpu().detach().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), priors_data, cfg['variance'])
scale_landm = torch.from_numpy(
np.array([
im.shape[3], im.shape[2], im.shape[3], im.shape[2], im.shape[3],
im.shape[2], im.shape[3], im.shape[2], im.shape[3], im.shape[2]
]))
scale_landm = scale_landm.float()
scale_landm = scale_landm.cuda()
landms = landms * scale_landm / resize
landms = landms.cpu().numpy()
# ignore low score
inds = np.where(scores > 0.6)[0]
boxes = boxes[inds]
scores = scores[inds]
# keep top-K before NMS
order = np.argsort(-scores)[:top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do nms
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(float, copy=False)
keep = py_cpu_nms(dets, nms_threshold)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K fater NMS
dets = dets[:keep_top_k, :]
landms = landms[:keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
result_data = dets[:, :5].tolist()
return result_data
def GetFacialPoints(img_raw):
img = np.float32(img_raw)
height, width, _ = img_raw.shape
scale = torch.Tensor([width, height, width, height])
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=(height, 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 / config.resize
boxes = boxes.cpu().detach().numpy()
scores = conf.squeeze(0).data.cpu().detach().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 / config.resize
landms = landms.cpu().detach().numpy()
# ignore low scores
inds = np.where(scores > config.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:config.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, config.nms_threshold)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
dets = dets[:config.keep_top_k, :]
landms = landms[:config.keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
torch.cuda.empty_cache()
return dets
def decode_output(image, detection_boxes, detection_scores, detection_landmark, cfg_plate):
# print(image.shape[2:])
image_h, image_w = image.shape[2:]
# image_h, image_w, _ = image.shape
# cfg_plate['image_size'] = (480, 640)
detection_scores = F.softmax(detection_scores, dim=-1)
# detection_scores = detection_scores.cpu().detach().numpy()
# priorbox = PriorBox(cfg_plate,
# image_size=(cfg_plate['image_size'], cfg_plate['image_size']), phase='test') # height, width
priorbox = PriorBox(cfg_plate,
image_size=(image_h, image_w), phase='test') # height, width
priors = priorbox.forward()
priors = priors.to(torch.device('cuda'))
prior_data = priors.data
boxes = decode(detection_boxes.data.squeeze(0), prior_data, cfg_plate['variance'])
# boxes[:, 0::2] = boxes[:, 0::2] * cfg_plate['image_size'] # width
# boxes[:, 1::2] = boxes[:, 1::2] * cfg_plate['image_size'] # height
boxes[:, 0::2] = boxes[:, 0::2] * image_w # width
boxes[:, 1::2] = boxes[:, 1::2] * image_h # height
boxes = boxes.cpu().numpy()
scores = scores = detection_scores.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(detection_landmark.data.squeeze(0), prior_data, cfg_plate['variance'])
# landms[:, 0::2] = landms[:, 0::2] * cfg_plate['image_size']
# landms[:, 1::2] = landms[:, 1::2] * cfg_plate['image_size']
landms[:, 0::2] = landms[:, 0::2] * image_w
landms[:, 1::2] = landms[:, 1::2] * image_h
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > cfg_plate['confidence_threshold'])[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:cfg_plate['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, cfg_plate['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[:cfg_plate['keep_top_k'], :]
landms = landms[:cfg_plate['keep_top_k'], :]
dets = np.concatenate((dets, landms), axis=1)
# draw_ouput2(image, dets)
return dets
def nms_gpu(_loc,
_conf,
_landms,
_prior_data,
_scale_boxes,
_scale_landms,
_scaling_ratio,
_variance,
_confidence_threshold,
_nms_threshold,
_nms='nms_torchvision'):
if _nms == 'nms_torchvision':
nms = nms_torchvision
else:
raise NotImplementedError
boxes = decode(_loc.data.squeeze(0), _prior_data, _variance)
boxes = boxes * _scale_boxes / _scaling_ratio
landms = decode_landm(_landms.data.squeeze(0), _prior_data, _variance)
landms = landms * _scale_landms / _scaling_ratio
scores = _conf.squeeze(0).data[:, 1]
# ignore low scores
keep_idx = scores > _confidence_threshold # Time bottleneck
boxes_ = boxes[keep_idx, :]
landms_ = landms[keep_idx, :]
scores_ = scores[keep_idx]
# NMS
keep_idx = nms(boxes_, scores_, _nms_threshold)
boxes_ = boxes_[keep_idx].view(-1, 4)
landms_ = landms_[keep_idx].view(-1, 10)
scores_ = scores_[keep_idx].view(-1, 1)
dets = torch.cat((boxes_, scores_, landms_), dim=1).cpu().numpy()
return dets
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 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()
def detect_one_image(name, input_path, output_path):
image_path = os.path.join(input_path, name)
img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
img = np.float32(img_raw)
# testing scale
target_size = long_side
max_size = long_side
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 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)
tic = time.time()
loc, conf, landms = net(img) # forward pass
print('Inferece {} take: {:.4f}'.format(name, 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 > 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)
# show image
bbox_res = {}
bbox_res[name] = []
if save_image:
for b in dets:
if b[4] < vis_thres:
continue
text = "{:.4f}".format(b[4])
b = list(map(int, b))
x, y, x_plus_w, y_plus_h = b[0], b[1], b[2], b[3]
bbox_res[name].append({
'x': x,
'y': y,
'w': x_plus_w - x,
'h': y_plus_h - y
})
# Blur face
# sub_face = img_raw[y:y_plus_h, x:x_plus_w]
# sub_face = cv2.GaussianBlur(sub_face, (81, 81), 75)
# img_raw[y:y_plus_h, x:x_plus_w] = sub_face
cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
# save image
out_name = os.path.join(output_path, name)
cv2.imwrite(out_name, img_raw)
return bbox_res
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()
def detect_faces(self, img_raw):
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)
if self.on_gpu:
img = img.to(self.device)
scale = scale.to(self.device)
# graph = 0
tic = time.time()
loc, conf, landms = self.detector(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()
if self.on_gpu:
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]
])
if self.on_gpu:
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)
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)
faces = []
for f in dets:
# fr: top, right, bottom, left
# retina: left, right, bottom, top
faces.append((int(f[1]), int(f[2]), int(f[3]), int(f[0])))
return faces
def Predict(self,
img_path="test.jpg",
thresh=0.5,
out_img_path="result.jpg"):
image_path = img_path
confidence_threshold = thresh
vis_thres = thresh
nms_threshold = 0.4
top_k = 1000
keep_top_k = 750
save_image = True
name = out_img_path
device = self.system_dict["local"]["device"]
net = self.system_dict["local"]["net"]
cfg = self.system_dict["local"]["cfg"]
resize = 1
img_raw = cv2.imread(image_path, cv2.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)
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 > 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)
# show image
tmp = {}
tmp["scores"] = []
tmp["bboxes"] = []
tmp["labels"] = []
for b in dets:
if b[4] < 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))
tmp["scores"].append(b[4])
tmp["bboxes"].append([b[0], b[1], b[2], b[3]])
tmp["labels"].append(text)
# 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 save_image:
cv2.imwrite(name, img_raw)
return tmp
def detect_faces(ops, img_raw):
img = np.float32(img_raw)
if ops.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 -= ops.color_mean
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(device)
scale = scale.to(device)
loc, conf, landms = detect_model(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 / ops.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 / ops.resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > ops.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:ops.keep_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, ops.nms_threshold)
dets = dets[keep, :]
landms = landms[keep]
dets = np.concatenate((dets, landms), axis=1)
return dets
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)
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)
def val_to_text():
txt_origin_size = True
txt_confidence_threshold = 0.02
txt_nms_threshold = 0.4
txt_save_folder = args.save_folder + 'widerface_txt/'
testset_list = 'data/widerface/val/wider_val.txt'
testset_folder = 'data/widerface/val/images/'
with open(testset_list, 'r') as fr:
test_dataset = fr.read().split()
# testing begin
for i, img_name in enumerate(tqdm(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 txt_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.cuda()
scale = scale.cuda()
net.phase = 'test'
loc, conf, landms = net(img) # forward pass
net.phase = 'train'
priorbox = PriorBox(cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.cuda()
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.cuda()
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > txt_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, txt_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)
# --------------------------------------------------------------------
save_name = txt_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)
return txt_save_folder
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()
def do_inference(net, img_raw):
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)
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)
def main():
args = parser.parse_args()
assert os.path.isfile(args.checkpoint)
checkpoint = torch.load(args.checkpoint, map_location="cpu")
cfg = checkpoint["config"]
device = torch.device("cpu" if args.cpu else "cuda")
# net and model
net = RetinaFace(**cfg)
net.load_state_dict(checkpoint["net_state_dict"], strict=False)
net.eval().requires_grad_(False)
net.to(device)
print('Finished loading model!')
resize = 1
# testing begin
for i 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
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
scores = conf.squeeze(0)[:, 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
# ignore low scores
inds = torch.where(scores > args.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()
boxes = boxes[order][:args.top_k]
landms = landms[order][:args.top_k]
scores = scores[order][:args.top_k]
# do NMS
keep = nms(boxes, scores, args.nms_threshold)
boxes = boxes[keep]
scores = scores[keep]
landms = landms[keep]
boxes = boxes.cpu().numpy()
scores = scores.cpu().numpy()
landms = landms.cpu().numpy()
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
dets = np.concatenate((dets, landms), axis=1)
# save image
if args.save_image:
draw_keypoint(img_raw, dets, args.vis_thres)
# save image
name = "test.jpg"
cv2.imwrite(name, img_raw)
def face_detector(frame):
img_raw = frame.copy()
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)
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 > CONFIDENCE)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:5000]
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[:750, :]
landms = landms[:750, :]
dets = np.concatenate((dets, landms), axis=1)
bboxs = []
for b in dets:
if b[4] < VIZ_THRESHOLD:
continue
b = list(map(int, b))
margin = 5
x1, y1, x2, y2 = b[0], b[1], b[2], b[3]
img_h, img_w, _ = frame.shape
w = x2 - x1
h = y2 - y1
margin = int(min(w, h) * margin / 100)
x_a = x1 - margin
y_a = y1 - margin
x_b = x1 + w + margin
y_b = y1 + h + margin
if x_a < 0:
x_b = min(x_b - x_a, img_w - 1)
x_a = 0
if y_a < 0:
y_b = min(y_b - y_a, img_h - 1)
y_a = 0
if x_b > img_w:
x_a = max(x_a - (x_b - img_w), 0)
x_b = img_w
if y_b > img_h:
y_a = max(y_a - (y_b - img_h), 0)
y_b = img_h
name = ""
face = frame[y_a:y_b, x_a:x_b]
rgb = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
encodings = face_recognition.face_encodings(rgb,
[(y_a, x_b, y_b, x_a)])
matches = face_recognition.compare_faces(face_data["encodings"],
encodings[0],
tolerance=0.55)
if True in matches:
matchedIdxs = [i for (i, b) in enumerate(matches) if b]
counts = {}
for i in matchedIdxs:
name = face_data["names"][i]
counts[name] = counts.get(name, 0) + 1
name = max(counts, key=counts.get)
cv2.putText(img_raw, name, (x_a + 10, y_a), cv2.FONT_HERSHEY_SIMPLEX,
1, (255, 0, 255), 1, cv2.LINE_AA)
cv2.rectangle(img_raw, (x_a, y_a), (x_b, y_b), (255, 0, 0), 1)
bboxs.append([x_a, y_a, x_b, y_b])
return img_raw, bboxs
def detect_image(self, image):
#---------------------------------------------------#
# 对输入图像进行一个备份，后面用于绘图
#---------------------------------------------------#
old_image = image.copy()
image = np.array(image,np.float32)
#---------------------------------------------------#
# 计算scale，用于将获得的预测框转换成原图的高宽
#---------------------------------------------------#
scale = [np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0]]
scale_for_landmarks = [np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0]]
im_height, im_width, _ = np.shape(image)
#---------------------------------------------------------#
# letterbox_image可以给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
if self.letterbox_image:
image = np.array(letterbox_image(image, [self.input_shape[1], self.input_shape[0]]), np.float32)
else:
self.anchors = Anchors(self.cfg, image_size=(im_height, im_width)).get_anchors()
with torch.no_grad():
#-----------------------------------------------------------#
# 图片预处理，归一化。
#-----------------------------------------------------------#
image = torch.from_numpy(preprocess_input(image).transpose(2, 0, 1)).unsqueeze(0)
if self.cuda:
self.anchors = self.anchors.cuda()
image = image.cuda()
loc, conf, landms = self.net(image)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
boxes = decode(loc.data.squeeze(0), self.anchors, self.cfg['variance'])
boxes = boxes.cpu().numpy()
conf = conf.data.squeeze(0)[:,1:2].cpu().numpy()
landms = decode_landm(landms.data.squeeze(0), self.anchors, self.cfg['variance'])
landms = landms.cpu().numpy()
boxes_conf_landms = np.concatenate([boxes, conf, landms],-1)
boxes_conf_landms = non_max_suppression(boxes_conf_landms, self.confidence)
if len(boxes_conf_landms)<=0:
return old_image
#---------------------------------------------------------#
# 如果使用了letterbox_image的话，要把灰条的部分去除掉。
#---------------------------------------------------------#
if self.letterbox_image:
boxes_conf_landms = retinaface_correct_boxes(boxes_conf_landms, \
np.array([self.input_shape[0], self.input_shape[1]]), np.array([im_height, im_width]))
boxes_conf_landms[:,:4] = boxes_conf_landms[:,:4]*scale
boxes_conf_landms[:,5:] = boxes_conf_landms[:,5:]*scale_for_landmarks
for b in boxes_conf_landms:
text = "{:.4f}".format(b[4])
b = list(map(int, b))
# b[0]-b[3]为人脸框的坐标，b[4]为得分
cv2.rectangle(old_image, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
cx = b[0]
cy = b[1] + 12
cv2.putText(old_image, text, (cx, cy),
cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
print(b[0], b[1], b[2], b[3], b[4])
# b[5]-b[14]为人脸关键点的坐标
cv2.circle(old_image, (b[5], b[6]), 1, (0, 0, 255), 4)
cv2.circle(old_image, (b[7], b[8]), 1, (0, 255, 255), 4)
cv2.circle(old_image, (b[9], b[10]), 1, (255, 0, 255), 4)
cv2.circle(old_image, (b[11], b[12]), 1, (0, 255, 0), 4)
cv2.circle(old_image, (b[13], b[14]), 1, (255, 0, 0), 4)
return old_image
def process_frames(
torched_frames: torch.tensor,
is_fp16: bool,
resize_factor: float,
video_path: Path,
frame_ids: np.array,
frames: np.array,
device: str,
batch_size: int,
cfg: dict,
nms_threshold: float,
confidence_threshold: float,
is_save_crops: bool,
is_save_boxes: bool,
output_path: Path,
net: torch.nn.Module,
min_size: int,
resize_scale: float,
keep_top_k: Optional[int],
) -> None:
if is_save_crops and output_path is not None:
output_image_path = output_path / "images"
output_image_path.mkdir(exist_ok=True, parents=True)
if is_save_boxes and output_path is not None:
output_label_path: Path = output_path / "labels"
output_label_path.mkdir(exist_ok=True, parents=True)
if is_fp16:
torched_frames = torched_frames.half()
num_frames = torched_frames.shape[0]
video_id = video_path.stem
labels: List[dict] = []
image_height, image_width = torched_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
for start_index in range(0, num_frames, batch_size):
end_index = min(start_index + batch_size, num_frames)
loc, conf, land = net(torched_frames[start_index:end_index].to(device))
for pred_id in range(loc.shape[0]):
frame_id = frame_ids[start_index + pred_id]
boxes = decode(loc.data[pred_id], prior_data, cfg["variance"])
boxes *= scale / resize_factor
scores = conf[pred_id][:, 1]
landmarks = decode_landm(land.data[pred_id], prior_data,
cfg["variance"])
landmarks *= scale1 / resize_factor
# ignore low scores
valid_index = torch.where(scores > confidence_threshold)[0]
boxes = boxes[valid_index]
landmarks = landmarks[valid_index]
scores = scores[valid_index]
order = scores.argsort(descending=True)
boxes = boxes[order][:keep_top_k, :]
landmarks = landmarks[order][:keep_top_k, :]
scores = scores[order][:keep_top_k]
# do NMS
keep = nms(boxes, scores, 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().tolist()
x_min, y_min, x_max, y_max = bbox
x_min = np.clip(x_min, 0, image_width - 1)
y_min = np.clip(y_min, 0, image_height - 1)
x_max = np.clip(x_max, x_min + 1, image_width - 1)
y_max = np.clip(y_max, y_min + 1, image_height - 1)
crop_width = x_max - x_min
crop_hegith = y_max - y_min
if crop_width < min_size or crop_hegith < min_size:
continue
labels += [{
"frame_id": int(frame_id),
"crop_id": crop_id,
"bbox": [x_min, y_min, x_max, y_max],
"score": scores[crop_id],
"landmarks": landmarks[crop_id].tolist(),
}]
if is_save_crops:
x_min, y_min, x_max, y_max = resize(
x_min,
y_min,
x_max,
y_max,
image_height,
image_width,
resize_coeff=resize_scale)
crop = frames[pred_id][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 is_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)
def crop_face(net,
device,
cfg,
data_dir,
target_dir,
left_scale=0.0,
right_scale=0.0,
up_scale=0.0,
low_scale=0.0):
resize = 1
landmark_target_dir = target_dir + '_landmark'
# testing begin
for dir, dirs, files in tqdm.tqdm(os.walk(data_dir)):
new_dir = dir.replace(data_dir, target_dir)
new_landmark_dir = dir.replace(data_dir, landmark_target_dir)
if not os.path.isdir(new_dir):
os.mkdir(new_dir)
if not os.path.isdir(new_landmark_dir):
os.mkdir(new_landmark_dir)
for file in files:
filepath = os.path.join(dir, file)
# print(filepath)
image_path = filepath
img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
if img_raw is None:
continue
im_height, im_width, _ = img_raw.shape
# print(img_raw.shape)
scale_with = 640
scale_height = 480
if im_height > scale_height:
scale_rate = scale_height / im_height
img_raw = cv2.resize(
img_raw, (int(im_width * scale_rate), scale_height))
elif im_width > scale_with:
scale_rate = scale_with / im_width
img_raw = cv2.resize(img_raw,
(scale_with, int(im_height * scale_rate)))
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)
# 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)
# save image
if args.save_image:
max_bb = 0
max_index = 0
if len(dets) == 0:
continue
elif len(dets) > 1:
print('warning detect more than one:', filepath)
# find maximum bounding box
for di, b in enumerate(dets):
if b[4] < args.vis_thres:
continue
b = list(map(int, b))
b = [p if p > 0 else 0 for p in b]
box_w = abs(b[1] - b[3])
box_h = abs(b[0] - b[2])
if max_bb < max(box_w, box_h):
max_bb = max(box_w, box_h)
max_index = di
di = max_index
b = list(map(int, dets[max_index]))
# for di, b in enumerate(dets):
# if b[4] < args.vis_thres:
# continue
# text = "{:.4f}".format(b[4])
# b = list(map(int, b))
# # print(b[0], b[1])
# # print(b[2], b[3])
b = [p if p > 0 else 0 for p in b]
b[1] -= int((b[3] - b[1]) * up_scale)
b[3] += int((b[3] - b[1]) * low_scale)
b[0] -= int((b[2] - b[0]) * left_scale)
b[2] += int((b[2] - b[0]) * right_scale)
b[1] = b[1] if b[1] >= 0 else 0
b[3] = b[3] if b[3] < im_height else im_height - 1
b[0] = b[0] if b[0] >= 0 else 0
b[2] = b[2] if b[2] < im_width else im_width - 1
# retain background
b_width = b[2] - b[0]
b_height = b[3] - b[1]
if b_width > b_height:
b[1] -= abs(b_width - b_height) // 2
b[3] += abs(b_width - b_height) // 2
elif b_width < b_height:
b[0] -= abs(b_width - b_height) // 2
b[2] += abs(b_width - b_height) // 2
b[1] = b[1] if b[1] >= 0 else 0
b[3] = b[3] if b[3] < im_height else im_height - 1
b[0] = b[0] if b[0] >= 0 else 0
b[2] = b[2] if b[2] < im_width else im_width - 1
roi_image = np.copy(img_raw[b[1]:b[3], b[0]:b[2]])
box_w = abs(b[1] - b[3])
box_h = abs(b[0] - b[2])
# print(b[1], b[3])
# print(b[0], b[2])
# print(box_w, box_h)
show_image = roi_image.copy()
leftEyeCenter = (int(landms[di][0] - b[0]),
int(landms[di][1] - b[1]))
rightEyeCenter = (int(landms[di][2] - b[0]),
int(landms[di][3] - b[1]))
noseCenter = (int(landms[di][4] - b[0]),
int(landms[di][5] - b[1]))
mouth1 = (int(landms[di][6] - b[0]), int(landms[di][7] - b[1]))
mouth2 = (int(landms[di][8] - b[0]), int(landms[di][9] - b[1]))
cv2.circle(show_image, (leftEyeCenter[0], leftEyeCenter[1]), 3,
(0, 255, 0), -1)
cv2.circle(show_image, (rightEyeCenter[0], rightEyeCenter[1]),
3, (0, 255, 0), -1)
cv2.circle(show_image, (noseCenter[0], noseCenter[1]), 3,
(0, 255, 0), -1)
cv2.circle(show_image, (mouth1[0], mouth1[1]), 3, (0, 255, 0),
-1)
cv2.circle(show_image, (mouth2[0], mouth2[1]), 3, (0, 255, 0),
-1)
# compute the angle between the eye centroids
eye_dis = np.sqrt((leftEyeCenter[0] - rightEyeCenter[0])**2 +
(leftEyeCenter[1] - rightEyeCenter[1])**2)
# print('eye_dis:', eye_dis)
if eye_dis < 16.0:
angle = 0
else:
dY = rightEyeCenter[1] - leftEyeCenter[1]
dX = rightEyeCenter[0] - leftEyeCenter[0]
angle = np.degrees(np.arctan2(dY, dX))
# print('angle:', angle)
desiredLeftEye = (1.0, 1.0)
desiredFaceWidth = roi_image.shape[1]
desiredFaceHeight = roi_image.shape[0]
# compute the desired right eye x-coordinate based on the
# desired x-coordinate of the left eye
desiredRightEyeX = 1.0 - desiredLeftEye[0]
# determine the scale of the new resulting image by taking
# the ratio of the distance between eyes in the *current*
# image to the ratio of distance between eyes in the
# *desired* image
# dist = np.sqrt((dX ** 2) + (dY ** 2))
# desiredDist = (desiredRightEyeX - desiredLeftEye[0])
# desiredDist *= desiredFaceWidth
# scale = desiredDist / dist
scale = desiredFaceWidth / max(roi_image.shape[:2])
resize_roi_image = cv2.resize(
roi_image, (int(roi_image.shape[1] * scale),
int(roi_image.shape[0] * scale)))
# cv2.imshow('resize_roi_image', resize_roi_image)
# print(max(roi_image.shape))
# print(scale)
# compute center (x, y)-coordinates (i.e., the median point)
# between the two eyes in the input image
eyesCenter = ((leftEyeCenter[0] + rightEyeCenter[0]) // 2,
(leftEyeCenter[1] + rightEyeCenter[1]) // 2)
# grab the rotation matrix for rotating and scaling the face
M = cv2.getRotationMatrix2D(eyesCenter, angle, 1.0)
# apply the affine transformation
(w, h) = (desiredFaceWidth, desiredFaceHeight)
aligned_image = cv2.warpAffine(roi_image,
M, (w, h),
flags=cv2.INTER_CUBIC)
if box_h < box_w:
padding_size = abs(box_w - box_h) // 2
aligned_image = cv2.copyMakeBorder(aligned_image,
0,
0,
padding_size,
padding_size,
cv2.BORDER_CONSTANT,
value=[0, 0, 0])
elif box_h > box_w:
padding_size = abs(box_w - box_h) // 2
aligned_image = cv2.copyMakeBorder(aligned_image,
padding_size,
padding_size,
0,
0,
cv2.BORDER_CONSTANT,
value=[0, 0, 0])
new_image = cv2.resize(aligned_image, (112, 112),
interpolation=cv2.INTER_AREA)
new_path = filepath.replace(data_dir, target_dir)
new_landmark_path = filepath.replace(data_dir,
landmark_target_dir)
new_path = new_path.replace(new_path.split('.')[-1], 'jpg')
new_landmark_path = new_landmark_path.replace(
new_landmark_path.split('.')[-1], 'jpg')
# print(new_path)
cv2.imwrite(new_path, new_image)
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]
def detect_face(net, img):
img = np.float32(img_raw)
# testing scale
target_size = args.long_side
max_size = args.long_side
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)
# print("\nimg shape resize: ", img.shape)
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)
tic = time.time()
loc, conf, landms = net(img) # forward pass
# print('net forward time: {:.4f}'.format(time.time() - tic))
tic = time.time()
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)
# print('post processing time: {:.4f}'.format(time.time() - tic))
return dets
