def get_bbox_landms(img, net, prior_data, device):
"""
return: dets type ndarray.
dets.shape = (face founds, 15)
dets[:,:3] -> 4 points of each bboxes
dets[:, 4] -> score of each faces
dets[:,5:] -> 10 points landmark of each faces
"""
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
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()
# ignore low scores
inds = np.where(scores > cfg_detect['confidence_threshold'])[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:cfg_detect['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_detect['nms_threshold'])
# keep = nms(dets, cfg_detect['nms_threshold'],force_cpu=cfg_detect['cpu'])
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
dets = dets[:cfg_detect['keep_top_k'], :]
landms = landms[:cfg_detect['keep_top_k'], :]
#dets - combine bboxes, scores and landmarks point
dets = np.concatenate((dets, landms), axis=1)
return dets
def detect_faces(img_raw, confidence_threshold=0.9, top_k=5000, nms_threshold=0.4, keep_top_k=750, resize=1):
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()
with torch.no_grad():
loc, conf, landms = model(img) # forward pass
# print('net forward time: {:.4f}'.format(time.time() - tic))
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 / resize
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 / 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, :]
# print(landms.shape)
landms = landms.reshape((-1, 5, 2))
# print(landms.shape)
landms = landms.transpose((0, 2, 1))
# print(landms.shape)
landms = landms.reshape(-1, 10, )
# print(landms.shape)
return dets, landms
def face_detector(img, net, out_name=None, save_image=True):
torch.set_grad_enabled(False)
cfg = cfg_re50
origin_size = True
confidence_threshold = 0.02
nms_threshold = 0.4
vis_thres = 0.998
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# load img in
img_raw = cv2.imread(img, 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 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, _ = 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]
# ignore low scores
inds = np.where(scores > confidence_threshold)[0]
boxes = boxes[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1]
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, nms_threshold)
dets = dets[keep, :]
# save image
if save_image:
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))
# save image
name = "./" + str(out_name) + ".jpg"
cv2.imwrite(name, img_raw)
results = []
for face in dets:
if face[4] > vis_thres:
results.append(face)
return results # returns the bounding boxes
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]
def run(self, img, frame_debug=None):
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)
self._t['forward_pass'].tic()
loc, conf, landms = self.net(img) # forward pass
self._t['forward_pass'].toc()
self._t['misc'].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 / 1
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 / 1
landms = landms.cpu().numpy()
confidence_threshold = 0.02
# 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][:args.top_k]
order = scores.argsort()[::-1]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
nms_threshold = 0.4
# 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[:args.keep_top_k, :]
# landms = landms[:args.keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
self._t['misc'].toc()
conf = dets[:, 4]
filtered_idx = np.where(conf > self.threshold)
dets = dets[filtered_idx[0]]
if frame_debug is not None:
frame_debug = self.debug(dets, frame_debug)
else:
frame_debug = img
return dets, frame_debug
