def detect_pnet(self, im):
"""Get face candidates through pnet
Parameters:
-----------
im: numpy array, input image array
Returns:
--------
bboxes_align: numpy array
bboxes after calibration
"""
h, w, c = im.shape
net_size = config.PNET_SIZE
current_scale = float(
net_size) / self.min_face_size # find initial scale
im_resized = self.resize_image(im, current_scale)
current_height, current_width, _ = im_resized.shape
# bounding boxes for all the pyramid scales
all_bboxes = list()
# generating bounding boxes for each scale
while min(current_height, current_width) > net_size:
image_tensor = utils.convert_image_to_tensor(im_resized)
feed_imgs = image_tensor.unsqueeze(0)
feed_imgs = feed_imgs.to(self.device)
cls_map, reg_map = self.pnet_detector(feed_imgs)
cls_map_np = utils.convert_chwTensor_to_hwcNumpy(cls_map.cpu())
reg_map_np = utils.convert_chwTensor_to_hwcNumpy(reg_map.cpu())
bboxes = self.generate_bounding_box(cls_map_np, reg_map_np,
current_scale, self.thresh[0])
current_scale *= self.scale_factor
im_resized = self.resize_image(im, current_scale)
current_height, current_width, _ = im_resized.shape
if bboxes.size == 0:
continue
keep = utils.nms(bboxes[:, :5], 0.5, 'Union')
bboxes = bboxes[keep]
all_bboxes.append(bboxes)
if len(all_bboxes) == 0:
return None
all_bboxes = np.vstack(all_bboxes)
# apply nms to the detections from all the scales
keep = utils.nms(all_bboxes[:, 0:5], 0.7, 'Union')
all_bboxes = all_bboxes[keep]
# 0-4: original bboxes, 5: score, 5: offsets
bboxes_align = utils.calibrate_box(all_bboxes[:, 0:5], all_bboxes[:,
5:])
bboxes_align = utils.convert_to_square(bboxes_align)
bboxes_align[:, 0:4] = np.round(bboxes_align[:, 0:4])
return bboxes_align
def detect_onet(self, im, bboxes):
"""Get face candidates using onet
Parameters:
----------
im: numpy array
input image array
bboxes: numpy array
detection results of rnet
Returns:
-------
bboxes_align: numpy array
bboxes after calibration
"""
net_size = config.ONET_SIZE
h, w, c = im.shape
if bboxes is None:
return None
[dy, edy, dx, edx, y, ey, x, ex, tmpw,
tmph] = utils.correct_bboxes(bboxes, w, h)
num_bboxes = bboxes.shape[0]
# crop face using rnet proposal
cropped_ims_tensors = []
for i in range(num_bboxes):
try:
if tmph[i] > 0 and tmpw[i] > 0:
tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8)
tmp[dy[i]:edy[i], dx[i]:edx[i], :] = im[y[i]:ey[i],
x[i]:ex[i], :]
crop_im = cv2.resize(tmp, (net_size, net_size))
crop_im_tensor = utils.convert_image_to_tensor(crop_im)
cropped_ims_tensors.append(crop_im_tensor)
except ValueError as e:
print(e)
feed_imgs = torch.stack(cropped_ims_tensors)
feed_imgs = feed_imgs.to(self.device)
cls, reg = self.onet_detector(feed_imgs)
cls = cls.cpu().data.numpy()
reg = reg.cpu().data.numpy()
keep_inds = np.where(cls[:, 1] > self.thresh[2])[0]
if len(keep_inds) > 0:
keep_bboxes = bboxes[keep_inds]
keep_cls = cls[keep_inds, :]
keep_reg = reg[keep_inds]
keep_bboxes[:, 4] = keep_cls[:, 1].reshape((-1, ))
else:
return None
bboxes_align = utils.calibrate_box(keep_bboxes, keep_reg)
keep = utils.nms(bboxes_align, 0.7, mode='Minimum')
if len(keep) == 0:
return None
bboxes_align = bboxes_align[keep]
bboxes_align = utils.convert_to_square(bboxes_align)
return bboxes_align
def detect_rnet(self, im, bboxes):
"""Get face candidates using rnet
Parameters:
----------
im: numpy array
input image array
bboxes: numpy array
detection results of pnet
Returns:
-------
bboxes_align: numpy array
bboxes after calibration
"""
net_size = config.RNET_SIZE
h, w, c = im.shape
if bboxes is None:
return None
num_bboxes = bboxes.shape[0]
[dy, edy, dx, edx, y, ey, x, ex, tmpw,
tmph] = utils.correct_bboxes(bboxes, w, h)
# crop face using pnet proposals
cropped_ims_tensors = []
for i in range(num_bboxes):
try:
if tmph[i] > 0 and tmpw[i] > 0:
tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8)
tmp[dy[i]:edy[i], dx[i]:edx[i], :] = im[y[i]:ey[i],
x[i]:ex[i], :]
crop_im = cv2.resize(tmp, (net_size, net_size))
crop_im_tensor = utils.convert_image_to_tensor(crop_im)
cropped_ims_tensors.append(crop_im_tensor)
except ValueError as e:
print('dy: {}, edy: {}, dx: {}, edx: {}'.format(
dy[i], edy[i], dx[i], edx[i]))
print('y: {}, ey: {}, x: {}, ex: {}'.format(
y[i], ey[i], x[i], ex[i]))
print(e)
# provide input tensor, if there are too many proposals in PNet
# there might be OOM
feed_imgs = torch.stack(cropped_ims_tensors)
feed_imgs = feed_imgs.to(self.device)
cls, reg = self.rnet_detector(feed_imgs)
cls = cls.cpu().data.numpy()
reg = reg.cpu().data.numpy()
keep_inds = np.where(cls[:, 1] > self.thresh[1])[0]
if len(keep_inds) > 0:
keep_bboxes = bboxes[keep_inds]
keep_cls = cls[keep_inds, :]
keep_reg = reg[keep_inds]
# using softmax 1 as cls score
keep_bboxes[:, 4] = keep_cls[:, 1].reshape((-1, ))
else:
return None
keep = utils.nms(keep_bboxes, 0.7)
if len(keep) == 0:
return None
keep_cls = keep_cls[keep]
keep_bboxes = keep_bboxes[keep]
keep_reg = keep_reg[keep]
bboxes_align = utils.calibrate_box(keep_bboxes, keep_reg)
bboxes_align = utils.convert_to_square(bboxes_align)
bboxes_align[:, 0:4] = np.round(bboxes_align[:, 0:4])
return bboxes_align
