def log_rpn(self,step=None, scope_name=''):
top_image = self.top_image
subdir = self.log_subdir
top_inds = self.batch_top_inds
top_labels = self.batch_top_labels
top_pos_inds = self.batch_top_pos_inds
top_targets = self.batch_top_targets
proposals = self.batch_proposals
proposal_scores = self.batch_proposal_scores
gt_top_boxes = self.batch_gt_top_boxes
gt_labels = self.batch_gt_labels
if gt_top_boxes is not None:
img_gt = draw_rpn_gt(top_image, gt_top_boxes, gt_labels)
# nud.imsave('img_rpn_gt', img_gt, subdir)
self.summary_image(img_gt, scope_name + '/img_rpn_gt', step=step)
if top_inds is not None:
img_label = draw_rpn_labels(top_image, self.top_view_anchors, top_inds, top_labels)
# nud.imsave('img_rpn_label', img_label, subdir)
self.summary_image(img_label, scope_name+ '/img_rpn_label', step=step)
if top_pos_inds is not None:
img_target = draw_rpn_targets(top_image, self.top_view_anchors, top_pos_inds, top_targets)
# nud.imsave('img_rpn_target', img_target, subdir)
self.summary_image(img_target, scope_name+ '/img_rpn_target', step=step)
if proposals is not None:
rpn_proposal = draw_rpn_proposal(top_image, proposals, proposal_scores, draw_num=20)
# nud.imsave('img_rpn_proposal', rpn_proposal, subdir)
self.summary_image(rpn_proposal, scope_name + '/img_rpn_proposal',step=step)
def log_rpn(self,step=None, scope_name=''):
top_image = self.top_image
subdir = self.log_subdir
top_inds = self.batch_top_inds
top_labels = self.batch_top_labels
top_pos_inds = self.batch_top_pos_inds
top_targets = self.batch_top_targets
proposals = self.batch_proposals
proposal_scores = self.batch_proposal_scores
gt_top_boxes = self.batch_gt_top_boxes
gt_labels = self.batch_gt_labels
if gt_top_boxes is not None:
img_gt = draw_rpn_gt(top_image, gt_top_boxes, gt_labels)
# nud.imsave('img_rpn_gt', img_gt, subdir)
self.summary_image(img_gt, scope_name + '/img_rpn_gt', step=step)
if top_inds is not None:
img_label = draw_rpn_labels(top_image, self.top_view_anchors, top_inds, top_labels)
# nud.imsave('img_rpn_label', img_label, subdir)
self.summary_image(img_label, scope_name+ '/img_rpn_label', step=step)
if top_pos_inds is not None:
img_target = draw_rpn_targets(top_image, self.top_view_anchors, top_pos_inds, top_targets)
# nud.imsave('img_rpn_target', img_target, subdir)
self.summary_image(img_target, scope_name+ '/img_rpn_target', step=step)
if proposals is not None:
rpn_proposal = draw_rpn_proposal(top_image, proposals, proposal_scores, draw_num=20)
# nud.imsave('img_rpn_proposal', rpn_proposal, subdir)
self.summary_image(rpn_proposal, scope_name + '/img_rpn_proposal',step=step)
def log_rpn(self,
step=None,
scope_name='',
loss=None,
tensor_board=True,
draw_rpn_target=False):
top_image = self.top_image
subdir = self.log_subdir
top_inds = self.batch_top_inds
top_labels = self.batch_top_labels
top_pos_inds = self.batch_top_pos_inds
top_targets = self.batch_top_targets
proposals = self.batch_proposals
proposal_scores = self.batch_proposal_scores
gt_top_boxes = self.batch_gt_top_boxes
gt_labels = self.batch_gt_labels
total_img = None
if gt_top_boxes is not None:
total_img = draw_rpn_gt(top_image, gt_top_boxes, gt_labels)
# nud.imsave('img_rpn_gt', img_gt, subdir)
if draw_rpn_target:
img_label = draw_rpn_labels(top_image, self.top_view_anchors,
top_inds, top_labels)
# nud.imsave('img_rpn_label', img_label, subdir)
total_img = np.concatenate(
(total_img,
img_label), 1) if total_img is not None else img_label
img_target = draw_rpn_targets(top_image, self.top_view_anchors,
top_pos_inds, top_targets)
# nud.imsave('img_rpn_target', img_target, subdir)
total_img = np.concatenate((total_img, img_target), 1)
if proposals is not None:
rpn_proposal = draw_rpn_proposal(top_image, proposals,
proposal_scores)
if loss != None:
text = 'loss c: %6f r: %6f' % loss
font = cv2.FONT_HERSHEY_SIMPLEX
text_pos = (0, 25)
cv2.putText(rpn_proposal, text, text_pos, font, 0.5,
(5, 255, 100), 0, cv2.LINE_AA)
if total_img is not None:
total_img = np.concatenate((total_img, rpn_proposal), 1)
else:
total_img = rpn_proposal
# print('\nproposal_scores= {}\n'.format(proposal_scores))
# nud.imsave('img_rpn_proposal', rpn_proposal, subdir)
if tensor_board:
self.summary_image(total_img,
scope_name + '/top_view',
step=step)
return total_img
def log_rpn(self, step=None, scope_name='', loss=None, tensor_board=True, draw_rpn_target=False):
top_image = self.top_image
subdir = self.log_subdir
top_inds = self.batch_top_inds
top_labels = self.batch_top_labels
top_pos_inds = self.batch_top_pos_inds
top_targets = self.batch_top_targets
proposals = self.batch_proposals
proposal_scores = self.batch_proposal_scores
gt_top_boxes = self.batch_gt_top_boxes
gt_labels = self.batch_gt_labels
total_img = None
if gt_top_boxes is not None:
total_img = draw_rpn_gt(top_image, gt_top_boxes, gt_labels)
# nud.imsave('img_rpn_gt', img_gt, subdir)
if draw_rpn_target:
img_label = draw_rpn_labels(top_image, self.top_view_anchors, top_inds, top_labels)
# nud.imsave('img_rpn_label', img_label, subdir)
total_img = np.concatenate((total_img, img_label), 1) if total_img is not None else img_label
img_target = draw_rpn_targets(top_image, self.top_view_anchors, top_pos_inds, top_targets)
# nud.imsave('img_rpn_target', img_target, subdir)
total_img = np.concatenate((total_img, img_target), 1)
if proposals is not None:
rpn_proposal = draw_rpn_proposal(top_image, proposals, proposal_scores)
if loss != None:
text = 'loss c: %6f r: %6f' % loss
font = cv2.FONT_HERSHEY_SIMPLEX
text_pos = (0, 25)
cv2.putText(rpn_proposal, text, text_pos, font, 0.5, (5, 255, 100), 0, cv2.LINE_AA)
if total_img is not None:
total_img = np.concatenate((total_img, rpn_proposal), 1)
else:
total_img = rpn_proposal
# print('\nproposal_scores= {}\n'.format(proposal_scores))
# nud.imsave('img_rpn_proposal', rpn_proposal, subdir)
if tensor_board: self.summary_image(total_img, scope_name + '/top_view', step=step)
return total_img