def detect(self, text_proposals, scores, size):
# 删除得分较低的proposal
keep_inds = np.where(scores > TextLineCfg.TEXT_PROPOSALS_MIN_SCORE)[0]
text_proposals, scores = text_proposals[keep_inds], scores[keep_inds]
# 按得分排序
sorted_indices = np.argsort(scores.ravel())[::-1]
text_proposals, scores = text_proposals[sorted_indices], scores[sorted_indices]
# 对proposal做nms
keep_inds = nms(np.hstack((text_proposals, scores)), TextLineCfg.TEXT_PROPOSALS_NMS_THRESH)
text_proposals, scores = text_proposals[keep_inds], scores[keep_inds]
# 获取检测结果
text_recs = self.text_proposal_connector.get_text_lines(text_proposals, scores, size)
keep_inds = self.filter_boxes(text_recs)
return text_recs[keep_inds]
def detect(self, text_proposals, scores, size):
# Delete lower scores
keep_inds = np.where(scores > TextLineCfg.TEXT_PROPOSALS_MIN_SCORE)[0]
text_proposals, scores = text_proposals[keep_inds], scores[keep_inds]
# Sort by score
sorted_indices = np.argsort(scores.ravel())[::-1]
text_proposals, scores = text_proposals[sorted_indices], scores[
sorted_indices]
# nms
keep_inds = nms(np.hstack((text_proposals, scores)),
TextLineCfg.TEXT_PROPOSALS_NMS_THRESH)
text_proposals, scores = text_proposals[keep_inds], scores[keep_inds]
# Get result
text_recs = self.text_proposal_connector.get_text_lines(
text_proposals, scores, size)
keep_inds = self.filter_boxes(text_recs)
return text_recs[keep_inds]
def proposal_layer(rpn_cls_prob_reshape,
rpn_bbox_pred,
im_info,
_feat_stride=[
16,
],
anchor_scales=[
16,
]):
"""
Parameters
----------
rpn_cls_prob_reshape: (1 , H , W , Ax2) outputs of RPN, prob of bg or fg
NOTICE: the old version is ordered by (1, H, W, 2, A) !!!!
rpn_bbox_pred: (1 , H , W , Ax4), rgs boxes output of RPN
im_info: a list of [image_height, image_width, scale_ratios]
_feat_stride: the downsampling ratio of feature map to the original input image
anchor_scales: the scales to the basic_anchor (basic anchor is [16, 16])
----------
Returns
----------
rpn_rois : (1 x H x W x A, 5) e.g. [0, x1, y1, x2, y2]
# Algorithm:
#
# for each (H, W) location i
# generate A anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the A anchors
# clip predicted boxes to image
# remove predicted boxes with either height or width < threshold
# sort all (proposal, score) pairs by score from highest to lowest
# take top pre_nms_topN proposals before NMS
# apply NMS with threshold 0.7 to remaining proposals
# take after_nms_topN proposals after NMS
# return the top proposals (-> RoIs top, scores top)
#layer_params = yaml.load(self.param_str_)
"""
_anchors = generate_anchors(
scales=np.array(anchor_scales)) # 生成基本的10个anchor
_num_anchors = _anchors.shape[0] # 10个anchor
im_info = im_info[0] # 原始图像的高宽、缩放尺度
assert rpn_cls_prob_reshape.shape[0] == 1, \
'Only single item batches are supported'
pre_nms_topN = cfg.RPN_PRE_NMS_TOP_N # 12000,在做nms之前，最多保留的候选box数目
post_nms_topN = cfg.RPN_POST_NMS_TOP_N # 1000，做完nms之后，最多保留的box的数目
nms_thresh = cfg.RPN_NMS_THRESH # nms用参数，阈值是0.7
min_size = cfg.RPN_MIN_SIZE # 候选box的最小尺寸，目前是16，高宽均要大于16
height, width = rpn_cls_prob_reshape.shape[1:3] # feature-map的高宽
width = width // 10
# the first set of _num_anchors channels are bg probs
# the second set are the fg probs, which we want
# (1, H, W, A)
scores = np.reshape(
np.reshape(rpn_cls_prob_reshape,
[1, height, width, _num_anchors, 2])[:, :, :, :, 1],
[1, height, width, _num_anchors])
# 提取到object的分数，non-object的我们不关心
bbox_deltas = rpn_bbox_pred # 模型输出的pred bbox是相对值，需要进一步处理成真实图像中的坐标
# im_info = bottom[2].data[0, :]
if DEBUG:
print('im_size: ({}, {})'.format(im_info[0], im_info[1]))
print('scale: {}'.format(im_info[2]))
# 1. Generate proposals from bbox deltas and shifted anchors
if DEBUG:
print('score map size: {}'.format(scores.shape))
# Enumerate all shifts
# 同anchor-target-layer-tf这个文件一样，生成anchor的shift，进一步得到整张图像上的所有anchor
shift_x = np.arange(0, width) * _feat_stride
shift_y = np.arange(0, height) * _feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(),
shift_y.ravel())).transpose()
# Enumerate all shifted anchors:
#
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = _num_anchors
K = shifts.shape[0]
anchors = _anchors.reshape((1, A, 4)) + \
shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4)) # 这里得到的anchor就是整张图像上的所有anchor
# Transpose and reshape predicted bbox transformations to get them
# into the same order as the anchors:
# bbox deltas will be (1, 4 * A, H, W) format
# transpose to (1, H, W, 4 * A)
# reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
# in slowest to fastest order
bbox_deltas = bbox_deltas.reshape((-1, 4)) # (HxWxA, 4)
# Same story for the scores:
scores = scores.reshape((-1, 1))
# Convert anchors into proposals via bbox transformations
proposals = bbox_transform_inv(anchors, bbox_deltas) # 做逆变换，得到box在图像上的真实坐标
# 2. clip predicted boxes to image
proposals = clip_boxes(proposals,
im_info[:2]) # 将所有的proposal修建一下，超出图像范围的将会被修剪掉
# 3. remove predicted boxes with either height or width < threshold
# (NOTE: convert min_size to input image scale stored in im_info[2])
keep = _filter_boxes(proposals, min_size) # 移除那些proposal小于一定尺寸的proposal
proposals = proposals[keep, :] # 保留剩下的proposal
scores = scores[keep]
bbox_deltas = bbox_deltas[keep, :]
# # remove irregular boxes, too fat too tall
# keep = _filter_irregular_boxes(proposals)
# proposals = proposals[keep, :]
# scores = scores[keep]
# 4. sort all (proposal, score) pairs by score from highest to lowest
# 5. take top pre_nms_topN (e.g. 6000)
order = scores.ravel().argsort()[::-1] # score按得分的高低进行排序
if pre_nms_topN > 0: # 保留12000个proposal进去做nms
order = order[:pre_nms_topN]
proposals = proposals[order, :]
scores = scores[order]
bbox_deltas = bbox_deltas[order, :]
# 6. apply nms (e.g. threshold = 0.7)
# 7. take after_nms_topN (e.g. 300)
# 8. return the top proposals (-> RoIs top)
keep = nms(np.hstack((proposals, scores)),
nms_thresh) # 进行nms操作，保留1000个proposal
if post_nms_topN > 0:
keep = keep[:post_nms_topN]
proposals = proposals[keep, :]
scores = scores[keep]
bbox_deltas = bbox_deltas[keep, :]
# Output rois blob
# Our RPN implementation only supports a single input image, so all
# batch inds are 0
blob = np.hstack(
(scores.astype(np.float32,
copy=False), proposals.astype(np.float32, copy=False)))
return blob, bbox_deltas
