def detected_bboxes(self, predictions, localisations, #通过SSD网络，得到检测到的bbox
select_threshold=None, nms_threshold=0.5,
clipping_bbox=None, top_k=400, keep_top_k=200):
"""Get the detected bounding boxes from the SSD network output.
"""
# Select top_k bboxes from predictions, and clip #选取top_k=400个框，并对框做修建（超出原图尺寸范围的切掉）
rscores, rbboxes = \ #得到对应某个类别的得分值以及bbox
ssd_common.tf_ssd_bboxes_select(predictions, localisations,
select_threshold=select_threshold,
num_classes=self.params.num_classes)
def detected_bboxes(self,
predictions,
localisations,
select_threshold=None,
nms_threshold=0.5,
clipping_bbox=None,
top_k=400,
keep_top_k=200):
"""Get the detected bounding boxes from the SSD network output.
"""
# Select top_k bboxes from predictions, and clip
rscores, rbboxes = \
ssd_common.tf_ssd_bboxes_select(predictions, localisations,
select_threshold=select_threshold,
num_classes=self.params.num_classes)
rscores, rbboxes = \
tfe.bboxes_sort(rscores, rbboxes, top_k=top_k)
# Apply NMS algorithm.
rscores, rbboxes = \
tfe.bboxes_nms_batch(rscores, rbboxes,
nms_threshold=nms_threshold,
keep_top_k=keep_top_k)
# if clipping_bbox is not None:
# rbboxes = tfe.bboxes_clip(clipping_bbox, rbboxes)
return rscores, rbboxes
def detected_bboxes(
self,
predictions,
localisations, #通过SSD网络，得到检测到的bbox
select_threshold=None,
nms_threshold=0.5,
clipping_bbox=None,
top_k=400,
keep_top_k=200):
"""Get the detected bounding boxes from the SSD network output.
"""
# Select top_k bboxes from predictions, and clip #选取top_k=400个框，并对框做修建（超出原图尺寸范围的切掉）
# 得到对应某个类别的得分值以及bbox
rscores, rbboxes = \
ssd_common.tf_ssd_bboxes_select(predictions, localisations,
select_threshold=select_threshold,
num_classes=self.params.num_classes)
# 按照得分高低，筛选出400个bbox和对应得分
rscores, rbboxes = \
tfe.bboxes_sort(rscores, rbboxes, top_k=top_k)
# Apply NMS algorithm. #应用非极大值抑制，筛选掉与得分最高bbox重叠率大于0.5的，保留200个
rscores, rbboxes = \
tfe.bboxes_nms_batch(rscores, rbboxes,
nms_threshold=nms_threshold,
keep_top_k=keep_top_k)
if clipping_bbox is not None:
rbboxes = tfe.bboxes_clip(clipping_bbox, rbboxes)
return rscores, rbboxes #返回裁剪好的bbox和对应得分
def detected_bboxes(self, predictions, localisations,
select_threshold=None, nms_threshold=0.5,
clipping_bbox=None, top_k=400, keep_top_k=200):
"""Get the detected bounding boxes from the SSD network output.
"""
# Select top_k bboxes from predictions, and clip
rscores, rbboxes = \
ssd_common.tf_ssd_bboxes_select(predictions, localisations,
select_threshold=select_threshold,
num_classes=self.params.num_classes)
rscores, rbboxes = \
tfe.bboxes_sort(rscores, rbboxes, top_k=top_k)
# Apply NMS algorithm.
rscores, rbboxes = \
tfe.bboxes_nms_batch(rscores, rbboxes,
nms_threshold=nms_threshold,
keep_top_k=keep_top_k)
# if clipping_bbox is not None:
# rbboxes = tfe.bboxes_clip(clipping_bbox, rbboxes)
return rscores, rbboxes
