Python mmdet.core.bbox_mapping_back() Examples
The following are 8
code examples of mmdet.core.bbox_mapping_back().
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Example #1
| Source File: reppoints_detector.py From RepPoints with MIT License | 6 votes |
|
def merge_aug_results(self, aug_bboxes, aug_scores, img_metas):
"""Merge augmented detection bboxes and scores.
Args:
aug_bboxes (list[Tensor]): shape (n, 4*#class)
aug_scores (list[Tensor] or None): shape (n, #class)
img_shapes (list[Tensor]): shape (3, ).
Returns:
tuple: (bboxes, scores)
"""
recovered_bboxes = []
for bboxes, img_info in zip(aug_bboxes, img_metas):
img_shape = img_info[0]['img_shape']
scale_factor = img_info[0]['scale_factor']
flip = img_info[0]['flip']
bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip)
recovered_bboxes.append(bboxes)
bboxes = torch.cat(recovered_bboxes, dim=0)
if aug_scores is None:
return bboxes
else:
scores = torch.cat(aug_scores, dim=0)
return bboxes, scores
Example #2
| Source File: reppoints_detector.py From kaggle-kuzushiji-recognition with MIT License | 6 votes |
|
def merge_aug_results(self, aug_bboxes, aug_scores, img_metas):
"""Merge augmented detection bboxes and scores.
Args:
aug_bboxes (list[Tensor]): shape (n, 4*#class)
aug_scores (list[Tensor] or None): shape (n, #class)
img_shapes (list[Tensor]): shape (3, ).
Returns:
tuple: (bboxes, scores)
"""
recovered_bboxes = []
for bboxes, img_info in zip(aug_bboxes, img_metas):
img_shape = img_info[0]['img_shape']
scale_factor = img_info[0]['scale_factor']
flip = img_info[0]['flip']
bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip)
recovered_bboxes.append(bboxes)
bboxes = torch.cat(recovered_bboxes, dim=0)
if aug_scores is None:
return bboxes
else:
scores = torch.cat(aug_scores, dim=0)
return bboxes, scores
Example #3
| Source File: reppoints_detector.py From RDSNet with Apache License 2.0 | 6 votes |
|
def merge_aug_results(self, aug_bboxes, aug_scores, img_metas):
"""Merge augmented detection bboxes and scores.
Args:
aug_bboxes (list[Tensor]): shape (n, 4*#class)
aug_scores (list[Tensor] or None): shape (n, #class)
img_shapes (list[Tensor]): shape (3, ).
Returns:
tuple: (bboxes, scores)
"""
recovered_bboxes = []
for bboxes, img_info in zip(aug_bboxes, img_metas):
img_shape = img_info[0]['img_shape']
scale_factor = img_info[0]['scale_factor']
flip = img_info[0]['flip']
bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip)
recovered_bboxes.append(bboxes)
bboxes = torch.cat(recovered_bboxes, dim=0)
if aug_scores is None:
return bboxes
else:
scores = torch.cat(aug_scores, dim=0)
return bboxes, scores
Example #4
| Source File: reppoints_detector.py From IoU-Uniform-R-CNN with Apache License 2.0 | 6 votes |
|
def merge_aug_results(self, aug_bboxes, aug_scores, img_metas):
"""Merge augmented detection bboxes and scores.
Args:
aug_bboxes (list[Tensor]): shape (n, 4*#class)
aug_scores (list[Tensor] or None): shape (n, #class)
img_shapes (list[Tensor]): shape (3, ).
Returns:
tuple: (bboxes, scores)
"""
recovered_bboxes = []
for bboxes, img_info in zip(aug_bboxes, img_metas):
img_shape = img_info[0]['img_shape']
scale_factor = img_info[0]['scale_factor']
flip = img_info[0]['flip']
bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip)
recovered_bboxes.append(bboxes)
bboxes = torch.cat(recovered_bboxes, dim=0)
if aug_scores is None:
return bboxes
else:
scores = torch.cat(aug_scores, dim=0)
return bboxes, scores
Example #5
| Source File: reppoints_detector.py From Cascade-RPN with Apache License 2.0 | 6 votes |
|
def merge_aug_results(self, aug_bboxes, aug_scores, img_metas):
"""Merge augmented detection bboxes and scores.
Args:
aug_bboxes (list[Tensor]): shape (n, 4*#class)
aug_scores (list[Tensor] or None): shape (n, #class)
img_shapes (list[Tensor]): shape (3, ).
Returns:
tuple: (bboxes, scores)
"""
recovered_bboxes = []
for bboxes, img_info in zip(aug_bboxes, img_metas):
img_shape = img_info[0]['img_shape']
scale_factor = img_info[0]['scale_factor']
flip = img_info[0]['flip']
bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip)
recovered_bboxes.append(bboxes)
bboxes = torch.cat(recovered_bboxes, dim=0)
if aug_scores is None:
return bboxes
else:
scores = torch.cat(aug_scores, dim=0)
return bboxes, scores
Example #6
| Source File: reppoints_detector.py From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 | 6 votes |
|
def merge_aug_results(self, aug_bboxes, aug_scores, img_metas):
"""Merge augmented detection bboxes and scores.
Args:
aug_bboxes (list[Tensor]): shape (n, 4*#class)
aug_scores (list[Tensor] or None): shape (n, #class)
img_shapes (list[Tensor]): shape (3, ).
Returns:
tuple: (bboxes, scores)
"""
recovered_bboxes = []
for bboxes, img_info in zip(aug_bboxes, img_metas):
img_shape = img_info[0]['img_shape']
scale_factor = img_info[0]['scale_factor']
flip = img_info[0]['flip']
bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip)
recovered_bboxes.append(bboxes)
bboxes = torch.cat(recovered_bboxes, dim=0)
if aug_scores is None:
return bboxes
else:
scores = torch.cat(aug_scores, dim=0)
return bboxes, scores
Example #7
| Source File: reppoints_detector.py From ttfnet with Apache License 2.0 | 6 votes |
|
def merge_aug_results(self, aug_bboxes, aug_scores, img_metas):
"""Merge augmented detection bboxes and scores.
Args:
aug_bboxes (list[Tensor]): shape (n, 4*#class)
aug_scores (list[Tensor] or None): shape (n, #class)
img_shapes (list[Tensor]): shape (3, ).
Returns:
tuple: (bboxes, scores)
"""
recovered_bboxes = []
for bboxes, img_info in zip(aug_bboxes, img_metas):
img_shape = img_info[0]['img_shape']
scale_factor = img_info[0]['scale_factor']
flip = img_info[0]['flip']
bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip)
recovered_bboxes.append(bboxes)
bboxes = torch.cat(recovered_bboxes, dim=0)
if aug_scores is None:
return bboxes
else:
scores = torch.cat(aug_scores, dim=0)
return bboxes, scores
Example #8
| Source File: reppoints_detector.py From mmdetection with Apache License 2.0 | 5 votes |
|
def merge_aug_results(self, aug_bboxes, aug_scores, img_metas):
"""Merge augmented detection bboxes and scores.
Args:
aug_bboxes (list[Tensor]): shape (n, 4*#class)
aug_scores (list[Tensor] or None): shape (n, #class)
img_shapes (list[Tensor]): shape (3, ).
Returns:
tuple: (bboxes, scores)
"""
recovered_bboxes = []
for bboxes, img_info in zip(aug_bboxes, img_metas):
img_shape = img_info[0]['img_shape']
scale_factor = img_info[0]['scale_factor']
flip = img_info[0]['flip']
flip_direction = img_info[0]['flip_direction']
bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip,
flip_direction)
recovered_bboxes.append(bboxes)
bboxes = torch.cat(recovered_bboxes, dim=0)
if aug_scores is None:
return bboxes
else:
scores = torch.cat(aug_scores, dim=0)
return bboxes, scores
