Merge branch 'linfangjian/PhotoMetricDistortion_RandomCrop' into 'refactor_dev'
[Refactor] Refactor RandomCrop and PhotoMetricDistortion See merge request openmmlab-enterprise/openmmlab-ce/mmsegmentation!5pull/1801/head
zhengmiao
commit
44ae07bbd6
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@ -8,7 +8,20 @@ train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', reduce_zero_label=True),
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dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(
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type='TransformBroadcaster',
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mapping={
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'img': ['img', 'gt_semantic_seg'],
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'img_shape': [..., 'img_shape']
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},
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auto_remap=True,
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share_random_params=True,
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transforms=[
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dict(
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type='mmseg.RandomCrop',
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crop_size=crop_size,
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cat_max_ratio=0.75),
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]),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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@ -8,7 +8,20 @@ train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', reduce_zero_label=True),
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dict(type='Resize', img_scale=(2560, 640), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(
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type='TransformBroadcaster',
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mapping={
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'img': ['img', 'gt_semantic_seg'],
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'img_shape': [..., 'img_shape']
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},
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auto_remap=True,
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share_random_params=True,
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transforms=[
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dict(
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type='mmseg.RandomCrop',
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crop_size=crop_size,
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cat_max_ratio=0.75),
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]),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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@ -9,7 +9,20 @@ train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(
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type='TransformBroadcaster',
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mapping={
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'img': ['img', 'gt_semantic_seg'],
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'img_shape': [..., 'img_shape']
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},
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auto_remap=True,
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share_random_params=True,
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transforms=[
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dict(
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type='mmseg.RandomCrop',
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crop_size=crop_size,
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cat_max_ratio=0.75),
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]),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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@ -8,7 +8,20 @@ train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(
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type='TransformBroadcaster',
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mapping={
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'img': ['img', 'gt_semantic_seg'],
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'img_shape': [..., 'img_shape']
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},
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auto_remap=True,
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share_random_params=True,
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transforms=[
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dict(
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type='mmseg.RandomCrop',
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crop_size=crop_size,
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cat_max_ratio=0.75),
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]),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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@ -6,7 +6,20 @@ train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(
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type='TransformBroadcaster',
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mapping={
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'img': ['img', 'gt_semantic_seg'],
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'img_shape': [..., 'img_shape']
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},
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auto_remap=True,
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share_random_params=True,
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transforms=[
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dict(
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type='mmseg.RandomCrop',
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crop_size=crop_size,
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cat_max_ratio=0.75),
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]),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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@ -6,7 +6,20 @@ train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(
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type='TransformBroadcaster',
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mapping={
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'img': ['img', 'gt_semantic_seg'],
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'img_shape': [..., 'img_shape']
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},
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auto_remap=True,
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share_random_params=True,
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transforms=[
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dict(
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type='mmseg.RandomCrop',
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crop_size=crop_size,
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cat_max_ratio=0.75),
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]),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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@ -6,7 +6,20 @@ train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(
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type='TransformBroadcaster',
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mapping={
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'img': ['img', 'gt_semantic_seg'],
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'img_shape': [..., 'img_shape']
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},
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auto_remap=True,
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share_random_params=True,
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transforms=[
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dict(
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type='mmseg.RandomCrop',
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crop_size=crop_size,
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cat_max_ratio=0.75),
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]),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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@ -6,7 +6,20 @@ train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(
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type='TransformBroadcaster',
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mapping={
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'img': ['img', 'gt_semantic_seg'],
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'img_shape': [..., 'img_shape']
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},
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auto_remap=True,
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share_random_params=True,
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transforms=[
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dict(
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type='mmseg.RandomCrop',
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crop_size=crop_size,
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cat_max_ratio=0.75),
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]),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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@ -8,7 +8,20 @@ train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', reduce_zero_label=True),
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dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(
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type='TransformBroadcaster',
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mapping={
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'img': ['img', 'gt_semantic_seg'],
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'img_shape': [..., 'img_shape']
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},
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auto_remap=True,
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share_random_params=True,
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transforms=[
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dict(
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type='mmseg.RandomCrop',
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crop_size=crop_size,
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cat_max_ratio=0.75),
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]),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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@ -8,7 +8,20 @@ train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(
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type='TransformBroadcaster',
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mapping={
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'img': ['img', 'gt_semantic_seg'],
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'img_shape': [..., 'img_shape']
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},
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auto_remap=True,
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share_random_params=True,
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transforms=[
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dict(
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type='mmseg.RandomCrop',
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crop_size=crop_size,
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cat_max_ratio=0.75),
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]),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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@ -9,7 +9,20 @@ train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(
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type='TransformBroadcaster',
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mapping={
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'img': ['img', 'gt_semantic_seg'],
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'img_shape': [..., 'img_shape']
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},
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auto_remap=True,
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share_random_params=True,
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transforms=[
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dict(
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type='mmseg.RandomCrop',
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crop_size=crop_size,
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cat_max_ratio=0.75),
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]),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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|
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@ -9,7 +9,20 @@ train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(
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type='TransformBroadcaster',
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mapping={
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'img': ['img', 'gt_semantic_seg'],
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'img_shape': [..., 'img_shape']
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},
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auto_remap=True,
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share_random_params=True,
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transforms=[
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dict(
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type='mmseg.RandomCrop',
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crop_size=crop_size,
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cat_max_ratio=0.75),
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]),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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@ -16,7 +16,20 @@ train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(896, 896), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(
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type='TransformBroadcaster',
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mapping={
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'img': ['img', 'gt_semantic_seg'],
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'img_shape': [..., 'img_shape']
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},
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auto_remap=True,
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share_random_params=True,
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transforms=[
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dict(
|
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type='mmseg.RandomCrop',
|
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crop_size=crop_size,
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cat_max_ratio=0.75),
|
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]),
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dict(type='RandomFlip', prob=0.5),
|
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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|
|
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|
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@ -8,7 +8,20 @@ train_pipeline = [
|
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dict(type='LoadImageFromFile'),
|
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dict(type='LoadAnnotations', reduce_zero_label=True),
|
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dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
|
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
|
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dict(
|
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type='TransformBroadcaster',
|
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mapping={
|
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'img': ['img', 'gt_semantic_seg'],
|
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'img_shape': [..., 'img_shape']
|
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},
|
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auto_remap=True,
|
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share_random_params=True,
|
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transforms=[
|
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dict(
|
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type='mmseg.RandomCrop',
|
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crop_size=crop_size,
|
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cat_max_ratio=0.75),
|
||||
]),
|
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dict(type='RandomFlip', prob=0.5),
|
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dict(type='PhotoMetricDistortion'),
|
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dict(type='Normalize', **img_norm_cfg),
|
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|
|
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|
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@ -11,7 +11,20 @@ train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
|
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dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
|
||||
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
|
||||
dict(
|
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type='TransformBroadcaster',
|
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mapping={
|
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'img': ['img', 'gt_semantic_seg'],
|
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'img_shape': [..., 'img_shape']
|
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},
|
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auto_remap=True,
|
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share_random_params=True,
|
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transforms=[
|
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dict(
|
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type='mmseg.RandomCrop',
|
||||
crop_size=crop_size,
|
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cat_max_ratio=0.75),
|
||||
]),
|
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dict(type='RandomFlip', prob=0.5),
|
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dict(type='PhotoMetricDistortion'),
|
||||
dict(type='Normalize', **img_norm_cfg),
|
||||
|
|
|
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|
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@ -11,7 +11,20 @@ train_pipeline = [
|
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dict(type='LoadImageFromFile'),
|
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dict(type='LoadAnnotations', reduce_zero_label=True),
|
||||
dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
|
||||
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
|
||||
dict(
|
||||
type='TransformBroadcaster',
|
||||
mapping={
|
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'img': ['img', 'gt_semantic_seg'],
|
||||
'img_shape': [..., 'img_shape']
|
||||
},
|
||||
auto_remap=True,
|
||||
share_random_params=True,
|
||||
transforms=[
|
||||
dict(
|
||||
type='mmseg.RandomCrop',
|
||||
crop_size=crop_size,
|
||||
cat_max_ratio=0.75),
|
||||
]),
|
||||
dict(type='RandomFlip', prob=0.5),
|
||||
dict(type='PhotoMetricDistortion'),
|
||||
dict(type='Normalize', **img_norm_cfg),
|
||||
|
|
|
|||
|
|
@ -8,7 +8,20 @@ train_pipeline = [
|
|||
dict(type='LoadImageFromFile'),
|
||||
dict(type='LoadAnnotations'),
|
||||
dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
|
||||
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
|
||||
dict(
|
||||
type='TransformBroadcaster',
|
||||
mapping={
|
||||
'img': ['img', 'gt_semantic_seg'],
|
||||
'img_shape': [..., 'img_shape']
|
||||
},
|
||||
auto_remap=True,
|
||||
share_random_params=True,
|
||||
transforms=[
|
||||
dict(
|
||||
type='mmseg.RandomCrop',
|
||||
crop_size=crop_size,
|
||||
cat_max_ratio=0.75),
|
||||
]),
|
||||
dict(type='RandomFlip', prob=0.5),
|
||||
dict(type='PhotoMetricDistortion'),
|
||||
dict(type='Normalize', **img_norm_cfg),
|
||||
|
|
|
|||
|
|
@ -8,7 +8,20 @@ train_pipeline = [
|
|||
dict(type='LoadImageFromFile'),
|
||||
dict(type='LoadAnnotations', reduce_zero_label=True),
|
||||
dict(type='Resize', img_scale=(512, 512), ratio_range=(0.5, 2.0)),
|
||||
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
|
||||
dict(
|
||||
type='TransformBroadcaster',
|
||||
mapping={
|
||||
'img': ['img', 'gt_semantic_seg'],
|
||||
'img_shape': [..., 'img_shape']
|
||||
},
|
||||
auto_remap=True,
|
||||
share_random_params=True,
|
||||
transforms=[
|
||||
dict(
|
||||
type='mmseg.RandomCrop',
|
||||
crop_size=crop_size,
|
||||
cat_max_ratio=0.75),
|
||||
]),
|
||||
dict(type='RandomFlip', prob=0.5),
|
||||
dict(type='PhotoMetricDistortion'),
|
||||
dict(type='Normalize', **img_norm_cfg),
|
||||
|
|
|
|||
|
|
@ -9,7 +9,20 @@ train_pipeline = [
|
|||
dict(type='LoadImageFromFile'),
|
||||
dict(type='LoadAnnotations'),
|
||||
dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
|
||||
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
|
||||
dict(
|
||||
type='TransformBroadcaster',
|
||||
mapping={
|
||||
'img': ['img', 'gt_semantic_seg'],
|
||||
'img_shape': [..., 'img_shape']
|
||||
},
|
||||
auto_remap=True,
|
||||
share_random_params=True,
|
||||
transforms=[
|
||||
dict(
|
||||
type='mmseg.RandomCrop',
|
||||
crop_size=crop_size,
|
||||
cat_max_ratio=0.75),
|
||||
]),
|
||||
dict(type='RandomFlip', prob=0.5),
|
||||
dict(type='PhotoMetricDistortion'),
|
||||
dict(type='Normalize', **img_norm_cfg),
|
||||
|
|
|
|||
|
|
@ -8,7 +8,20 @@ train_pipeline = [
|
|||
dict(type='LoadImageFromFile'),
|
||||
dict(type='LoadAnnotations', reduce_zero_label=True),
|
||||
dict(type='Resize', img_scale=(512, 512), ratio_range=(0.5, 2.0)),
|
||||
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
|
||||
dict(
|
||||
type='TransformBroadcaster',
|
||||
mapping={
|
||||
'img': ['img', 'gt_semantic_seg'],
|
||||
'img_shape': [..., 'img_shape']
|
||||
},
|
||||
auto_remap=True,
|
||||
share_random_params=True,
|
||||
transforms=[
|
||||
dict(
|
||||
type='mmseg.RandomCrop',
|
||||
crop_size=crop_size,
|
||||
cat_max_ratio=0.75),
|
||||
]),
|
||||
dict(type='RandomFlip', prob=0.5),
|
||||
dict(type='PhotoMetricDistortion'),
|
||||
dict(type='Normalize', **img_norm_cfg),
|
||||
|
|
|
|||
|
|
@ -1,8 +1,11 @@
|
|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
import copy
|
||||
from typing import Sequence, Tuple, Union
|
||||
|
||||
import mmcv
|
||||
import numpy as np
|
||||
from mmcv.transforms.base import BaseTransform
|
||||
from mmcv.transforms.utils import cache_randomness
|
||||
from mmcv.utils import deprecated_api_warning, is_tuple_of
|
||||
from numpy import random
|
||||
|
||||
|
|
@ -581,40 +584,110 @@ class CLAHE(object):
|
|||
|
||||
|
||||
@TRANSFORMS.register_module()
|
||||
class RandomCrop(object):
|
||||
class RandomCrop(BaseTransform):
|
||||
"""Random crop the image & seg.
|
||||
|
||||
Required Keys:
|
||||
|
||||
- img
|
||||
- gt_semantic_seg
|
||||
|
||||
Modified Keys:
|
||||
|
||||
- img
|
||||
- img_shape
|
||||
- gt_semantic_seg
|
||||
|
||||
|
||||
Args:
|
||||
crop_size (tuple): Expected size after cropping, (h, w).
|
||||
crop_size (Union[int, Tuple[int, int]]): Expected size after cropping
|
||||
with the format of (h, w). If set to an integer, then cropping
|
||||
width and height are equal to this integer.
|
||||
cat_max_ratio (float): The maximum ratio that single category could
|
||||
occupy.
|
||||
ignore_index (int): The label index to be ignored. Default: 255
|
||||
"""
|
||||
|
||||
def __init__(self, crop_size, cat_max_ratio=1., ignore_index=255):
|
||||
def __init__(self,
|
||||
crop_size: Union[int, Tuple[int, int]],
|
||||
cat_max_ratio: float = 1.,
|
||||
ignore_index: int = 255):
|
||||
super().__init__()
|
||||
assert isinstance(crop_size, int) or (
|
||||
isinstance(crop_size, tuple) and len(crop_size) == 2
|
||||
), 'The expected crop_size is an integer, or a tuple containing two '
|
||||
'intergers'
|
||||
|
||||
if isinstance(crop_size, int):
|
||||
crop_size = (crop_size, crop_size)
|
||||
assert crop_size[0] > 0 and crop_size[1] > 0
|
||||
self.crop_size = crop_size
|
||||
self.cat_max_ratio = cat_max_ratio
|
||||
self.ignore_index = ignore_index
|
||||
|
||||
def get_crop_bbox(self, img):
|
||||
"""Randomly get a crop bounding box."""
|
||||
margin_h = max(img.shape[0] - self.crop_size[0], 0)
|
||||
margin_w = max(img.shape[1] - self.crop_size[1], 0)
|
||||
offset_h = np.random.randint(0, margin_h + 1)
|
||||
offset_w = np.random.randint(0, margin_w + 1)
|
||||
crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0]
|
||||
crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1]
|
||||
@cache_randomness
|
||||
def crop_bbox(self, results: dict) -> tuple:
|
||||
"""get a crop bounding box.
|
||||
|
||||
return crop_y1, crop_y2, crop_x1, crop_x2
|
||||
Args:
|
||||
results (dict): Result dict from loading pipeline.
|
||||
|
||||
Returns:
|
||||
tuple: Coordinates of the cropped image.
|
||||
"""
|
||||
|
||||
def generate_crop_bbox(img: np.ndarray) -> tuple:
|
||||
"""Randomly get a crop bounding box.
|
||||
|
||||
Args:
|
||||
img (np.ndarray): Original input image.
|
||||
|
||||
Returns:
|
||||
tuple: Coordinates of the cropped image.
|
||||
"""
|
||||
|
||||
margin_h = max(img.shape[0] - self.crop_size[0], 0)
|
||||
margin_w = max(img.shape[1] - self.crop_size[1], 0)
|
||||
offset_h = np.random.randint(0, margin_h + 1)
|
||||
offset_w = np.random.randint(0, margin_w + 1)
|
||||
crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0]
|
||||
crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1]
|
||||
|
||||
return crop_y1, crop_y2, crop_x1, crop_x2
|
||||
|
||||
img = results['img']
|
||||
crop_bbox = generate_crop_bbox(img)
|
||||
if self.cat_max_ratio < 1.:
|
||||
# Repeat 10 times
|
||||
for _ in range(10):
|
||||
seg_temp = self.crop(results['gt_semantic_seg'], crop_bbox)
|
||||
labels, cnt = np.unique(seg_temp, return_counts=True)
|
||||
cnt = cnt[labels != self.ignore_index]
|
||||
if len(cnt) > 1 and np.max(cnt) / np.sum(
|
||||
cnt) < self.cat_max_ratio:
|
||||
break
|
||||
crop_bbox = generate_crop_bbox(img)
|
||||
|
||||
return crop_bbox
|
||||
|
||||
def crop(self, img: np.ndarray, crop_bbox: tuple) -> np.ndarray:
|
||||
"""Crop from ``img``
|
||||
|
||||
Args:
|
||||
img (np.ndarray): Original input image.
|
||||
crop_bbox (tuple): Coordinates of the cropped image.
|
||||
|
||||
Returns:
|
||||
np.ndarray: The cropped image.
|
||||
"""
|
||||
|
||||
def crop(self, img, crop_bbox):
|
||||
"""Crop from ``img``"""
|
||||
crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox
|
||||
img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
|
||||
return img
|
||||
|
||||
def __call__(self, results):
|
||||
"""Call function to randomly crop images, semantic segmentation maps.
|
||||
def transform(self, results: dict) -> dict:
|
||||
"""Transform function to randomly crop images, semantic segmentation
|
||||
maps.
|
||||
|
||||
Args:
|
||||
results (dict): Result dict from loading pipeline.
|
||||
|
|
@ -625,28 +698,13 @@ class RandomCrop(object):
|
|||
"""
|
||||
|
||||
img = results['img']
|
||||
crop_bbox = self.get_crop_bbox(img)
|
||||
if self.cat_max_ratio < 1.:
|
||||
# Repeat 10 times
|
||||
for _ in range(10):
|
||||
seg_temp = self.crop(results['gt_semantic_seg'], crop_bbox)
|
||||
labels, cnt = np.unique(seg_temp, return_counts=True)
|
||||
cnt = cnt[labels != self.ignore_index]
|
||||
if len(cnt) > 1 and np.max(cnt) / np.sum(
|
||||
cnt) < self.cat_max_ratio:
|
||||
break
|
||||
crop_bbox = self.get_crop_bbox(img)
|
||||
crop_bbox = self.crop_bbox(results)
|
||||
|
||||
# crop the image
|
||||
img = self.crop(img, crop_bbox)
|
||||
img_shape = img.shape
|
||||
results['img'] = img
|
||||
results['img_shape'] = img_shape
|
||||
|
||||
# crop semantic seg
|
||||
for key in results.get('seg_fields', []):
|
||||
results[key] = self.crop(results[key], crop_bbox)
|
||||
|
||||
return results
|
||||
|
||||
def __repr__(self):
|
||||
|
|
@ -858,7 +916,7 @@ class SegRescale(object):
|
|||
|
||||
|
||||
@TRANSFORMS.register_module()
|
||||
class PhotoMetricDistortion(object):
|
||||
class PhotoMetricDistortion(BaseTransform):
|
||||
"""Apply photometric distortion to image sequentially, every transformation
|
||||
is applied with a probability of 0.5. The position of random contrast is in
|
||||
second or second to last.
|
||||
|
|
@ -871,6 +929,14 @@ class PhotoMetricDistortion(object):
|
|||
6. convert color from HSV to BGR
|
||||
7. random contrast (mode 1)
|
||||
|
||||
Required Keys:
|
||||
|
||||
- img
|
||||
|
||||
Modified Keys:
|
||||
|
||||
- img
|
||||
|
||||
Args:
|
||||
brightness_delta (int): delta of brightness.
|
||||
contrast_range (tuple): range of contrast.
|
||||
|
|
@ -879,23 +945,45 @@ class PhotoMetricDistortion(object):
|
|||
"""
|
||||
|
||||
def __init__(self,
|
||||
brightness_delta=32,
|
||||
contrast_range=(0.5, 1.5),
|
||||
saturation_range=(0.5, 1.5),
|
||||
hue_delta=18):
|
||||
brightness_delta: int = 32,
|
||||
contrast_range: Sequence[float] = (0.5, 1.5),
|
||||
saturation_range: Sequence[float] = (0.5, 1.5),
|
||||
hue_delta: int = 18):
|
||||
self.brightness_delta = brightness_delta
|
||||
self.contrast_lower, self.contrast_upper = contrast_range
|
||||
self.saturation_lower, self.saturation_upper = saturation_range
|
||||
self.hue_delta = hue_delta
|
||||
|
||||
def convert(self, img, alpha=1, beta=0):
|
||||
"""Multiple with alpha and add beat with clip."""
|
||||
def convert(self,
|
||||
img: np.ndarray,
|
||||
alpha: int = 1,
|
||||
beta: int = 0) -> np.ndarray:
|
||||
"""Multiple with alpha and add beat with clip.
|
||||
|
||||
Args:
|
||||
img (np.ndarray): The input image.
|
||||
alpha (int): Image weights, change the contrast/saturation
|
||||
of the image. Default: 1
|
||||
beta (int): Image bias, change the brightness of the
|
||||
image. Default: 0
|
||||
|
||||
Returns:
|
||||
np.ndarray: The transformed image.
|
||||
"""
|
||||
|
||||
img = img.astype(np.float32) * alpha + beta
|
||||
img = np.clip(img, 0, 255)
|
||||
return img.astype(np.uint8)
|
||||
|
||||
def brightness(self, img):
|
||||
"""Brightness distortion."""
|
||||
def brightness(self, img: np.ndarray) -> np.ndarray:
|
||||
"""Brightness distortion.
|
||||
|
||||
Args:
|
||||
img (np.ndarray): The input image.
|
||||
Returns:
|
||||
np.ndarray: Image after brightness change.
|
||||
"""
|
||||
|
||||
if random.randint(2):
|
||||
return self.convert(
|
||||
img,
|
||||
|
|
@ -903,16 +991,30 @@ class PhotoMetricDistortion(object):
|
|||
self.brightness_delta))
|
||||
return img
|
||||
|
||||
def contrast(self, img):
|
||||
"""Contrast distortion."""
|
||||
def contrast(self, img: np.ndarray) -> np.ndarray:
|
||||
"""Contrast distortion.
|
||||
|
||||
Args:
|
||||
img (np.ndarray): The input image.
|
||||
Returns:
|
||||
np.ndarray: Image after contrast change.
|
||||
"""
|
||||
|
||||
if random.randint(2):
|
||||
return self.convert(
|
||||
img,
|
||||
alpha=random.uniform(self.contrast_lower, self.contrast_upper))
|
||||
return img
|
||||
|
||||
def saturation(self, img):
|
||||
"""Saturation distortion."""
|
||||
def saturation(self, img: np.ndarray) -> np.ndarray:
|
||||
"""Saturation distortion.
|
||||
|
||||
Args:
|
||||
img (np.ndarray): The input image.
|
||||
Returns:
|
||||
np.ndarray: Image after saturation change.
|
||||
"""
|
||||
|
||||
if random.randint(2):
|
||||
img = mmcv.bgr2hsv(img)
|
||||
img[:, :, 1] = self.convert(
|
||||
|
|
@ -922,8 +1024,15 @@ class PhotoMetricDistortion(object):
|
|||
img = mmcv.hsv2bgr(img)
|
||||
return img
|
||||
|
||||
def hue(self, img):
|
||||
"""Hue distortion."""
|
||||
def hue(self, img: np.ndarray) -> np.ndarray:
|
||||
"""Hue distortion.
|
||||
|
||||
Args:
|
||||
img (np.ndarray): The input image.
|
||||
Returns:
|
||||
np.ndarray: Image after hue change.
|
||||
"""
|
||||
|
||||
if random.randint(2):
|
||||
img = mmcv.bgr2hsv(img)
|
||||
img[:, :,
|
||||
|
|
@ -932,8 +1041,8 @@ class PhotoMetricDistortion(object):
|
|||
img = mmcv.hsv2bgr(img)
|
||||
return img
|
||||
|
||||
def __call__(self, results):
|
||||
"""Call function to perform photometric distortion on images.
|
||||
def transform(self, results: dict) -> dict:
|
||||
"""Transform function to perform photometric distortion on images.
|
||||
|
||||
Args:
|
||||
results (dict): Result dict from loading pipeline.
|
||||
|
|
|
|||
|
|
@ -0,0 +1,64 @@
|
|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
import os.path as osp
|
||||
|
||||
import mmcv
|
||||
import numpy as np
|
||||
import pytest
|
||||
from mmcv.transforms.wrappers import TransformBroadcaster
|
||||
from PIL import Image
|
||||
|
||||
from mmseg.datasets.pipelines import PhotoMetricDistortion, RandomCrop
|
||||
|
||||
|
||||
def test_random_crop():
|
||||
# test assertion for invalid random crop
|
||||
with pytest.raises(AssertionError):
|
||||
RandomCrop(crop_size=(-1, 0))
|
||||
|
||||
results = dict()
|
||||
img = mmcv.imread(osp.join('tests/data/color.jpg'), 'color')
|
||||
seg = np.array(Image.open(osp.join('tests/data/seg.png')))
|
||||
results['img'] = img
|
||||
results['gt_semantic_seg'] = seg
|
||||
results['seg_fields'] = ['gt_semantic_seg']
|
||||
results['img_shape'] = img.shape
|
||||
results['ori_shape'] = img.shape
|
||||
# Set initial values for default meta_keys
|
||||
results['pad_shape'] = img.shape
|
||||
results['scale_factor'] = 1.0
|
||||
|
||||
h, w, _ = img.shape
|
||||
pipeline = TransformBroadcaster(
|
||||
transforms=[RandomCrop(crop_size=(h - 20, w - 20))],
|
||||
mapping={
|
||||
'img': ['img', 'gt_semantic_seg'],
|
||||
'img_shape': [..., 'img_shape']
|
||||
},
|
||||
auto_remap=True,
|
||||
share_random_params=True)
|
||||
results = pipeline(results)
|
||||
assert results['img'].shape[:2] == (h - 20, w - 20)
|
||||
assert results['img_shape'][:2] == (h - 20, w - 20)
|
||||
assert results['gt_semantic_seg'].shape[:2] == (h - 20, w - 20)
|
||||
|
||||
|
||||
def test_photo_metric_distortion():
|
||||
|
||||
results = dict()
|
||||
img = mmcv.imread(osp.join('tests/data/color.jpg'), 'color')
|
||||
seg = np.array(Image.open(osp.join('tests/data/seg.png')))
|
||||
results['img'] = img
|
||||
results['gt_semantic_seg'] = seg
|
||||
results['seg_fields'] = ['gt_semantic_seg']
|
||||
results['img_shape'] = img.shape
|
||||
results['ori_shape'] = img.shape
|
||||
# Set initial values for default meta_keys
|
||||
results['pad_shape'] = img.shape
|
||||
results['scale_factor'] = 1.0
|
||||
|
||||
pipeline = PhotoMetricDistortion()
|
||||
results = pipeline(results)
|
||||
|
||||
assert not ((results['img'] == img).all())
|
||||
assert (results['gt_semantic_seg'] == seg).all()
|
||||
assert results['img_shape'] == img.shape
|
||||
Loading…
Reference in New Issue