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[Feature] Support iSAID aerial dataset. (#1115)
* support iSAID aerial dataset * Update and rename docs/dataset_prepare.md to 博士/dataset_prepare.md * Update dataset_prepare.md * fix typo * fix typo * fix typo * remove imgviz * fix wrong order in annotation name * upload models&logs * upload models&logs * add load_annotations * fix unittest coverage * fix unittest coverage * fix correct crop size in config * fix iSAID unit test * fix iSAID unit test * fix typos * fix wrong crop size in readme * use smaller figure as test data * add smaller dataset in test data * add blank in docs * use 0 bytes pseudo data * add footnote and comments for crop size * change iSAID to isaid and add default value in it * change iSAID to isaid in _base_ Co-authored-by: MengzhangLI <mcmong@pku.edu.cn>
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@ -138,6 +138,7 @@ Supported datasets:
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- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#loveda)
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- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-potsdam)
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- [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-vaihingen)
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- [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isaid)
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## Installation
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@ -137,6 +137,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
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- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#loveda)
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- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isprs-potsdam)
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- [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isprs-vaihingen)
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- [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isaid)
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## 安装
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62
configs/_base_/datasets/isaid.py
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62
configs/_base_/datasets/isaid.py
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@ -0,0 +1,62 @@
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# dataset settings
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dataset_type = 'iSAIDDataset'
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data_root = 'data/iSAID'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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"""
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This crop_size setting is followed by the implementation of
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`PointFlow: Flowing Semantics Through Points for Aerial Image
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Segmentation <https://arxiv.org/pdf/2103.06564.pdf>`_.
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"""
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crop_size = (896, 896)
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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(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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dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(896, 896),
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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=4,
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workers_per_gpu=4,
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train=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='img_dir/train',
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ann_dir='ann_dir/train',
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='img_dir/val',
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ann_dir='ann_dir/val',
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pipeline=test_pipeline),
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test=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='img_dir/val',
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ann_dir='ann_dir/val',
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pipeline=test_pipeline))
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@ -114,8 +114,16 @@ Spatial pyramid pooling module or encode-decoder structure are used in deep neur
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| DeepLabV3+ | R-50-D8 | 512x512 | 80000 | 7.36 | 26.91 | 73.97 | 75.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816-5040938d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json) |
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| DeepLabV3+ | R-101-D8 | 512x512 | 80000 | 10.83 | 18.59 | 73.06 | 74.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816-8a095afa.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json) |
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### iSAID
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
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| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| DeepLabV3+ | R-18-D8 | 896x896 | 80000 | 6.19 | 24.81 | 61.35 | 62.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526-7059991d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
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| DeepLabV3+ | R-50-D8 | 896x896 | 80000 | 21.45 | 8.42 | 67.06 | 68.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526-598be439.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
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Note:
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- `D-8`/`D-16` here corresponding to the output stride 8/16 setting for DeepLab series.
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- `MG-124` stands for multi-grid dilation in the last stage of ResNet.
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- `FP16` means Mixed Precision (FP16) is adopted in training.
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- `896x896` is the Crop Size of iSAID dataset, which is followed by the implementation of [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://arxiv.org/pdf/2103.06564.pdf)
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@ -10,6 +10,7 @@ Collections:
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- LoveDA
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- Potsdam
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- Vaihingen
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- iSAID
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Paper:
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URL: https://arxiv.org/abs/1802.02611
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Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
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@ -803,3 +804,47 @@ Models:
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mIoU(ms+flip): 74.14
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Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816-8a095afa.pth
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- Name: deeplabv3plus_r18-d8_4x4_896x896_80k_isaid
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In Collection: deeplabv3plus
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Metadata:
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backbone: R-18-D8
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crop size: (896,896)
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lr schd: 80000
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inference time (ms/im):
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- value: 40.31
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (896,896)
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Training Memory (GB): 6.19
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Results:
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- Task: Semantic Segmentation
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Dataset: iSAID
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Metrics:
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mIoU: 61.35
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mIoU(ms+flip): 62.61
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Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526-7059991d.pth
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- Name: deeplabv3plus_r50-d8_4x4_896x896_80k_isaid
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In Collection: deeplabv3plus
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Metadata:
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backbone: R-50-D8
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crop size: (896,896)
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lr schd: 80000
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inference time (ms/im):
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- value: 118.76
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (896,896)
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Training Memory (GB): 21.45
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Results:
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- Task: Semantic Segmentation
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Dataset: iSAID
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Metrics:
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mIoU: 67.06
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mIoU(ms+flip): 68.02
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Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526-598be439.pth
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@ -0,0 +1,11 @@
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_base_ = './deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py'
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model = dict(
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pretrained='open-mmlab://resnet18_v1c',
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backbone=dict(depth=18),
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decode_head=dict(
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c1_in_channels=64,
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c1_channels=12,
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in_channels=512,
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channels=128,
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),
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auxiliary_head=dict(in_channels=256, channels=64))
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@ -0,0 +1,6 @@
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_base_ = [
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'../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/isaid.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
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]
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model = dict(
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decode_head=dict(num_classes=16), auxiliary_head=dict(num_classes=16))
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@ -107,3 +107,15 @@ High-resolution representations are essential for position-sensitive vision prob
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| FCN | HRNetV2p-W18-Small | 512x512 | 80000 | 1.58 | 38.11 | 71.81 | 73.1 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909-b23aae02.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909.log.json) |
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| FCN | HRNetV2p-W18 | 512x512 | 80000 | 2.76 | 19.55 | 72.57 | 74.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216-2ec3ae8a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216.log.json) |
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| FCN | HRNetV2p-W48 | 512x512 | 80000 | 6.20 | 17.25 | 72.50 | 73.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244-7133cb22.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244.log.json) |
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### iSAID
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
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| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| FCN | HRNetV2p-W18-Small | 896x896 | 80000 | 4.95 | 13.84 | 62.30 | 62.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603-3cc0769b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603.log.json) |
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| FCN | HRNetV2p-W18 | 896x896 | 80000 | 8.30 | 7.71 | 65.06 | 65.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230-49bf752e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230.log.json) |
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| FCN | HRNetV2p-W48 | 896x896 | 80000 | 16.89 | 7.34 | 67.80 | 68.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643-547fc420.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643.log.json) |
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Note:
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- `896x896` is the Crop Size of iSAID dataset, which is followed by the implementation of [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://arxiv.org/pdf/2103.06564.pdf)
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5
configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.py
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5
configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.py
Normal file
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_base_ = [
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'../_base_/models/fcn_hr18.py', '../_base_/datasets/isaid.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
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]
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model = dict(decode_head=dict(num_classes=16))
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9
configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py
Normal file
9
configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py
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_base_ = './fcn_hr18_4x4_896x896_80k_isaid.py'
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model = dict(
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pretrained='open-mmlab://msra/hrnetv2_w18_small',
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backbone=dict(
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extra=dict(
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stage1=dict(num_blocks=(2, )),
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stage2=dict(num_blocks=(2, 2)),
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stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
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stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
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10
configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.py
Normal file
10
configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.py
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_base_ = './fcn_hr18_4x4_896x896_80k_isaid.py'
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model = dict(
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pretrained='open-mmlab://msra/hrnetv2_w48',
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backbone=dict(
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extra=dict(
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stage2=dict(num_channels=(48, 96)),
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stage3=dict(num_channels=(48, 96, 192)),
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stage4=dict(num_channels=(48, 96, 192, 384)))),
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decode_head=dict(
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in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
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@ -10,6 +10,7 @@ Collections:
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- LoveDA
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- Potsdam
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- Vaihingen
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- iSAID
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Paper:
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||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
@ -648,3 +649,69 @@ Models:
|
||||
mIoU(ms+flip): 73.52
|
||||
Config: configs/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244-7133cb22.pth
|
||||
- Name: fcn_hr18s_4x4_896x896_80k_isaid
|
||||
In Collection: hrnet
|
||||
Metadata:
|
||||
backbone: HRNetV2p-W18-Small
|
||||
crop size: (896,896)
|
||||
lr schd: 80000
|
||||
inference time (ms/im):
|
||||
- value: 72.25
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (896,896)
|
||||
Training Memory (GB): 4.95
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: iSAID
|
||||
Metrics:
|
||||
mIoU: 62.3
|
||||
mIoU(ms+flip): 62.97
|
||||
Config: configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603-3cc0769b.pth
|
||||
- Name: fcn_hr18_4x4_896x896_80k_isaid
|
||||
In Collection: hrnet
|
||||
Metadata:
|
||||
backbone: HRNetV2p-W18
|
||||
crop size: (896,896)
|
||||
lr schd: 80000
|
||||
inference time (ms/im):
|
||||
- value: 129.7
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (896,896)
|
||||
Training Memory (GB): 8.3
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: iSAID
|
||||
Metrics:
|
||||
mIoU: 65.06
|
||||
mIoU(ms+flip): 65.6
|
||||
Config: configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230-49bf752e.pth
|
||||
- Name: fcn_hr48_4x4_896x896_80k_isaid
|
||||
In Collection: hrnet
|
||||
Metadata:
|
||||
backbone: HRNetV2p-W48
|
||||
crop size: (896,896)
|
||||
lr schd: 80000
|
||||
inference time (ms/im):
|
||||
- value: 136.24
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (896,896)
|
||||
Training Memory (GB): 16.89
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: iSAID
|
||||
Metrics:
|
||||
mIoU: 67.8
|
||||
mIoU(ms+flip): 68.53
|
||||
Config: configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643-547fc420.pth
|
||||
|
||||
@ -148,6 +148,14 @@ We support evaluation results on these two datasets using models above trained o
|
||||
| PSPNet | R-50-D8 | 512x512 | 80000 | 6.14 | 30.29 | 72.36 | 73.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355-382f8f5b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355.log.json) |
|
||||
| PSPNet | R-101-D8 | 512x512 | 80000 | 9.61 | 19.97 | 72.61 | 74.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806-8eba0a09.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806.log.json) |
|
||||
|
||||
### iSAID
|
||||
|
||||
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
|
||||
| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| PSPNet | R-18-D8 | 896x896 | 80000 | 4.52 | 26.91 | 60.22 | 61.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526-e84c0b6a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
|
||||
| PSPNet | R-50-D8 | 896x896 | 80000 | 16.58 | 8.88 | 65.36 | 66.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629-1f21dc32.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629.log.json) |
|
||||
|
||||
Note:
|
||||
|
||||
- `FP16` means Mixed Precision (FP16) is adopted in training.
|
||||
- `896x896` is the Crop Size of iSAID dataset, which is followed by the implementation of [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://arxiv.org/pdf/2103.06564.pdf)
|
||||
|
||||
@ -13,6 +13,7 @@ Collections:
|
||||
- LoveDA
|
||||
- Potsdam
|
||||
- Vaihingen
|
||||
- iSAID
|
||||
Paper:
|
||||
URL: https://arxiv.org/abs/1612.01105
|
||||
Title: Pyramid Scene Parsing Network
|
||||
@ -942,3 +943,47 @@ Models:
|
||||
mIoU(ms+flip): 74.18
|
||||
Config: configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806-8eba0a09.pth
|
||||
- Name: pspnet_r18-d8_4x4_896x896_80k_isaid
|
||||
In Collection: pspnet
|
||||
Metadata:
|
||||
backbone: R-18-D8
|
||||
crop size: (896,896)
|
||||
lr schd: 80000
|
||||
inference time (ms/im):
|
||||
- value: 37.16
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (896,896)
|
||||
Training Memory (GB): 4.52
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: iSAID
|
||||
Metrics:
|
||||
mIoU: 60.22
|
||||
mIoU(ms+flip): 61.25
|
||||
Config: configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526-e84c0b6a.pth
|
||||
- Name: pspnet_r50-d8_4x4_896x896_80k_isaid
|
||||
In Collection: pspnet
|
||||
Metadata:
|
||||
backbone: R-50-D8
|
||||
crop size: (896,896)
|
||||
lr schd: 80000
|
||||
inference time (ms/im):
|
||||
- value: 112.61
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (896,896)
|
||||
Training Memory (GB): 16.58
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: iSAID
|
||||
Metrics:
|
||||
mIoU: 65.36
|
||||
mIoU(ms+flip): 66.48
|
||||
Config: configs/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629-1f21dc32.pth
|
||||
|
||||
9
configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py
Normal file
9
configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py
Normal file
@ -0,0 +1,9 @@
|
||||
_base_ = './pspnet_r50-d8_4x4_896x896_80k_isaid.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet18_v1c',
|
||||
backbone=dict(depth=18),
|
||||
decode_head=dict(
|
||||
in_channels=512,
|
||||
channels=128,
|
||||
),
|
||||
auxiliary_head=dict(in_channels=256, channels=64))
|
||||
6
configs/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.py
Normal file
6
configs/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.py
Normal file
@ -0,0 +1,6 @@
|
||||
_base_ = [
|
||||
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/isaid.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
|
||||
]
|
||||
model = dict(
|
||||
decode_head=dict(num_classes=16), auxiliary_head=dict(num_classes=16))
|
||||
@ -123,6 +123,21 @@ mmsegmentation
|
||||
│ │ ├── ann_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
│ ├── vaihingen
|
||||
│ │ ├── img_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
│ │ ├── ann_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
│ ├── iSAID
|
||||
│ │ ├── img_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
│ │ │ ├── test
|
||||
│ │ ├── ann_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
```
|
||||
|
||||
### Cityscapes
|
||||
@ -325,3 +340,38 @@ python tools/convert_datasets/vaihingen.py /path/to/vaihingen
|
||||
```
|
||||
|
||||
In our default setting (`clip_size` =512, `stride_size`=256), it will generate 344 images for training and 398 images for validation.
|
||||
|
||||
### iSAID
|
||||
The data images could be download from [DOTA-v1.0](https://captain-whu.github.io/DOTA/dataset.html) (train/val/test)
|
||||
|
||||
The data annotations could be download from [iSAID](https://captain-whu.github.io/iSAID/dataset.html) (train/val)
|
||||
|
||||
The dataset is a Large-scale Dataset for Instance Segmentation (also have segmantic segmentation) in Aerial Images.
|
||||
|
||||
You may need to follow the following structure for dataset preparation after downloading iSAID dataset.
|
||||
|
||||
```
|
||||
│ ├── iSAID
|
||||
│ │ ├── train
|
||||
│ │ │ ├── images
|
||||
│ │ │ │ ├── part1.zip
|
||||
│ │ │ │ ├── part2.zip
|
||||
│ │ │ │ ├── part3.zip
|
||||
│ │ │ ├── Semantic_masks
|
||||
│ │ │ │ ├── images.zip
|
||||
│ │ ├── val
|
||||
│ │ │ ├── images
|
||||
│ │ │ │ ├── part1.zip
|
||||
│ │ │ ├── Semantic_masks
|
||||
│ │ │ │ ├── images.zip
|
||||
│ │ ├── test
|
||||
│ │ │ ├── images
|
||||
│ │ │ │ ├── part1.zip
|
||||
│ │ │ │ ├── part2.zip
|
||||
```
|
||||
|
||||
```shell
|
||||
python tools/convert_datasets/isaid.py /path/to/iSAID
|
||||
```
|
||||
|
||||
In our default setting (`clip_size` =512, `stride_size`=256), it will generate 33978 images for training and 11644 images for validation.
|
||||
|
||||
@ -104,6 +104,21 @@ mmsegmentation
|
||||
│ │ ├── ann_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
│ ├── vaihingen
|
||||
│ │ ├── img_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
│ │ ├── ann_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
│ ├── iSAID
|
||||
│ │ ├── img_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
│ │ │ ├── test
|
||||
│ │ ├── ann_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
```
|
||||
|
||||
### Cityscapes
|
||||
@ -265,4 +280,39 @@ python tools/convert_datasets/potsdam.py /path/to/potsdam
|
||||
python tools/convert_datasets/vaihingen.py /path/to/vaihingen
|
||||
```
|
||||
|
||||
使用我们默认的配置 (`clip_size` =512, `stride_size`=256)， 将生成 344 张图片的训练集和 398 张图片的验证集。
|
||||
使用我们默认的配置 (`clip_size`=512, `stride_size`=256)， 将生成 344 张图片的训练集和 398 张图片的验证集。
|
||||
|
||||
### iSAID
|
||||
iSAID 数据集(训练集/验证集/测试集)的图像可以从 [DOTA-v1.0](https://captain-whu.github.io/DOTA/dataset.html) 下载.
|
||||
|
||||
iSAID 数据集(训练集/验证集)的注释可以从 [iSAID](https://captain-whu.github.io/iSAID/dataset.html) 下载.
|
||||
|
||||
该数据集是一个大规模的实例分割(也可以用于语义分割)的遥感数据集.
|
||||
|
||||
下载后，在数据集转换前，您需要将数据集文件夹调整成如下格式.
|
||||
|
||||
```
|
||||
│ ├── iSAID
|
||||
│ │ ├── train
|
||||
│ │ │ ├── images
|
||||
│ │ │ │ ├── part1.zip
|
||||
│ │ │ │ ├── part2.zip
|
||||
│ │ │ │ ├── part3.zip
|
||||
│ │ │ ├── Semantic_masks
|
||||
│ │ │ │ ├── images.zip
|
||||
│ │ ├── val
|
||||
│ │ │ ├── images
|
||||
│ │ │ │ ├── part1.zip
|
||||
│ │ │ ├── Semantic_masks
|
||||
│ │ │ │ ├── images.zip
|
||||
│ │ ├── test
|
||||
│ │ │ ├── images
|
||||
│ │ │ │ ├── part1.zip
|
||||
│ │ │ │ ├── part2.zip
|
||||
```
|
||||
|
||||
```shell
|
||||
python tools/convert_datasets/isaid.py /path/to/iSAID
|
||||
```
|
||||
|
||||
使用我们默认的配置 (`patch_width`=896, `patch_height`=896, `overlap_area`=384)， 将生成 33978 张图片的训练集和 11644 张图片的验证集。
|
||||
|
||||
@ -111,6 +111,16 @@ def vaihingen_classes():
|
||||
]
|
||||
|
||||
|
||||
def isaid_classes():
|
||||
"""iSAID class names for external use."""
|
||||
return [
|
||||
'background', 'ship', 'store_tank', 'baseball_diamond', 'tennis_court',
|
||||
'basketball_court', 'Ground_Track_Field', 'Bridge', 'Large_Vehicle',
|
||||
'Small_Vehicle', 'Helicopter', 'Swimming_pool', 'Roundabout',
|
||||
'Soccer_ball_field', 'plane', 'Harbor'
|
||||
]
|
||||
|
||||
|
||||
def cityscapes_palette():
|
||||
"""Cityscapes palette for external use."""
|
||||
return [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156],
|
||||
@ -236,6 +246,15 @@ def vaihingen_palette():
|
||||
[255, 255, 0], [255, 0, 0]]
|
||||
|
||||
|
||||
def isaid_palette():
|
||||
"""iSAID palette for external use."""
|
||||
return [[0, 0, 0], [0, 0, 63], [0, 63, 63], [0, 63, 0], [0, 63, 127],
|
||||
[0, 63, 191], [0, 63, 255], [0, 127, 63], [0, 127,
|
||||
127], [0, 0, 127],
|
||||
[0, 0, 191], [0, 0, 255], [0, 191, 127], [0, 127, 191],
|
||||
[0, 127, 255], [0, 100, 155]]
|
||||
|
||||
|
||||
dataset_aliases = {
|
||||
'cityscapes': ['cityscapes'],
|
||||
'ade': ['ade', 'ade20k'],
|
||||
@ -247,7 +266,8 @@ dataset_aliases = {
|
||||
'cocostuff', 'cocostuff10k', 'cocostuff164k', 'coco-stuff',
|
||||
'coco-stuff10k', 'coco-stuff164k', 'coco_stuff', 'coco_stuff10k',
|
||||
'coco_stuff164k'
|
||||
]
|
||||
],
|
||||
'isaid': ['isaid', 'iSAID']
|
||||
}
|
||||
|
||||
|
||||
|
||||
@ -10,6 +10,7 @@ from .dataset_wrappers import (ConcatDataset, MultiImageMixDataset,
|
||||
RepeatDataset)
|
||||
from .drive import DRIVEDataset
|
||||
from .hrf import HRFDataset
|
||||
from .isaid import iSAIDDataset
|
||||
from .isprs import ISPRSDataset
|
||||
from .loveda import LoveDADataset
|
||||
from .night_driving import NightDrivingDataset
|
||||
@ -25,5 +26,5 @@ __all__ = [
|
||||
'PascalContextDataset59', 'ChaseDB1Dataset', 'DRIVEDataset', 'HRFDataset',
|
||||
'STAREDataset', 'DarkZurichDataset', 'NightDrivingDataset',
|
||||
'COCOStuffDataset', 'LoveDADataset', 'MultiImageMixDataset',
|
||||
'ISPRSDataset', 'PotsdamDataset'
|
||||
'iSAIDDataset', 'ISPRSDataset', 'PotsdamDataset'
|
||||
]
|
||||
|
||||
82
mmseg/datasets/isaid.py
Normal file
82
mmseg/datasets/isaid.py
Normal file
@ -0,0 +1,82 @@
|
||||
import os.path as osp
|
||||
|
||||
import mmcv
|
||||
from mmcv.utils import print_log
|
||||
|
||||
from ..utils import get_root_logger
|
||||
from .builder import DATASETS
|
||||
from .custom import CustomDataset
|
||||
|
||||
|
||||
@DATASETS.register_module()
|
||||
class iSAIDDataset(CustomDataset):
|
||||
""" iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
|
||||
In segmentation map annotation for iSAID dataset, which is included
|
||||
in 16 categories. ``reduce_zero_label`` is fixed to False. The
|
||||
``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to
|
||||
'_manual1.png'.
|
||||
"""
|
||||
|
||||
CLASSES = ('background', 'ship', 'store_tank', 'baseball_diamond',
|
||||
'tennis_court', 'basketball_court', 'Ground_Track_Field',
|
||||
'Bridge', 'Large_Vehicle', 'Small_Vehicle', 'Helicopter',
|
||||
'Swimming_pool', 'Roundabout', 'Soccer_ball_field', 'plane',
|
||||
'Harbor')
|
||||
|
||||
PALETTE = [[0, 0, 0], [0, 0, 63], [0, 63, 63], [0, 63, 0], [0, 63, 127],
|
||||
[0, 63, 191], [0, 63, 255], [0, 127, 63], [0, 127, 127],
|
||||
[0, 0, 127], [0, 0, 191], [0, 0, 255], [0, 191, 127],
|
||||
[0, 127, 191], [0, 127, 255], [0, 100, 155]]
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(iSAIDDataset, self).__init__(
|
||||
img_suffix='.png',
|
||||
seg_map_suffix='.png',
|
||||
ignore_index=255,
|
||||
**kwargs)
|
||||
assert osp.exists(self.img_dir)
|
||||
|
||||
def load_annotations(self,
|
||||
img_dir,
|
||||
img_suffix,
|
||||
ann_dir,
|
||||
seg_map_suffix=None,
|
||||
split=None):
|
||||
"""Load annotation from directory.
|
||||
|
||||
Args:
|
||||
img_dir (str): Path to image directory
|
||||
img_suffix (str): Suffix of images.
|
||||
ann_dir (str|None): Path to annotation directory.
|
||||
seg_map_suffix (str|None): Suffix of segmentation maps.
|
||||
split (str|None): Split txt file. If split is specified, only file
|
||||
with suffix in the splits will be loaded. Otherwise, all images
|
||||
in img_dir/ann_dir will be loaded. Default: None
|
||||
|
||||
Returns:
|
||||
list[dict]: All image info of dataset.
|
||||
"""
|
||||
|
||||
img_infos = []
|
||||
if split is not None:
|
||||
with open(split) as f:
|
||||
for line in f:
|
||||
name = line.strip()
|
||||
img_info = dict(filename=name + img_suffix)
|
||||
if ann_dir is not None:
|
||||
ann_name = name + '_instance_color_RGB'
|
||||
seg_map = ann_name + seg_map_suffix
|
||||
img_info['ann'] = dict(seg_map=seg_map)
|
||||
img_infos.append(img_info)
|
||||
else:
|
||||
for img in mmcv.scandir(img_dir, img_suffix, recursive=True):
|
||||
img_info = dict(filename=img)
|
||||
if ann_dir is not None:
|
||||
seg_img = img
|
||||
seg_map = seg_img.replace(
|
||||
img_suffix, '_instance_color_RGB' + seg_map_suffix)
|
||||
img_info['ann'] = dict(seg_map=seg_map)
|
||||
img_infos.append(img_info)
|
||||
|
||||
print_log(f'Loaded {len(img_infos)} images', logger=get_root_logger())
|
||||
return img_infos
|
||||
@ -16,4 +16,4 @@ default_section = THIRDPARTY
|
||||
skip = *.po,*.ts,*.ipynb
|
||||
count =
|
||||
quiet-level = 3
|
||||
ignore-words-list = formating,sur,hist
|
||||
ignore-words-list = formating,sur,hist,dota
|
||||
|
||||
1
tests/data/pseudo_isaid_dataset/splits/train.txt
Normal file
1
tests/data/pseudo_isaid_dataset/splits/train.txt
Normal file
@ -0,0 +1 @@
|
||||
P0000_0_896_1536_2432
|
||||
1
tests/data/pseudo_isaid_dataset/splits/val.txt
Normal file
1
tests/data/pseudo_isaid_dataset/splits/val.txt
Normal file
@ -0,0 +1 @@
|
||||
P0000_0_896_1024_1920
|
||||
@ -16,7 +16,7 @@ from mmseg.datasets import (DATASETS, ADE20KDataset, CityscapesDataset,
|
||||
COCOStuffDataset, ConcatDataset, CustomDataset,
|
||||
ISPRSDataset, LoveDADataset, MultiImageMixDataset,
|
||||
PascalVOCDataset, PotsdamDataset, RepeatDataset,
|
||||
build_dataset)
|
||||
build_dataset, iSAIDDataset)
|
||||
|
||||
|
||||
def test_classes():
|
||||
@ -25,10 +25,11 @@ def test_classes():
|
||||
'pascal_voc')
|
||||
assert list(
|
||||
ADE20KDataset.CLASSES) == get_classes('ade') == get_classes('ade20k')
|
||||
assert list(COCOStuffDataset.CLASSES) == get_classes('cocostuff')
|
||||
assert list(LoveDADataset.CLASSES) == get_classes('loveda')
|
||||
assert list(PotsdamDataset.CLASSES) == get_classes('potsdam')
|
||||
assert list(ISPRSDataset.CLASSES) == get_classes('vaihingen')
|
||||
assert list(COCOStuffDataset.CLASSES) == get_classes('cocostuff')
|
||||
assert list(iSAIDDataset.CLASSES) == get_classes('isaid')
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
get_classes('unsupported')
|
||||
@ -73,6 +74,7 @@ def test_palette():
|
||||
assert LoveDADataset.PALETTE == get_palette('loveda')
|
||||
assert PotsdamDataset.PALETTE == get_palette('potsdam')
|
||||
assert COCOStuffDataset.PALETTE == get_palette('cocostuff')
|
||||
assert iSAIDDataset.PALETTE == get_palette('isaid')
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
get_palette('unsupported')
|
||||
@ -730,6 +732,27 @@ def test_vaihingen():
|
||||
assert len(test_dataset) == 1
|
||||
|
||||
|
||||
def test_isaid():
|
||||
test_dataset = iSAIDDataset(
|
||||
pipeline=[],
|
||||
img_dir=osp.join(
|
||||
osp.dirname(__file__), '../data/pseudo_isaid_dataset/img_dir'),
|
||||
ann_dir=osp.join(
|
||||
osp.dirname(__file__), '../data/pseudo_isaid_dataset/ann_dir'))
|
||||
assert len(test_dataset) == 2
|
||||
isaid_info = test_dataset.load_annotations(
|
||||
img_dir=osp.join(
|
||||
osp.dirname(__file__), '../data/pseudo_isaid_dataset/img_dir'),
|
||||
img_suffix='.png',
|
||||
ann_dir=osp.join(
|
||||
osp.dirname(__file__), '../data/pseudo_isaid_dataset/ann_dir'),
|
||||
seg_map_suffix='.png',
|
||||
split=osp.join(
|
||||
osp.dirname(__file__),
|
||||
'../data/pseudo_isaid_dataset/splits/train.txt'))
|
||||
assert len(isaid_info) == 1
|
||||
|
||||
|
||||
@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
|
||||
@patch('mmseg.datasets.CustomDataset.__getitem__',
|
||||
MagicMock(side_effect=lambda idx: idx))
|
||||
|
||||
244
tools/convert_datasets/isaid.py
Normal file
244
tools/convert_datasets/isaid.py
Normal file
@ -0,0 +1,244 @@
|
||||
import argparse
|
||||
import glob
|
||||
import os
|
||||
import os.path as osp
|
||||
import shutil
|
||||
import tempfile
|
||||
import zipfile
|
||||
|
||||
import mmcv
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
iSAID_palette = \
|
||||
{
|
||||
0: (0, 0, 0),
|
||||
1: (0, 0, 63),
|
||||
2: (0, 63, 63),
|
||||
3: (0, 63, 0),
|
||||
4: (0, 63, 127),
|
||||
5: (0, 63, 191),
|
||||
6: (0, 63, 255),
|
||||
7: (0, 127, 63),
|
||||
8: (0, 127, 127),
|
||||
9: (0, 0, 127),
|
||||
10: (0, 0, 191),
|
||||
11: (0, 0, 255),
|
||||
12: (0, 191, 127),
|
||||
13: (0, 127, 191),
|
||||
14: (0, 127, 255),
|
||||
15: (0, 100, 155)
|
||||
}
|
||||
|
||||
iSAID_invert_palette = {v: k for k, v in iSAID_palette.items()}
|
||||
|
||||
|
||||
def iSAID_convert_from_color(arr_3d, palette=iSAID_invert_palette):
|
||||
"""RGB-color encoding to grayscale labels."""
|
||||
arr_2d = np.zeros((arr_3d.shape[0], arr_3d.shape[1]), dtype=np.uint8)
|
||||
|
||||
for c, i in palette.items():
|
||||
m = np.all(arr_3d == np.array(c).reshape(1, 1, 3), axis=2)
|
||||
arr_2d[m] = i
|
||||
|
||||
return arr_2d
|
||||
|
||||
|
||||
def slide_crop_image(src_path, out_dir, mode, patch_H, patch_W, overlap):
|
||||
img = np.asarray(Image.open(src_path).convert('RGB'))
|
||||
|
||||
img_H, img_W, _ = img.shape
|
||||
|
||||
if img_H < patch_H and img_W > patch_W:
|
||||
|
||||
img = mmcv.impad(img, shape=(patch_H, img_W), pad_val=0)
|
||||
|
||||
img_H, img_W, _ = img.shape
|
||||
|
||||
elif img_H > patch_H and img_W < patch_W:
|
||||
|
||||
img = mmcv.impad(img, shape=(img_H, patch_W), pad_val=0)
|
||||
|
||||
img_H, img_W, _ = img.shape
|
||||
|
||||
elif img_H < patch_H and img_W < patch_W:
|
||||
|
||||
img = mmcv.impad(img, shape=(patch_H, patch_W), pad_val=0)
|
||||
|
||||
img_H, img_W, _ = img.shape
|
||||
|
||||
for x in range(0, img_W, patch_W - overlap):
|
||||
for y in range(0, img_H, patch_H - overlap):
|
||||
x_str = x
|
||||
x_end = x + patch_W
|
||||
if x_end > img_W:
|
||||
diff_x = x_end - img_W
|
||||
x_str -= diff_x
|
||||
x_end = img_W
|
||||
y_str = y
|
||||
y_end = y + patch_H
|
||||
if y_end > img_H:
|
||||
diff_y = y_end - img_H
|
||||
y_str -= diff_y
|
||||
y_end = img_H
|
||||
|
||||
img_patch = img[y_str:y_end, x_str:x_end, :]
|
||||
img_patch = Image.fromarray(img_patch.astype(np.uint8))
|
||||
image = osp.splitext(
|
||||
src_path.split('/')[-1])[0] + '_' + str(y_str) + '_' + str(
|
||||
y_end) + '_' + str(x_str) + '_' + str(x_end) + '.png'
|
||||
# print(image)
|
||||
save_path_image = osp.join(out_dir, 'img_dir', mode, str(image))
|
||||
img_patch.save(save_path_image)
|
||||
|
||||
|
||||
def slide_crop_label(src_path, out_dir, mode, patch_H, patch_W, overlap):
|
||||
label = mmcv.imread(src_path, channel_order='rgb')
|
||||
label = iSAID_convert_from_color(label)
|
||||
img_H, img_W = label.shape
|
||||
|
||||
if img_H < patch_H and img_W > patch_W:
|
||||
|
||||
label = mmcv.impad(label, shape=(patch_H, img_W), pad_val=255)
|
||||
|
||||
img_H = patch_H
|
||||
|
||||
elif img_H > patch_H and img_W < patch_W:
|
||||
|
||||
label = mmcv.impad(label, shape=(img_H, patch_W), pad_val=255)
|
||||
|
||||
img_W = patch_W
|
||||
|
||||
elif img_H < patch_H and img_W < patch_W:
|
||||
|
||||
label = mmcv.impad(label, shape=(patch_H, patch_W), pad_val=255)
|
||||
|
||||
img_H = patch_H
|
||||
img_W = patch_W
|
||||
|
||||
for x in range(0, img_W, patch_W - overlap):
|
||||
for y in range(0, img_H, patch_H - overlap):
|
||||
x_str = x
|
||||
x_end = x + patch_W
|
||||
if x_end > img_W:
|
||||
diff_x = x_end - img_W
|
||||
x_str -= diff_x
|
||||
x_end = img_W
|
||||
y_str = y
|
||||
y_end = y + patch_H
|
||||
if y_end > img_H:
|
||||
diff_y = y_end - img_H
|
||||
y_str -= diff_y
|
||||
y_end = img_H
|
||||
|
||||
lab_patch = label[y_str:y_end, x_str:x_end]
|
||||
lab_patch = Image.fromarray(lab_patch.astype(np.uint8), mode='P')
|
||||
|
||||
image = osp.splitext(src_path.split('/')[-1])[0].split(
|
||||
'_')[0] + '_' + str(y_str) + '_' + str(y_end) + '_' + str(
|
||||
x_str) + '_' + str(x_end) + '_instance_color_RGB' + '.png'
|
||||
lab_patch.save(osp.join(out_dir, 'ann_dir', mode, str(image)))
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(
|
||||
description='Convert iSAID dataset to mmsegmentation format')
|
||||
parser.add_argument('dataset_path', help='iSAID folder path')
|
||||
parser.add_argument('--tmp_dir', help='path of the temporary directory')
|
||||
parser.add_argument('-o', '--out_dir', help='output path')
|
||||
|
||||
parser.add_argument(
|
||||
'--patch_width',
|
||||
default=896,
|
||||
type=int,
|
||||
help='Width of the cropped image patch')
|
||||
parser.add_argument(
|
||||
'--patch_height',
|
||||
default=896,
|
||||
type=int,
|
||||
help='Height of the cropped image patch')
|
||||
parser.add_argument(
|
||||
'--overlap_area', default=384, type=int, help='Overlap area')
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
dataset_path = args.dataset_path
|
||||
# image patch width and height
|
||||
patch_H, patch_W = args.patch_width, args.patch_height
|
||||
|
||||
overlap = args.overlap_area # overlap area
|
||||
|
||||
if args.out_dir is None:
|
||||
out_dir = osp.join('data', 'iSAID')
|
||||
else:
|
||||
out_dir = args.out_dir
|
||||
|
||||
print('Making directories...')
|
||||
mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
|
||||
mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
|
||||
mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'test'))
|
||||
|
||||
mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
|
||||
mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
|
||||
mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'test'))
|
||||
|
||||
assert os.path.exists(os.path.join(dataset_path, 'train')), \
|
||||
'train is not in {}'.format(dataset_path)
|
||||
assert os.path.exists(os.path.join(dataset_path, 'val')), \
|
||||
'val is not in {}'.format(dataset_path)
|
||||
assert os.path.exists(os.path.join(dataset_path, 'test')), \
|
||||
'test is not in {}'.format(dataset_path)
|
||||
|
||||
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
|
||||
for dataset_mode in ['train', 'val', 'test']:
|
||||
|
||||
# for dataset_mode in [ 'test']:
|
||||
print('Extracting {}ing.zip...'.format(dataset_mode))
|
||||
img_zipp_list = glob.glob(
|
||||
os.path.join(dataset_path, dataset_mode, 'images', '*.zip'))
|
||||
print('Find the data', img_zipp_list)
|
||||
for img_zipp in img_zipp_list:
|
||||
zip_file = zipfile.ZipFile(img_zipp)
|
||||
zip_file.extractall(os.path.join(tmp_dir, dataset_mode, 'img'))
|
||||
src_path_list = glob.glob(
|
||||
os.path.join(tmp_dir, dataset_mode, 'img', 'images', '*.png'))
|
||||
|
||||
src_prog_bar = mmcv.ProgressBar(len(src_path_list))
|
||||
for i, img_path in enumerate(src_path_list):
|
||||
if dataset_mode != 'test':
|
||||
slide_crop_image(img_path, out_dir, dataset_mode, patch_H,
|
||||
patch_W, overlap)
|
||||
|
||||
else:
|
||||
shutil.move(img_path,
|
||||
os.path.join(out_dir, 'img_dir', dataset_mode))
|
||||
src_prog_bar.update()
|
||||
|
||||
if dataset_mode != 'test':
|
||||
label_zipp_list = glob.glob(
|
||||
os.path.join(dataset_path, dataset_mode, 'Semantic_masks',
|
||||
'*.zip'))
|
||||
for label_zipp in label_zipp_list:
|
||||
zip_file = zipfile.ZipFile(label_zipp)
|
||||
zip_file.extractall(
|
||||
os.path.join(tmp_dir, dataset_mode, 'lab'))
|
||||
|
||||
lab_path_list = glob.glob(
|
||||
os.path.join(tmp_dir, dataset_mode, 'lab', 'images',
|
||||
'*.png'))
|
||||
lab_prog_bar = mmcv.ProgressBar(len(lab_path_list))
|
||||
for i, lab_path in enumerate(lab_path_list):
|
||||
slide_crop_label(lab_path, out_dir, dataset_mode, patch_H,
|
||||
patch_W, overlap)
|
||||
lab_prog_bar.update()
|
||||
|
||||
print('Removing the temporary files...')
|
||||
|
||||
print('Done!')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Loading…
x
Reference in New Issue
Block a user