[Feature] Support Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets (#2194)
add custom dataset add face occlusion dataset add config file for occlusion face fix format update prepare.md formatting formatting fix typo error for doc update downloading process Update dataset_prepare.md PR fix version to original repository. change to original repository.pull/2489/head
jinwonkim93
MeowZheng
parent
7b09967fcc
commit
a1d8174017
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dataset_type = 'FaceOccludedDataset'
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data_root = 'data/occlusion-aware-face-dataset'
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crop_size = (512, 512)
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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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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=(512, 512)),
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dict(type='RandomFlip', prob=0.5),
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dict(type='RandomRotate', degree=(-30, 30), prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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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=(512, 512),
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img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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flip=True,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='ResizeToMultiple', size_divisor=32),
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dict(type='RandomFlip'),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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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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dataset_train_A = dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='NatOcc_hand_sot/img',
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ann_dir='NatOcc_hand_sot/mask',
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split='train.txt',
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pipeline=train_pipeline)
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dataset_train_B = dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='NatOcc_object/img',
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ann_dir='NatOcc_object/mask',
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split='train.txt',
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pipeline=train_pipeline)
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dataset_train_C = dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='RandOcc/img',
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ann_dir='RandOcc/mask',
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split='train.txt',
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pipeline=train_pipeline)
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dataset_valid = dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='RealOcc/image',
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ann_dir='RealOcc/mask',
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split='RealOcc/split/val.txt',
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pipeline=test_pipeline)
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data = dict(
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samples_per_gpu=2,
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workers_per_gpu=2,
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train=[dataset_train_A, dataset_train_B, dataset_train_C],
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val=dataset_valid)
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# +
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_base_ = '../_base_/datasets/occlude_face.py'
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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model = dict(
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type='EncoderDecoder',
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pretrained='open-mmlab://resnet101_v1c',
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backbone=dict(
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type='ResNetV1c',
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depth=101,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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dilations=(1, 1, 2, 4),
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strides=(1, 2, 1, 1),
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norm_cfg=dict(type='SyncBN', requires_grad=True),
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norm_eval=False,
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style='pytorch',
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contract_dilation=True),
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decode_head=dict(
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type='DepthwiseSeparableASPPHead',
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in_channels=2048,
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in_index=3,
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channels=512,
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dilations=(1, 12, 24, 36),
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c1_in_channels=256,
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c1_channels=48,
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dropout_ratio=0.1,
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num_classes=2,
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norm_cfg=dict(type='SyncBN', requires_grad=True),
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
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sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000)),
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auxiliary_head=dict(
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type='FCNHead',
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in_channels=1024,
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in_index=2,
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channels=256,
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num_convs=1,
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concat_input=False,
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dropout_ratio=0.1,
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num_classes=2,
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norm_cfg=dict(type='SyncBN', requires_grad=True),
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
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train_cfg=dict(),
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test_cfg=dict(mode='whole'))
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log_config = dict(
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interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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load_from = None
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resume_from = None
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workflow = [('train', 1)]
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cudnn_benchmark = True
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optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
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optimizer_config = dict()
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lr_config = dict(policy='poly', power=0.9, min_lr=0.0001, by_epoch=False)
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runner = dict(type='IterBasedRunner', max_iters=30000)
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checkpoint_config = dict(by_epoch=False, interval=400)
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evaluation = dict(
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interval=400, metric=['mIoU', 'mDice', 'mFscore'], pre_eval=True)
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auto_resume = False
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@ -1,3 +1,5 @@
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<!-- #region -->
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## Prepare datasets
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It is recommended to symlink the dataset root to `$MMSEGMENTATION/data`.
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@ -138,6 +140,21 @@ mmsegmentation
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│ │ ├── ann_dir
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│ │ │ ├── train
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│ │ │ ├── val
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│ ├── occlusion-aware-face-dataset
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│ │ ├── train.txt
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│ │ ├── NatOcc_hand_sot
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│ │ │ ├── img
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│ │ │ ├── mask
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│ │ ├── NatOcc_object
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│ │ │ ├── img
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│ │ │ ├── mask
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│ │ ├── RandOcc
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│ │ │ ├── img
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│ │ │ ├── mask
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│ │ ├── RealOcc
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│ │ │ ├── img
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│ │ │ ├── mask
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│ │ │ ├── split
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```
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### Cityscapes
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@ -376,3 +393,190 @@ python tools/convert_datasets/isaid.py /path/to/iSAID
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```
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In our default setting (`patch_width`=896, `patch_height`=896, `overlap_area`=384), it will generate 33978 images for training and 11644 images for validation.
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### Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets
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The dataset is generated by two techniques, Naturalistic occlusion generation, Random occlusion generation. you must install face-occlusion-generation and dataset. see more guide in https://github.com/kennyvoo/face-occlusion-generation.git
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## Dataset Preparation
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step 1
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Create a folder for data generation materials on mmsegmentation folder.
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```shell
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mkdir data_materials
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```
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step 2
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Please download the masks (11k-hands_mask.7z,CelebAMask-HQ-masks_corrected.7z) from this [drive](https://drive.google.com/drive/folders/15nZETWlGMdcKY6aHbchRsWkUI42KTNs5?usp=sharing)
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Please download the images from [CelebAMask-HQ](https://github.com/switchablenorms/CelebAMask-HQ), [11k Hands.zip](https://sites.google.com/view/11khands) and [dtd-r1.0.1.tar.gz](https://www.robots.ox.ac.uk/~vgg/data/dtd/).
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step 3
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Download a upsampled COCO objects images and masks (coco_object.7z). files can be found in this [drive](https://drive.google.com/drive/folders/15nZETWlGMdcKY6aHbchRsWkUI42KTNs5?usp=sharing).
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Download CelebAMask-HQ and 11k Hands images split txt files. (11k_hands_sample.txt, CelebAMask-HQ-WO-train.txt) found in [drive](https://drive.google.com/drive/folders/15nZETWlGMdcKY6aHbchRsWkUI42KTNs5?usp=sharing).
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download file to ./data_materials
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```none
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CelebAMask-HQ.zip
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CelebAMask-HQ-masks_corrected.7z
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CelebAMask-HQ-WO-train.txt
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RealOcc.7z
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RealOcc-Wild.7z
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11k-hands_mask.7z
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11k Hands.zip
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11k_hands_sample.txt
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coco_object.7z
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dtd-r1.0.1.tar.gz
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```
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______________________________________________________________________
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```bash
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apt-get install p7zip-full
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cd data_materials
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#make occlusion-aware-face-dataset folder
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mkdir path-to-mmsegmentaion/data/occlusion-aware-face-dataset
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#extract celebAMask-HQ and split by train-set
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unzip CelebAMask-HQ.zip
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7za x CelebAMask-HQ-masks_corrected.7z -o./CelebAMask-HQ
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#copy training data to train-image-folder
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rsync -a ./CelebAMask-HQ/CelebA-HQ-img/ --files-from=./CelebAMask-HQ-WO-train.txt ./CelebAMask-HQ-WO-Train_img
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#create a file-name txt file for copying mask
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basename -s .jpg ./CelebAMask-HQ-WO-Train_img/* > train.txt
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#add .png to file-name txt file
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xargs -n 1 -i echo {}.png < train.txt > mask_train.txt
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#copy training data to train-mask-folder
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rsync -a ./CelebAMask-HQ/CelebAMask-HQ-masks_corrected/ --files-from=./mask_train.txt ./CelebAMask-HQ-WO-Train_mask
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mv train.txt ../data/occlusion-aware-face-dataset
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#extract DTD
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tar -zxvf dtd-r1.0.1.tar.gz
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mv dtd DTD
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#extract hands dataset and split by 200 samples
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7za x 11k-hands_masks.7z -o.
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unzip Hands.zip
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rsync -a ./Hands/ --files-from=./11k_hands_sample.txt ./11k-hands_img
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#extract upscaled coco object
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7za x coco_object.7z -o.
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mv coco_object/* .
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#extract validation set
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7za x RealOcc.7z -o../data/occlusion-aware-face-dataset
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```
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**Dataset material Organization:**
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```none
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├── data_materials
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│ ├── CelebAMask-HQ-WO-Train_img
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│ │ ├── {image}.jpg
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│ ├── CelebAMask-HQ-WO-Train_mask
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│ │ ├── {mask}.png
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│ ├── DTD
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│ │ ├── images
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│ │ │ ├── {classA}
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│ │ │ │ ├── {image}.jpg
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│ │ │ ├── {classB}
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│ │ │ │ ├── {image}.jpg
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│ ├── 11k-hands_img
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│ │ ├── {image}.jpg
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│ ├── 11k-hands_mask
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│ │ ├── {mask}.png
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│ ├── object_image_sr
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│ │ ├── {image}.jpg
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│ ├── object_mask_x4
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│ │ ├── {mask}.png
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```
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## Data Generation
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```bash
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git clone https://github.com/kennyvoo/face-occlusion-generation.git
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cd face_occlusion-generation
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```
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Example script to generate NatOcc hand dataset
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```bash
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CUDA_VISIBLE_DEVICES=0 NUM_WORKERS=4 python main.py \
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--config ./configs/natocc_hand.yaml \
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--opts OUTPUT_PATH "path/to/mmsegmentation/data/occlusion-aware-face-dataset/NatOcc_hand_sot"\
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AUGMENTATION.SOT True \
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SOURCE_DATASET.IMG_DIR "path/to/data_materials/CelebAMask-HQ-WO-Train_img" \
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SOURCE_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_mask" \
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OCCLUDER_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/11k-hands_img" \
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OCCLUDER_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/11k-hands_masks"
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```
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Example script to generate NatOcc object dataset
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```bash
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CUDA_VISIBLE_DEVICES=0 NUM_WORKERS=4 python main.py \
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--config ./configs/natocc_objects.yaml \
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--opts OUTPUT_PATH "path/to/mmsegmentation/data/occlusion-aware-face-dataset/NatOcc_object" \
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SOURCE_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_img" \
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SOURCE_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_mask" \
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OCCLUDER_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/object_image_sr" \
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OCCLUDER_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/object_mask_x4"
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```
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Example script to generate RandOcc dataset
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```bash
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CUDA_VISIBLE_DEVICES=0 NUM_WORKERS=4 python main.py \
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--config ./configs/randocc.yaml \
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--opts OUTPUT_PATH "path/to/mmsegmentation/data/occlusion-aware-face-dataset/RandOcc" \
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SOURCE_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_img/" \
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SOURCE_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_mask" \
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OCCLUDER_DATASET.IMG_DIR "path/to/jw93/mmsegmentation/data_materials/DTD/images"
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```
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**Dataset Organization:**
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```none
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├── data
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│ ├── occlusion-aware-face-dataset
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│ │ ├── train.txt
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│ │ ├── NatOcc_hand_sot
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│ │ │ ├── img
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│ │ │ │ ├── {image}.jpg
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│ │ │ ├── mask
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│ │ │ │ ├── {mask}.png
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│ │ ├── NatOcc_object
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│ │ │ ├── img
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│ │ │ │ ├── {image}.jpg
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│ │ │ ├── mask
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│ │ │ │ ├── {mask}.png
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│ │ ├── RandOcc
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│ │ │ ├── img
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│ │ │ │ ├── {image}.jpg
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│ │ │ ├── mask
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│ │ │ │ ├── {mask}.png
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│ │ ├── RealOcc
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│ │ │ ├── img
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│ │ │ │ ├── {image}.jpg
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│ │ │ ├── mask
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│ │ │ │ ├── {mask}.png
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│ │ │ ├── split
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│ │ │ │ ├── val.txt
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```
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<!-- #endregion -->
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```python
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```
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@ -9,6 +9,7 @@ from .dark_zurich import DarkZurichDataset
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from .dataset_wrappers import (ConcatDataset, MultiImageMixDataset,
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RepeatDataset)
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from .drive import DRIVEDataset
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from .face import FaceOccludedDataset
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from .hrf import HRFDataset
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from .isaid import iSAIDDataset
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from .isprs import ISPRSDataset
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@ -26,5 +27,5 @@ __all__ = [
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'PascalContextDataset59', 'ChaseDB1Dataset', 'DRIVEDataset', 'HRFDataset',
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'STAREDataset', 'DarkZurichDataset', 'NightDrivingDataset',
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'COCOStuffDataset', 'LoveDADataset', 'MultiImageMixDataset',
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'iSAIDDataset', 'ISPRSDataset', 'PotsdamDataset'
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'iSAIDDataset', 'ISPRSDataset', 'PotsdamDataset', 'FaceOccludedDataset'
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]
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@ -0,0 +1,23 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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from .builder import DATASETS
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from .custom import CustomDataset
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@DATASETS.register_module()
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class FaceOccludedDataset(CustomDataset):
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"""Face Occluded dataset.
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Args:
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split (str): Split txt file for Pascal VOC.
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"""
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CLASSES = ('background', 'face')
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PALETTE = [[0, 0, 0], [128, 0, 0]]
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def __init__(self, split, **kwargs):
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super(FaceOccludedDataset, self).__init__(
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img_suffix='.jpg', seg_map_suffix='.png', split=split, **kwargs)
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assert osp.exists(self.img_dir) and self.split is not None
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