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[Dataset] Support GID dataset on project (#3038)
## Motivation Support GID dataset on project --------- Co-authored-by: 谢昕辰 <xiexinch@outlook.com>
This commit is contained in:
Tianlong Ai
GitHub
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67
projects/gid_dataset/configs/_base_/datasets/gid.py
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67
projects/gid_dataset/configs/_base_/datasets/gid.py
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# dataset settings
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dataset_type = 'GID_Dataset' # 注册的类名
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data_root = 'data/gid/' # 数据集根目录
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crop_size = (256, 256) # 图像裁剪大小
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train_pipeline = [
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dict(type='LoadImageFromFile'), # 从文件中加载图像
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dict(type='LoadAnnotations'), # 从文件中加载标注
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dict(
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type='RandomResize', # 随机缩放
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scale=(512, 512), # 缩放尺寸
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ratio_range=(0.5, 2.0), # 缩放比例范围
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keep_ratio=True), # 是否保持长宽比
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), # 随机裁剪
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dict(type='RandomFlip', prob=0.5), # 随机翻转
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dict(type='PhotoMetricDistortion'), # 图像增强
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dict(type='PackSegInputs') # 打包数据
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'), # 从文件中加载图像
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dict(type='Resize', scale=(256, 256), keep_ratio=True), # 缩放
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# add loading annotation after ``Resize`` because ground truth
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# does not need to do resize data transform
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dict(type='LoadAnnotations'), # 从文件中加载标注
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dict(type='PackSegInputs') # 打包数据
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]
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img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] # 多尺度预测缩放比例
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tta_pipeline = [ # 多尺度测试
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dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
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dict(
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type='TestTimeAug',
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transforms=[
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[
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dict(type='Resize', scale_factor=r, keep_ratio=True)
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for r in img_ratios
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],
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[
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dict(type='RandomFlip', prob=0., direction='horizontal'),
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dict(type='RandomFlip', prob=1., direction='horizontal')
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], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')]
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])
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]
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train_dataloader = dict( # 训练数据加载器
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batch_size=2, # 训练时的数据批量大小
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num_workers=4, # 数据加载线程数
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persistent_workers=True, # 是否持久化线程
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sampler=dict(type='InfiniteSampler', shuffle=True), # 无限采样器
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dataset=dict(
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type=dataset_type, # 数据集类名
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data_root=data_root, # 数据集根目录
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data_prefix=dict(
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img_path='img_dir/train',
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seg_map_path='ann_dir/train'), # 训练集图像和标注路径
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pipeline=train_pipeline))
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val_dataloader = dict(
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batch_size=1, # 验证时的数据批量大小
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num_workers=4, # 数据加载线程数
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
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pipeline=test_pipeline))
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test_dataloader = val_dataloader
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val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
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test_evaluator = val_evaluator
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_base_ = [
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'../../../configs/_base_/models/deeplabv3plus_r50-d8.py',
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'./_base_/datasets/gid.py', '../../../configs/_base_/default_runtime.py',
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'../../../configs/_base_/schedules/schedule_240k.py'
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]
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custom_imports = dict(imports=['projects.gid_dataset.mmseg.datasets.gid'])
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crop_size = (256, 256)
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data_preprocessor = dict(size=crop_size)
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model = dict(
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data_preprocessor=data_preprocessor,
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pretrained='open-mmlab://resnet101_v1c',
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backbone=dict(depth=101),
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decode_head=dict(num_classes=6),
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auxiliary_head=dict(num_classes=6))
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55
projects/gid_dataset/mmseg/datasets/gid.py
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projects/gid_dataset/mmseg/datasets/gid.py
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# Copyright (c) OpenMMLab. All rights reserved.
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from mmseg.datasets.basesegdataset import BaseSegDataset
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from mmseg.registry import DATASETS
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# 注册数据集类
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@DATASETS.register_module()
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class GID_Dataset(BaseSegDataset):
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"""Gaofen Image Dataset (GID)
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Dataset paper link:
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https://www.sciencedirect.com/science/article/pii/S0034425719303414
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https://x-ytong.github.io/project/GID.html
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GID 6 classes: others, built-up, farmland, forest, meadow, water
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In this example, select 15 images from GID dataset as training set,
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and select 5 images as validation set.
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The selected images are listed as follows:
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GF2_PMS1__L1A0000647767-MSS1
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GF2_PMS1__L1A0001064454-MSS1
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GF2_PMS1__L1A0001348919-MSS1
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GF2_PMS1__L1A0001680851-MSS1
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GF2_PMS1__L1A0001680853-MSS1
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GF2_PMS1__L1A0001680857-MSS1
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GF2_PMS1__L1A0001757429-MSS1
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GF2_PMS2__L1A0000607681-MSS2
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GF2_PMS2__L1A0000635115-MSS2
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GF2_PMS2__L1A0000658637-MSS2
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GF2_PMS2__L1A0001206072-MSS2
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GF2_PMS2__L1A0001471436-MSS2
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GF2_PMS2__L1A0001642620-MSS2
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GF2_PMS2__L1A0001787089-MSS2
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GF2_PMS2__L1A0001838560-MSS2
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The ``img_suffix`` is fixed to '.tif' and ``seg_map_suffix`` is
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fixed to '.tif' for GID.
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"""
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METAINFO = dict(
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classes=('Others', 'Built-up', 'Farmland', 'Forest', 'Meadow',
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'Water'),
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palette=[[0, 0, 0], [255, 0, 0], [0, 255, 0], [0, 255, 255],
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[255, 255, 0], [0, 0, 255]])
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def __init__(self,
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img_suffix='.png',
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seg_map_suffix='.png',
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reduce_zero_label=None,
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**kwargs) -> None:
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super().__init__(
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img_suffix=img_suffix,
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seg_map_suffix=seg_map_suffix,
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reduce_zero_label=reduce_zero_label,
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**kwargs)
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181
projects/gid_dataset/tools/dataset_converters/gid.py
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projects/gid_dataset/tools/dataset_converters/gid.py
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import argparse
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import glob
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import math
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import os
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import os.path as osp
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import mmcv
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import numpy as np
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from mmengine.utils import ProgressBar, mkdir_or_exist
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Convert GID dataset to mmsegmentation format')
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parser.add_argument('dataset_img_path', help='GID images folder path')
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parser.add_argument('dataset_label_path', help='GID labels folder path')
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parser.add_argument('--tmp_dir', help='path of the temporary directory')
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parser.add_argument(
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'-o', '--out_dir', help='output path', default='data/gid')
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parser.add_argument(
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'--clip_size',
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type=int,
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help='clipped size of image after preparation',
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default=256)
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parser.add_argument(
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'--stride_size',
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type=int,
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help='stride of clipping original images',
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default=256)
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args = parser.parse_args()
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return args
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GID_COLORMAP = dict(
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Background=(0, 0, 0), # 0-背景-黑色
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Building=(255, 0, 0), # 1-建筑-红色
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Farmland=(0, 255, 0), # 2-农田-绿色
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Forest=(0, 0, 255), # 3-森林-蓝色
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Meadow=(255, 255, 0), # 4-草地-黄色
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Water=(0, 0, 255) # 5-水-蓝色
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)
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palette = list(GID_COLORMAP.values())
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classes = list(GID_COLORMAP.keys())
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# 用列表来存一个 RGB 和一个类别的对应
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def colormap2label(palette):
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colormap2label_list = np.zeros(256**3, dtype=np.longlong)
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for i, colormap in enumerate(palette):
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colormap2label_list[(colormap[0] * 256 + colormap[1]) * 256 +
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colormap[2]] = i
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return colormap2label_list
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# 给定那个列表，和vis_png然后生成masks_png
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def label_indices(RGB_label, colormap2label_list):
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RGB_label = RGB_label.astype('int32')
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idx = (RGB_label[:, :, 0] * 256 +
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RGB_label[:, :, 1]) * 256 + RGB_label[:, :, 2]
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return colormap2label_list[idx]
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def RGB2mask(RGB_label, colormap2label_list):
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mask_label = label_indices(RGB_label, colormap2label_list)
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return mask_label
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colormap2label_list = colormap2label(palette)
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def clip_big_image(image_path, clip_save_dir, args, to_label=False):
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"""Original image of GID dataset is very large, thus pre-processing of them
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is adopted.
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Given fixed clip size and stride size to generate
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clipped image, the intersection of width and height is determined.
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For example, given one 6800 x 7200 original image, the clip size is
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256 and stride size is 256, thus it would generate 29 x 27 = 783 images
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whose size are all 256 x 256.
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"""
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image = mmcv.imread(image_path, channel_order='rgb')
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# image = mmcv.bgr2gray(image)
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h, w, c = image.shape
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clip_size = args.clip_size
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stride_size = args.stride_size
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num_rows = math.ceil((h - clip_size) / stride_size) if math.ceil(
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(h - clip_size) /
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stride_size) * stride_size + clip_size >= h else math.ceil(
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(h - clip_size) / stride_size) + 1
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num_cols = math.ceil((w - clip_size) / stride_size) if math.ceil(
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(w - clip_size) /
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stride_size) * stride_size + clip_size >= w else math.ceil(
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(w - clip_size) / stride_size) + 1
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x, y = np.meshgrid(np.arange(num_cols + 1), np.arange(num_rows + 1))
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xmin = x * clip_size
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ymin = y * clip_size
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xmin = xmin.ravel()
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ymin = ymin.ravel()
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xmin_offset = np.where(xmin + clip_size > w, w - xmin - clip_size,
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np.zeros_like(xmin))
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ymin_offset = np.where(ymin + clip_size > h, h - ymin - clip_size,
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np.zeros_like(ymin))
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boxes = np.stack([
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xmin + xmin_offset, ymin + ymin_offset,
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np.minimum(xmin + clip_size, w),
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np.minimum(ymin + clip_size, h)
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],
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axis=1)
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if to_label:
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image = RGB2mask(image, colormap2label_list)
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for count, box in enumerate(boxes):
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start_x, start_y, end_x, end_y = box
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clipped_image = image[start_y:end_y,
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start_x:end_x] if to_label else image[
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start_y:end_y, start_x:end_x, :]
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img_name = osp.basename(image_path).replace('.tif', '')
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img_name = img_name.replace('_label', '')
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if count % 3 == 0:
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mmcv.imwrite(
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clipped_image.astype(np.uint8),
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osp.join(
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clip_save_dir.replace('train', 'val'),
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f'{img_name}_{start_x}_{start_y}_{end_x}_{end_y}.png'))
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else:
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mmcv.imwrite(
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clipped_image.astype(np.uint8),
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osp.join(
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clip_save_dir,
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f'{img_name}_{start_x}_{start_y}_{end_x}_{end_y}.png'))
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count += 1
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def main():
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args = parse_args()
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"""
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According to this paper: https://ieeexplore.ieee.org/document/9343296/
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select 15 images contained in GID, , which cover the whole six
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categories, to generate train set and validation set.
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"""
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if args.out_dir is None:
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out_dir = osp.join('data', 'gid')
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else:
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out_dir = args.out_dir
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print('Making directories...')
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mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
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mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
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mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
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mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
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src_path_list = glob.glob(os.path.join(args.dataset_img_path, '*.tif'))
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print(f'Find {len(src_path_list)} pictures')
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prog_bar = ProgressBar(len(src_path_list))
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dst_img_dir = osp.join(out_dir, 'img_dir', 'train')
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dst_label_dir = osp.join(out_dir, 'ann_dir', 'train')
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for i, img_path in enumerate(src_path_list):
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label_path = osp.join(
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args.dataset_label_path,
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osp.basename(img_path.replace('.tif', '_label.tif')))
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clip_big_image(img_path, dst_img_dir, args, to_label=False)
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clip_big_image(label_path, dst_label_dir, args, to_label=True)
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prog_bar.update()
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print('Done!')
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if __name__ == '__main__':
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main()
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import argparse
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import os
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import shutil
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# select 15 images from GID dataset
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img_list = [
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'GF2_PMS1__L1A0000647767-MSS1.tif', 'GF2_PMS1__L1A0001064454-MSS1.tif',
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'GF2_PMS1__L1A0001348919-MSS1.tif', 'GF2_PMS1__L1A0001680851-MSS1.tif',
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'GF2_PMS1__L1A0001680853-MSS1.tif', 'GF2_PMS1__L1A0001680857-MSS1.tif',
|
||||||
|
'GF2_PMS1__L1A0001757429-MSS1.tif', 'GF2_PMS2__L1A0000607681-MSS2.tif',
|
||||||
|
'GF2_PMS2__L1A0000635115-MSS2.tif', 'GF2_PMS2__L1A0000658637-MSS2.tif',
|
||||||
|
'GF2_PMS2__L1A0001206072-MSS2.tif', 'GF2_PMS2__L1A0001471436-MSS2.tif',
|
||||||
|
'GF2_PMS2__L1A0001642620-MSS2.tif', 'GF2_PMS2__L1A0001787089-MSS2.tif',
|
||||||
|
'GF2_PMS2__L1A0001838560-MSS2.tif'
|
||||||
|
]
|
||||||
|
|
||||||
|
labels_list = [
|
||||||
|
'GF2_PMS1__L1A0000647767-MSS1_label.tif',
|
||||||
|
'GF2_PMS1__L1A0001064454-MSS1_label.tif',
|
||||||
|
'GF2_PMS1__L1A0001348919-MSS1_label.tif',
|
||||||
|
'GF2_PMS1__L1A0001680851-MSS1_label.tif',
|
||||||
|
'GF2_PMS1__L1A0001680853-MSS1_label.tif',
|
||||||
|
'GF2_PMS1__L1A0001680857-MSS1_label.tif',
|
||||||
|
'GF2_PMS1__L1A0001757429-MSS1_label.tif',
|
||||||
|
'GF2_PMS2__L1A0000607681-MSS2_label.tif',
|
||||||
|
'GF2_PMS2__L1A0000635115-MSS2_label.tif',
|
||||||
|
'GF2_PMS2__L1A0000658637-MSS2_label.tif',
|
||||||
|
'GF2_PMS2__L1A0001206072-MSS2_label.tif',
|
||||||
|
'GF2_PMS2__L1A0001471436-MSS2_label.tif',
|
||||||
|
'GF2_PMS2__L1A0001642620-MSS2_label.tif',
|
||||||
|
'GF2_PMS2__L1A0001787089-MSS2_label.tif',
|
||||||
|
'GF2_PMS2__L1A0001838560-MSS2_label.tif'
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
def parse_args():
|
||||||
|
parser = argparse.ArgumentParser(
|
||||||
|
description='From 150 images of GID dataset to select 15 images')
|
||||||
|
parser.add_argument('dataset_img_dir', help='150 GID images folder path')
|
||||||
|
parser.add_argument('dataset_label_dir', help='150 GID labels folder path')
|
||||||
|
|
||||||
|
parser.add_argument('dest_img_dir', help='15 GID images folder path')
|
||||||
|
parser.add_argument('dest_label_dir', help='15 GID labels folder path')
|
||||||
|
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
return args
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""This script is used to select 15 images from GID dataset, According to
|
||||||
|
paper: https://ieeexplore.ieee.org/document/9343296/"""
|
||||||
|
args = parse_args()
|
||||||
|
|
||||||
|
img_path = args.dataset_img_dir
|
||||||
|
label_path = args.dataset_label_dir
|
||||||
|
|
||||||
|
dest_img_dir = args.dest_img_dir
|
||||||
|
dest_label_dir = args.dest_label_dir
|
||||||
|
|
||||||
|
# copy images of 'img_list' to 'desr_dir'
|
||||||
|
print('Copy images of img_list to desr_dir ing...')
|
||||||
|
for img in img_list:
|
||||||
|
shutil.copy(os.path.join(img_path, img), dest_img_dir)
|
||||||
|
print('Done!')
|
||||||
|
|
||||||
|
print('copy labels of labels_list to desr_dir ing...')
|
||||||
|
for label in labels_list:
|
||||||
|
shutil.copy(os.path.join(label_path, label), dest_label_dir)
|
||||||
|
print('Done!')
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
||||||
53
projects/gid_dataset/user_guides/2_dataset_prepare.md
Normal file
53
projects/gid_dataset/user_guides/2_dataset_prepare.md
Normal file
@ -0,0 +1,53 @@
|
|||||||
|
## Gaofen Image Dataset (GID)
|
||||||
|
|
||||||
|
- GID 数据集可在[此处](https://x-ytong.github.io/project/GID.html)进行下载。
|
||||||
|
- GID 数据集包含 150 张 6800x7200 的大尺寸图像，标签为 RGB 标签。
|
||||||
|
- 根据[文献](https://ieeexplore.ieee.org/document/9343296/)，此处选择 15 张图像生成训练集和验证集，该 15 张图像包含了所有六类信息。所选的图像名称如下：
|
||||||
|
|
||||||
|
```None
|
||||||
|
GF2_PMS1__L1A0000647767-MSS1
|
||||||
|
GF2_PMS1__L1A0001064454-MSS1
|
||||||
|
GF2_PMS1__L1A0001348919-MSS1
|
||||||
|
GF2_PMS1__L1A0001680851-MSS1
|
||||||
|
GF2_PMS1__L1A0001680853-MSS1
|
||||||
|
GF2_PMS1__L1A0001680857-MSS1
|
||||||
|
GF2_PMS1__L1A0001757429-MSS1
|
||||||
|
GF2_PMS2__L1A0000607681-MSS2
|
||||||
|
GF2_PMS2__L1A0000635115-MSS2
|
||||||
|
GF2_PMS2__L1A0000658637-MSS2
|
||||||
|
GF2_PMS2__L1A0001206072-MSS2
|
||||||
|
GF2_PMS2__L1A0001471436-MSS2
|
||||||
|
GF2_PMS2__L1A0001642620-MSS2
|
||||||
|
GF2_PMS2__L1A0001787089-MSS2
|
||||||
|
GF2_PMS2__L1A0001838560-MSS2
|
||||||
|
```
|
||||||
|
|
||||||
|
这里也提供了一个脚本来方便的筛选出15张图像，
|
||||||
|
|
||||||
|
```
|
||||||
|
python projects/gid_dataset/tools/dataset_converters/gid_select15imgFromAll.py {150 张图像的路径} {150 张标签的路径} {15 张图像的路径} {15 张标签的路径}
|
||||||
|
```
|
||||||
|
|
||||||
|
在选择出 15 张图像后，执行以下命令进行裁切及标签的转换，需要修改为您所存储 15 张图像及标签的路径。
|
||||||
|
|
||||||
|
```
|
||||||
|
python projects/gid_dataset/tools/dataset_converters/gid.py {15 张图像的路径} {15 张标签的路径}
|
||||||
|
```
|
||||||
|
|
||||||
|
完成裁切后的 GID 数据结构如下：
|
||||||
|
|
||||||
|
```none
|
||||||
|
mmsegmentation
|
||||||
|
├── mmseg
|
||||||
|
├── tools
|
||||||
|
├── configs
|
||||||
|
├── data
|
||||||
|
│ ├── gid
|
||||||
|
│ │ ├── ann_dir
|
||||||
|
| │ │ │ ├── train
|
||||||
|
| │ │ │ ├── val
|
||||||
|
│ │ ├── img_dir
|
||||||
|
| │ │ │ ├── train
|
||||||
|
| │ │ │ ├── val
|
||||||
|
|
||||||
|
```
|
||||||
Loading…
x
Reference in New Issue
Block a user