图像分割简介

主要介绍了图像分割发展历程。之前了解了一些图像分割，主要是使用调色板，png图像来进行保存图像。因此对每一个标签都需要绘画一种颜色。

为了让每一个像素值都是一个类别，需要使用膨胀卷积，保存图像不变，输出 的是图像，不是类别

因此在训练的时候，需要不仅仅写cfg，还需要再写dataset，这个是核心函数，定义颜色，还有类别，分割的显示

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# 同济子豪兄 2023-2-15
from mmseg.registry import DATASETS
from .basesegdataset import BaseSegDataset

@DATASETS.register_module()
class WatermelonDataset(BaseSegDataset):
    # 类别和对应的可视化配色
    METAINFO = {
        'classes':['red', 'green', 'white', 'seed-black', 'seed-white', 'Unlabeled'],
        'palette':[[132,41,246], [228,193,110], [152,16,60], [58,221,254], [41,169,226], [155,155,155]]
    }
    
    # 指定图像扩展名、标注扩展名
    def __init__(self,
                 img_suffix='.jpg',
                 seg_map_suffix='.png',
                 reduce_zero_label=False, # 类别ID为0的类别是否需要除去
                 **kwargs) -> None:
        super().__init__(
            img_suffix=img_suffix,
            seg_map_suffix=seg_map_suffix,
            reduce_zero_label=reduce_zero_label,
            **kwargs)

之后进行设置cfg

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norm_cfg = dict(type='BN', requires_grad=True)
data_preprocessor = dict(
    type='SegDataPreProcessor',
    mean=[123.675, 116.28, 103.53],
    std=[58.395, 57.12, 57.375],
    bgr_to_rgb=True,
    pad_val=0,
    seg_pad_val=255,
    size=(64, 64))
model = dict(
    type='EncoderDecoder',
    data_preprocessor=dict(
        type='SegDataPreProcessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True,
        pad_val=0,
        seg_pad_val=255,
        size=(256, 256)),
    pretrained='open-mmlab://resnet50_v1c',
    backbone=dict(
        type='ResNetV1c',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        dilations=(1, 1, 2, 4),
        strides=(1, 2, 1, 1),
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=False,
        style='pytorch',
        contract_dilation=True),
    decode_head=dict(
        type='PSPHead',
        in_channels=2048,
        in_index=3,
        channels=512,
        pool_scales=(1, 2, 3, 6),
        dropout_ratio=0.1,
        num_classes=6,
        norm_cfg=dict(type='BN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
    auxiliary_head=dict(
        type='FCNHead',
        in_channels=1024,
        in_index=2,
        channels=256,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=6,
        norm_cfg=dict(type='BN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))

dataset_type = 'WatermelonDataset'
data_root = 'watermelon/Watermelon87_Semantic_Seg_Mask/'
crop_size = (256, 256)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(
        type='RandomResize',
        scale=(2048, 1024),
        ratio_range=(0.5, 2.0),
        keep_ratio=True),
    dict(type='RandomCrop', crop_size=(64, 64), cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(type='PackSegInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
    dict(type='LoadAnnotations'),
    dict(type='PackSegInputs')
]
img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
tta_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(
        type='TestTimeAug',
        transforms=[[{
            'type': 'Resize',
            'scale_factor': 0.5,
            'keep_ratio': True
        }, {
            'type': 'Resize',
            'scale_factor': 0.75,
            'keep_ratio': True
        }, {
            'type': 'Resize',
            'scale_factor': 1.0,
            'keep_ratio': True
        }, {
            'type': 'Resize',
            'scale_factor': 1.25,
            'keep_ratio': True
        }, {
            'type': 'Resize',
            'scale_factor': 1.5,
            'keep_ratio': True
        }, {
            'type': 'Resize',
            'scale_factor': 1.75,
            'keep_ratio': True
        }],
                    [{
                        'type': 'RandomFlip',
                        'prob': 0.0,
                        'direction': 'horizontal'
                    }, {
                        'type': 'RandomFlip',
                        'prob': 1.0,
                        'direction': 'horizontal'
                    }], [{
                        'type': 'LoadAnnotations'
                    }], [{
                        'type': 'PackSegInputs'
                    }]])
]
train_dataloader = dict(
    batch_size=8,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(type='InfiniteSampler', shuffle=True),
    dataset=dict(
        type='DubaiDataset',
        data_root='watermelon/Watermelon87_Semantic_Seg_Mask/',
        data_prefix=dict(
            img_path='img_dir/train', seg_map_path='ann_dir/train'),
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations'),
            dict(
                type='RandomResize',
                scale=(2048, 1024),
                ratio_range=(0.5, 2.0),
                keep_ratio=True),
            dict(type='RandomCrop', crop_size=(64, 64), cat_max_ratio=0.75),
            dict(type='RandomFlip', prob=0.5),
            dict(type='PhotoMetricDistortion'),
            dict(type='PackSegInputs')
        ]))
val_dataloader = dict(
    batch_size=1,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='DubaiDataset',
        data_root='watermelon/Watermelon87_Semantic_Seg_Mask/',
        data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
            dict(type='LoadAnnotations'),
            dict(type='PackSegInputs')
        ]))
test_dataloader = dict(
    batch_size=1,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='DubaiDataset',
        data_root='watermelon/Watermelon87_Semantic_Seg_Mask/',
        data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
            dict(type='LoadAnnotations'),
            dict(type='PackSegInputs')
        ]))
val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
test_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
default_scope = 'mmseg'
env_cfg = dict(
    cudnn_benchmark=True,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='SegLocalVisualizer',
    vis_backends=[dict(type='LocalVisBackend')],
    name='visualizer')
log_processor = dict(by_epoch=False)
log_level = 'INFO'
load_from = None
resume = False
tta_model = dict(type='SegTTAModel')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005),
    clip_grad=None)
param_scheduler = [
    dict(
        type='PolyLR',
        eta_min=0.0001,
        power=0.9,
        begin=0,
        end=40000,
        by_epoch=False)
]
train_cfg = dict(type='IterBasedTrainLoop', max_iters=3000, val_interval=400)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=100, log_metric_by_epoch=False),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=1500),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='SegVisualizationHook'))
work_dir = './work_dirs/watermelon'
randomness = dict(seed=0)

主要是参考子豪兄的代码

https://github.com/TommyZihao/MMSegmentation_Tutorials.git