数据标注

# 安装
pip install labelme
# 启动labelme
labelme

然后 ctrl +N 开启多边形标注即可，命名类为person

之后会保存到同目录下json文件：

json转mask

下载labelme代码里的转换代码：

labels里存储的如下形式

运行指令

python labelme2voc.py ./img output labels.txt

生成如下

运行源码MMSegmentation

mmseg/datasets里生成一个my_data.py文件，这个文件存储的是类别信息和seg颜色
需要多加一个backbone

# Copyright (c) OpenMMLab. All rights reserved.
from mmseg.registry import DATASETS
from .basesegdataset import BaseSegDataset


@DATASETS.register_module()
class mydata(BaseSegDataset):
    """Cityscapes dataset.

    The ``img_suffix`` is fixed to '_leftImg8bit.png' and ``seg_map_suffix`` is
    fixed to '_gtFine_labelTrainIds.png' for Cityscapes dataset.
    """
    METAINFO = dict(
        classes=('backbone','person'),
        palette=[[128, 64, 128], [244, 35, 232]])

    def __init__(self,
                 img_suffix='.jpg',
                 seg_map_suffix='.png',
                 reduce_zero_label=True,
                 **kwargs) -> None:
        super().__init__(
            img_suffix=img_suffix,
            seg_map_suffix=seg_map_suffix,
            reduce_zero_label=reduce_zero_label,
            **kwargs)

mmseg/utils/class_names.py文件里添加：不加backbone也不报错，这里没加，最好加上另外，seg颜色要与上面文件一致

def mydata_classes():
    """shengteng class names for external use."""
    return [
        'person'
    ]

def mydata_palette():
    return [[244, 35, 232]]

mmseg/datasets/init.py中加引入，

from .my_data import mydata

configs/base/datasets文件下新建一个my_data.py文件：
这个就是一个读取数据的文件了，包含数据地址、type和加载增加等方式

# dataset settings
dataset_type = 'mydata' #改
data_root = 'data/my_dataset'  #改
crop_size = (512, 512)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', reduce_zero_label=True),
    dict(
        type='RandomResize',
        scale=(2048, 512),
        ratio_range=(0.5, 2.0),
        keep_ratio=True),
    dict(type='RandomCrop', crop_size=crop_size, 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, 512), keep_ratio=True),
    # add loading annotation after ``Resize`` because ground truth
    # does not need to do resize data transform
    dict(type='LoadAnnotations', reduce_zero_label=True),
    dict(type='PackSegInputs')
]
img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
tta_pipeline = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(
        type='TestTimeAug',
        transforms=[
            [
                dict(type='Resize', scale_factor=r, keep_ratio=True)
                for r in img_ratios
            ],
            [
                dict(type='RandomFlip', prob=0., direction='horizontal'),
                dict(type='RandomFlip', prob=1., direction='horizontal')
            ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')]
        ])
]
train_dataloader = dict(
    batch_size=4,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='InfiniteSampler', shuffle=True),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        data_prefix=dict(
            img_path='images/training', seg_map_path='annotations/training'),  #改
        pipeline=train_pipeline))
val_dataloader = dict(
    batch_size=1,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        data_prefix=dict(
            img_path='images/validation', #改
            seg_map_path='annotations/validation'), #改
        pipeline=test_pipeline))
test_dataloader = val_dataloader

val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
test_evaluator = val_evaluator

模型选择运行部分

我选择的是configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py，主要是修改继承的数据部分

运行
每次修改配置文件，最好是运行一遍python setup.py install

python setup.py install
python ./tools/train.py ./configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py

![在这里插入图片描述](https://img-blog.csdnimg.cn/2ec531af24a94c6b982f55bffe7024bf.png)