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【Deeplabv3+】Ubutu18.04中使用pytorch复现Deeplabv3+第三步)-----CityscapesScripts生成自己的标签

news 来源：原创 2025/5/10 15:28:56

本文是在前面两篇文章的基础上，讲解如何更改训练数据集颜色，需要与前面两篇文章连起来看。

本文用于修改cityscapes数据集的标签颜色与Semankitti数据集的标签一致，对修改后的数据集进行训练。需要下载两个开发工具包和一个数据集，分别是cityscapesScripts-master、semantic-kitti-api-master和cityscapes数据集：

  • cityscapesScripts是用于检查、准备和评估 Cityscapes 数据集的脚本。下载路径：

https://github.com/mcordts/cityscapesScripts

  •  cityscapes数据集需要注册登录才能下载，下载链接：

Login – Cityscapes Dataset

下载完成后，在cityscapesScripts-master中创建一个cityscapes文件夹，将下载好的两个文件分别放入其中，解压出来的说明文件直接删除即可，最终如下图：

  • semantic-kitti-api是用于打开、可视化、处理和评估 SemanticKITTI 数据集中的点云和标签结果的帮助程序脚本。下载路径：

https://github.com/PRBonn/semantic-kitti-api

一、制作标签步骤

1.1 更改标签颜色

进入目录cityscapesScripts-master\cityscapesscripts\helpers\labels.py中修改标签颜色与semantic-kitti-api-master\config\semanic-kitti.yaml中一致。注意：semantic-kitti-api-maste中的颜色是BGR颜色，cityscapesScripts中的颜色是RGB颜色，颠倒一下

cityscapesScripts-master\cityscapesscripts\helpers\labels.py标签：

semantic-kitti-api-master\config\semanic-kitti.yaml标签：

修改后的cityscapesScripts-master\cityscapesscripts\helpers\labels.py标签，可以直接拷贝使用：

labels = [
    #       name                     id    trainId   category            catId     hasInstances   ignoreInEval   color
    Label(  'unlabeled'            ,  0 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'ego vehicle'          ,  1 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'rectification border' ,  2 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'out of roi'           ,  3 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'static'               ,  4 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'dynamic'              ,  5 ,      255 , 'void'            , 0       , False        , True         , (111, 74,  0) ),
# Label(  'ground'               ,  6 ,      255 , 'void'            , 0       , False        , True         , ( 81,  0, 81) ),
    Label(  'ground'               ,  6 ,      255 , 'void'            , 0       , False        , True         , ( 175,  0, 75) ),
# Label(  'road'                 ,  7 ,        0 , 'flat'            , 1       , False        , False        , (128, 64,128) ),
    Label(  'road'                 ,  7 ,        0 , 'flat'            , 1       , False        , False        , (255, 0,255) ),
# Label(  'sidewalk'             ,  8 ,        1 , 'flat'            , 1       , False        , False        , (244, 35,232) ),
    Label(  'sidewalk'             ,  8 ,        1 , 'flat'            , 1       , False        , False        , (75, 0,75) ),
# Label(  'parking'              ,  9 ,      255 , 'flat'            , 1       , False        , True         , (250,170,160) ),
    Label(  'parking'              ,  9 ,      255 , 'flat'            , 1       , False        , True         , (255,150,255) ),
# Label(  'rail track'           , 10 ,      255 , 'flat'            , 1       , False        , True         , (230,150,140) ),
    Label(  'rail track'           , 10 ,      255 , 'flat'            , 1       , False        , True         , (0,0,255) ),
# Label(  'building'             , 11 ,        2 , 'construction'    , 2       , False        , False        , ( 70, 70, 70) ),
    Label(  'building'             , 11 ,        2 , 'construction'    , 2       , False        , False        , ( 255, 200, 0) ),
# Label(  'wall'                 , 12 ,        3 , 'construction'    , 2       , False        , False        , (102,102,156) ),
    Label(  'wall'                 , 12 ,        3 , 'construction'    , 2       , False        , False        , (255,150,0) ),
# Label(  'fence'                , 13 ,        4 , 'construction'    , 2       , False        , False        , (190,153,153) ),
    Label(  'fence'                , 13 ,        4 , 'construction'    , 2       , False        , False        , (255,120,50) ),
# Label(  'guard rail'           , 14 ,      255 , 'construction'    , 2       , False        , True         , (180,165,180) ),
    Label(  'guard rail'           , 14 ,      255 , 'construction'    , 2       , False        , True         , (255,150,0) ),
# Label(  'bridge'               , 15 ,      255 , 'construction'    , 2       , False        , True         , (150,100,100) ),
    Label(  'bridge'               , 15 ,      255 , 'construction'    , 2       , False        , True         , (255,150,0) ),
# Label(  'tunnel'               , 16 ,      255 , 'construction'    , 2       , False        , True         , (150,120, 90) ),
    Label(  'tunnel'               , 16 ,      255 , 'construction'    , 2       , False        , True         , (255,150, 0) ),
# Label(  'pole'                 , 17 ,        5 , 'object'          , 3       , False        , False        , (153,153,153) ),
    Label(  'pole'                 , 17 ,        5 , 'object'          , 3       , False        , False        , (255,240,150) ),
# Label(  'polegroup'            , 18 ,      255 , 'object'          , 3       , False        , True         , (153,153,153) ),
    Label(  'polegroup'            , 18 ,      255 , 'object'          , 3       , False        , True         , (50,255,255) ),
# Label(  'traffic light'        , 19 ,        6 , 'object'          , 3       , False        , False        , (250,170, 30) ),
    Label(  'traffic light'        , 19 ,        6 , 'object'          , 3       , False        , False        , (50,255, 255) ),
# Label(  'traffic sign'         , 20 ,        7 , 'object'          , 3       , False        , False        , (220,220,  0) ),
    Label(  'traffic sign'         , 20 ,        7 , 'object'          , 3       , False        , False        , (255,0,  0) ),
# Label(  'vegetation'           , 21 ,        8 , 'nature'          , 4       , False        , False        , (107,142, 35) ),
    Label(  'vegetation'           , 21 ,        8 , 'nature'          , 4       , False        , False        , (0,175, 0) ),
# Label(  'terrain'              , 22 ,        9 , 'nature'          , 4       , False        , False        , (152,251,152) ),
    Label(  'terrain'              , 22 ,        9 , 'nature'          , 4       , False        , False        , (150,240,80) ),
# Label(  'sky'                  , 23 ,       10 , 'sky'             , 5       , False        , False        , ( 70,130,180) ),
    Label(  'sky'                  , 23 ,       10 , 'sky'             , 5       , False        , False        , ( 0,0,0) ),
# Label(  'person'               , 24 ,       11 , 'human'           , 6       , True         , False        , (220, 20, 60) ),
    Label(  'person'               , 24 ,       11 , 'human'           , 6       , True         , False        , (255, 30, 30) ),
# Label(  'rider'                , 25 ,       12 , 'human'           , 6       , True         , False        , (255,  0,  0) ),
    Label(  'rider'                , 25 ,       12 , 'human'           , 6       , True         , False        , (255,  40,  200) ),
# Label(  'car'                  , 26 ,       13 , 'vehicle'         , 7       , True         , False        , (  0,  0,142) ),
    Label(  'car'                  , 26 ,       13 , 'vehicle'         , 7       , True         , False        , (  100,  150,245) ),
# Label(  'truck'                , 27 ,       14 , 'vehicle'         , 7       , True         , False        , (  0,  0, 70) ),
    Label(  'truck'                , 27 ,       14 , 'vehicle'         , 7       , True         , False        , (  80,  30, 180) ),
# Label(  'bus'                  , 28 ,       15 , 'vehicle'         , 7       , True         , False        , (  0, 60,100) ),
    Label(  'bus'                  , 28 ,       15 , 'vehicle'         , 7       , True         , False        , (  100, 80,250) ),
# Label(  'caravan'              , 29 ,      255 , 'vehicle'         , 7       , True         , True         , (  0,  0, 90) ),
    Label(  'caravan'              , 29 ,      255 , 'vehicle'         , 7       , True         , True         , (  0,  0, 255) ),
# Label(  'trailer'              , 30 ,      255 , 'vehicle'         , 7       , True         , True         , (  0,  0,110) ),
    Label(  'trailer'              , 30 ,      255 , 'vehicle'         , 7       , True         , True         , (  0,  0,255) ),
# Label(  'train'                , 31 ,       16 , 'vehicle'         , 7       , True         , False        , (  0, 80,100) ),
    Label(  'train'                , 31 ,       16 , 'vehicle'         , 7       , True         , False        , (  0, 0,255) ),
# Label(  'motorcycle'           , 32 ,       17 , 'vehicle'         , 7       , True         , False        , (  0,  0,230) ),
    Label(  'motorcycle'           , 32 ,       17 , 'vehicle'         , 7       , True         , False        , (  30,  60,150) ),
# Label(  'bicycle'              , 33 ,       18 , 'vehicle'         , 7       , True         , False        , (119, 11, 32) ),
    Label(  'bicycle'              , 33 ,       18 , 'vehicle'         , 7       , True         , False        , (100, 230, 245) ),
# Label(  'license plate'        , -1 ,       -1 , 'vehicle'         , 7       , False        , True         , (  0,  0,142) ),
    Label(  'license plate'        , -1 ,       -1 , 'vehicle'         , 7       , False        , True         , (  0,  0,255) ),
]

1.2 生成训练标签

1.2.1 生成labelIds标签

进入目录：cityscapesScripts-master\cityscapesscripts\preparation中

运行下面代码：

 # 运行成功后会在cityscapes数据集中生成_labelTrainIds结尾的训练文件
 python  .\createTrainIdLabelImgs.py

此时进入cityscapesScripts-master\cityscapes\gtFine\train中任何一个城市，会发现多了一个修改好的训练标签（gtFine中test、train和val中均多了一个训练标签，不一一展示）：

2.2 生成instanceIds标签

进入目录：cityscapesScripts-master\cityscapesscripts\preparation中

运行下面代码

#  # 运行成功后会在cityscapes数据集中生成_instanceTrainIds结尾的训练文件
python .\createTrainIdInstanceImgs.py

此时进入cityscapesScripts-master\cityscapes\gtFine\train中任何一个城市，会发现多了一个另一个实例训练标签，（gtFine中test、train和val中均多了一个训练标签，不一一展示）：

2.3 修改DeepLabV3Plus-Pytorch中datasets\cityscapes.py中RGB值

训练之前，修改datasets\cityscapes.py文件中标签RGB值与cityscapesScripts-master中一致，可直接使用：

修改好的标签代码如下：

CityscapesClass = namedtuple('CityscapesClass', ['name', 'id', 'train_id', 'category', 'category_id',
                                                     'has_instances', 'ignore_in_eval', 'color'])
    classes = [
        CityscapesClass('unlabeled',            0, 255, 'void', 0, False, True, (0, 0, 0)),
        CityscapesClass('ego vehicle',          1, 255, 'void', 0, False, True, (0, 0, 0)),
        CityscapesClass('rectification border', 2, 255, 'void', 0, False, True, (0, 0, 0)),
        CityscapesClass('out of roi',           3, 255, 'void', 0, False, True, (0, 0, 0)),
        CityscapesClass('static',               4, 255, 'void', 0, False, True, (0, 0, 0)),
        CityscapesClass('dynamic',              5, 255, 'void', 0, False, True, (111, 74, 0)),
    # CityscapesClass('ground',               6, 255, 'void', 0, False, True, (81, 0, 81)),
        CityscapesClass('ground',               6, 255, 'void', 0, False, True, (175, 0, 75)),
    # CityscapesClass('road',                 7, 0, 'flat', 1, False, False, (128, 64, 128)),
        CityscapesClass('road',                 7, 0, 'flat', 1, False, False, (255, 0, 255)),
    # CityscapesClass('sidewalk',             8, 1, 'flat', 1, False, False, (244, 35, 232)),
        CityscapesClass('sidewalk',             8, 1, 'flat', 1, False, False, (75, 0, 75)),
    # CityscapesClass('parking',              9, 255, 'flat', 1, False, True, (250, 170, 160)),
        CityscapesClass('parking',              9, 255, 'flat', 1, False, True, (255, 150, 255)),
    # CityscapesClass('rail track',           10, 255, 'flat', 1, False, True, (230, 150, 140)),
        CityscapesClass('rail track',           10, 255, 'flat', 1, False, True, (0, 0, 255)),
    # CityscapesClass('building',             11, 2, 'construction', 2, False, False, (70, 70, 70)),
        CityscapesClass('building',             11, 2, 'construction', 2, False, False, (255, 200, 0)),
    # CityscapesClass('wall',                 12, 3, 'construction', 2, False, False, (102, 102, 156)),
        CityscapesClass('wall',                 12, 3, 'construction', 2, False, False, (255, 150, 0)),
    # CityscapesClass('fence',                13, 4, 'construction', 2, False, False, (190, 153, 153)),
        CityscapesClass('fence',                13, 4, 'construction', 2, False, False, (255, 120, 50)),
    # CityscapesClass('guard rail',           14, 255, 'construction', 2, False, True, (180, 165, 180)),
        CityscapesClass('guard rail',           14, 255, 'construction', 2, False, True, (255, 150, 0)),
    # CityscapesClass('bridge',               15, 255, 'construction', 2, False, True, (150, 100, 100)),
        CityscapesClass('bridge',               15, 255, 'construction', 2, False, True, (255, 150, 0)),
    # CityscapesClass('tunnel',               16, 255, 'construction', 2, False, True, (150, 120, 90)),
        CityscapesClass('tunnel',               16, 255, 'construction', 2, False, True, (255, 150, 0)),
    # CityscapesClass('pole',                 17, 5, 'object', 3, False, False, (153, 153, 153)),
        CityscapesClass('pole',                 17, 5, 'object', 3, False, False, (255, 240, 150)),
    # CityscapesClass('polegroup',            18, 255, 'object', 3, False, True, (153, 153, 153)),
        CityscapesClass('polegroup',            18, 255, 'object', 3, False, True, (50, 255, 255)),
    # CityscapesClass('traffic light',        19, 6, 'object', 3, False, False, (250, 170, 30)),
        CityscapesClass('traffic light',        19, 6, 'object', 3, False, False, (50, 255, 255)),
    # CityscapesClass('traffic sign',         20, 7, 'object', 3, False, False, (220, 220, 0)),
        CityscapesClass('traffic sign',         20, 7, 'object', 3, False, False, (255, 0, 0)),
    # CityscapesClass('vegetation',           21, 8, 'nature', 4, False, False, (107, 142, 35)),
        CityscapesClass('vegetation',           21, 8, 'nature', 4, False, False, (0, 175, 0)),
    # CityscapesClass('terrain',              22, 9, 'nature', 4, False, False, (152, 251, 152)),
        CityscapesClass('terrain',              22, 9, 'nature', 4, False, False, (150, 240, 80)),
    # CityscapesClass('sky',                  23, 10, 'sky', 5, False, False, (70, 130, 180)),
        CityscapesClass('sky',                  23, 10, 'sky', 5, False, False, (0, 0, 0)),
    # CityscapesClass('person',               24, 11, 'human', 6, True, False, (220, 20, 60)),
        CityscapesClass('person',               24, 11, 'human', 6, True, False, (255, 30, 30)),
    # CityscapesClass('rider',                25, 12, 'human', 6, True, False, (255, 0, 0)),
        CityscapesClass('rider',                25, 12, 'human', 6, True, False, (255, 40, 200)),
    # CityscapesClass('car',                  26, 13, 'vehicle', 7, True, False, (0, 0, 142)),
        CityscapesClass('car',                  26, 13, 'vehicle', 7, True, False, (100, 150, 245)),
    # CityscapesClass('truck',                27, 14, 'vehicle', 7, True, False, (0, 0, 70)),
        CityscapesClass('truck',                27, 14, 'vehicle', 7, True, False, (80, 30, 180)),
    # CityscapesClass('bus',                  28, 15, 'vehicle', 7, True, False, (0, 60, 100)),
        CityscapesClass('bus',                  28, 15, 'vehicle', 7, True, False, (100, 80, 250)),
    # CityscapesClass('caravan',              29, 255, 'vehicle', 7, True, True, (0, 0, 90)),
        CityscapesClass('caravan',              29, 255, 'vehicle', 7, True, True, (0, 0, 255)),
    # CityscapesClass('trailer',              30, 255, 'vehicle', 7, True, True, (0, 0, 110)),
        CityscapesClass('trailer',              30, 255, 'vehicle', 7, True, True, (0, 0, 255)),
    # CityscapesClass('train',                31, 16, 'vehicle', 7, True, False, (0, 80, 100)),
        CityscapesClass('train',                31, 16, 'vehicle', 7, True, False, (0, 0, 255)),
    # CityscapesClass('motorcycle',           32, 17, 'vehicle', 7, True, False, (0, 0, 230)),
        CityscapesClass('motorcycle',           32, 17, 'vehicle', 7, True, False, (30, 60, 150)),
    # CityscapesClass('bicycle',              33, 18, 'vehicle', 7, True, False, (119, 11, 32)),
        CityscapesClass('bicycle',              33, 18, 'vehicle', 7, True, False, (100, 230, 245)),
        CityscapesClass('license plate',        -1, 255, 'vehicle', 7, False, True, (0, 0, 255)),
    ]

更改完成后，在DeepLabV3Plus-Pytorch-master中训练，即可得到训练后的新结果：

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