labelme使用教程

labelme使用教程

官方代码

0. 安装 pip install labelme 1. 运行

demo

labelme img_dir –nodata –autosave –output output_dir

运行上面的命令会弹出labelme的标注界面，标注完成之后会在output_dir目录下生成json文件，生成的文件如下.

其中的一些key的含义:

label:类别名称points:[[x1,y1],[x2,y2],…] 图像左上角为坐标原点shape_type rectangle：矩形框标注polygon：多边形框标注 { “version”: “5.0.1”, “flags”: {}, “shapes”: [ { “label”: “window”, “points”: [ [ 618.8, 279.2 ], [ 1278.0, 716.8 ] ], “group_id”: null, “shape_type”: “rectangle”, “flags”: {} }, { “label”: “window”, “points”: [ [ 678.8, 347.2 ], [ 707.6, 700.8 ], [ 1266.0, 652.0 ], [ 1205.2, 288.0 ] ], “group_id”: null, “shape_type”: “polygon”, “flags”: {} } ], “imagePath”: “0.jpg”, “imageData”: null, “imageHeight”: 720, “imageWidth”: 1280}

标注视频

需要先将视频帧转换成图像帧，然后运行下面的命令对生成的图像文件夹进行标注

labelme img_dir –nodata –keep-prev –autosave -o output_dir

其中–keep-prev参数会将上一帧的标注保存到当前帧

labelme D:DataSetimgleft_fish –nodata –keep-prev –autosave -o D:DataSetimgleft_fish_label

语义分割

./labelme2voc.py data_annotated data_dataset_voc –labels labels.txt

其中，

labelme2voc是官方的转换文件 你 data_annotated是你标注得到的json文件路径data_dataset_voc是输出的文件目录label.txt是标注信息

data_dataset_voc的目录结构如下:

data_dataset_voc├── JPEGImages├── SegmentationClass├── SegmentationClassVisualization 2. 命令行参数 positional arguments: filename image or label filenameoptional arguments: -h, –help show this help message and exit –version, -V show version –reset-config reset qt config –logger-level {debug,info,warning,fatal,error} logger level –output OUTPUT, -O OUTPUT, -o OUTPUT output file or directory (if it ends with .json it is recognized as file, else as directory) –config CONFIG config file or yaml-format string (default: C:UsersLenovo.labelmerc) –nodata stop storing image data to JSON file –autosave auto save –nosortlabels stop sorting labels –flags FLAGS comma separated list of flags OR file containing flags –labelflags LABEL_FLAGS yaml string of label specific flags OR file containing json string of label specific flags (ex. {person-d+: [male, tall], dog-d+: [black, brown, white], .*: [occluded]}) –labels LABELS comma separated list of labels OR file containing labels –validatelabel {exact} label validation types –keep-prev keep annotation of previous frame –epsilon EPSILON epsilon to find nearest vertex on canvas 3. labelme2voc.py

可以将标注生成的json文件生成voc格式的语义mask，可以通过运行一下命令来使用。

./labelme2voc.py data_annotated data_dataset_voc –labels labels.txt