Labelme分割标注软件

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0、VOC数据集目录

1、环境配置与安装

1.1 创建conda虚拟环境(建议)

conda create -n labelme_env python=3.8 

创建完成后，进入新环境：

conda activate labelme_env 

1.2 安装Labelme

安装非常简单，直接使用pip安装即可：

pip install labelme 

安装pycocotools

# ubutnu pip install pycocotools # Windows pip install pycocotools-windows 

安装完成后在终端输入labelme即可启动：

labelme 

2、简单使用

├── img_data: 存放你要标注的所有图片 ├── data_annotated: 存放后续标注好的所有json文件 └── labels.txt: 所有类别信息 

2.1 创建label标签文件

__ignore__ _background_ dog cat 

2.2 启动labelme

labelme --labels labels.txt 

2.3 打开文件/文件夹

2.4 设置保存结果路径

2.5 标注目标

2.6 保存json文件格式

{ 
    "version": "4.5.9", "flags": { 
   }, "shapes": [ { 
    "label": "dog", "points": [ [ 108.007, 687.81 ], .... [ 538.0, 668.0 ], [ 534.54, 689.0 ] ], "group_id": null, "shape_type": "polygon", "flags": { 
   } } ], "imagePath": "../img_data/1.jpg", "imageData": null, "imageHeight": 690, "imageWidth": 690 } 

3 格式转换

3.1 转换语义分割标签

python labelme2voc.py data_annotated data_dataset_voc --labels labels.txt 

执行后会生成如下目录：

- data_dataset_voc/JPEGImages - data_dataset_voc/SegmentationClass - data_dataset_voc/SegmentationClassPNG - data_dataset_voc/SegmentationClassVisualization - data_dataset_voc/class_names.txt 

class_names.txt存储的是所有的类别信息，包括背景。

_background_ dog cat 

#!/usr/bin/env python from __future__ import print_function import argparse import glob import os import os.path as osp import sys import imgviz import numpy as np import labelme def main(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument("input_dir", help="Input annotated directory") parser.add_argument("output_dir", help="Output dataset directory") parser.add_argument( "--labels", help="Labels file or comma separated text", required=True ) parser.add_argument( "--noobject", help="Flag not to generate object label", action="store_true" ) parser.add_argument( "--nonpy", help="Flag not to generate .npy files", action="store_true" ) parser.add_argument( "--noviz", help="Flag to disable visualization", action="store_true" ) args = parser.parse_args() if osp.exists(args.output_dir): print("Output directory already exists:", args.output_dir) sys.exit(1) os.makedirs(args.output_dir) os.makedirs(osp.join(args.output_dir, "JPEGImages")) os.makedirs(osp.join(args.output_dir, "SegmentationClass")) if not args.nonpy: os.makedirs(osp.join(args.output_dir, "SegmentationClassNpy")) if not args.noviz: os.makedirs(osp.join(args.output_dir, "SegmentationClassVisualization")) if not args.noobject: os.makedirs(osp.join(args.output_dir, "SegmentationObject")) if not args.nonpy: os.makedirs(osp.join(args.output_dir, "SegmentationObjectNpy")) if not args.noviz: os.makedirs(osp.join(args.output_dir, "SegmentationObjectVisualization")) print("Creating dataset:", args.output_dir) if osp.exists(args.labels): with open(args.labels) as f: labels = [label.strip() for label in f if label] else: labels = [label.strip() for label in args.labels.split(",")] class_names = [] class_name_to_id = { 
   } for i, label in enumerate(labels): class_id = i - 1 # starts with -1 class_name = label.strip() class_name_to_id[class_name] = class_id if class_id == -1: print("------",class_name) assert class_name == "__ignore__" continue elif class_id == 0: assert class_name == "_background_" class_names.append(class_name) class_names = tuple(class_names) print("class_names:", class_names) out_class_names_file = osp.join(args.output_dir, "class_names.txt") with open(out_class_names_file, "w") as f: f.writelines("\n".join(class_names)) print("Saved class_names:", out_class_names_file) for filename in sorted(glob.glob(osp.join(args.input_dir, "*.json"))): print("Generating dataset from:", filename) label_file = labelme.LabelFile(filename=filename) base = osp.splitext(osp.basename(filename))[0] out_img_file = osp.join(args.output_dir, "JPEGImages", base + ".jpg") out_clsp_file = osp.join(args.output_dir, "SegmentationClass", base + ".png") if not args.nonpy: out_cls_file = osp.join( args.output_dir, "SegmentationClassNpy", base + ".npy" ) if not args.noviz: out_clsv_file = osp.join( args.output_dir, "SegmentationClassVisualization", base + ".jpg", ) if not args.noobject: out_insp_file = osp.join( args.output_dir, "SegmentationObject", base + ".png" ) if not args.nonpy: out_ins_file = osp.join( args.output_dir, "SegmentationObjectNpy", base + ".npy" ) if not args.noviz: out_insv_file = osp.join( args.output_dir, "SegmentationObjectVisualization", base + ".jpg", ) img = labelme.utils.img_data_to_arr(label_file.imageData) imgviz.io.imsave(out_img_file, img) cls, ins = labelme.utils.shapes_to_label( img_shape=img.shape, shapes=label_file.shapes, label_name_to_value=class_name_to_id, ) ins[cls == -1] = 0 # ignore it. # class label labelme.utils.lblsave(out_clsp_file, cls) if not args.nonpy: np.save(out_cls_file, cls) if not args.noviz: clsv = imgviz.label2rgb( cls, imgviz.rgb2gray(img), label_names=class_names, font_size=15, loc="rb", ) imgviz.io.imsave(out_clsv_file, clsv) if not args.noobject: # instance label labelme.utils.lblsave(out_insp_file, ins) if not args.nonpy: np.save(out_ins_file, ins) if not args.noviz: instance_ids = np.unique(ins) instance_names = [str(i) for i in range(max(instance_ids) + 1)] insv = imgviz.label2rgb( ins, imgviz.rgb2gray(img), label_names=instance_names, font_size=15, loc="rb", ) imgviz.io.imsave(out_insv_file, insv) if __name__ == "__main__": main() 

3.2 转换实例分割标签

https://github.com/wkentaro/labelme/tree/main/examples/instance_segmentation

python labelme2voc.py data_annotated data_dataset_voc --labels labels.txt 

执行后会生成如下目录：

- data_dataset_voc/JPEGImages - data_dataset_voc/SegmentationClass - data_dataset_voc/SegmentationClassPNG - data_dataset_voc/SegmentationClassVisualization - data_dataset_voc/SegmentationObject - data_dataset_voc/SegmentationObjectPNG - data_dataset_voc/SegmentationObjectVisualization - data_dataset_voc/class_names.txt 

#!/usr/bin/env python from __future__ import print_function import argparse import glob import os import os.path as osp import sys import imgviz import numpy as np import labelme def main(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument("input_dir", help="Input annotated directory") parser.add_argument("output_dir", help="Output dataset directory") parser.add_argument( "--labels", help="Labels file or comma separated text", required=True ) parser.add_argument( "--noobject", help="Flag not to generate object label", action="store_true" ) parser.add_argument( "--nonpy", help="Flag not to generate .npy files", action="store_true" ) parser.add_argument( "--noviz", help="Flag to disable visualization", action="store_true" ) args = parser.parse_args() if osp.exists(args.output_dir): print("Output directory already exists:", args.output_dir) sys.exit(1) os.makedirs(args.output_dir) os.makedirs(osp.join(args.output_dir, "JPEGImages")) os.makedirs(osp.join(args.output_dir, "SegmentationClass")) if not args.nonpy: os.makedirs(osp.join(args.output_dir, "SegmentationClassNpy")) if not args.noviz: os.makedirs(osp.join(args.output_dir, "SegmentationClassVisualization")) if not args.noobject: os.makedirs(osp.join(args.output_dir, "SegmentationObject")) if not args.nonpy: os.makedirs(osp.join(args.output_dir, "SegmentationObjectNpy")) if not args.noviz: os.makedirs(osp.join(args.output_dir, "SegmentationObjectVisualization")) print("Creating dataset:", args.output_dir) if osp.exists(args.labels): with open(args.labels) as f: labels = [label.strip() for label in f if label] else: labels = [label.strip() for label in args.labels.split(",")] class_names = [] class_name_to_id = { 
   } for i, label in enumerate(labels): class_id = i - 1 # starts with -1 class_name = label.strip() class_name_to_id[class_name] = class_id if class_id == -1: print("------",class_name) assert class_name == "__ignore__" continue elif class_id == 0: assert class_name == "_background_" class_names.append(class_name) class_names = tuple(class_names) print("class_names:", class_names) out_class_names_file = osp.join(args.output_dir, "class_names.txt") with open(out_class_names_file, "w") as f: f.writelines("\n".join(class_names)) print("Saved class_names:", out_class_names_file) for filename in sorted(glob.glob(osp.join(args.input_dir, "*.json"))): print("Generating dataset from:", filename) label_file = labelme.LabelFile(filename=filename) base = osp.splitext(osp.basename(filename))[0] out_img_file = osp.join(args.output_dir, "JPEGImages", base + ".jpg") out_clsp_file = osp.join(args.output_dir, "SegmentationClass", base + ".png") if not args.nonpy: out_cls_file = osp.join( args.output_dir, "SegmentationClassNpy", base + ".npy" ) if not args.noviz: out_clsv_file = osp.join( args.output_dir, "SegmentationClassVisualization", base + ".jpg", ) if not args.noobject: out_insp_file = osp.join( args.output_dir, "SegmentationObject", base + ".png" ) if not args.nonpy: out_ins_file = osp.join( args.output_dir, "SegmentationObjectNpy", base + ".npy" ) if not args.noviz: out_insv_file = osp.join( args.output_dir, "SegmentationObjectVisualization", base + ".jpg", ) img = labelme.utils.img_data_to_arr(label_file.imageData) imgviz.io.imsave(out_img_file, img) cls, ins = labelme.utils.shapes_to_label( img_shape=img.shape, shapes=label_file.shapes, label_name_to_value=class_name_to_id, ) ins[cls == -1] = 0 # ignore it. # class label labelme.utils.lblsave(out_clsp_file, cls) if not args.nonpy: np.save(out_cls_file, cls) if not args.noviz: clsv = imgviz.label2rgb( cls, imgviz.rgb2gray(img), label_names=class_names, font_size=15, loc="rb", ) imgviz.io.imsave(out_clsv_file, clsv) if not args.noobject: # instance label labelme.utils.lblsave(out_insp_file, ins) if not args.nonpy: np.save(out_ins_file, ins) if not args.noviz: instance_ids = np.unique(ins) instance_names = [str(i) for i in range(max(instance_ids) + 1)] insv = imgviz.label2rgb( ins, imgviz.rgb2gray(img), label_names=instance_names, font_size=15, loc="rb", ) imgviz.io.imsave(out_insv_file, insv) if __name__ == "__main__": main() 

python labelme2coco.py data_annotated data_dataset_coco --labels labels.txt 

执行后会生成如下目录：

- data_dataset_coco/JPEGImages - data_dataset_coco/annotations.json 

代码：

#!/usr/bin/env python import argparse import collections import datetime import glob import json import os import os.path as osp import sys import uuid import imgviz import numpy as np import labelme try: import pycocotools.mask except ImportError: print("Please install pycocotools:\n\n pip install pycocotools\n") sys.exit(1) def main(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument("input_dir", help="input annotated directory") parser.add_argument("output_dir", help="output dataset directory") parser.add_argument("--labels", help="labels file", required=True) parser.add_argument( "--noviz", help="no visualization", action="store_true" ) args = parser.parse_args() if osp.exists(args.output_dir): print("Output directory already exists:", args.output_dir) sys.exit(1) os.makedirs(args.output_dir) os.makedirs(osp.join(args.output_dir, "JPEGImages")) if not args.noviz: os.makedirs(osp.join(args.output_dir, "Visualization")) print("Creating dataset:", args.output_dir) now = datetime.datetime.now() data = dict( info=dict( description=None, url=None, version=None, year=now.year, contributor=None, date_created=now.strftime("%Y-%m-%d %H:%M:%S.%f"), ), licenses=[ dict( url=None, id=0, name=None, ) ], images=[ # license, url, file_name, height, width, date_captured, id ], type="instances", annotations=[ # segmentation, area, iscrowd, image_id, bbox, category_id, id ], categories=[ # supercategory, id, name ], ) class_name_to_id = { 
   } for i, line in enumerate(open(args.labels).readlines()): class_id = i - 1 # starts with -1 class_name = line.strip() if class_id == -1: assert class_name == "__ignore__" continue class_name_to_id[class_name] = class_id data["categories"].append( dict( supercategory=None, id=class_id, name=class_name, ) ) out_ann_file = osp.join(args.output_dir, "annotations.json") label_files = glob.glob(osp.join(args.input_dir, "*.json")) for image_id, filename in enumerate(label_files): print("Generating dataset from:", filename) label_file = labelme.LabelFile(filename=filename) base = osp.splitext(osp.basename(filename))[0] out_img_file = osp.join(args.output_dir, "JPEGImages", base + ".jpg") img = labelme.utils.img_data_to_arr(label_file.imageData) imgviz.io.imsave(out_img_file, img) data["images"].append( dict( license=0, url=None, file_name=osp.relpath(out_img_file, osp.dirname(out_ann_file)), height=img.shape[0], width=img.shape[1], date_captured=None, id=image_id, ) ) masks = { 
   } # for area segmentations = collections.defaultdict(list) # for segmentation for shape in label_file.shapes: points = shape["points"] label = shape["label"] group_id = shape.get("group_id") shape_type = shape.get("shape_type", "polygon") mask = labelme.utils.shape_to_mask( img.shape[:2], points, shape_type ) if group_id is None: group_id = uuid.uuid1() instance = (label, group_id) if instance in masks: masks[instance] = masks[instance] | mask else: masks[instance] = mask if shape_type == "rectangle": (x1, y1), (x2, y2) = points x1, x2 = sorted([x1, x2]) y1, y2 = sorted([y1, y2]) points = [x1, y1, x2, y1, x2, y2, x1, y2] if shape_type == "circle": (x1, y1), (x2, y2) = points r = np.linalg.norm([x2 - x1, y2 - y1]) # r(1-cos(a/2))<x, a=2*pi/N => N>pi/arccos(1-x/r) # x: tolerance of the gap between the arc and the line segment n_points_circle = max(int(np.pi / np.arccos(1 - 1 / r)), 12) i = np.arange(n_points_circle) x = x1 + r * np.sin(2 * np.pi / n_points_circle * i) y = y1 + r * np.cos(2 * np.pi / n_points_circle * i) points = np.stack((x, y), axis=1).flatten().tolist() else: points = np.asarray(points).flatten().tolist() segmentations[instance].append(points) segmentations = dict(segmentations) for instance, mask in masks.items(): cls_name, group_id = instance if cls_name not in class_name_to_id: continue cls_id = class_name_to_id[cls_name] mask = np.asfortranarray(mask.astype(np.uint8)) mask = pycocotools.mask.encode(mask) area = float(pycocotools.mask.area(mask)) bbox = pycocotools.mask.toBbox(mask).flatten().tolist() data["annotations"].append( dict( id=len(data["annotations"]), image_id=image_id, category_id=cls_id, segmentation=segmentations[instance], area=area, bbox=bbox, iscrowd=0, ) ) if not args.noviz: viz = img if masks: labels, captions, masks = zip( *[ (class_name_to_id[cnm], cnm, msk) for (cnm, gid), msk in masks.items() if cnm in class_name_to_id ] ) viz = imgviz.instances2rgb( image=img, labels=labels, masks=masks, captions=captions, font_size=15, line_width=2, ) out_viz_file = osp.join( args.output_dir, "Visualization", base + ".jpg" ) imgviz.io.imsave(out_viz_file, viz) with open(out_ann_file, "w") as f: json.dump(data, f) if __name__ == "__main__": main()