labelme数据转coco instance segmentation

本文主要是介绍labelme数据转coco instance segmentation，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

转数据参考：https://www.freesion.com/article/1518170289/
需要安装pycocotools，参考这里：https://blog.csdn.net/summermaoz/article/details/115969308?spm=1001.2014.3001.5501

可能需要根据自己的标注情况做一点点修改 labelme2coco.py

#!/usr/bin/env pythonimport argparse
import collections
import datetime
import glob
import json
import os
import os.path as osp
import sys
import numpy as np
import PIL.Image
import labelmetry: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('--filename', help='output filename')parser.add_argument('--labels', help='labels file', required=True)args = parser.parse_args()if osp.exists(args.output_dir):print('Output directory already exists:', args.output_dir)# sys.exit(1)# if not os.path.exists()else:os.makedirs(args.output_dir)os.makedirs(osp.join(args.output_dir, 'JPEGImages'))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 -1class_name = line.strip()if class_id == -1:assert class_name == '__ignore__'continueclass_name_to_id[class_name] = class_iddata['categories'].append(dict(supercategory=None,id=class_id,name=class_name,))out_ann_file = osp.join(args.output_dir,  args.filename+'.json')label_files = glob.glob(osp.join(args.input_dir, '*.json'))for image_id, label_file in enumerate(label_files):print('Generating dataset from:', label_file)with open(label_file) as f:label_data = json.load(f)base = osp.splitext(osp.basename(label_file))[0]out_img_file = osp.join(args.output_dir, 'JPEGImages', base + '.jpg')path = label_data['imagePath']img_file = osp.join(osp.dirname(label_file), path).replace('png', 'jpg')img = np.asarray(PIL.Image.open(img_file))	PIL.Image.fromarray(img).save(out_img_file)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 areasegmentations = collections.defaultdict(list)  # for segmentationfor shape in label_data['shapes']:points = shape['points']label = shape['label']shape_type = shape.get('shape_type', None)mask = labelme.utils.shape_to_mask(img.shape[:2], points, shape_type)if label in masks:masks[label] = masks[label] | maskelse:masks[label] = maskpoints = np.asarray(points).flatten().tolist()segmentations[label].append(points)for label, mask in masks.items():cls_name = label[:10]if cls_name not in class_name_to_id:continuecls_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[label],area=area,bbox=bbox,iscrowd=0,))print('data:', data)with open(out_ann_file, 'w') as f:json.dump(data, f)if __name__ == '__main__':main()