labelme

2023-08-13 17:34| 来源: 网络整理| 查看: 265

项目场景：

使用labelme批量将json文件转换成png文件

问题描述：

labelme_json_to_dataset.exe只能将单个json文件转换为png文件

原因分析：

D:\software\Anaconda3\envs\labelme\Lib\site-packages\labelme\cli路径下的json_to_dataset.py文件中定义的函数只执行一次，固只能转换单个json文件

import argparse import base64 import json import os import os.path as osp import imgviz import PIL.Image import yaml from labelme.logger import logger from labelme import utils '''single json file''' def main(): logger.warning( "This script is aimed to demonstrate how to convert the " "JSON file to a single image dataset." ) logger.warning( "It won't handle multiple JSON files to generate a " "real-use dataset." ) parser = argparse.ArgumentParser() parser.add_argument("json_file") parser.add_argument("-o", "--out", default=None) args = parser.parse_args() json_file = args.json_file if args.out is None: out_dir = osp.basename(json_file).replace(".", "_") out_dir = osp.join(osp.dirname(json_file), out_dir) else: out_dir = args.out if not osp.exists(out_dir): os.mkdir(out_dir) data = json.load(open(json_file)) imageData = data.get("imageData") if not imageData: imagePath = os.path.join(os.path.dirname(json_file), data["imagePath"]) with open(imagePath, "rb") as f: imageData = f.read() imageData = base64.b64encode(imageData).decode("utf-8") img = utils.img_b64_to_arr(imageData) label_name_to_value = {"_background_": 0} for shape in sorted(data["shapes"], key=lambda x: x["label"]): label_name = shape["label"] if label_name in label_name_to_value: label_value = label_name_to_value[label_name] else: label_value = len(label_name_to_value) label_name_to_value[label_name] = label_value lbl, _ = utils.shapes_to_label( img.shape, data["shapes"], label_name_to_value ) label_names = [None] * (max(label_name_to_value.values()) + 1) for name, value in label_name_to_value.items(): label_names[value] = name lbl_viz = imgviz.label2rgb( label=lbl, img=imgviz.asgray(img), label_names=label_names, loc="rb" ) PIL.Image.fromarray(img).save(osp.join(out_dir, "img.png")) utils.lblsave(osp.join(out_dir, "label.png"), lbl) PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, "label_viz.png")) with open(osp.join(out_dir, "label_names.txt"), "w") as f: for lbl_name in label_names: f.write(lbl_name + "\n") # 生成info.yaml文件 # logger.warning('info.yaml is being replaced by label_names.txt') # info = dict(label_names=label_names) # with open(osp.join(out_dir, 'info.yaml'), 'w') as f: # yaml.safe_dump(info, f, default_flow_style=False) logger.info("Saved to: {}".format(out_dir)) if __name__ == "__main__": main() 解决方案：

修改json_to_dataset.py文件，加入一个遍历json文件的循环即可

import argparse import base64 import json import os import os.path as osp import imgviz import PIL.Image import yaml from labelme.logger import logger from labelme import utils '''multiple json files''' def main(): parser = argparse.ArgumentParser() parser.add_argument('json_file') # 标注文件json所在的文件夹 parser.add_argument('-o', '--out', default=None) args = parser.parse_args() json_file = args.json_file list = os.listdir(json_file) # 获取json文件列表 for i in range(0, len(list)): path = os.path.join(json_file, list[i]) # 获取每个json文件的绝对路径 filename = list[i][:-5] # 提取出.json前的字符作为文件名，以便后续保存Label图片的时候使用 extension = list[i][-4:] if extension == 'json': if os.path.isfile(path): # 判断是否是文件 data = json.load(open(path)) # 加载json文件 img = utils.image.img_b64_to_arr(data['imageData']) # 根据'imageData'字段的字符得到原图像 # data['shapes']是json文件中记录着标注的位置及label等信息的字段 label_name_to_value = {"_background_": 0} for shape in sorted(data["shapes"], key=lambda x: x["label"]): label_name = shape["label"] if label_name in label_name_to_value: label_value = label_name_to_value[label_name] else: label_value = len(label_name_to_value) label_name_to_value[label_name] = label_value # lbl为label图片（标注的地方用类别名对应的数字来标，其他为0） lbl, _ = utils.shapes_to_label( img.shape, data["shapes"], label_name_to_value ) # lbl_names为label名和数字的对应关系字典 label_names = [None] * (max(label_name_to_value.values()) + 1) for name, value in label_name_to_value.items(): label_names[value] = name lbl_viz = imgviz.label2rgb( label=lbl, img=imgviz.asgray(img), label_names=label_names, loc="rb" ) out_dir = osp.basename(list[i])[:-5]+'_json' # 创建“*_json”文件夹 # os.path.dirname（）返回去掉文件名的路径 out_dir = osp.join(osp.dirname(list[i]), out_dir) if not osp.exists(out_dir): os.mkdir(out_dir) PIL.Image.fromarray(img).save(osp.join(out_dir, '{}_source.png'.format(filename))) utils.lblsave(osp.join(out_dir, "{}_label.png".format(filename)), lbl) PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, '{}_mask.png'.format(filename))) #PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, '{}_viz.jpg'.format(filename))) with open(osp.join(out_dir, 'label_names.txt'), 'w') as f: for lbl_name in label_names: f.write(lbl_name + '\n') # warnings.warn('info.yaml is being replaced by label_names.txt') # info = dict(label_names=lbl_names) # with open(osp.join(out_dir, 'info.yaml'), 'w') as f: # yaml.safe_dump(info, f, default_flow_style=False) print('Saved to: %s' % out_dir) if __name__ == "__main__": main()

然后进入安装了labelme的虚拟环境下，在控制台窗口执行转换程序即可：

labelme_json_to_dataset H:\\dataset\\train\\label # "H:\\dataset\\train\\label"为json所在的文件夹

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