labelme batch processing json files, json_to_dataset method
This is the data generated after processing the. JSON file. The usage method is labelme_json_to_dataset + space + file name. JSON, the premise is that labelme should be installed and activated accurately. But this will cause a problem. It's too troublesome to process multiple images. Here we provide a tool to convert all JSON files directly under the. JSON file directory,
Step 1: find json_to_dataset.py file
Open the abelme installation directory:
1.labelme is directly installed in the main environment of anaconda, in the directory of C: \ programdata \ anaconda3 \ lib \ site packages \ labelme \ cli (depending on the installation directory of Anaconda on your computer). You can see json_to_dataset.py Documents.
2.labelme is installed in the newly created virtual environment. You need to find the virtual environment file in the envs folder under the anananda installation directory, C: \ programdata \ anaconda3 \ envs \ LB \ lib \ site packages \ labelme \ cli.
Step 2 modify json_to_dataset.py file
Replace JSON with the code below_ To_ dataset.py File and save
import argparse import json import os import os.path as osp import warnings import PIL.Image import yaml from labelme import utils import base64 def main(): warnings.warn("This script is aimed to demonstrate how to convert the\n" "JSON file to a single image dataset, and not to handle\n" "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) count = os.listdir(json_file) for i in range(0, len(count)): path = os.path.join(json_file, count[i]) if os.path.isfile(path): data = json.load(open(path)) if data['imageData']: imageData = data['imageData'] else: imagePath = os.path.join(os.path.dirname(path), 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 data['shapes']: 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 # label_values must be dense label_values, label_names = [], [] for ln, lv in sorted(label_name_to_value.items(), key=lambda x: x[1]): label_values.append(lv) label_names.append(ln) assert label_values == list(range(len(label_values))) lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value) captions = ['{}: {}'.format(lv, ln) for ln, lv in label_name_to_value.items()] lbl_viz = utils.draw_label(lbl, img, captions) out_dir = osp.basename(count[i]).replace('.', '_') out_dir = osp.join(osp.dirname(count[i]), out_dir) if not osp.exists(out_dir): os.mkdir(out_dir) PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png')) #PIL.Image.fromarray(lbl).save(osp.join(out_dir, 'label.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') warnings.warn('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) print('Saved to: %s' % out_dir) if __name__ == '__main__': main()
Step 3 find labelme_json_to_dataset.exe file
The method is the same as the first step, I will only show the directory under my virtual environment: C:\ProgramData\Anaconda3\envs\lb\Scripts
Step 4 switch paths
Open anaconda prompt to activate the virtual environment
Use the cd command to switch paths: cd C:\ProgramData\Anaconda3\envs\lb\Scripts
Step 5: pass in the. json file path and perform the conversion
Executed by: labelme_ json_ To_ dataset.exe +Path to JSON file
Automatically generate conversion files in scripts:
complete
If an error occurs AttributeError: module ' labelme.utils ’No 'draw_label 'attribute, attributeerrormodule label
You need to change the labelme version. You need to reduce the labelme version to 3.16.2. Enter the labelme environment by typing pip install labelme==3.16.2 to download this version automatically. You can succeed.