1、修改D:\Anaconda3\Lib\site-packages\labelme\cli下的json_to_dataset.py.
import argparseimport jsonimport osimport os.path as ospimport warningsimport PIL.Imageimport yamlfrom labelme import utilsimport base64def 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_fileif 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.outif 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 denselabel_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()
2、将所有json文件放在一个文件夹中E:\oralcell\test327\json 进入环境D:\Anaconda3\Scripts 这个目录下有labelme_json_to_dataset.exe 执行
labelme_json_to_dataset.exe E:\oralcell\test327\json
生成的文件夹在D:\Anaconda3\Scripts中，将他们放在E:\oralcell\test327\package中
每个json文件生成一个json文件夹，文件夹中有五项，要用的是label.png。
如果出错可能是labelme版本不对
pip install labelme==3.16.7
3、将每个json文件夹中的label取出来放在一个文件夹中E:\oralcell\test327\png,并重命名为原图的名字。
# -*- coding: utf-8 -*-import osimport numpy as npimport jsonimport shutildef find_dir_path(path, keyword_name, dir_list):files = os.listdir(path)for file_name in files:file_path = os.path.join(path, file_name)if os.path.isdir(file_path) and keyword_name not in file_path:find_dir_path(file_path, keyword_name, dir_list)elif os.path.isdir(file_path) and keyword_name in file_path:dir_list.append(file_path)all_result_path = []src_path = r'E:\oralcell\test327\package'#json文件夹的目录label_save_path = r'E:\oralcell\test327\png'#png存放的目录find_dir_path(src_path, '_json', all_result_path) # 找出所有带着关键词(_json)的所有目标文件夹# print(all_result_path)for dir_path in all_result_path:# print(dir_path)file_name = dir_path.split('\\')[-1]key_word = file_name[:-5]# print(key_word)label_file = dir_path + "\\" + "label.png"new_label_save_path = label_save_path + "\\" + key_word + ".png" # 复制生成的label.png到新的文件夹# print(new_label_save_path)shutil.copyfile(label_file, new_label_save_path)
4、改通道数
#批量图片转换：将RGB模式或P模式转换为L灰度拉伸值(可以自定义拉伸大小)import numpy as npfrom PIL import Imageimport osdef Mode_P_to_L(img_file,stretch_value):"""将当前文件下的所有图片进行灰度值转换:param img_file: 图片文件存放目录:param stretch_value: 拉伸值， 图片原始值*stretch_value的结果理论上应该小于255"""##获取目录下所有文件名file_name_list = os.listdir(img_file)#遍历所有图片文件for file in file_name_list:#获取某个图片的全路径img_path = os.path.join(img_file, file)#打开图片image = Image.open(img_path)print(image.mode) #p模式img_arry = Image.fromarray(np.uint8(image))print(img_arry.mode)#L模式img_L = img_arry.convert("L")print(img_L.mode)#灰度拉伸img_end = Image.fromarray(np.uint8(img_L) * stretch_value)print(img_end.mode)#保存图片，并覆盖原图img_end.save(img_path)#提示print("完成对图片：",file," 的转换！")print("所有图片均已完成转换！")#程序主入口if __name__ == "__main__":# 需要转换的图片所在文件目录img_file = r"E:\oralcell\label327\png\segmentations"#自己输入上一步生成的png格式的标签图stretch_value = 1#自定义拉伸值，但要注意，图片标签值*stretch_value的结果理论上应该小于255Mode_P_to_L(img_file, stretch_value)#调用自定义的转换方法
