操作步骤
执行json_to_dataset.py文件，批量将json转为dataset执行get_jpg_and_png.py文件，生成对应的png灰度图注意：使用 3.16.2版本的labelme, 博主试过4.2.10版本的labelme，会报错，没有draw.py文件。 且博主试过了，将3.16.2版本的draw.py文件拷贝到4.2.10文件夹下的utils文件中，自己构建也是不行的，会报错。 应该得改动draw.py文件中的内容吧。
以下代码为全部代码，可直接运行。
1. json_to_dataset.py 代码（批量将json转为dataset）：
# -*- encoding: utf-8 -*-"""@File : json_to_dataset.py.py@Time : /5/28 18:09@Author : ligang@WeChat : by15188607997@Software: PyCharm@explain：本文件为将json文件批量转为dataset"""import argparseimport jsonimport osimport os.path as ospimport warningsimport PIL.Imageimport yamlfrom labelme import utilsimport base64# 使用 3.16.2版本的labelme, 博主试过4.2.10版本的labelme，会报错，没有draw.py文件。# 且博主试过了，将3.16.2版本的draw.py文件拷贝到4.2.10文件夹下的utils文件中，自己构建也是不行的，会报错。# 应该得改动draw.py文件中的内容吧。def main(frompath, outputpath):count = os.listdir(frompath)for i in range(0, len(count)):path = os.path.join(frompath, count[i])if os.path.isfile(path) and path.endswith('json'):data = json.load(open(path))if data['imageData']:imageData = data['imageData']else:imagePath = os.path.join(os.path.dirname(path), data['imagePath'])print(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)out_dir = osp.join(outputpath, out_dir)if not osp.exists(out_dir):os.mkdir(out_dir)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')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__':# 源图片、json 文件路径frompath = "./before/"# 生成数据保存路径outputpath = "./output"if not osp.exists(outputpath):os.mkdir(outputpath)main(frompath, outputpath)
结果：保存在输出的output文件中，此文件作用同 labelme_json_to_dataset <**.json> 作用。上面代码是批量的。
2. get_jpg_and_png.py代码（生成对应的png灰度图）：
注意：class_name.txt文件中的类需要手动填写。
# -*- encoding: utf-8 -*-"""@File : get_jpg_and_png.py@Time : /5/28 18:07@Author : ligang@WeChat : by15188607997@Software: PyCharm"""import osfrom PIL import Imageimport numpy as npdef main(frompath_jpg, outputpath_json, output_jpg, output_png, path_allclass):# 读取原文件夹count = os.listdir(frompath_jpg)for i in range(0, len(count)):# 如果里的文件以jpg结尾# 则寻找它对应的pngif count[i].endswith("jpg"):path = os.path.join(frompath_jpg, count[i])img = Image.open(path)img.save(os.path.join(output_jpg, count[i]))print(count[i].split(".")[1])# 找到对应的pngpath = outputpath_json + count[i].split(".")[0] + "_json/label.png"img = Image.open(path)# 找到全局的类class_txt = open(path_allclass, "r")class_name = class_txt.read().splitlines()# ["bk","cat","dog"]# 打开json文件里面存在的类，称其为局部类with open(outputpath_json + count[i].split(".")[0] + "_json/label_names.txt", "r") as f:names = f.read().splitlines()# ["bk","dog"]new = Image.new("RGB", [np.shape(img)[1], np.shape(img)[0]])for name in names:# index_json是json文件里存在的类，局部类index_json = names.index(name)# index_all是全局的类index_all = class_name.index(name)# 将局部类转换成为全局类new = new + np.expand_dims(index_all * (np.array(img) == index_json), -1)new = Image.fromarray(np.uint8(new))print(output_png)new.save(os.path.join(output_png, count[i].replace("jpg", "png")))print(np.max(new), np.min(new))if __name__ == '__main__':# 全局类（所有标签总共有多少类） 如：# _background_（不可少）# Albatross# _Yellowthroatpath_allclass = "./before/class_name.txt"# 源图片、json 文件路径frompath_jpg = "./before/"# 生成数据保存路径outputpath_json = "./output/"# 生成jpg数据的保存位置output_jpg = "./traindata/jpg/"# 生成png数据的保存位置output_png = "./traindata/png/"if not os.path.exists(output_jpg):os.makedirs(output_jpg)if not os.path.exists(output_png):os.makedirs(output_png)main(frompath_jpg, outputpath_json, output_jpg, output_png, path_allclass)
结果：成果保存在traindata文件夹下。生成的png图片为二值化图。像素变化从 （1,1，1）开始的眼是看不来的，可以用取色器进行取色验证下。
目录结构：
