import base64
import json
import os
import os.path as osp

import numpy as np
import PIL.Image
from labelme import utils

'''
制作自己的语义分割数据集需要注意以下几点：
1、我使用的labelme版本是3.16.7,建议使用该版本的labelme,有些版本的labelme会发生错误.
   具体错误为Too many dimensions: 3 > 2
   安装方式为命令行pip install labelme==3.16.7
2、此处生成的标签图是8位彩色图与视频中看起来的数据集格式不太一样。
   虽然看起来是彩图但事实上只有8位此时每个像素点的值就是这个像素点所属的种类。
   所以其实和视频中VOC数据集的格式一样。因此这样制作出来的数据集是可以正常使用的。也是正常的。
'''
if __name__ == '__main__':
    jpgs_path = "datasets/JPEGImages"
    pngs_path = "datasets/SegmentationClass"
    #classes = ["_background_","Egg white", "yolk"]
    classes = ["_background_","Germinal disc"]
    # classes     = ["_background_","cat","dog"]

    count = os.listdir(r"./datasets/output_images_copy/")
    for i in range(0, len(count)):
        path = os.path.join(r"./datasets/output_images_copy", 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'])
                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)

            # 将图像从 RGBA 模式转换为 RGB 模式
            pil_img = PIL.Image.fromarray(img)
            if pil_img.mode == 'RGBA':
                pil_img = pil_img.convert('RGB')
            pil_img.save(osp.join(jpgs_path, count[i].split(".")[0]+'.jpg'))

            new = np.zeros([np.shape(img)[0], np.shape(img)[1]])
            for name in label_names:
                try:
                    index_json = label_names.index(name)
                    index_all = classes.index(name)
                    new = new + index_all * (np.array(lbl) == index_json)
                except ValueError:
                    print(f"类别 {name} 不在 classes 列表中，跳过该类别。")

            utils.lblsave(osp.join(pngs_path, count[i].split(".")[0]+'.png'), new)
            print('Saved ' + count[i].split(".")[0] + '.jpg and ' + count[i].split(".")[0] + '.png')

