[人工智能]深度学习分割json_to_data报错Too many dimensions: 3 ＞ 2

包这个错的原因是labelme（我的是5.0.1）的版本太新了，与旧版本labelme标注生成的json文件有所区别

解决办法1：把labelme版本降低，降到3.16.7

解决办法2：直接换代码，json_to_data的目的其实就是根据标注完的json文件来获取到mask图。

因为在语义分割在训练的时候用到的其实也就是原图和mask图，所以只要达到目的就好了。

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

import imgviz
import PIL.Image

from labelme.logger import logger
from labelme import utils


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."
    )

    # json_file是标注完之后生成的json文件的目录。out_dir是输出目录，即数据处理完之后文件保存的路径
    json_file = r"D:\Code\python\pytorch\unet-pytorch-main\myself_datas\01_cat_dog\before"
    out_dir1 = r"D:\Code\python\pytorch\unet-pytorch-main\myself_datas\01_cat_dog\SegmentationClass"

    # 如果输出的路径不存在，则自动创建这个路径
    if not osp.exists(out_dir1):
        os.mkdir(out_dir1)

    for file_name in os.listdir(json_file):
        # 遍历json_file里面所有的文件，并判断这个文件是不是以.json结尾
        if file_name.endswith(".json"):
            path = os.path.join(json_file, file_name)
            if os.path.isfile(path):
                data = json.load(open(path))

                # 获取json里面的图片数据，也就是二进制数据
                imageData = data.get("imageData")
                # 如果通过data.get获取到的数据为空，就重新读取图片数据
                if not imageData:
                    imagePath = os.path.join(json_file, data["imagePath"])
                    with open(imagePath, "rb") as f:
                        imageData = f.read()
                        imageData = base64.b64encode(imageData).decode("utf-8")
                #  将二进制数据转变成numpy格式的数据
                img = utils.img_b64_to_arr(imageData)

                # 将类别名称转换成数值，以便于计算
                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, image=imgviz.asgray(img), label_names=label_names, loc="rb"
                )

                # out_dir = osp.basename(file_name).replace('.', '_')
                # out_dir = osp.join(out_dir1, out_dir)
                # if not osp.exists(out_dir):
                #     os.mkdir(out_dir)
                #     print(out_dir)

                # 将输出结果保存，
                # PIL.Image.fromarray(img).save(osp.join(out_dir, "%s_img.jpg" % file_name.split(".")[0]))
                utils.lblsave(osp.join(out_dir1, "%s.png" % file_name.split(".")[0]), 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")

                logger.info("Saved to: {}".format(out_dir1))



if __name__ == "__main__":
    main()