json_to_mask

'''
修改后的json_to_dataset文件，直接复制替换你自己原始的json_to_dataset，建议保存一下原版
'''
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."
    )
    """
    parser = argparse.ArgumentParser()
    parser.add_argument("json_file")
    parser.add_argument("-o", "--out", default=None)
    args = parser.parse_args()
 
    json_file = args.json_file
    print(osp.dirname(json_file))
 
    if osp.isdir(osp.join(osp.dirname(json_file), 'json_data')) is False:
        os.mkdir(osp.join(osp.dirname(json_file), 'json_data'))
    else:
        print("文件已存在")
    if args.out is None:
        out_dir = osp.basename(json_file).replace(".", "_")
        out_dir1 = osp.join(osp.dirname(json_file), 'json_data')
        out_dir = osp.join(out_dir1, out_dir)
        print(out_dir)
        print("#" * 10)
    else:
        out_dir = args.out
    if not osp.exists(out_dir):
        os.mkdir(out_dir)
 
    data = json.load(open(json_file))
    imageData = data.get("imageData")
 
    if not imageData:
        imagePath = os.path.join(os.path.dirname(json_file), 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 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(
        lbl, imgviz.asgray(img), label_names=label_names, loc="rb"
    )
 
    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")
 
    logger.info("Saved to: {}".format(out_dir))
 
 
if __name__ == "__main__":
    main()

修改地方：删除lbl后的“,-” ，只让lbl接受函数结果，要不然会报返回值与接受值不匹配的错误，ValueError: too many values to unpack (expected 2)。这类错误出现原因是比如返回三个值，但你用2个变量接受。