图像分割报Polygon must have points more than 2

在做图像分割,用labelme文件打标数据后,解析json文件显示此错误

查资料，是因为 labelme 打标时一般都是多边形，所有 points 的个数必然大于等于3，但是我的数据集中存在只有一个 point 的标签，所以加了一个 fause_json 函数来筛选出错的 json，以便于转化可以顺利进行

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

import numpy as np

warnings.filterwarnings('ignore')
import PIL.Image
import base64


def fause_json(jsonfilelist, json_path, fause_json_list):
    '''
    筛选因为形状问题无法转化的 json 文件
    '''
    for i in jsonfilelist:
        f = open(json_path   "\\"   i, "r")
        jsonfile = json.loads(f.read())
        for j in jsonfile['shapes']:
            point_list = j['points']
            if len(point_list) < 3:
                fause_json_list.append(i)
    return fause_json_list


# def main():
#     '''
#     json 转我们需要的数据格式
#     '''
#     count = os.listdir("F:\luzice\\")  # 自己的路径
#     index = 0
#     old_jsonlist = os.listdir("F:\luzice")  # 自己的路径
#     repeat_list = []
#     for i in range(0, len(count)):
#         if (count[i] not in old_jsonlist) and (count[i] not in fause_json_list):
#             path = os.path.join("F:\luzice", 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, label_names = [], []  # label_values must be dense
#                 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)
#
#                 if not os.path.exists("D:\\train_dataset"):
#                     os.mkdir("D:\\train_dataset")
#                 label_path = "D:\\train_dataset\\mask"
#                 if not os.path.exists(label_path):
#                     os.mkdir(label_path)
#                 img_path = "D:\\train_dataset\\imgs"
#                 if not os.path.exists(img_path):
#                     os.mkdir(img_path)
#                 yaml_path = "D:\\train_dataset\\yaml"
#                 if not os.path.exists(yaml_path):
#                     os.mkdir(yaml_path)
#                 label_viz_path = "D:\\train_dataset\\label_viz"
#                 if not os.path.exists(label_viz_path):
#                     os.mkdir(label_viz_path)
#
#                 PIL.Image.fromarray(img).save(osp.join(img_path, str(index)   '.jpg'))
#
#                 utils.lblsave(osp.join(label_path, str(index)   '.png'), lbl)
#                 PIL.Image.fromarray(lbl_viz).save(osp.join(label_viz_path, str(index)   '.png'))
#
#                 warnings.warn('info.yaml is being replaced by label_names.txt')
#                 info = dict(label_names=label_names)
#                 with open(osp.join(yaml_path, str(index)   '.yaml'), 'w') as f:
#                     yaml.safe_dump(info, f, default_flow_style=False)
#                 index = index   1
#                 print(f"Saved : {str(index)}")
#         else:
#             repeat_list.append(count[i])
#             print(f"{count[i]} 已存在")
#
#     print("全部转化完成!")
#     return repeat_list


if __name__ == '__main__':
    json_path = "F:\luzice"  # 存储 json 文件的路径，用自己的路径
    jsonfilelist = os.listdir(json_path)
    fause_json_list = []
    fause_json_list = fause_json(jsonfilelist, json_path, fause_json_list)
    print(np.array(fause_json_list))
#  repeat_list = main()

# 运行该函数后输出的两个比较重要的列表
# fause_json_list：因为形状问题无法转化的 json 文件列表;
# repeat_list:和已存在的 json 文件重复的 json 文件列表

出现Windows下UnicodeDecodeError: ‘gbk‘ codec can‘t decode byte 0xff in position 0错误的方法

解决的方法是因为文件中有非.json文件

:
参考地址:labelme 打标数据 json_to_dataset,以及 yaml 文件标签替换问题

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