After segmentation, the mask image data is converted into the json format corresponding to coco

After segmentation,
the mask binary image data is converted into the json format corresponding to coco

import os, json, numpy as np
from tqdm import tqdm
from imantics import Mask, Image, Category, Dataset
import cv2

dataset = Dataset('foot') # 先定义一个数据库对象，后续需要往里面添加具体的image和annotation

path1 = r'\\10.214.150.97\Public\datasets\real_foot\real_foot_2021_03' # image对应的mask的文件路径
for index, j in enumerate(tqdm(os.listdir(path1))):
    if str(j)[-1] == 'r' or str(j)[-1] == 'l':
        path = os.path.join(path1, j, 'masks')
        if not os.path.exists(path):
            path = os.path.join(path1, j, 'mask')
        for index, i in enumerate(tqdm(os.listdir(path))):
            mask_file = os.path.join(path, i)
            name = i.split('.')[0]
            file = os.path.join(path1, j, '{}.jpg'.format(name))

            image = cv2.imread(file)[:,:,::-1]
            image = Image(image, id=index+1) # 定义一个Image对象
            image.file_name = '{}.jpg'.format(name) # 为上面的Image对象添加coco标签格式的'file_name'属性
            image.path = file # 为Image对象添加coco标签格式的'path'属性

            mask = cv2.imread(mask_file, 0)
            ret, mask = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)
            t = cv2.imread(file)
            if t.shape[:-1] != mask.shape:
                h, w, _ = t.shape
                mask = cv2.resize(mask, (w, h), cv2.INTER_CUBIC)

            mask = Mask(mask) # 定义一个Mask对象，并传入上面所定义的image对应的mask数组

            categ = 'foot'
            t = Category(categ) # 这里是定义Category对象
            t.id = 1
            image.add(mask, t) # 将mask信息和类别信息传给image
            dataset.add(image) # 往dataset里添加图像以及gt信息

t = dataset.coco() # 将dataset转化为coco格式的，还可以转化为yolo等格式
with open('foot.json', 'w') as output_json_file: # 最后输出为json数据
    json.dump(t, output_json_file)

There will be some problems in the segmentation part of the json data after the conversion. It is not a 1 * n-dimensional data but contains many segments, which need to be merged. As shown in the figure: before merging: after merging, all edge
points
are

in a list:

The final converted format is as follows:

Merge code:

import os
import json
import numpy as np
from tkinter import _flatten

with open('foot1.json','r') as f:
    load_dict = json.load(f)
    list1 = load_dict["annotations"]
    for i in range(0, len(list1)):
        a = list(_flatten(list1[i]["segmentation"]))
        b = np.asarray(a).reshape(1, len(a)).tolist()
        load_dict["annotations"][i]["segmentation"] = b

    # print(len(list1))
    # print(np.asarray(list1).shape)

with open("test.json",'w',encoding='utf-8') as f:
    json.dump(load_dict, f,ensure_ascii=False)