python把ChestX-Det-Dataset的json样本转为COCO数据集的json格式

ChestX-Det-Dataset数据集网址：https://github.com/Deepwise-AILab/ChestX-Det-Dataset/tree/main

数据集JSON内容：

[
    {
        "file_name": "36199.png",
        "syms": [],
        "boxes": [],
        "polygons": []
    },
    {
        "file_name": "36302.png",
        "syms": [
            "Effusion"
        ],
        "boxes": [
            [
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                666,
                937,
                761
            ]
        ],
        "polygons": [
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                ]
            ]
        ]
    },

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转化后coco格式样本json：

使用的python代码如下：

import json
import os
import sys
import cv2
from tqdm import tqdm
import math

che_json = './chetrain.json'
dst_json = './chestrain_coco.json'
test_img = './train_data/train'

# che_json = './chetest.json'
# dst_json = './chetest_coco.json'
# test_img = './test_data/test'

def polygon_area(vertices):
    n = len(vertices)
    area = 0.0
    for i in range(n):
        x1, y1 = vertices[i]
        x2, y2 = vertices[(i + 1) % n]
        area += (x1 * y2 - x2 * y1)
    return abs(area) / 2.0

def main():
    coco_data = {
        "info": {},
        "licenses": [],
        "categories": [],
        "images": [],
        "annotations": []
    }
	category_mapping = {}
    category_id = 1
    image_id = 1
    annotation_id = 1
    with open(che_json,'r',encoding='utf-8') as js:
        json_info = json.load(js)
    
    image = {}
    annotation = {}
    boxid = 0
    for jsfo in tqdm(json_info):
        image['file_name'] = jsfo['file_name']
        img =cv2.imread(test_img + '/' + jsfo['file_name']) 
        image['height'] = img.shape[0]
        image['width'] = img.shape[1]
        image['id'] = image_id
        coco_data["images"].append(image)
        image = {}
        category_name = jsfo['syms']
        for ii in range(len(category_name)):
            if category_name[ii] not in category_mapping:
                category_mapping[category_name[ii]] = category_id
                coco_data["categories"].append({
                    "supercategory": category_name[ii],
                    "id": category_id,
                    "name": category_name[ii]
                })
                category_id += 1
        box_cnt = len(jsfo['boxes'])
        for i in range(box_cnt):
            boxid = boxid + 1
            segpnts = []
            segtmp = jsfo['polygons'][i]
            for segt in segtmp:
                segpnts.append(segt[0])
                segpnts.append(segt[1])
            segarea = polygon_area(segtmp)
            annotation['segmentation'] = [segpnts]
            annotation['image_id'] = image_id
            annotation['area'] = segarea
            boxtmp = jsfo['boxes'][i]
            x_left,y_left,x_br,y_br = boxtmp
            box_w,box_h = x_br-x_left, y_br-y_left
            annotation['bbox'] = [x_left,y_left,box_w,box_h]
            annotation['category_id'] = category_mapping[category_name[i]]
            annotation['id'] = boxid
        coco_data["annotations"].append(annotation)
        annotation = {}
        image_id = image_id + 1
    with open(dst_json,'w') as jsout:
        json.dump(coco_data, jsout)

if __name__=='__main__':
    main()
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先跑训练集或先跑验证集会生成category_mapping 的字典内容，生成后统一用同一个，就可以保证训练集和验证集的标签一致