ChestX-Det-Dataset data set URL: https://github.com/Deepwise-AILab/ChestX-Det-Dataset/tree/main
Dataset JSON content:
[
{<!-- -->
"file_name": "36199.png",
"syms": [],
"boxes": [],
"polygons": []
},
{<!-- -->
"file_name": "36302.png",
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},
Converted coco format sample json:
The python code used is as follows:
import json
import os
importsys
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()
Run the training set first or the validation set first to generate the dictionary content of category_mapping. After generation, use the same one to ensure that the labels of the training set and the validation set are consistent.