bddk100 转coco
时间: 2023-08-08 12:11:16 浏览: 44
你可以使用以下代码将bddk100数据集转换为COCO格式:
```python
import os
import json
from PIL import Image
# BDDK100数据集的路径
bddk100_path = 'path_to_bddk100_dataset'
output_path = 'path_to_save_coco_annotations'
# 创建COCO格式的注释和图像列表
coco_data = {
"info": {},
"licenses": [],
"categories": [],
"images": [],
"annotations": []
}
# 添加类别列表
category_list = ['car', 'bus', 'person', 'bike'] # 根据你的数据集进行修改
for i, category_name in enumerate(category_list):
category = {
"id": i+1,
"name": category_name,
"supercategory": "object"
}
coco_data["categories"].append(category)
# 处理图像和注释
image_id = 1
annotation_id = 1
image_list = os.listdir(os.path.join(bddk100_path, 'images'))
for image_filename in image_list:
image_filepath = os.path.join(bddk100_path, 'images', image_filename)
image = Image.open(image_filepath)
image_info = {
"file_name": image_filename,
"height": image.height,
"width": image.width,
"id": image_id
}
coco_data["images"].append(image_info)
annotation_filename = image_filename.replace(".jpg", ".json")
annotation_filepath = os.path.join(bddk100_path, 'annotations', annotation_filename)
with open(annotation_filepath, 'r') as f:
annotation_data = json.load(f)
for annotation in annotation_data['objects']:
bbox = annotation['bbox']
annotation_info = {
"id": annotation_id,
"image_id": image_id,
"category_id": category_list.index(annotation['category']) + 1,
"bbox": bbox,
"segmentation": [],
"area": bbox[2] * bbox[3],
"iscrowd": 0
}
coco_data["annotations"].append(annotation_info)
annotation_id += 1
image_id += 1
# 保存COCO格式的注释
with open(os.path.join(output_path, 'bddk100_coco.json'), 'w') as f:
json.dump(coco_data, f)
```
请替换`bddk100_path`为你的BDDK100数据集的路径,并设置`output_path`为你想要保存COCO注释的路径。此代码将遍历图像文件夹中的图像,并将其与相应的注释转换为COCO格式的JSON文件。注意,你需要根据你的数据集修改类别列表`category_list`。
运行以上代码后,你将得到一个名为`bddk100_coco.json`的COCO格式注释文件。