yolo数据集格式转换成coco

将YOLO数据集转换为COCO格式需要进行以下步骤：

1. 将YOLO标注文件(.txt)转换为COCO标注文件(.json)
2. 将YOLO图像文件(.jpg)复制到COCO数据集目录下的images文件夹中
3. 在COCO标注文件中添加图像信息

具体实现可以参考以下步骤：

1. 安装cocoapi

pip install pycocotools

2. 创建COCO数据集目录结构

coco_dataset/
    annotations/
        instances_train.json
        instances_val.json
    images/
        train/
        val/

3. 编写转换脚本

import json
import os
from PIL import Image

# YOLO标注文件路径
yolo_annotation_path = 'yolo_dataset/annotations/train.txt'

# COCO数据集路径
coco_dataset_path = 'coco_dataset'

# COCO标注文件路径
coco_annotation_path = os.path.join(coco_dataset_path, 'annotations', 'instances_train.json')

# 图像目录路径
image_dir = os.path.join(coco_dataset_path, 'images', 'train')

# 类别名称映射表
class_name_map = {
    '0': 'person',
    '1': 'car',
    # ...
}

# COCO标注文件格式
coco_annotation = {
    "info": {},
    "licenses": [],
    "images": [],
    "annotations": [],
    "categories": []
}

# 添加类别信息
for class_id, class_name in class_name_map.items():
    coco_annotation['categories'].append({
        'id': int(class_id),
        'name': class_name,
        'supercategory': ''
    })

# 读取YOLO标注文件
with open(yolo_annotation_path, 'r') as f:
    lines = f.readlines()

# 处理每个标注文件
for line in lines:
    line = line.strip()
    image_path, *bboxes = line.split(' ')

    # 添加图像信息
    image_id = len(coco_annotation['images']) + 1
    image_name = os.path.basename(image_path)
    image = Image.open(image_path)
    width, height = image.size
    coco_annotation['images'].append({
        'id': image_id,
        'file_name': image_name,
        'width': width,
        'height': height
    })

    # 处理每个bbox
    for bbox in bboxes:
        class_id, x_center, y_center, w, h = bbox.split(',')
        x_min = int(float(x_center) - float(w) / 2 * width)
        y_min = int(float(y_center) - float(h) / 2 * height)
        x_max = int(float(x_center) + float(w) / 2 * width)
        y_max = int(float(y_center) + float(h) / 2 * height)

        # 添加标注信息
        annotation_id = len(coco_annotation['annotations']) + 1
        coco_annotation['annotations'].append({
            'id': annotation_id,
            'image_id': image_id,
            'category_id': int(class_id),
            'bbox': [x_min, y_min, x_max - x_min, y_max - y_min],
            'area': (x_max - x_min) * (y_max - y_min),
            'iscrowd': 0
        })

# 保存COCO标注文件
with open(coco_annotation_path, 'w') as f:
    json.dump(coco_annotation, f)

4. 运行转换脚本

python yolo2coco.py