给我写一段代码，实现ApolloScape数据集格式转换为coco数据集格式

### 回答1：

import json
from os.path import join
import numpy as np# ApolloScape格式
apollo_ann_file = "./apollo_annotation.json"# Coco格式
coco_ann_file = "./coco_annotation.json"# 读取Apollo格式的注释文件
with open(apollo_ann_file) as f:
apollo_ann = json.load(f)# 初始化Coco格式的注释文件
coco_ann = {
"info": {
"year": 2020,
"version": "1.0",
"description": "ApolloScape to Coco Conversion",
"contributor": "ChatGPT",
"url": "https://github.com/openai/chatgpt"
},
"images": [],
"annotations": [],
"categories": []
}# 遍历ApolloScape格式的注释文件
for image_info in apollo_ann['images']:
# 构建Coco格式的图像信息
image_id = image_info['id']
image_width = image_info['width']
image_height = image_info['height']
coco_image_info = {
"id": image_id,
"width": image_width,
"height": image_height
}
coco_ann['images'].append(coco_image_info) # 构建Coco格式的标注信息
for label_info in image_info['labels']:
category_id = label_info['category_id']
bbox = label_info['box2d']
coco_annotation_info = {
"image_id": image_id,
"category_id": category_id,
"bbox": [bbox['x1'], bbox['y1'], bbox['x2'] - bbox['x1'], bbox['y2'] - bbox['y1']],
"area": (bbox['x2'] - bbox['x1']) * (bbox['y2'] - bbox['y1'])
}
coco_ann['annotations'].append(coco_annotation_info)# 构建Coco格式的类别信息
for category_info in apollo_ann['categories']:
category_id = category_info['id']
category_name = category_info['name']
coco_category_info = {
"id": category_id,
"name": category_name
}
coco_ann['categories'].append(coco_category_info)# 将Coco格式的注释文件写入文件
with open(coco_ann_file, "w") as f:
json.dump(coco_ann, f)

### 回答2：
实现ApolloScape数据集格式转换为COCO数据集格式的代码如下：

import json
from PIL import Image

# 读取ApolloScape数据集标注文件
with open('apollo_annotation.json', 'r') as f:
    apollo_data = json.load(f)

# 创建COCO数据集格式的字典
coco_data = {
    "info": {},
    "licenses": [],
    "images": [],
    "annotations": [],
    "categories": []
}

# 遍历ApolloScape数据集的每个图像
for image_data in apollo_data['images']:
    image_id = image_data['id']
    image_name = image_data['file_name']
    image_file = f'apollo_images/{image_name}'  # ApolloScape图像文件夹路径

    # 读取图像尺寸
    image = Image.open(image_file)
    width, height = image.size

    # 添加图像信息到COCO数据集字典中
    coco_data['images'].append({
        'id': image_id,
        'file_name': image_name,
        'width': width,
        'height': height
    })

    # 遍历ApolloScape数据集的每个标注
    for annotation in apollo_data['annotations']:
        if annotation['image_id'] == image_id:
            category_id = annotation['category_id']
            bbox = annotation['bbox']
            area = bbox[2] * bbox[3]

            # 添加标注信息到COCO数据集字典中
            coco_data['annotations'].append({
                'id': annotation['id'],
                'image_id': image_id,
                'category_id': category_id,
                'bbox': bbox,
                'area': area,
                'iscrowd': 0
            })

# 保存COCO格式数据集为JSON文件
with open('coco_annotation.json', 'w') as f:
    json.dump(coco_data, f)

此段代码通过读取ApolloScape数据集的标注文件，将其转换为COCO数据集格式的字典，然后保存为JSON文件。代码首先读取ApolloScape数据集的标注文件，然后创建一个COCO数据集格式的空字典。接着遍历每个ApolloScape数据集中的图像，读取图像的尺寸，并将图像信息添加到COCO数据集字典中。然后遍历每个标注，找到与图像ID匹配的标注，获取标注的类别ID、边界框、面积等信息，并将标注信息添加到COCO数据集字典中。最后将COCO格式的数据集字典保存为JSON文件。

### 回答3：
import json
from PIL import Image

# 读取ApolloScape标注文件
with open('annotations.json', 'r') as f:
annotations = json.load(f)

coco_data = {
"info": {},
"licenses": [],
"images": [],
"annotations": [],
"categories": []
}

# 添加categories
category_mapping = {} # 用于记录类别映射关系
for i, category in enumerate(annotations["categories"]):
category_mapping[category["id"]] = i + 1
coco_data["categories"].append({"id": i + 1, "name": category["name"], "supercategory": ""})

# 转换images和annotations
for image in annotations["images"]:
img = Image.open(image["file_name"])
width, height = img.size
coco_data["images"].append({
"id": image["id"],
"file_name": image["file_name"],
"width": width,
"height": height,
})

for ann in annotations["annotations"]:
if ann["image_id"] == image["id"]:
coco_data["annotations"].append({
"id": ann["id"],
"image_id": ann["image_id"],
"category_id": category_mapping[ann["category_id"]],
"segmentation": [ann["segmentation"]],
"bbox": ann["bbox"],
"area": ann["area"],
})

# 保存为coco格式文件
with open('coco_annotations.json', 'w') as f:
json.dump(coco_data, f)

以上代码用于将ApolloScape数据集的标注文件annotations.json转换为COCO数据集格式的标注文件coco_annotations.json。首先，我们读取ApolloScape标注文件中的内容。然后，创建一个空的COCO数据集格式字典，并为annotations中的每个类别创建一个映射关系。接着，我们遍历ApolloScape数据集中的每张图片，在COCO数据集中添加相应的images信息，并将annotations中对应的标注信息转换为COCO数据集中的annotations格式。最后，将转换后的COCO数据集格式字典保存为coco_annotations.json文件。