将tt100k_2021数据集中的annotations.json变成yolo的txt数据集

将tt100k_2021数据集中的annotations.json文件转换为YOLO格式的txt文件，可以按照以下步骤进行：

1. 首先，需要导入Python中的json和os模块，以及PIL库。

import json
import os
from PIL import Image

2. 设置数据集路径和输出路径，以及类别标签。

# 数据集路径
data_dir = "/path/to/tt100k_2021"

# 输出路径
output_dir = "/path/to/output"

# 类别标签
classes = ["car", "bus", "truck", "van", "others"]

3. 定义一个函数`convert_annotation`，用于将一张图片的标注信息转换为YOLO格式的标注信息。

def convert_annotation(image_id, image_width, image_height, annotations):
    txt_file = open(os.path.join(output_dir, f"{image_id}.txt"), "w")
    for annotation in annotations:
        if annotation["type"] in classes:
            class_id = classes.index(annotation["type"])
            bbox = annotation["bbox"]
            x_center = bbox["x"] + bbox["w"] / 2
            y_center = bbox["y"] + bbox["h"] / 2
            width = bbox["w"]
            height = bbox["h"]
            x_center /= image_width
            y_center /= image_height
            width /= image_width
            height /= image_height
            txt_file.write(f"{class_id} {x_center} {y_center} {width} {height}\n")
    txt_file.close()

该函数的输入参数包括图片ID、图片的宽度和高度，以及图片的标注信息。函数会将每个目标的类别转换为对应的类别ID，计算目标的中心点坐标、宽度和高度，并将其进行归一化处理后写入YOLO格式的txt文件中。

4. 遍历数据集中的所有图片，对每张图片调用`convert_annotation`函数进行转换。

# 加载annotations.json文件
with open(os.path.join(data_dir, "annotations.json"), "r") as f:
    annotations = json.load(f)

# 遍历所有图片
for image in annotations["imgs"].values():
    image_id = image["id"]
    image_path = os.path.join(data_dir, image["path"])
    image_width, image_height = Image.open(image_path).size
    convert_annotation(image_id, image_width, image_height, image["objects"])

5. 完成转换后，YOLO格式的txt文件将保存在输出路径中，每个文件的名称与对应的图片ID相同。

# 输出转换完成提示信息
print(f"Convert annotations to YOLO format successfully! Output directory: {output_dir}")

完整代码示例：

import json
import os
from PIL import Image

# 数据集路径
data_dir = "/path/to/tt100k_2021"

# 输出路径
output_dir = "/path/to/output"

# 类别标签
classes = ["car", "bus", "truck", "van", "others"]

def convert_annotation(image_id, image_width, image_height, annotations):
    txt_file = open(os.path.join(output_dir, f"{image_id}.txt"), "w")
    for annotation in annotations:
        if annotation["type"] in classes:
            class_id = classes.index(annotation["type"])
            bbox = annotation["bbox"]
            x_center = bbox["x"] + bbox["w"] / 2
            y_center = bbox["y"] + bbox["h"] / 2
            width = bbox["w"]
            height = bbox["h"]
            x_center /= image_width
            y_center /= image_height
            width /= image_width
            height /= image_height
            txt_file.write(f"{class_id} {x_center} {y_center} {width} {height}\n")
    txt_file.close()

# 加载annotations.json文件
with open(os.path.join(data_dir, "annotations.json"), "r") as f:
    annotations = json.load(f)

# 遍历所有图片
for image in annotations["imgs"].values():
    image_id = image["id"]
    image_path = os.path.join(data_dir, image["path"])
    image_width, image_height = Image.open(image_path).size
    convert_annotation(image_id, image_width, image_height, image["objects"])

# 输出转换完成提示信息
print(f"Convert annotations to YOLO format successfully! Output directory: {output_dir}")