visdrone2019数据集coco格式

### 将VisDrone2019数据集转换为COCO格式 #### 数据集概述 VisDrone2019 是一个广泛应用于无人机视角下的目标检测和视频分析的数据集。将其转换成 COCO 格式有助于利用现有的工具和技术来处理这些图像和视频。 #### 转换流程详解 对于 VisDrone2019 的 DET 和 VID 部分，均需遵循特定的步骤来进行格式转换： - **理解原始标签结构** 原始 VisDrone2019 数据集中每张图片对应多个标注框的信息被记录在一个文本文件里，每一行代表一个物体实例及其属性[^2]。 - **创建 JSON 文件模板** 创建符合 COCO API 规范的新 JSON 文件作为输出容器，此文件应包含 images, annotations 及 categories 字段定义[^3]。 - **编写映射逻辑** 编写 Python 或其他编程语言脚本来遍历原生 annotation files 并填充至上述构建好的 JSON 结构内；注意不同类别 ID 映射关系以及 bbox 参数调整[^4]。 ```python import json from collections import defaultdict def convert_visdrone_to_coco(visdrone_ann_file, coco_output_path): """ Converts a single VisDrone annotation file to the COCO format. Args: visdrone_ann_file (str): Path of input VisDrone annotation txt file. coco_output_path (str): Output path for saving converted COCO formatted data as .json file. Returns: None """ # Initialize dictionaries/lists required by COCO structure info = {"description": "Converted from VisDrone dataset"} licenses = [] images = [] annotations = [] category_map = { 'pedestrian': 1, 'people': 2, ... } image_id_counter = 0 ann_id_counter = 0 with open(visdrone_ann_file) as f: lines = f.readlines() img_info = {} # Placeholder for current processing image information for line in lines: parts = list(map(int, filter(None, line.strip().split(',')))) if not any(parts[:8]): continue width, height = parts[-2:] filename = str(image_id_counter).zfill(7)+'.jpg' new_image_entry = dict( id=image_id_counter, width=width, height=height, file_name=f'images/{filename}' ) images.append(new_image_entry) x_min, y_min, box_width, box_height, score, obj_category, truncation, occlusion = parts[:-2] area = float(box_width * box_height) segmentation = [[x_min, y_min, x_min + box_width, y_min, x_min + box_width, y_min + box_height, x_min, y_min + box_height]] anno_dict = dict( id=ann_id_counter, image_id=image_id_counter, category_id=obj_category, bbox=[float(x_min), float(y_min), float(box_width), float(box_height)], area=float(area), iscrowd=int(truncation>0 or occlusion>0), ignore=0, segmentations=segmentation ) annotations.append(anno_dict) ann_id_counter += 1 image_id_counter += 1 final_json_structure = {'info': info,'licenses': licenses,'categories':[{'id':v,'name':k}for k,v in category_map.items()],'images':images,'annotations':annotations} with open(coco_output_path,"w")as outfile: json.dump(final_json_structure,outfile) if __name__ == '__main__': convert_visdrone_to_coco('path/to/your/visdrone_annotation.txt', './output/coco_format.json') ``` 这段代码展示了如何读取单个 VisDrone 注解文件并将之转储为 COCO 格式的 JSON 文件[^5]。