目录格式

yolov8仅支持YOLO格式的标签，COCO的默认标签为JSON格式，所以需要将COCO格式转换为YOLO格式。

如果训练COCO数据集的话一定要按照这个格式，摆放目录images，labels这两个目录名不可以改变

因为在内部已经写好了就这么去找数据，如果不按照这个规则写就会报错：No labels found in

datasets|coco|images|train2017val2017labels|train2017val2017

代码

该代码可将COCO格式转换为YOLO格式并保存在labels/下。这里需要运行两次，train和val都需要转换。

import os 
import json
from tqdm import tqdm
import argparseparser = argparse.ArgumentParser()
parser.add_argument('--json_path', default='/home/ubuntu/data/coco2017/annotations/instances_train2017.json',type=str, help="input: coco format(json)")
parser.add_argument('--save_path', default='/home/ubuntu/data/coco2017/labels/train2017', type=str, help="specify where to save the output dir of labels")
arg = parser.parse_args()def convert(size, box):dw = 1. / (size[0])dh = 1. / (size[1])x = box[0] + box[2] / 2.0y = box[1] + box[3] / 2.0w = box[2]h = box[3]x = x * dww = w * dwy = y * dhh = h * dhreturn (x, y, w, h)if __name__ == '__main__':json_file =   arg.json_path # COCO Object Instance 类型的标注ana_txt_save_path = arg.save_path  # 保存的路径data = json.load(open(json_file, 'r'))if not os.path.exists(ana_txt_save_path):os.makedirs(ana_txt_save_path)id_map = {} # coco数据集的id不连续！重新映射一下再输出！for i, category in enumerate(data['categories']): id_map[category['id']] = i# 通过事先建表来降低时间复杂度max_id = 0for img in data['images']:max_id = max(max_id, img['id'])# 注意这里不能写作 [[]]*(max_id+1)，否则列表内的空列表共享地址img_ann_dict = [[] for i in range(max_id+1)] for i, ann in enumerate(data['annotations']):img_ann_dict[ann['image_id']].append(i)for img in tqdm(data['images']):filename = img["file_name"]img_width = img["width"]img_height = img["height"]img_id = img["id"]head, tail = os.path.splitext(filename)ana_txt_name = head + ".txt"  # 对应的txt名字，与jpg一致f_txt = open(os.path.join(ana_txt_save_path, ana_txt_name), 'w')'''for ann in data['annotations']:if ann['image_id'] == img_id:box = convert((img_width, img_height), ann["bbox"])f_txt.write("%s %s %s %s %s\n" % (id_map[ann["category_id"]], box[0], box[1], box[2], box[3]))'''# 这里可以直接查表而无需重复遍历for ann_id in img_ann_dict[img_id]:ann = data['annotations'][ann_id]box = convert((img_width, img_height), ann["bbox"])f_txt.write("%s %s %s %s %s\n" % (id_map[ann["category_id"]], box[0], box[1], box[2], box[3]))f_txt.close()

参考文章