yolov8 pose coco2yolo

import os
import json
from tqdm import tqdm
import argparseparser = argparse.ArgumentParser()
# 这里根据自己的json文件位置，换成自己的就行
parser.add_argument('--json_path',default=r'C:\Users\k167\Desktop\dataset\person_dataset/instances_val2017_person_3dataset_merged_hip.json', type=str,help="input: coco format(json)")
# 这里设置.txt文件保存位置
parser.add_argument('--save_path', default=r'C:\Users\k167\Desktop\dataset\person_dataset', type=str,help="specify where to save the output dir of labels")
parser.add_argument('--root', default=r'C:\Users\k167\Desktop\dataset\person_dataset', 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 = round(x * dw, 6)w = round(w * dw, 6)y = round(y * dh, 6)h = round(h * dh, 6)return (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root = arg.rootdata = 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！with open(os.path.join(ana_txt_save_path, 'classes.txt'), 'w') as f:# 写入classes.txtfor i, category in enumerate(data['categories']):f.write(f"{category['name']}\n")id_map[category['id']] = i# print(id_map)# 这里需要根据自己的需要，更改写入图像相对路径的文件位置。list_file = open(os.path.join(ana_txt_save_path, 'train2017.txt'), 'w')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# print(os.path.join(root,filename))# exit()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" % (id_map[ann["category_id"]], box[0], box[1], box[2], box[3]))counter=0for i in range(len(ann["keypoints"])):if (i+1)%3 == 0 and (ann["keypoints"][i] == 2 or ann["keypoints"][i] == 1 or ann["keypoints"][i] == 0):f_txt.write(" %s " % format(ann["keypoints"][i],'6f'))counter=0else:if counter==0:f_txt.write(" %s " % round((ann["keypoints"][i] / img_width),6))else:f_txt.write(" %s " % round((ann["keypoints"][i] / img_height),6))counter+=1f_txt.write("\n")f_txt.close()# 将图片的路径写入train2017或val2017的路径# list_file.write('E:/edgeai-yolov5-yolo-pose/coco_kpts/images/train2017/%s.jpg\n' % (head))list_file.write(os.path.join(root,filename)+'\n')list_file.close()