coco格式转yolo格式

#COCO 格式的数据集转化为 YOLO 格式的数据集
#--json_path 输入的json文件路径
#--save_path 保存的文件夹名字，默认为当前目录下的labels。

import os
import json
from tqdm import tqdm
import argparse



parser = 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='./annotations/instances_train2017.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='./labels/train', 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] / 2.0
    y = box[1] + box[3] / 2.0
    w = box[2]
    h = box[3]
#round函数确定(xmin, ymin, xmax, ymax)的小数位数
    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
    global count
    count=0
    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！

    #所含80个类别的文件
    with open('classes.txt', 'w') as f:
        # 写入classes.txt
        for 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('train2017.txt', 'w')
    images=tqdm(data['images'])
    # 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]))
    #     f_txt.close()
    #     #将图片的相对路径写入train2017或val2017的路径
    #     list_file.write('./images/train2017/%s.jpg\n' %(head))

    for img in 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
        txt_path = os.path.join(ana_txt_save_path, ana_txt_name)
        f_txt = open(txt_path, 'w')
        tmp = []    #记录已匹配部分的下标
        for i, ann in enumerate(data['annotations']):
            if ann['image_id'] == img_id:
                tmp.append(i)
                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()
        for i in tmp:
            del data['annotations'][i]   #删除已匹配的信息，降低时间复杂度
        count += 1
        # print(txt_path + "-------" + str(count))
        # 将图片的相对路径写入train2017或val2017的路径
        list_file.write('./images/train2017/%s.jpg\n' % (head))
        print('annotations'+str(len(data['annotations'])))
    list_file.close()