处理BOP数据集，将其和COCO数据集结合

我就是我 2024-02-18 12:02 12阅读 0赞

#### 处理BOP数据集，将其和COCO数据集结合 ####

[BOP][]

取消映射关系，并自增80  
取消文件名的images前缀

import os
    import json
    from tqdm import tqdm
    import argparse
    
    parser = argparse.ArgumentParser()
    parser.add_argument('--json_path', default='H:/Dataset/COCO/train_pbr/000002/coco/annotations/scene_gt_coco.json', type=str,
                        help="input: coco format(json)")
    parser.add_argument('--save_path', default='H:/Dataset/COCO/train_pbr/000002/coco/labels', 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]
    
        x = x * dw
        w = w * dw
        y = y * dh
        h = h * dh
        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
    
        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 = 0
        for 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)
            head2 = head.split("/")
            head3 = head2[1]
            ana_txt_name = head3 + ".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"])
                #print(box[0],box[1],box[2],box[3])
                f_txt.write("%s %s %s %s %s\n" % (ann["category_id"]+80, box[0], box[1], box[2], box[3]))
            f_txt.close()

![在这里插入图片描述][418d69d1604e41608ad4a664c3c97a44.png]

[BOP]: https://github.com/thodan/bop_toolkit
[418d69d1604e41608ad4a664c3c97a44.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/18/ad9bbe4cf2c9437f90c979baa48e4d47.png