bop数据合并到COCO

朴灿烈づ我的快乐病毒、 2024-02-17 10:17 35阅读 0赞

#### bop数据合并到COCO ####

*  JSON转TXT
 *  重命名
 *  txt文件中类别信息的转换

## JSON转TXT ##

import json
    import os,glob
    
    
    
    categories = [
            {
        
                "id": 12,
                "name": "OREO",
                "supercategory": "icbin"
            },
            {
        
                "id": 16,
                "name": "Paper Cup",
                "supercategory": "icbin"
            },
            {
        
                "id": 4,
                "name": "School Glue",
                "supercategory": "icbin"
            },
            {
        
                "id": 7,
                "name": "Straw Cups",
                "supercategory": "icbin"
            },
            {
        
                "id": 9,
                "name": "Highland",
                "supercategory": "icbin"
            },
            {
        
                "id": 10,
                "name": "Soueakair",
                "supercategory": "icbin"
            },
            {
        
                "id": 2,
                "name": "Cheez-it",
                "supercategory": "icbin"
            },
            {
        
                "id": 1,
                "name": "Copper Plus",
                "supercategory": "icbin"
            },
            {
        
                "id": 8,
                "name": "Stir Stick",
                "supercategory": "icbin"
            },
            {
        
                "id": 14,
                "name": "Stanley",
                "supercategory": "icbin"
            },
            {
        
                "id": 3,
                "name": "Crayola",
                "supercategory": "icbin"
            },
            {
        
                "id": 13,
                "name": "Mirado",
                "supercategory": "icbin"
            },
            {
        
                "id": 11,
                "name": "Munchkin",
                "supercategory": "icbin"
            },
            {
        
                "id": 6,
                "name": "Greenies",
                "supercategory": "icbin"
            },
            {
        
                "id": 5,
                "name": "White Board Cake",
                "supercategory": "icbin"
            },
            {
        
                "id": 15,
                "name": "Main Arm",
                "supercategory": "icbin"
            }
        ]
    
    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)
     
    def to_yolo(data_path):
        json_path=data_path+'/scene_gt_coco.json' 
        save_path = data_path+ '/labels/'
    
        json_file =   json_path # COCO Object Instance 类型的标注
        ana_txt_save_path = 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！
        print(data['categories'])
        # # categories = sorted(data['categories'], key=lambda x: x['id'])
    
        for i, category in enumerate(categories): 
            # id_map[category['id']] = int(category['id'])
            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 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.split('/')[-1] + ".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()
        print(f'==> coco to yolo images:{
          len(data["images"])}, save path: {
          save_path}')
    
        
    def train_val_test(data_path):
        sets = ['train','val','test']#生成txt的文件名称
    
        image_ids = glob.glob(os.path.join(data_path, 'images', '*.jpg'))
    
        train_ratio = 0.7  # 训练集比例
        val_ratio = 0.2  # 验证集比例
        test_ratio = 0.1  # 测试集比例
    
        train_size = int(len(image_ids) * train_ratio)
        val_size = int(len(image_ids) * val_ratio)
        test_size = len(image_ids) - train_size - val_size
    
        data  = [image_ids[:train_size], image_ids[train_size:train_size + val_size], image_ids[train_size + val_size:]]
        
        
    
        for i, image_set in enumerate(sets):
        
            image_ids = data[i]
            list_file = open(data_path+'/%s.txt' % (image_set), 'w')
            for image_id in image_ids:
                image_id = image_id.replace('/rgb','/images')
                list_file.write(image_id + "\n")
                # convert_annotation(image_id)
            # 关闭文件
            list_file.close()
        
        print(f'==> train image: {
          train_size}')
        print(f'==> valid image: {
          val_size}')
        print(f'==> test  image: {
          test_size}')
    
    if __name__ == '__main__':
        data_path = 'H:/Dataset/COCO/train_pbr/000002'
        to_yolo(data_path)
        train_val_test(data_path)
        # print([cat['name'] for cat in categories])

![在这里插入图片描述][78e0ef1b8d1f4fc29edbaa6df3685728.png]

## 重命名 ##

以00000061\*开头

![在这里插入图片描述][bbb9eae231fe4456a3d74a1f0fef50e2.png]

## txt文件中类别信息的转换 ##

加79（从0开始，80类的COCO）

import codecs
    import os
    
    path = 'H:/Dataset/COCO/train_pbr/000002/labelNew/'  # 标签文件train路径
    m = os.listdir(path)
    # 读取路径下的txt文件
    for n in range(0, len(m)):
        t = codecs.open('H:/Dataset/COCO/train_pbr/000002/labelNew/' + m[n], mode='r', encoding='utf-8')
        line = t.readline()  # 以行的形式进行读取文件
        list1 = []
        while line:
            a = line.split()
            list1.append(a)
            line = t.readline()
        t.close()
    
        lt = open('H:/Dataset/COCO/train_pbr/000002/labelNew/' + m[n], "w")
        for num in range(0, len(list1)):
            list1[num][0] = str(int(list1[num][0])+79)  # 第一列为0时，将0改为1
            lt.writelines(' '.join(list1[num]) + '\n')  # 每个元素以空格间隔，一行元素写完并换行
        lt.close()
        print(m[n] + " 修改完成")

![在这里插入图片描述][0b1b7699d578421e9293206e2559073d.png]

![在这里插入图片描述][e3e63136fd224d5a820957ae27d7ff6c.png]

[78e0ef1b8d1f4fc29edbaa6df3685728.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/f1f8c217c7f54108a88e15bce14ca5b9.png
[bbb9eae231fe4456a3d74a1f0fef50e2.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/7a645d6ac9f44265bb6863bd38cde25f.png
[0b1b7699d578421e9293206e2559073d.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/9338453bfd6c4748aa5d0ea75fb318b2.png
[e3e63136fd224d5a820957ae27d7ff6c.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/03866d2c0fbc4cd7b988de73b66cf4db.png