将 RSOD 遥感图像数据集转为 PASCAL VOC 格式

客官°小女子只卖身不卖艺 2023-01-18 02:25 235阅读 0赞

### PASCAL VOC 格式的RSOD ###

*  原
 *  2021.07.10 改
 *  附：对比代码

PASCAL VOC 格式的 RSOD 数据集：[下载地址（所需积分/C币: 0）][C_ 0]（原始数据集 playground.zip 中的图片有189张，而标注只有149张，PASCAL VOC 格式的数据集中，只选用了有标注的149张图片）

# 原 #

1、下载原数据集 https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-  
2、将四个类别中的`Annotation\xml\`和`JPEGImages`分别放在`Annotations`和`JPEGImages`里（类似 VOC 的文件顺序）  
3、重命名文件

import os
    print(os.getcwd())
    
    ann_filepath = 'F:/VOC2007/VOC2007/Annotations/'
    img_filepath = 'F:/VOC2007/VOC2007/JPEGImages/'
    
    ann_num = 0
    img_num = 0
    
    for file in os.listdir(ann_filepath):
        new_name = '%06d' % int(ann_num)+'.xml'
        ann_num += 1
        print(file, new_name)
        os.rename(os.path.join(ann_filepath, file), 
                  os.path.join(ann_filepath, new_name))
        
    for file in os.listdir(img_filepath):
        new_name = '%06d' % int(img_num)+'.jpg'
        img_num += 1
        print(file, new_name)
        os.rename(os.path.join(img_filepath, file), 
                  os.path.join(img_filepath, new_name))

4、最后，自己制作 trainval.txt、test.txt 等文件，里面放的是**图片名**（可能会报“没有这个文件”的错误，需要手动新建……能力有限）。

from sklearn.model_selection import train_test_split
    import os
    
    name_path = r'F:/VOC2007/VOC2007/JPEGImages'
    name_list = os.listdir(name_path)
    names = []
    
    for i in name_list:
        names.append(i.split('.')[0])
    
    trainval, test = train_test_split(names, test_size=0.5, shuffle=10)
    val, train = train_test_split(trainval, test_size=0.5, shuffle=10)
    
    with open('F:/VOC2007/VOC2007/ImageSets/Main/trainval.txt', 'w') as fw:
        for i in trainval:
            fw.write(i + '\n')
    
    with open('F:/VOC2007/VOC2007/ImageSets/Main/test.txt', 'w') as fw:
        for i in test:
            fw.write(i + '\n')
    
    with open('F:/VOC2007/VOC2007/ImageSets/Main/val.txt', 'w') as fw:
        for i in val:
            fw.write(i + '\n')
    
    with open('F:/VOC2007/VOC2007/ImageSets/Main/train.txt', 'w') as fw:
        for i in train:
            fw.write(i + '\n')
    
    print('done!')

# 2021.07.10 改 #

多谢评论大佬提醒，按上述**3、重命名文件**时，私以为 `Annotations` 和 `JPEGImages`中的文件名都是同一排序方式，其实并不是……

将重命名后的 `Annotations` 标注在 `JPEGImages`上时，发现只有`aircraft`能够一一对应，`oiltank` 、`playground` 和 `overpass` 则对应不上，如下：

<table> 
 <thead> 
  <tr> 
   <th>aircraft</th> 
   <th>oiltank</th> 
   <th>overpass</th> 
   <th>playground</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td><img src="https://img-blog.csdnimg.cn/20210710174005600.png" height="150" width="150"></td> 
   <td><img src="https://img-blog.csdnimg.cn/2021071017462234.png" height="150" width="150"></td> 
   <td><img src="https://img-blog.csdnimg.cn/20210710174205655.png" height="150" width="150"></td> 
   <td><img src="https://img-blog.csdnimg.cn/20210710174454651.png" height="150" width="150"></td> 
  </tr> 
 </tbody> 
</table>

修改后的**3、重命名文件**如下：

import os
    
    print(os.getcwd())
    
    ann_filepath = 'F:/VOC2007/VOC2007/Annotations/'
    img_filepath = 'F:/VOC2007/VOC2007/JPEGImages/'
    
    ann_num = 0
    img_num = 0
    
    for old_file in os.listdir(ann_filepath):
        new_file_name = '%06d' % int(ann_num) + '.xml'
        new_img_name = '%06d' % int(ann_num) + '.jpg'
        ann_num += 1
    
        os.rename(os.path.join(ann_filepath, old_file),
                  os.path.join(ann_filepath, new_file_name))
    
        old_img = old_file.split('.')[0] + '.jpg'
        os.rename(os.path.join(img_filepath, old_img),
                  os.path.join(img_filepath, new_img_name))

每个类别如下：

<table> 
 <thead> 
  <tr> 
   <th>aircraft</th> 
   <th>oiltank</th> 
   <th>overpass</th> 
   <th>playground</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td><img src="https://img-blog.csdnimg.cn/2021071017542185.png" height="150" width="150"></td> 
   <td><img src="https://img-blog.csdnimg.cn/20210710175330932.png" height="150" width="150"></td> 
   <td><img src="https://img-blog.csdnimg.cn/20210710175504758.png" height="150" width="150"></td> 
   <td><img src="https://img-blog.csdnimg.cn/20210710175604102.png" height="150" width="150"></td> 
  </tr> 
 </tbody> 
</table>

# 附：对比代码 #

# 对比 RSOD 数据集变为 PASCAL VOC 前后是否一一对应
    
    import numpy as np
    import cv2
    import os
    import xml.etree.ElementTree as ET
    
    color_gt = (255, 0, 0)
    color_md = (0, 255, 0)
    thickness = 1
    
    img_path = 'F:/VOC2007/VOC2007/JPEGImages/'
    file_path = 'F:/VOC2007/VOC2007/Annotations/'
    
    for f in os.listdir(file_path):
        file = os.path.join(file_path, f)
        tree = ET.parse(file)
        root = tree.getroot()
    
        size = tree.find('size')
        width = float(size.find('width').text)
        height = float(size.find('height').text)
        objs = root.findall('object')
    
        file_name = f.split('.')
        img_source = os.path.join(img_path, file_name[0] + '.jpg')
        img = cv2.imread(img_source, 0)
    
        for ix, obj in enumerate(objs):
            bbox = obj.find('bndbox')
            obj_name = str(obj.find('name').text)
            y_max = float(bbox.find('ymax').text) - 1
            y_min = float(bbox.find('ymin').text) - 1
            x_max = float(bbox.find('xmax').text) - 1
            x_min = float(bbox.find('xmin').text) - 1
    
            ptLeftTop = (int(x_min), int(y_min))
            ptRightBottom = (int(x_max), int(y_max))
    
            cv2.rectangle(img, ptLeftTop, ptRightBottom, color_md)
            cv2.putText(img, obj_name, (int(x_min), int(y_min)), cv2.FONT_HERSHEY_COMPLEX, thickness, color_md)
    
        image = np.expand_dims(img, axis=2)
        image = np.concatenate((image, image, image), axis=-1)
    
        if obj_name == 'aircraft':
                # ['aircraft', 'oiltank', 'overpass', 'playground']
            cv2.imshow('image', img)
            k = cv2.waitKey(0)
            if k == 27:  # wait for ESC key to exit
                cv2.destroyAllWindows()
            elif k == ord('s'):  # wait for 's' key to save and exit
                cv2.imwrite(file_name[0] + '.png', img)
                cv2.destroyAllWindows()
    
    # 对比依次取 VOC 数据集与原数据集标注文件中的内容，查看是否一致
    
    img_path = 'F:/VOC2007/VOC2007/JPEGImages/'
    file_path = 'F:/VOC2007/VOC2007/Annotations/'
    
    origin_path = "F:/RSOD"
    origin_name_list = ['aircraft', 'oiltank', 'overpass', 'playground']
    
    voc_list = []
    origin_list = []
    
    for f in os.listdir(file_path):
        file = os.path.join(file_path, f)
        tree = ET.parse(file)
        root = tree.getroot()
    
        size = tree.find('size')
        width = float(size.find('width').text)
        height = float(size.find('height').text)
        objs = root.findall('object')
    
        for ix, obj in enumerate(objs):
            obj_name = str(obj.find('name').text)
            bbox = obj.find('bndbox')
            y_max = float(bbox.find('ymax').text)
            y_min = float(bbox.find('ymin').text)
            x_max = float(bbox.find('xmax').text)
            x_min = float(bbox.find('xmin').text)
    
            voc_list.append([y_max, y_min, x_max, x_min, obj_name])
    
    for name in origin_name_list:
        origin_class_path = origin_path + '\\' + name + '\Annotation\\xml'
        for f in os.listdir(origin_class_path):
            file = os.path.join(origin_class_path, f)
            tree = ET.parse(file)
            root = tree.getroot()
    
            size = tree.find('size')
            width = float(size.find('width').text)
            height = float(size.find('height').text)
            objs = root.findall('object')
    
            for ix, obj in enumerate(objs):
                obj_name = str(obj.find('name').text)
                bbox = obj.find('bndbox')
                y_max = float(bbox.find('ymax').text)
                y_min = float(bbox.find('ymin').text)
                x_max = float(bbox.find('xmax').text)
                x_min = float(bbox.find('xmin').text)
    
                origin_list.append([y_max, y_min, x_max, x_min, obj_name])
    
    print(voc_list)
    print(origin_list)
    print(voc_list == origin_list)

[C_ 0]: https://download.csdn.net/download/dear_jing/20161351