Caltech行人数据集转化VOC格式

小咪咪 2022-12-07 12:18 347阅读 0赞

该数据库是目前规模较大的行人数据库，采用车载摄像头拍摄，约10个小时左右，视频的分辨率为640x480，30帧/秒。标注了约**249,884帧**（约137分钟），350000个矩形框，2300个行人，另外还对矩形框之间的时间对应关系及其遮挡的情况进行标注。

下载好的Caltech行人数据集，解压放在Caltech文件夹内，目录情况如下图。  
![0][]

### 目录） ###

*  1 图片转换
 *  2 文本转换
 *  3 数据筛选
 *   *  3.1 根据目标面积进行筛选
     *  3.2 根据xml保存jpg

# 1 图片转换 #

将`.seq`文件转换为`.jpg`文件并保存在某一目录下。

**`seq2jpg.py`**

# -*- coding:utf-8 -*-
    import os
    import fnmatch
    
    
    def open_save(file, savepath):
        f = open(file, 'rb')
        # 将seq文件的内容转化成str类型
        string = f.read().decode('latin-1')
    
        # splitstring是图片的前缀，可以理解成seq是以splitstring为分隔的多个jpg合成的文件
        splitstring = "\xFF\xD8\xFF\xE0\x00\x10\x4A\x46\x49\x46"  
        # split函数做一个测试,因此返回结果的第一个是在seq文件中是空，因此后面省略掉第一个
        strlist = string.split(splitstring)
    
        f.close()
        count = 0  
    
        # 遍历每一个jpg文件内容，然后加上前缀合成图片
        for img in strlist:        
            filename = str(count)+'.jpg'
            filename = savepath + filename
            if count > 0:                                    
                i = open(filename, 'wb+')
                i.write(splitstring.encode('latin-1'))
                i.write(img.encode('latin-1'))
                i.close()
            count = count + 1
            print("Generating JPEGImages jpg file of picture:{}".format(filename))
    
    
    def main():
        seq_inputdir = "E:\\dataset\\Caltech"
        jpg_outputdir = "E:\\dataset\\Caltech_VOC\\JPEGImages"
    
        for dir_set in os.listdir(seq_inputdir):
            # 若果是set01、set02……这类文件夹
            if dir_set[0:3] == "set" and os.path.splitext(dir_set)[1] == '':
                dir_set_seq = os.path.join(seq_inputdir, dir_set)
                for dir_seq in os.listdir(dir_set_seq):
                    if fnmatch.fnmatch(dir_seq, '*.seq'):
                        file_dir = os.path.join(dir_set_seq, dir_seq)
                        save_dir = os.path.join(jpg_outputdir, dir_set) + '_' + dir_seq.split('.')[0] + '_'
                        open_save(file_dir, save_dir)
    
    
    if __name__ == '__main__':
        main()
        print("Success！")

# 2 文本转换 #

将`.vbb`文件转换为`.xml`文件并保存在某一目录下。

**`vbb2voc.py`**

# -*- coding:utf-8 -*-
    # -*- coding:utf-8 -*-
    import os, glob
    from scipy.io import loadmat
    from collections import defaultdict
    import numpy as np
    from lxml import etree, objectify
    
    
    def vbb_anno2dict(vbb_file, cam_id):
        filename = os.path.splitext(os.path.basename(vbb_file))[0]
    
        # 定义字典对象annos
        annos = defaultdict(dict)
        vbb = loadmat(vbb_file)
        # object info in each frame: id, pos, occlusion, lock, posv
        objLists = vbb['A'][0][0][1][0]
        objLbl = [str(v[0]) for v in vbb['A'][0][0][4][0]]     # 可查看所有类别
        # person index
        person_index_list = np.where(np.array(objLbl) == "person")[0]   # 只选取类别为‘person’的xml
        for frame_id, obj in enumerate(objLists):
            if len(obj) > 0:
                frame_name = str(cam_id) + "_" + str(filename) + "_" + str(frame_id+1) + ".jpg"
                annos[frame_name] = defaultdict(list)
                annos[frame_name]["id"] = frame_name
                annos[frame_name]["label"] = "person"
                for id, pos, occl in zip(obj['id'][0], obj['pos'][0], obj['occl'][0]):
                    id = int(id[0][0]) - 1  # for matlab start from 1 not 0
                    if not id in person_index_list:  # only use bbox whose label is person
                        continue
                    pos = pos[0].tolist()
                    occl = int(occl[0][0])
                    annos[frame_name]["occlusion"].append(occl)
                    annos[frame_name]["bbox"].append(pos)
                if not annos[frame_name]["bbox"]:
                    del annos[frame_name]
        print(annos)
        return annos
    
    
    def instance2xml_base(anno, bbox_type='xyxy'):
        """bbox_type: xyxy (xmin, ymin, xmax, ymax); xywh (xmin, ymin, width, height)"""
        assert bbox_type in ['xyxy', 'xywh']
        E = objectify.ElementMaker(annotate=False)
        anno_tree = E.annotation(
            E.folder('VOC2014_instance/person'),
            E.filename(anno['id']),
            E.source(
                E.database('Caltech pedestrian'),
                E.annotation('Caltech pedestrian'),
                E.image('Caltech pedestrian'),
                E.url('None')
            ),
            E.size(
                E.width(640),
                E.height(480),
                E.depth(3)
            ),
            E.segmented(0),
        )
        for index, bbox in enumerate(anno['bbox']):
            bbox = [float(x) for x in bbox]
            if bbox_type == 'xyxy':
                xmin, ymin, w, h = bbox
                xmax = xmin+w
                ymax = ymin+h
            else:
                xmin, ymin, xmax, ymax = bbox
            E = objectify.ElementMaker(annotate=False)
            anno_tree.append(
                E.object(
                E.name(anno['label']),
                E.bndbox(
                    E.xmin(int(xmin)),
                    E.ymin(int(ymin)),
                    E.xmax(int(xmax)),
                    E.ymax(int(ymax))
                ),
                E.difficult(0),
                E.occlusion(anno["occlusion"][index])
                )
            )
        return anno_tree
    
    
    def parse_anno_file(vbb_inputdir, vbb_outputdir):
        # annotation sub-directories in hda annotation input directory
        assert os.path.exists(vbb_inputdir)
        sub_dirs = os.listdir(vbb_inputdir)     # 对应set00,set01...
        for sub_dir in sub_dirs:
            print("Parsing annotations of camera: ", sub_dir)
            cam_id = sub_dir
            # 获取某一个子set下面的所有vbb文件
            vbb_files = glob.glob(os.path.join(vbb_inputdir, sub_dir, "*.vbb"))
            for vbb_file in vbb_files:
                # 返回一个vbb文件中所有的帧的标注结果
                annos = vbb_anno2dict(vbb_file, cam_id)
                
                if annos:
                    # 组成xml文件的存储文件夹，形如“/Users/chenguanghao/Desktop/Caltech/xmlresult/”
                    vbb_outdir = vbb_outputdir
                                                  
                    # 如果不存在 vbb_outdir
                    if not os.path.exists(vbb_outdir):
                        os.makedirs(vbb_outdir)
    
                    for filename, anno in sorted(annos.items(), key=lambda x: x[0]):                  
                        if "bbox" in anno:
                            anno_tree = instance2xml_base(anno)
                            outfile = os.path.join(vbb_outdir, os.path.splitext(filename)[0]+".xml")
                            print("Generating annotation xml file of picture: ", filename)
                            # 生成最终的xml文件，对应一张图片
                            etree.ElementTree(anno_tree).write(outfile, pretty_print=True)
    
    
    def visualize_bbox(xml_file, img_file):
        import cv2
        tree = etree.parse(xml_file)
        # load image
        image = cv2.imread(img_file)
        origin = cv2.imread(img_file)
        # 获取一张图片的所有bbox
        for bbox in tree.xpath('//bndbox'):
            coord = []
            for corner in bbox.getchildren():
                coord.append(int(float(corner.text)))
            print(coord)
            cv2.rectangle(image, (coord[0], coord[1]), (coord[2], coord[3]), (0, 0, 255), 2)
        # visualize image
        cv2.imshow("test", image)
        cv2.imshow('origin', origin)
        cv2.waitKey(0)
    
    
    def main():
        vbb_inputdir = "F:\\dataset\\Caltech\\annotations"
        vbb_outputdir = "F:\\dataset\\Caltech_VOC\\Annotations"
        parse_anno_file(vbb_inputdir, vbb_outputdir)
    
    
    if __name__ == "__main__":
        main()
        print("Success!")

得到如下文件：  
![在这里插入图片描述][2020091519503957.png_pic_left]  
Annotations内包含122,187个xml文件。（每一个xml文件内都包含“person”标签）  
JPEGImages内包含249,884个jpg文件。（很多jpg文件内不含有目标，所以要比xml文件多）

# 3 数据筛选 #

由于Annotations内包含122,187个xml文件，JPEGImages内包含249,884个jpg文件，不能实现一一对应，所以要去除多余的jpg文件。

但，其实很多xml文件内标注的person由于目标太小，导致及其不清晰，应该将其去除，再进一步处理。

这是数据集中的一张图片，猜猜人在哪？![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NlZV9TdGFy_size_16_color_FFFFFF_t_70_pic_center]  
欸，在这里！！  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NlZV9TdGFy_size_16_color_FFFFFF_t_70_pic_center 1]  
![在这里插入图片描述][20200919161248291.png_pic_center]  
由于这些目标让我难以描述，所以将此类目标（尺寸较小）去除掉。

## 3.1 根据目标面积进行筛选 ##

将符合条件的目标保存到另一个目录下。

# -*- coding:utf-8 -*-
    import xml.etree.ElementTree as ET
    import os
    
    space = 40*70
    xml_path = "F:\\dataset\\Caltech_VOC\\Annotations"
    save_path = "F:\\dataset\\Caltech_VOC\\Annotations_"
    
    
    def filter_xml():
        for file_name in os.listdir(xml_path):
            file_path = os.path.join(xml_path, file_name)
            print(file_path)
            in_file = open(file_path)
            tree = ET.parse(in_file)  # ET是一个xml文件解析库，ET.parse（）打开xml文件。parse--"解析"
            root = tree.getroot()  # 获取根节点
    
            write_flag = False
    
            for obj in root.findall('object'):  # 找到根节点下所有“object”节点
                bbox = obj.find('bndbox')
                x1 = int(bbox.find('xmin').text)
                y1 = int(bbox.find('ymin').text)
                x2 = int(bbox.find('xmax').text)
                y2 = int(bbox.find('ymax').text)
    
                if (x2-x1)*(y2-y1) >= space:
                    write_flag = True
            if write_flag:
                tree.write(os.path.join(save_path, file_name))
                print("save file: {}".format(os.path.join(save_path, file_name)))
    
    
    if __name__ == '__main__':
        filter_xml()

## 3.2 根据xml保存jpg ##

将含有xml文件的jpg保存在另一目录下。

import os
    import shutil
    from tqdm import tqdm
    
    xml_path = "F:\\dataset\\Caltech_VOC\\Annotations_"
    jpg_path = "F:\\dataset\\Caltech_VOC\\JPEGImages"
    save_path = "F:\\dataset\\Caltech_VOC\\JPEGImages_"
    
    
    if __name__ == '__main__':
        filter = []
        print("filter loading...")
        for file in tqdm(os.listdir(xml_path)):
            filter.append(os.path.splitext(file)[0])
    
        for file in os.listdir(jpg_path):
            if os.path.splitext(file)[0] in filter:
                old_path = os.path.join(jpg_path, file)
                new_path = os.path.join(save_path, file)
                shutil.copyfile(old_path, new_path)
                print("Success save jpg file: {}".format(new_path))
    
        print("Success!")

处理完成后：  
![在这里插入图片描述][20200919162214707.png_pic_left]

[0]: /images/20221123/b7f251050c7b4aefbd5d85e35f324db2.png
[2020091519503957.png_pic_left]: /images/20221123/052c9d569eed47a5acabee5ad55c2e0a.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NlZV9TdGFy_size_16_color_FFFFFF_t_70_pic_center]: /images/20221123/bdec1da429ab483bb4024db49af0ea52.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NlZV9TdGFy_size_16_color_FFFFFF_t_70_pic_center 1]: /images/20221123/b4ef2de9b5a84e588ee1b002872cc05f.png
[20200919161248291.png_pic_center]: /images/20221123/dd1a113c5bf241e7ba102a1d0adecb67.png
[20200919162214707.png_pic_left]: /images/20221123/3406cbb221b145039038a7f7e1cadbde.png