前言：
由于作者最近在做行人目标检测这方面的研究，需要用到Caltech Pedestrian数据集，但该数据集存在的一些问题使我不得不对其进行一些适配性的处理。趁着炼丹的功夫来记录一下处理过程。
正文：
Caltech Pedestrian dataset 下载链接：Caltech—USA
下载原数据可能需要爬墙，若需要的话也可在文末获取本文处理后的数据。
言归正传，原数据的标签和图片格式分是.vbb和.seq，我希望对其进行一个xml和jpg的转换，在Caltech数据集目录同级处创建两个python文件，复制以下代码并运行即可。代码如下：
1.标签处理：
# -*- 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 = "./Caltech/annotations/annotations"
vbb_outputdir = "./Caltech/Annotations_voc"
parse_anno_file(vbb_inputdir, vbb_outputdir)
if __name__ == "__main__":
main()
print("Success!")
2.图片处理
import os.path
import fnmatch
import shutil
def open_save(file, savepath):
"""
read .seq file, and save the images into the savepath
:param file: .seq文件路径
:param savepath: 保存的图像路径
:return:
"""
# 读入一个seq文件，然后拆分成image存入savepath当中
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
"""
>>> a = ".12121.3223.4343"
>>> a.split('.')
['', '12121', '3223', '4343']
"""
# split .seq file into segment with the image prefix
strlist = string.split(splitstring)
f.close()
count = 0
# delete the image folder path if it exists
if os.path.exists(savepath):
shutil.rmtree(savepath)
# create the image folder path
if not os.path.exists(savepath):
os.makedirs(savepath)
# deal with file segment, every segment is an image except the first one
for img in strlist:
filename = str(count) + '.jpg'
filenamewithpath = os.path.join(savepath, filename)
# abandon the first one, which is filled with .seq header
if count > 0:
i = open(filenamewithpath, 'wb+')
i.write(splitstring.encode('latin-1'))
i.write(img.encode('latin-1'))
i.close()
count += 1
if __name__ == "__main__":
rootdir = "./Caltech"
saveroot = "./Caltech/VOC_process"
# walk in the rootdir, take down the .seq filename and filepath
for parent, dirnames, filenames in os.walk(rootdir):
for filename in filenames:
# check .seq file with suffix
# fnmatch 全称是 filename match，主要是用来匹配文件名是否符合规则的
if fnmatch.fnmatch(filename, '*.seq'):
# take down the filename with path of .seq file
thefilename = os.path.join(parent, filename)
# create the image folder by combining .seq file path with .seq filename
parent_path = parent
parent_path = parent_path.replace('\\', '/')
thesavepath = saveroot + '/' + parent_path.split('/')[-1] + '/' + filename.split('.')[0]
print("Filename=" + thefilename)
print("Savepath=" + thesavepath)
open_save(thefilename, thesavepath)
经过上述处理后，实际上图片名和标签名并没有一一对应，标签名是：set00_V000_id.xml样式的，而图片名则是1，2，3，4…样式的，我想让它们一一对应起来，就先要修改图片名为set00_V000_id.jpg：
3.图片重命名：
import os
image_train_path="./train_images"
def renamfile(path1):
total_xml = os.listdir(path1)
nw = [] # V000,V0001
for j in total_xml:#set00
second_dir=os.listdir(os.path.join(path1,j))
nw.append(second_dir)
txz = 0
for i in total_xml:#i:set00 set01 set02
s_dir = os.path.join(path1, i)
for j in nw[txz]:
v_dir = os.path.join(s_dir, j)
sx = os.listdir(v_dir)
sx.sort(key=lambda x: int(x.split('.')[0]))
for tt in sx:
final_dir=os.path.join(v_dir,tt)
houzhui=i+'_'+j+'_'+tt
re_dir=os.path.join(v_dir, houzhui)
try:
os.rename(final_dir, re_dir)
except Exception as e:
print(e)
print('rename file fail\r\n')
else:
print('rename file success\r\n')
txz += 1
renamfile(image_train_path)
实际上，由于该数据集是由车载摄像头的视频切帧得到的，并不是每一帧上都有person目标物，所以某些图片是没有annotations的（作者采用的annotation是旧版的，不是新版），所以在这里对一些多余的图片进行删除：
4.图片删除：
import os#首先导入os模块，进行文件的查找，修改，删除等操作一般都要事先导入os模块
xmlfilepath = "./Caltech/Annotations_voc"
image_train_path="./Caltech/VOC_process"
def del_files(path,path2):
total_xml = os.listdir(path)#遍历xml标签目录，获得所有标签名，存在total_xml中
nw = [] # V000,V0001
total_image=os.listdir(path2)#遍历第一级目录，获得set00,set01...set10
for j in total_image:#set00
second_dir=os.listdir(os.path.join(path2,j))#遍历第二级目录，获得V000,V001...
nw.append(second_dir)#
txz = 0
sx1 = []#保存图片的文件名，如set00_V000_1
for i in total_image:#i:set00 set01 set02
s_dir = os.path.join(path2, i)#./Caltech/VOC_process/set00
for j in nw[txz]:#j:V000
v_dir = os.path.join(s_dir, j)#./Caltech/VOC_process/set00/V000
sx = os.listdir(v_dir)#69.xml 70.xml
sx.sort(key=lambda x: int(x.split('_')[2].split('.')[0]))
for x in sx:
x1,x2=x.split(".")
sx1.append(x1)
txz += 1
sx2=[]#保存xml的文件名，如set00_V000_69
for j in total_xml:
name=j.split(".")[0]
sx2.append(name)
for a in sx1:
if a not in sx2:
p1,p2,p3=a.split('_')
del_path=p1+'/'+p2+'/'+a+'.jpg'
print('图片不存在',a)
os.remove(os.path.join(path2, del_path))
print('del success!')
del_files(xmlfilepath,image_train_path)
然后再将annotation像图片一样放到如set00/V000等的文件夹中就可以了，最后再对所有图片和标签进行0，1，2，3…这样的重命名，在此不再赘述。
处理后的数据集：链接：https://pan.baidu.com/s/1jLt1MwoWo7hd3bMZ7MvYMQ
提取码：lhvw
