【目标检测】数据集

VOC

• Annotation里面是xml文件，包含了每张图片的标注信息。
• JPEGImage包含了所有的图片。
• ImageSets里有四个文件夹，但是常用的是Main文件夹，里面有对训练、验证和测试集的划分，只有图片名字的前缀。

下载
VOC2007数据集

# 下载训练和验证集
curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
# 下载测试集
curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
# 解压
tar -xvf VOCtrainval_06-Nov-2007.tar
tar -xvf VOCtest_06-Nov-2007.tar
# 移除压缩包
rm VOCtrainval_06-Nov-2007.tar
rm VOCtest_06-Nov-2007.tar

VOC2012数据集

# 下载
curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
# 解压
tar -xvf VOCtrainval_11-May-2012.tar
# 移除压缩包
rm VOCtrainval_11-May-2012.tar

Annotation

Annotation存放的是xml文件，每张图片都有一个同名的xml文件。

<annotation>
	<folder>VOC2012</folder>  #图片来源
	<filename>2007_000027.jpg</filename> #名称
	<source>                  
		<database>The VOC2007 Database</database>
		<annotation>PASCAL VOC2007</annotation>
		<image>flickr</image>
	</source>
	<size>     #尺寸
		<width>486</width>
		<height>500</height>
		<depth>3</depth>
	</size>
	<segmented>0</segmented> #是否用于分割
	<object>  #包含物体
		<name>person</name> #物体类别
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>  #物体的bbox
			<xmin>174</xmin>
			<ymin>101</ymin>
			<xmax>349</xmax>
			<ymax>351</ymax>
		</bndbox>
		<part> 
			<name>head</name>
			<bndbox>
				<xmin>169</xmin>
				<ymin>104</ymin>
				<xmax>209</xmax>
				<ymax>146</ymax>
			</bndbox>
		</part>
		<part>   
			<name>hand</name>
			<bndbox>
				<xmin>278</xmin>
				<ymin>210</ymin>
				<xmax>297</xmax>
				<ymax>233</ymax>
			</bndbox>
		</part>
		<part>
			<name>foot</name>
			<bndbox>
				<xmin>273</xmin>
				<ymin>333</ymin>
				<xmax>297</xmax>
				<ymax>354</ymax>
			</bndbox>
		</part>
		<part>
			<name>foot</name>
			<bndbox>
				<xmin>319</xmin>
				<ymin>307</ymin>
				<xmax>340</xmax>
				<ymax>326</ymax>
			</bndbox>
		</part>
	</object>
</annotation>

基本操作

"""VOC Dataset Classes
Original author: Francisco Massa
https://github.com/fmassa/vision/blob/voc_dataset/torchvision/datasets/voc.py
Updated by: Ellis Brown, Max deGroot
"""
import os.path as osp
import sys
import torch
import torch.utils.data as data
import cv2
import numpy as np
if sys.version_info[0] == 2:
    import xml.etree.cElementTree as ET
else:
    import xml.etree.ElementTree as ET

VOC_CLASSES = (  # always index 0
    'aeroplane', 'bicycle', 'bird', 'boat',
    'bottle', 'bus', 'car', 'cat', 'chair',
    'cow', 'diningtable', 'dog', 'horse',
    'motorbike', 'person', 'pottedplant',
    'sheep', 'sofa', 'train', 'tvmonitor')

# note: if you used our download scripts, this should be right
VOC_ROOT = osp.join(HOME, "data/VOCdevkit/")

class VOCAnnotationTransform(object):
    """Transforms a VOC annotation into a Tensor of bbox coords and label index
    Initilized with a dictionary lookup of classnames to indexes
    Arguments:
        class_to_ind (dict, optional): dictionary lookup of classnames -> indexes
            (default: alphabetic indexing of VOC's 20 classes)
        keep_difficult (bool, optional): keep difficult instances or not
            (default: False)
        height (int): height
        width (int): width
    """

    def __init__(self, class_to_ind=None, keep_difficult=False):
        self.class_to_ind = class_to_ind or dict(
            zip(VOC_CLASSES, range(len(VOC_CLASSES))))
        self.keep_difficult = keep_difficult

    def __call__(self, target, width, height):
        """
        Arguments:
            target (annotation) : the target annotation to be made usable
                will be an ET.Element
        Returns:
            a list containing lists of bounding boxes  [bbox coords, class name]
        """
        res = []
        for obj in target.iter('object'):
            difficult = int(obj.find('difficult').text) == 1
            if not self.keep_difficult and difficult:
                continue
            name = obj.find('name').text.lower().strip()
            bbox = obj.find('bndbox')

            pts = ['xmin', 'ymin', 'xmax', 'ymax']
            bndbox = []
            for i, pt in enumerate(pts):
                cur_pt = int(bbox.find(pt).text) - 1
                # scale height or width
                cur_pt = cur_pt / width if i % 2 == 0 else cur_pt / height
                bndbox.append(cur_pt)
            label_idx = self.class_to_ind[name]
            bndbox.append(label_idx)
            res += [bndbox]  # [xmin, ymin, xmax, ymax, label_ind]
            # img_id = target.find('filename').text[:-4]

        return res  # [[xmin, ymin, xmax, ymax, label_ind], ... ]
 
_annopath = osp.join('%s', 'Annotations', '%s.xml')
_imgpath = osp.join('%s', 'JPEGImages', '%s.jpg')
target = ET.parse(_annopath % img_id).getroot()
img = cv2.imread(_imgpath % img_id)

COCO

下载
通过get_coco_dataset.sh 下载数据集并且制作训练集绝对路径文本

#!/bin/bash
# CREDIT: https://github.com/pjreddie/darknet/tree/master/scripts/get_coco_dataset.sh
 
# Clone COCO API
git clone https://github.com/pdollar/coco
cd coco
 
mkdir images
cd images
 
# Download Images
wget -c https://pjreddie.com/media/files/train2014.zip
wget -c https://pjreddie.com/media/files/val2014.zip
 
# Unzip 且不回显
unzip -q train2014.zip 
unzip -q val2014.zip
 
cd ..
 
# Download COCO Metadata
wget -c https://pjreddie.com/media/files/instances_train-val2014.zip
wget -c https://pjreddie.com/media/files/coco/5k.part
wget -c https://pjreddie.com/media/files/coco/trainvalno5k.part
wget -c https://pjreddie.com/media/files/coco/labels.tgz
tar xzf labels.tgz
unzip -q instances_train-val2014.zip
 
# Set Up Image Lists
paste <(awk "{print \"$PWD\"}" <5k.part) 5k.part | tr -d '\t' > 5k.txt
paste <(awk "{print \"$PWD\"}" <trainvalno5k.part) trainvalno5k.part | tr -d '\t' > trainvalno5k.txt

读取json文件

import json
f = open("Settings.json", encoding='utf-8')  #encoding参数必须设置，否则默认以gbk模式读取文件，当文件中有中文时，会报错
temp = json.load(f)