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  • SUN dataset图像数据集下载

    千次阅读 2016-04-27 12:32:35
    SUN dataset数据集,有两个不错的网址: http://vision.princeton.edu/projects/2010/SUN/ (普林斯顿大学) ... ...普林斯顿大学的SUN数据集主页: SUN Database: Scene Categorization B

    SUN dataset数据集,有两个不错的网址:

    http://vision.princeton.edu/projects/2010/SUN/ (普林斯顿大学)

    http://groups.csail.mit.edu/vision/SUN/ (麻省理工学院)

     

    普林斯顿大学的SUN数据集主页:

    SUN Database: Scene Categorization Benchmark


    Abstract

    Scene categorization is a fundamental problem in computer vision. However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories. Whereas standard databases for object categorization contain hundreds of different classes of objects, the largest available dataset of scene categories contains only 15 classes. In this paper we propose the extensive Scene UNderstanding (SUN) database that contains 899 categories and 130,519 images. We use 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of performance. We measure human scene classification performance on the SUN database and compare this with computational methods.

    Paper

    J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba.
    SUN Database: Large-scale Scene Recognition from Abbey to Zoo.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    J. Xiao, K. A. Ehinger, J. Hays, A. Torralba, and A. Oliva.
    SUN Database: Exploring a Large Collection of Scene Categories
    International Journal of Computer Vision (IJCV)

    Benchmark Evaluation

    We use 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of performance. The results are shown in the figure on the right.

    Results Visualization

    We visualize the results using the combined kernel from all features for the first training and testing partition in the following webpage. For each of the 397 categories, we show the class name, the ROC curve, 5 sample traning images, 5 sample correct predictions, 5 most confident false positives (with true label), and 5 least confident false negatives (with wrong predicted label).

    Image Database

    The database contains 397 categories SUN dataset used in the benchmark of the paper. The number of images varies across categories, but there are at least 100 images per category, and 108,754 images in total. Images are in jpg, png, or gif format. The images provided here are for research purposes only.

    Training and Testing Partition

    For the results in the paper we use a subset of the dataset that has 50 training images and 50 testing images per class, averaging over the 10 partitions in the following. To plot the curve in Figure 4(b) of the paper, we use the first n=(1, 5, 10, 20) images outof the 50 training images per class for training, and use all the same 50 testing images for testing no matter what size the training set is. (If you are using Microsoft Windows, you may need to replace / by \ in the following files.)

    Soucre Code for Benchmark Evaluation

    Scene Hierarchy

    We have manually built an overcomplete three-level hierarchy for all 908 scene categories. The scene categories are arranged in a 3-level tree: with 908 leaf nodes (SUN categories) connected to 15 parent nodes at the second level (basic-level categories) that are in turn connected to 3 nodes at the first level (superordinate categories) with the root node at the top. The hierarchy is not a tree, but a Directed Acyclic Graph. Many categories such as "hayfield" are duplicated in the hierarchy because there might be confusion over whether such a category belongs in the natural or man-made sub-hierarchies.

    Explore SUN Database

    Kernel Matrices for SVM

    Feature Matrices

    The feature matrices are avialble at THIS LINK.

    Human Classification Experiments

    DrawMe: A light-weight Javascript library for line drawing on a picture

    DrawMe is a light-weight Javascript library to enable client-end line drawing on a picture in a web browser. It is targeted to provide a basis for self-define labeling tasks for computer vision researchers. It is different from LabelMe, which provides full support but fixed labeling interface. DrawMe is a Javascript library only and the users are required to write their own code to make use of this library for their specific need of labeling. DrawMe does not provide any server or server-end code for labeling, but gives the user greater flexibility for their specific need. It also comes with a simple example with Amazon Mechanical Turk interface that serializes Javascript DOM object into text for HTML form submission. The user can easily build their own labeling interface based on this MTurk example to make use for the Amazon Mechanical Turk for labeling, either using paid workers or the researchers themselves with MTurk sandbox.

     

    ——————————————————————————————我是分割线——————————————————————————————

     

    麻省理工学院的SUN数据集主页:

     

    Goals

    The goal of the SUN database project is to provide researchers in computer vision, human perception, cognition and neuroscience, machine learning and data mining, computer graphics and robotics, with a comprehensive collection of annotated images covering a large variety of environmental scenes, places and the objects within. To build the core of the dataset, we counted all the entries that corresponded to names of scenes, places and environments (any concrete noun which could reasonably complete the phrase I am in a place, or Let’s go to the place), using WordNet English dictionary. Once we established a vocabulary for scenes, we collected images belonging to each scene category using online image search engines by quering for each scene category term, and annotate the objects in the images manually.

    Scene Recognition Benchmark

    To evaluate descriptors and classifiers for scene classification:

    Object Detection Benchmark

    The next collections contains only the fully annotated images from SUN. Each release contains the images from previous years.

    Citation

    If you find this dataset useful, please cite this paper (and refer the data as SUN397, SUN2012, or SUN):

    To know more about the object annotation process (and the annotator), check this technical note:

     

    Download Latest Dataset

    You can download the raw SUN database using the LabelMe toolbox. If you do not have the latest version of the toolbox (or if you do not have the function SUNinstall.m), you should download the toolbox first:

     LabelMe toolbox

    To download the latest version of the database enter the Matlab commands:

    >> yourpathimages = 'SUNDATABASE/Images';
    >> yourpathannotations = 'SUNDATABASE/Annotations';
    >> SUNinstall(yourpathimages, yourpathannotations);

    The variables yourpathimages and yourpathannotations should point to the local paths where you want to download the images and annotations.

    The first time that you call SUNinstall it will download the full set of images and annotations. Subsequent calls to SUNinstall will only download any new images added since the last download and the full set of annotations. If the download is interrupted the next call will not download again the images already downloaded.

    If you want to download only one folder, you can specify a folder name:

    >> folder = 'b/beach';
    >> SUNinstall(yourpathimages, yourpathannotations, folder);

    As new images are annotated everyday, you will get a slightly changing version if you download the database several times. If you are looking for a frozen copy of the database, use the links in the benchmark sections above.

    展开全文
  • SUN360 panorama数据集

    2020-02-05 23:21:15
    数据集主页上的下载资源都挂了,也联系不上作者,有没有大佬下载过这个数据集啊跪求分享,毕业论文要用,不想延毕QAQ 可以只提供能用的下载链接,可以额外有偿,谢谢!
  • SUN2012pascalformat 链接: https://pan.baidu.com/s/1yNb0c0hTLT0MJkX6M_5xBA 密码: f51d Linemod_preprocessed 链接: https://pan.baidu.com/s/12WcjfVza2LVtu72fRAn7nQ 密码: l8lj

    SUN2012pascalformat
    链接: https://pan.baidu.com/s/1yNb0c0hTLT0MJkX6M_5xBA 密码: f51d
    Linemod_preprocessed
    链接: https://pan.baidu.com/s/12WcjfVza2LVtu72fRAn7nQ 密码: l8lj

    展开全文
  • SUN RGB-D数据集的理解

    千次阅读 2020-06-14 17:35:25
    SUN RGB-D数据集是普灵斯顿大学的 Vision & Robotics Group 公开的一个有关场景理解的数据集。 官方介绍在此,其中有视频介绍。视频介绍已经很详细了,建议先看懂视频。 此博客仅仅列出个人认为的一些理解要点...

    SUN RGB-D数据集是普灵斯顿大学的 Vision & Robotics Group 公开的一个有关场景理解的数据集。

    官方介绍在此,其中有视频介绍。视频介绍已经很详细了,建议先看懂视频。

    此博客仅仅列出个人认为的一些理解要点,如有错误,欢迎指正。

    一、数据采集

    通过四款3D摄像机采集图像和深度信息:

    • Intel Realsence
    • Asus Xtion
    • Kinect v1
    • Kinect v2

    这四款相机均含有色彩传感器+红外发射器+红外接收器。其中色彩传感器获取RGB信息,红外发射器+红外接收器获取深度信息。

    从数据上来看,RGB和深度信息是分开存放的。

    如下图所示,左边是一副彩色图片,包含RGB信息,右边是一副灰度图片,其灰度值代表着深度信息:

       

    一般而言,使用不同相机拍摄出来的彩色图片,差别不会太大。但由于硬件和算法上的差异,不同3D相机得出的深度估计差别较大,这是我们需要认识到的一个变量。

    使用笔记本电脑+移动电源+相机的方式,方便在不同场景下做数据采集,见下图:

    二、数据标注

    每份数据标注了:

    • scene category (场景种类)
    • 2D segmentation (二维分割)
    • 3D room layout (三维房间布局)
    • 3D object box (三维物体边框)
    • 3D object orientation (三维物体方向)

    三、数据量级

    SUN RGB-D 数据集包含10,335张不同场景的室内图片,146,617个2D多边形标注(应该指的是2D分割),和58,657个3D边框。

    将SUN RGB-D的数据量级与PASCAL VOC2017的数据量级作对比:

      图片个数 2D分割个数 2D 物体框个数 3D物体框个数
    SUN RGB-D 10,335 146,617   58,657
    PASCAL VOC2017 11,530 6,929 27,450  

    就图片数量而言,SUN RGB-D与PASCAL VOC2017有着相同的量级,适合训练数据驱动模型,并适合作为一种评价基准。

    展开全文
  • 【语义分割】——SUN_RGBD数据集解析

    千次阅读 2020-12-14 17:35:17
    简介: 虽然RGB-D传感器已经在一些视觉任务上实现了重大突破,比如3D重建,但我们还没有在高级场景理解上实现类似的性能...我们的数据集由四个不同的传感器捕获,包含10,000张RGB-D图像,其规模与PASCAL VOC类似。整.

    地址:http://rgbd.cs.princeton.edu/

    简介:
    虽然RGB-D传感器已经在一些视觉任务上实现了重大突破,比如3D重建,但我们还没有在高级场景理解上实现类似的性能飞跃。造成这种情况的主要原因之一可能是缺乏一个具有合理大小的基准,其中包括用于培训的3D注释和用于评估的3D度量标准。在本文中,我们提出了一个RGB-D基准套件,目的是为了在所有主要场景理解任务中推进最新的技术水平。我们的数据集由四个不同的传感器捕获,包含10,000张RGB-D图像,其规模与PASCAL VOC类似。整个数据集被密集地注释,包括146,617个2D多边形和58,657个具有精确对象方向的3D边框,以及一个3D房间布局和场景类别。这个数据集使我们能够训练需要大量数据的算法来完成场景理解任务,使用直接和有意义的3D度量来评估它们,避免对小测试集进行过拟合,并研究交叉传感器的偏差。

    这里我们主要解析其语义分割的部分

    1. 语义标注解析

    这里我们主要解析其语义分割的部分。目标是得到原始图像,语义label图像,语义可视化图像,类别label.txt文件。

    Code(参考自:prepare_dataset.py):

    import os
    import os.path as osp
    import shutil
    from PIL import Image
    from scipy.io import loadmat
    import matplotlib.pyplot as plt
    import numpy as np
    import random
    from tqdm import tqdm
    import h5py
    import scipy
    
    imgpath = './SUNRGBD'
    SUNRGBDMeta_dir = './SUNRGBDtoolbox/Metadata/SUNRGBDMeta.mat'
    SUNRGBD2Dseg_dir = './SUNRGBDtoolbox/Metadata/SUNRGBD2Dseg.mat'
    labeltxt   = "./label.txt"
    imagepath = './images'
    labelpath = './labels'
    visualpath = './visual'
    for path in [imagepath, labelpath, visualpath]:
        if not osp.exists(path):
            os.makedirs(path)
    
    bin_colormap = np.random.randint(0, 255, (256, 3))      # 可视化的颜色
    bin_colormap = bin_colormap.astype(np.uint8)
    
    labels = []
    processed = []
    # load the data from the matlab file
    SUNRGBD2Dseg = h5py.File(SUNRGBD2Dseg_dir, mode='r', libver='latest')
    SUNRGBDMeta = scipy.io.loadmat(SUNRGBDMeta_dir, squeeze_me=True,
                                    struct_as_record=False)['SUNRGBDMeta']
    seglabel = SUNRGBD2Dseg['SUNRGBD2Dseg']['seglabel']
    
    # classlabels
    seg37list = SUNRGBD2Dseg['seg37list']
    for i in range(seg37list.size):
        classstring = np.array(SUNRGBD2Dseg[seg37list[i][0]]).tostring().decode('utf-8')
        classstring = classstring.replace("\x00", "")
        print(classstring)
        labels.append(classstring)
    
    with open(labeltxt, 'w') as f:
        content = ','.join(labels)
        f.write(content)
    
    for i, meta in tqdm(enumerate(SUNRGBDMeta)):
        meta_dir = '/'.join(meta.rgbpath.split('/')[:-2])
        real_dir = meta_dir.split('/n/fs/sun3d/data/SUNRGBD/')[1]
        rgb_path = os.path.join(real_dir, 'image/' + meta.rgbname)
    
        # rgbimage
        srcname = osp.join(imgpath, rgb_path)
        t = "sun_{}".format(meta.rgbname)
        dstname = osp.join(imagepath, t)
        shutil.copy(srcname, dstname)
        rgbimg = Image.open(srcname)
    
    
        # labelimage
        label = np.array(
            SUNRGBD2Dseg[seglabel[i][0]][:].transpose(1, 0)).\
            astype(np.uint8)
        labelname = osp.join(labelpath, t.replace(".jpg", ".png"))
        labelimg = Image.fromarray(label, 'L')
        labelimg.save(labelname)
    
        # debug show
        # plt.subplot(1, 2, 1)
        # plt.imshow(rgbimg)
        # plt.subplot(1, 2, 2)
        # plt.imshow(labelimg)
        # plt.show()
    
        # visualimage
        visualname = osp.join(visualpath, t.replace(".jpg", ".png"))
        visualimg  = Image.fromarray(label, "P")
        palette = bin_colormap          #long palette of 768 items
        visualimg.putpalette(palette) 
        visualimg.save(visualname, format='PNG')
    

    可视化结果:
    在这里插入图片描述
    labellist
    共有37个类别,

    wall,floor,cabinet,bed,chair,sofa,table,door,window,bookshelf,picture,counter,blinds,desk,shelves,curtain,dresser,pillow,mirror,floor_mat,clothes,ceiling,books,fridge,tv,paper,towel,shower_curtain,box,whiteboard,person,night_stand,toilet,sink,lamp,bathtub,bag
    

    2. 关键点解析

    2.1 h5py数据的解析

    要先索引到,然后再从总的数据中读取SUNRGBD2Dseg[]
    np.array(SUNRGBD2Dseg[seglabel[i][0]][:].transpose(1, 0))

    2.2 PIL灰度图以platte保存成彩图
    关键是用:putpalette(palette)指定颜色

    bin_colormap = np.random.randint(0, 255, (256, 3))      # 可视化的颜色
    bin_colormap = bin_colormap.astype(np.uint8)
    visualimg  = Image.fromarray(label, "P")
    palette = bin_colormap          #long palette of 768 items
    visualimg.putpalette(palette) 
    visualimg.save(visualname, format='PNG')
    
    展开全文
  • (1)生成trainval.txt和test.txtload allsplit.matfid=fopen('trainval.txt','wt')[row,col]=size(alltrain)c=alltrainstr1='/n/fs/sun3d/data/'str2='/home/zhaohuaqing/Downloads/'f='/image/'f1='image/'kv2ok='...
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