cv.Dataset/Dataset

Constructor

ds = cv.Dataset(dstype)

Input

dstype Dataset class implementation. One of:
- AR_hmdb HMDB: A Large Human Motion Database
- AR_sports Sports-1M Dataset
- FR_adience Adience
- FR_lfw Labeled Faces in the Wild
- GR_chalearn ChaLearn Looking at People
- GR_skig Sheffield Kinect Gesture Dataset
- HPE_humaneva HumanEva Dataset
- HPE_parse PARSE Dataset
- IR_affine Affine Covariant Regions Datasets
- IR_robot Robot Data Set
- IS_bsds The Berkeley Segmentation Dataset and Benchmark
- IS_weizmann Weizmann Segmentation Evaluation Database
- MSM_epfl EPFL Multi-View Stereo
- MSM_middlebury Stereo - Middlebury Computer Vision
- OR_imagenet ImageNet
- OR_mnist MNIST
- OR_pascal PASCAL Object Recognition Database
- OR_sun SUN Database
- PD_caltech Caltech Pedestrian Detection Benchmark
- PD_inria INRIA Person Dataset
- SLAM_kitti KITTI Vision Benchmark
- SLAM_tumindoor TUMindoor Dataset
- TR_chars The Chars74K Dataset
- TR_icdar ICDAR
- TR_svt The Street View Text Dataset
- TRACK_vot VOT 2015 Database
- TRACK_alov Amsterdam Library of Ordinary Videos (ALOV++)

HMDB: A Large Human Motion Database

Implements loading dataset: "HMDB: A Large Human Motion Database" Link

Usage:

From link above download dataset files: hmdb51_org.rar & test_train_splits.rar.
Unpack them. Unpack all archives from directory: hmdb51_org/ and remove them.

To load data run:

ds = cv.Dataset('AR_hmdb');
ds.load('/home/user/path_to_unpacked_folders/');

Benchmark:

For this dataset was implemented benchmark with accuracy: 0.107407 (using precomputed HOG/HOF "STIP" features from site, averaging for 3 splits)

Note:

Precomputed features should be unpacked in the same folder: /home/user/path_to_unpacked_folders/hmdb51_org_stips/.
Also unpack all archives from directory: hmdb51_org_stips/ and remove them.

Sports-1M Dataset

Implements loading dataset: "Sports-1M Dataset" Link

Usage:

From link above download dataset files.

To load data run:

ds = cv.Dataset('AR_sports');
ds.load('/home/user/path_to_downloaded_folders/');

Adience

Implements loading dataset: "Adience" Link

Usage:

From link above download any dataset file: faces.tar.gz\aligned.tar.gz and files with splits: fold_0_data.txt-fold_4_data.txt, fold_frontal_0_data.txt-fold_frontal_4_data.txt. (For face recognition task another splits should be created)
Unpack dataset file to some folder and place split files into the same folder.

To load data run:

ds = cv.Dataset('FR_adience');
ds.load('/home/user/path_to_created_folder/');

Labeled Faces in the Wild

Implements loading dataset: "Labeled Faces in the Wild" Link

Usage:

From link above download any dataset file: lfw.tgz\lfwa.tar.gz\lfw-deepfunneled.tgz\lfw-funneled.tgz and files with pairs: 10 test splits: pairs.txt and developer train split: pairsDevTrain.txt.
Unpack dataset file and place pairs.txt and pairsDevTrain.txt in created folder.

To load data run:

ds = cv.Dataset('FR_lfw');
ds.load('/home/user/path_to_unpacked_folder/lfw2/');

Benchmark:

For this dataset was implemented benchmark with accuracy: 0.623833 +/- 0.005223 (train split: pairsDevTrain.txt, dataset: lfwa)

ChaLearn Looking at People

Implements loading dataset: "ChaLearn Looking at People" Link

Usage

Follow instruction from site above, download files for dataset "Track 3: Gesture Recognition": Train1.zip-Train5.zip, Validation1.zip-Validation3.zip (Register on site and accept the terms and conditions of competition. There are three mirrors for downloading dataset files. When I downloaded data only mirror: "Universitat Oberta de Catalunya" works).
Unpack train archives Train1.zip-Train5.zip to folder Train/, validation archives Validation1.zip-Validation3.zip to folder Validation/
Unpack all archives in Train/ & Validation/ in the folders with the same names, for example: Sample0001.zip to Sample0001/

To load data run:

ds = cv.Dataset('GR_chalearn');
ds.load('/home/user/path_to_unpacked_folders/');

Sheffield Kinect Gesture Dataset

Implements loading dataset: "Sheffield Kinect Gesture Dataset" Link

Usage:

From link above download dataset files: subject1_dep.7z-subject6_dep.7z, subject1_rgb.7z-subject6_rgb.7z.
Unpack them.

To load data run:

ds = cv.Dataset('GR_skig');
ds.load('/home/user/path_to_unpacked_folders/');

HumanEva Dataset

Implements loading dataset: "HumanEva Dataset" Link

Usage:

From link above download dataset files for HumanEva-I (tar) and HumanEva-II.
Unpack them to HumanEva_1 and HumanEva_2 accordingly.

To load data run:

ds = cv.Dataset('HPE_humaneva');
ds.load('/home/user/path_to_unpacked_folders/');

PARSE Dataset

Implements loading dataset: "PARSE Dataset" Link

Usage:

From link above download dataset file: people.zip.
Unpack it.

To load data run:

ds = cv.Dataset('HPE_parse');
ds.load('/home/user/path_to_unpacked_folder/people_all/');

Affine Covariant Regions Datasets

Implements loading dataset: "Affine Covariant Regions Datasets" Link

Usage:

From link above download dataset files: bark\bikes\boat\graf\leuven\trees\ubc\wall.tar.gz.
Unpack them.

To load data, for example, for "bark", run:

ds = cv.Dataset('IR_affine');
ds.load('/home/user/path_to_unpacked_folder/bark/');

Robot Data Set

Implements loading dataset: "Robot Data Set, Point Feature Data Set - 2010" Link

Usage:

From link above download dataset files: SET001_6.tar.gz-SET055_60.tar.gz
Unpack them to one folder.

To load data run:

ds = cv.Dataset('IR_robot');
ds.load('/home/user/path_to_unpacked_folder/');

The Berkeley Segmentation Dataset and Benchmark

Implements loading dataset: "The Berkeley Segmentation Dataset and Benchmark" Link

Usage:

From link above download dataset files: BSDS300-human.tgz & BSDS300-images.tgz.
Unpack them.

To load data run:

ds = cv.Dataset('IS_bsds');
ds.load('/home/user/path_to_unpacked_folder/BSDS300/');

Weizmann Segmentation Evaluation Database

Implements loading dataset: "Weizmann Segmentation Evaluation Database" Link

Usage:

From link above download dataset files: Weizmann_Seg_DB_1obj.ZIP & Weizmann_Seg_DB_2obj.ZIP.
Unpack them.

To load data, for example, for 1 object dataset, run:

ds = cv.Dataset('IS_weizmann');
ds.load('/home/user/path_to_unpacked_folder/1obj/');

EPFL Multi-View Stereo

Implements loading dataset: "EPFL Multi-View Stereo" Link

Usage:

From link above download dataset files: castle_dense\castle_dense_large\castle_entry\fountain\herzjesu_dense\herzjesu_dense_large_bounding\cameras\images\p.tar.gz.
Unpack them in separate folder for each object. For example, for "fountain", in folder fountain/ : fountain_dense_bounding.tar.gz -> bounding/, fountain_dense_cameras.tar.gz -> camera/, fountain_dense_images.tar.gz -> png/, fountain_dense_p.tar.gz -> P/

To load data, for example, for "fountain", run:

ds = cv.Dataset('MSM_epfl');
ds.load('/home/user/path_to_unpacked_folder/fountain/');

Stereo - Middlebury Computer Vision

Implements loading dataset: "Stereo - Middlebury Computer Vision" Link

Usage:

From link above download dataset files: dino\dinoRing\dinoSparseRing\temple\templeRing\templeSparseRing.zip
Unpack them.

To load data, for example "temple" dataset, run:

ds = cv.Dataset('MSM_middlebury');
ds.load('/home/user/path_to_unpacked_folder/temple/');

ImageNet

Implements loading dataset: "ImageNet" Link

Usage:

From link above download dataset files: ILSVRC2010_images_train.tar, ILSVRC2010_images_test.tar, ILSVRC2010_images_val.tar & devkit: ILSVRC2010_devkit-1.0.tar.gz (Implemented loading of 2010 dataset as only this dataset has ground truth for test data, but structure for ILSVRC2014 is similar)
Unpack them to: some_folder/train/, some_folder/test/, some_folder/val & some_folder/ILSVRC2010_validation_ground_truth.txt, some_folder/ILSVRC2010_test_ground_truth.txt.
Create file with labels: some_folder/labels.txt, for example, using python script below (each file's row format: synset,labelID,description. For example: "n07751451,18,plum").
Unpack all tar files in train.

To load data run:

ds = cv.Dataset('OR_imagenet');
ds.load('/home/user/some_folder/');

Python script to parse meta.mat:

import scipy.io
meta_mat = scipy.io.loadmat("devkit-1.0/data/meta.mat")
 
labels_dic = dict((m[0][1][0], m[0][0][0][0]-1) for m in meta_mat['synsets']
label_names_dic = dict((m[0][1][0], m[0][2][0]) for m in meta_mat['synsets']
 
for label in labels_dic.keys():
    print "{0},{1},{2}".format(label, labels_dic[label], label_names_dic[label])

MNIST

Implements loading dataset: "MNIST" Link

Usage:

From link above download dataset files: t10k-images-idx3-ubyte.gz, t10k-labels-idx1-ubyte.gz, train-images-idx3-ubyte.gz, train-labels-idx1-ubyte.gz.
Unpack them.

To load data run:

ds = cv.Dataset('OR_mnist');
ds.load('/home/user/path_to_unpacked_files/');

SUN Database

Implements loading dataset: "SUN Database, Scene Recognition Benchmark. SUN397" Link

Usage:

From link above download dataset file: SUN397.tar & file with splits: Partitions.zip
Unpack SUN397.tar into folder: SUN397/ & Partitions.zip into folder: SUN397/Partitions/

To load data run:

ds = cv.Dataset('OR_sun');
ds.load('/home/user/path_to_unpacked_files/SUN397/');

Caltech Pedestrian Detection Benchmark

Implements loading dataset: "Caltech Pedestrian Detection Benchmark" Link

Usage:

From link above download dataset files: set00.tar-set10.tar.
Unpack them to separate folder.

To load data run:

ds = cv.Dataset('PD_caltech');
ds.load('/home/user/path_to_unpacked_folders/');

Note:

First version of Caltech Pedestrian dataset loading. Code to unpack all frames from seq files commented as their number is huge! So currently load only meta information without data.
Also ground truth isn't processed, as need to convert it from mat files first.

KITTI Vision Benchmark

Implements loading dataset: "KITTI Vision Benchmark" Link

Usage:

From link above download "Odometry" dataset files: data_odometry_gray, data_odometry_color, data_odometry_velodyne, data_odometry_poses, data_odometry_calib.zip.
Unpack data_odometry_poses.zip, it creates folder dataset/poses/. After that unpack data_odometry_gray.zip, data_odometry_color.zip, data_odometry_velodyne.zip. Folder dataset/sequences/ will be created with folders 00/..21/. Each of these folders will contain: image_0/, image_1/, image_2/, image_3/, velodyne/ and files calib.txt & times.txt. These two last files will be replaced after unpacking data_odometry_calib.zip at the end.

To load data run:

ds = cv.Dataset('SLAM_kitti');
ds.load('/home/user/path_to_unpacked_folder/dataset/');

TUMindoor Dataset

Implements loading dataset: "TUMindoor Dataset" Link

Usage:

From link above download dataset files: dslr\info\ladybug\pointcloud.tar.bz2 for each dataset: 11-11-28 (1st floor), 11-12-13 (1st floor N1), 11-12-17a (4th floor), 11-12-17b (3rd floor), 11-12-17c (Ground I), 11-12-18a (Ground II), 11-12-18b (2nd floor)
Unpack them in separate folder for each dataset. dslr.tar.bz2 -> dslr/, info.tar.bz2 -> info/, ladybug.tar.bz2 -> ladybug/, pointcloud.tar.bz2 -> pointcloud/.

To load each dataset run:

ds = cv.Dataset('SLAM_tumindoor');
ds.load('/home/user/path_to_unpacked_folders/');

The Chars74K Dataset

Implements loading dataset: "The Chars74K Dataset" Link

Usage:

From link above download dataset files: EnglishFnt\EnglishHnd\EnglishImg\KannadaHnd\KannadaImg.tgz, ListsTXT.tgz.
Unpack them.
Move .m files from folder ListsTXT/ to appropriate folder. For example, English/list_English_Img.m for EnglishImg.tgz.

To load data, for example "EnglishImg", run:

ds = cv.Dataset('TR_chars');
ds.load('/home/user/path_to_unpacked_folder/English/');

The Street View Text Dataset

Implements loading dataset: "The Street View Text Dataset" Link

Usage:

From link above download dataset file: svt.zip.
Unpack it.

To load data run:

ds = cv.Dataset('TR_svt');
ds.load('/home/user/path_to_unpacked_folder/svt/svt1/');

Benchmark:

For this dataset was implemented benchmark with accuracy (mean f1): 0.217

VOT 2015 Database

Implements loading dataset: VOT 2015 Link

VOT 2015 dataset comprises 60 short sequences showing various objects in challenging backgrounds. The sequences were chosen from a large pool of sequences including the ALOV dataset, OTB2 dataset, non-tracking datasets, Computer Vision Online, Professor Bob Fisher's Image Database, Videezy, Center for Research in Computer Vision, University of Central Florida, USA, NYU Center for Genomics and Systems Biology, Data Wrangling, Open Access Directory and Learning and Recognition in Vision Group, INRIA, France. The VOT sequence selection protocol was applied to obtain a representative set of challenging sequences.

Usage:

From link above download dataset file: vot2015.zip
Unpack vot2015.zip into folder: VOT2015/

To load data run:

ds = cv.Dataset('TRACK_vot');
ds.load('/home/user/path_to_unpacked_files/VOT2015/');

Amsterdam Library of Ordinary Videos for tracking

Implements loading daataset: ALOV++ Link