datasetstools. Tools for working with different datasets.

The datasetstools module includes classes for working with different datasets.

Action Recognition

AR_hmdb

class AR_hmdb

Implements loading dataset:

“HMDB: A Large Human Motion Database”: http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/

Note

Usage

1. From link above download dataset files: hmdb51_org.rar & test_train_splits.rar.
2. Unpack them.
3. To load data run: ./opencv/build/bin/example_datasetstools_ar_hmdb -p=/home/user/path_to_unpacked_folders/

AR_sports

class AR_sports

Implements loading dataset:

“Sports-1M Dataset”: http://cs.stanford.edu/people/karpathy/deepvideo/

Note

Usage

1. From link above download dataset files (git clone https://code.google.com/p/sports-1m-dataset/).
2. To load data run: ./opencv/build/bin/example_datasetstools_ar_sports -p=/home/user/path_to_downloaded_folders/

Face Recognition

FR_lfw

class FR_lfw

Implements loading dataset:

“Labeled Faces in the Wild”: http://vis-www.cs.umass.edu/lfw/

Note

Usage

1. From link above download any dataset file: lfw.tgzlfwa.tar.gzlfw-deepfunneled.tgzlfw-funneled.tgz and file with 10 test splits: pairs.txt.
2. Unpack dataset file and place pairs.txt in created folder.
3. To load data run: ./opencv/build/bin/example_datasetstools_fr_lfw -p=/home/user/path_to_unpacked_folder/lfw2/

Gesture Recognition

GR_chalearn

class GR_chalearn

Implements loading dataset:

“ChaLearn Looking at People”: http://gesture.chalearn.org/

Note

Usage

1. Follow instruction from site above, download files for dataset “Track 3: Gesture Recognition”: Train1.zip-Train5.zip, Validation1.zip-Validation3.zip (Register on site: www.codalab.org and accept the terms and conditions of competition: https://www.codalab.org/competitions/991#learn_the_details There are three mirrors for downloading dataset files. When I downloaded data only mirror: “Universitat Oberta de Catalunya” works).
2. Unpack train archives Train1.zip-Train5.zip to folder Train/, validation archives Validation1.zip-Validation3.zip to folder Validation/
3. Unpack all archives in Train/ & Validation/ in the folders with the same names, for example: Sample0001.zip to Sample0001/
4. To load data run: ./opencv/build/bin/example_datasetstools_gr_chalearn -p=/home/user/path_to_unpacked_folders/

GR_skig

class GR_skig

Implements loading dataset:

“Sheffield Kinect Gesture Dataset”: http://lshao.staff.shef.ac.uk/data/SheffieldKinectGesture.htm

Note

Usage

1. From link above download dataset files: subject1_dep.7z-subject6_dep.7z, subject1_rgb.7z-subject6_rgb.7z.
2. Unpack them.
3. To load data run: ./opencv/build/bin/example_datasetstools_gr_skig -p=/home/user/path_to_unpacked_folders/

Human Pose Estimation

HPE_parse

class HPE_parse

Implements loading dataset:

“PARSE Dataset”: http://www.ics.uci.edu/~dramanan/papers/parse/

Note

Usage

1. From link above download dataset file: people.zip.
2. Unpack it.
3. To load data run: ./opencv/build/bin/example_datasetstools_hpe_parse -p=/home/user/path_to_unpacked_folder/people_all/

Image Registration

IR_affine

class IR_affine

Implements loading dataset:

“Affine Covariant Regions Datasets”: http://www.robots.ox.ac.uk/~vgg/data/data-aff.html

Note

Usage

1. From link above download dataset files: bark\bikes\boat\graf\leuven\trees\ubc\wall.tar.gz.
2. Unpack them.
3. To load data, for example, for “bark”, run: ./opencv/build/bin/example_datasetstools_ir_affine -p=/home/user/path_to_unpacked_folder/bark/

IR_robot

class IR_robot

Implements loading dataset:

“Robot Data Set”: http://roboimagedata.compute.dtu.dk/?page_id=24

Note

Usage

1. From link above download files for dataset “Point Feature Data Set – 2010”: SET001_6.tar.gz-SET055_60.tar.gz (there are two data sets: - Full resolution images (1200×1600), ~500 Gb and - Half size image (600×800), ~115 Gb.)
2. Unpack them to one folder.
3. To load data run: ./opencv/build/bin/example_datasetstools_ir_robot -p=/home/user/path_to_unpacked_folder/

Image Segmentation

IS_bsds

class IS_bsds

Implements loading dataset:

“The Berkeley Segmentation Dataset and Benchmark”: https://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/

Note

Usage

1. From link above download dataset files: BSDS300-human.tgz & BSDS300-images.tgz.
2. Unpack them.
3. To load data run: ./opencv/build/bin/example_datasetstools_is_bsds -p=/home/user/path_to_unpacked_folder/BSDS300/

IS_weizmann

class IS_weizmann

Implements loading dataset:

“Weizmann Segmentation Evaluation Database”: http://www.wisdom.weizmann.ac.il/~vision/Seg_Evaluation_DB/

Note

Usage

1. From link above download dataset files: Weizmann_Seg_DB_1obj.ZIP & Weizmann_Seg_DB_2obj.ZIP.
2. Unpack them.
3. To load data, for example, for 1 object dataset, run: ./opencv/build/bin/example_datasetstools_is_weizmann -p=/home/user/path_to_unpacked_folder/1obj/

Multiview Stereo Matching

MSM_epfl

class MSM_epfl

Implements loading dataset:

“EPFL Multi-View Stereo”: http://cvlabwww.epfl.ch/~strecha/multiview/denseMVS.html

Note

Usage

1. 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.
2. 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/
3. To load data, for example, for “fountain”, run: ./opencv/build/bin/example_datasetstools_msm_epfl -p=/home/user/path_to_unpacked_folder/fountain/

MSM_middlebury

class MSM_middlebury

Implements loading dataset:

“Stereo – Middlebury Computer Vision”: http://vision.middlebury.edu/mview/

Note

Usage

1. From link above download dataset files: dino\dinoRing\dinoSparseRing\temple\templeRing\templeSparseRing.zip
2. Unpack them.
3. To load data, for example “temple” dataset, run: ./opencv/build/bin/example_datasetstools_msm_middlebury -p=/home/user/path_to_unpacked_folder/temple/

Object Recognition

OR_imagenet

class OR_imagenet

Implements loading dataset:

“ImageNet”: http://www.image-net.org/

Currently implemented loading full list with urls. Planned to implement dataset from ILSVRC challenge.

Note

Usage

1. From link above download dataset file: imagenet_fall11_urls.tgz
2. Unpack it.
3. To load data run: ./opencv/build/bin/example_datasetstools_or_imagenet -p=/home/user/path_to_unpacked_file/

OR_mnist

class OR_mnist

Implements loading dataset:

“MNIST”: http://yann.lecun.com/exdb/mnist/

Note

Usage

1. 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.
2. Unpack them.
3. To load data run: ./opencv/build/bin/example_datasetstools_or_mnist -p=/home/user/path_to_unpacked_files/

OR_sun

class OR_sun

Implements loading dataset:

“SUN Database”: http://sundatabase.mit.edu/

Currently implemented loading “Scene Recognition Benchmark. SUN397”. Planned to implement also “Object Detection Benchmark. SUN2012”.

Note

Usage

1. From link above download dataset file: SUN397.tar
2. Unpack it.
3. To load data run: ./opencv/build/bin/example_datasetstools_or_sun -p=/home/user/path_to_unpacked_folder/SUN397/

SLAM

SLAM_kitti

class SLAM_kitti

Implements loading dataset:

“KITTI Vision Benchmark”: http://www.cvlibs.net/datasets/kitti/eval_odometry.php

Note

Usage

1. From link above download “Odometry” dataset files: data_odometry_gray\data_odometry_color\data_odometry_velodyne\data_odometry_poses\data_odometry_calib.zip.
2. 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.
3. To load data run: ./opencv/build/bin/example_datasetstools_slam_kitti -p=/home/user/path_to_unpacked_folder/dataset/

SLAM_tumindoor

class SLAM_tumindoor

Implements loading dataset:

“TUMindoor Dataset”: http://www.navvis.lmt.ei.tum.de/dataset/

Note

Usage

1. 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)
2. Unpack them in separate folder for each dataset. dslr.tar.bz2 -> dslr/, info.tar.bz2 -> info/, ladybug.tar.bz2 -> ladybug/, pointcloud.tar.bz2 -> pointcloud/.
3. To load each dataset run: ./opencv/build/bin/example_datasetstools_slam_tumindoor -p=/home/user/path_to_unpacked_folders/

Text Recognition

TR_chars

class TR_chars

Implements loading dataset:

“The Chars74K Dataset”: http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/

Note

Usage

1. From link above download dataset files: EnglishFnt\EnglishHnd\EnglishImg\KannadaHnd\KannadaImg.tgz, ListsTXT.tgz.
2. Unpack them.
3. Move .m files from folder ListsTXT/ to appropriate folder. For example, English/list_English_Img.m for EnglishImg.tgz.
4. To load data, for example “EnglishImg”, run: ./opencv/build/bin/example_datasetstools_tr_chars -p=/home/user/path_to_unpacked_folder/English/

TR_svt

class TR_svt

Implements loading dataset:

“The Street View Text Dataset”: http://vision.ucsd.edu/~kai/svt/

Note

Usage

1. From link above download dataset file: svt.zip.
2. Unpack it.
3. To load data run: ./opencv/build/bin/example_datasetstools_tr_svt -p=/home/user/path_to_unpacked_folder/svt/svt1/