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+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2014, Itseez Inc, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Itseez Inc or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_DATASETS_DATASET_HPP
+#define OPENCV_DATASETS_DATASET_HPP
+
+#include <string>
+#include <vector>
+
+#include <opencv2/core.hpp>
+
+/** @defgroup datasets Framework for working with different datasets
+
+The datasets module includes classes for working with different datasets: load data, evaluate
+different algorithms on them, contains benchmarks, etc.
+
+It is planned to have:
+
+-   basic: loading code for all datasets to help start work with them.
+-   next stage: quick benchmarks for all datasets to show how to solve them using OpenCV and
+implement evaluation code.
+-   finally: implement on OpenCV state-of-the-art algorithms, which solve these tasks.
+
+@{
+@defgroup datasets_ar Action Recognition
+
+### HMDB: A Large Human Motion Database
+
+Implements loading dataset:
+
+"HMDB: A Large Human Motion Database": <http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/>
+
+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:
+~~~
+./opencv/build/bin/example_datasets_ar_hmdb -p=/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)
+
+To run this benchmark execute:
+~~~
+./opencv/build/bin/example_datasets_ar_hmdb_benchmark -p=/home/user/path_to_unpacked_folders/
+~~~
+
+@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": <http://cs.stanford.edu/people/karpathy/deepvideo/>
+
+Usage:
+-# From link above download dataset files (`git clone https://code.google.com/p/sports-1m-dataset/`).
+-# To load data run:
+~~~
+./opencv/build/bin/example_datasets_ar_sports -p=/home/user/path_to_downloaded_folders/
+~~~
+
+@defgroup datasets_fr Face Recognition
+
+### Adience
+
+Implements loading dataset:
+
+"Adience": <http://www.openu.ac.il/home/hassner/Adience/data.html>
+
+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:
+~~~
+./opencv/build/bin/example_datasets_fr_adience -p=/home/user/path_to_created_folder/
+~~~
+
+### Labeled Faces in the Wild
+
+Implements loading dataset:
+
+"Labeled Faces in the Wild": <http://vis-www.cs.umass.edu/lfw/>
+
+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:
+~~~
+./opencv/build/bin/example_datasets_fr_lfw -p=/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)
+
+To run this benchmark execute:
+~~~
+./opencv/build/bin/example_datasets_fr_lfw_benchmark -p=/home/user/path_to_unpacked_folder/lfw2/
+~~~
+
+@defgroup datasets_gr Gesture Recognition
+
+### ChaLearn Looking at People
+
+Implements loading dataset:
+
+"ChaLearn Looking at People": <http://gesture.chalearn.org/>
+
+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: 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).
+-# 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:
+~~~
+./opencv/build/bin/example_datasets_gr_chalearn -p=/home/user/path_to_unpacked_folders/
+~~~
+
+### Sheffield Kinect Gesture Dataset
+
+Implements loading dataset:
+
+"Sheffield Kinect Gesture Dataset": <http://lshao.staff.shef.ac.uk/data/SheffieldKinectGesture.htm>
+
+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:
+~~~
+./opencv/build/bin/example_datasets_gr_skig -p=/home/user/path_to_unpacked_folders/
+~~~
+
+@defgroup datasets_hpe Human Pose Estimation
+
+### HumanEva Dataset
+
+Implements loading dataset:
+
+"HumanEva Dataset": <http://humaneva.is.tue.mpg.de>
+
+Usage:
+-# From link above download dataset files for `HumanEva-I` (tar) & `HumanEva-II`.
+-# Unpack them to `HumanEva_1` & `HumanEva_2` accordingly.
+-# To load data run:
+~~~
+./opencv/build/bin/example_datasets_hpe_humaneva -p=/home/user/path_to_unpacked_folders/
+~~~
+
+### PARSE Dataset
+
+Implements loading dataset:
+
+"PARSE Dataset": <http://www.ics.uci.edu/~dramanan/papers/parse/>
+
+Usage:
+-# From link above download dataset file: `people.zip`.
+-# Unpack it.
+-# To load data run:
+~~~
+./opencv/build/bin/example_datasets_hpe_parse -p=/home/user/path_to_unpacked_folder/people_all/
+~~~
+
+@defgroup datasets_ir Image Registration
+
+### Affine Covariant Regions Datasets
+
+Implements loading dataset:
+
+"Affine Covariant Regions Datasets": <http://www.robots.ox.ac.uk/~vgg/data/data-aff.html>
+
+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:
+```
+./opencv/build/bin/example_datasets_ir_affine -p=/home/user/path_to_unpacked_folder/bark/
+```
+
+### Robot Data Set
+
+Implements loading dataset:
+
+"Robot Data Set, Point Feature Data Set – 2010": <http://roboimagedata.compute.dtu.dk/?page_id=24>
+
+Usage:
+-# From link above download dataset files: `SET001_6.tar.gz`-`SET055_60.tar.gz`
+-# Unpack them to one folder.
+-# To load data run:
+~~~
+./opencv/build/bin/example_datasets_ir_robot -p=/home/user/path_to_unpacked_folder/
+~~~
+
+@defgroup datasets_is Image Segmentation
+
+### The Berkeley Segmentation Dataset and Benchmark
+
+Implements loading dataset:
+
+"The Berkeley Segmentation Dataset and Benchmark": <https://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/>
+
+Usage:
+-# From link above download dataset files: `BSDS300-human.tgz` & `BSDS300-images.tgz`.
+-# Unpack them.
+-# To load data run:
+~~~
+./opencv/build/bin/example_datasets_is_bsds -p=/home/user/path_to_unpacked_folder/BSDS300/
+~~~
+
+### Weizmann Segmentation Evaluation Database
+
+Implements loading dataset:
+
+"Weizmann Segmentation Evaluation Database": <http://www.wisdom.weizmann.ac.il/~vision/Seg_Evaluation_DB/>
+
+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:
+~~~
+./opencv/build/bin/example_datasets_is_weizmann -p=/home/user/path_to_unpacked_folder/1obj/
+~~~
+
+@defgroup datasets_msm Multiview Stereo Matching
+
+### EPFL Multi-View Stereo
+
+Implements loading dataset:
+
+"EPFL Multi-View Stereo": <http://cvlab.epfl.ch/data/strechamvs>
+
+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:
+~~~
+./opencv/build/bin/example_datasets_msm_epfl -p=/home/user/path_to_unpacked_folder/fountain/
+~~~
+
+### Stereo – Middlebury Computer Vision
+
+Implements loading dataset:
+
+"Stereo – Middlebury Computer Vision": <http://vision.middlebury.edu/mview/>
+
+Usage:
+-# From link above download dataset files:
+`dino\dinoRing\dinoSparseRing\temple\templeRing\templeSparseRing.zip`
+-# Unpack them.
+-# To load data, for example "temple" dataset, run:
+~~~
+./opencv/build/bin/example_datasets_msm_middlebury -p=/home/user/path_to_unpacked_folder/temple/
+~~~
+
+@defgroup datasets_or Object Recognition
+
+### ImageNet
+
+Implements loading dataset:  "ImageNet": <http://www.image-net.org/>
+
+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:
+~~~
+./opencv/build/bin/example_datasets_or_imagenet -p=/home/user/some_folder/
+~~~
+
+Python script to parse `meta.mat`:
+~~~{py}
+    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": <http://yann.lecun.com/exdb/mnist/>
+
+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:
+~~~
+./opencv/build/bin/example_datasets_or_mnist -p=/home/user/path_to_unpacked_files/
+~~~
+
+### SUN Database
+
+Implements loading dataset:
+
+"SUN Database, Scene Recognition Benchmark. SUN397": <http://vision.cs.princeton.edu/projects/2010/SUN/>
+
+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:
+~~~
+./opencv/build/bin/example_datasets_or_sun -p=/home/user/path_to_unpacked_files/SUN397/
+~~~
+
+@defgroup datasets_pd Pedestrian Detection
+
+### Caltech Pedestrian Detection Benchmark
+
+Implements loading dataset:
+
+"Caltech Pedestrian Detection Benchmark": <http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/>
+
+@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.
+
+Usage:
+-# From link above download dataset files: `set00.tar`-`set10.tar`.
+-# Unpack them to separate folder.
+-# To load data run:
+~~~
+./opencv/build/bin/example_datasets_pd_caltech -p=/home/user/path_to_unpacked_folders/
+~~~
+
+@defgroup datasets_slam SLAM
+
+### KITTI Vision Benchmark
+
+Implements loading dataset:
+
+"KITTI Vision Benchmark": <http://www.cvlibs.net/datasets/kitti/eval_odometry.php>
+
+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:
+~~~
+./opencv/build/bin/example_datasets_slam_kitti -p=/home/user/path_to_unpacked_folder/dataset/
+~~~
+
+### TUMindoor Dataset
+
+Implements loading dataset:
+
+"TUMindoor Dataset": <http://www.navvis.lmt.ei.tum.de/dataset/>
+
+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:
+~~~
+./opencv/build/bin/example_datasets_slam_tumindoor -p=/home/user/path_to_unpacked_folders/
+~~~
+
+@defgroup datasets_tr Text Recognition
+
+### The Chars74K Dataset
+
+Implements loading dataset:
+
+"The Chars74K Dataset": <http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/>
+
+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:
+~~~
+./opencv/build/bin/example_datasets_tr_chars -p=/home/user/path_to_unpacked_folder/English/
+~~~
+
+### The Street View Text Dataset
+
+Implements loading dataset:
+
+"The Street View Text Dataset": <http://vision.ucsd.edu/~kai/svt/>
+
+Usage:
+-# From link above download dataset file: `svt.zip`.
+-# Unpack it.
+-# To load data run:
+~~~
+./opencv/build/bin/example_datasets_tr_svt -p=/home/user/path_to_unpacked_folder/svt/svt1/
+~~~
+
+#### Benchmark
+
+For this dataset was implemented benchmark with accuracy (mean f1): 0.217
+
+To run benchmark execute:
+~~~
+./opencv/build/bin/example_datasets_tr_svt_benchmark -p=/home/user/path_to_unpacked_folders/svt/svt1/
+~~~
+
+@defgroup datasets_track Tracking
+
+### VOT 2015 Database
+
+Implements loading dataset:
+
+"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.": <http://box.vicos.si/vot/vot2015.zip>
+
+Usage:
+-# From link above download dataset file: `vot2015.zip`
+-# Unpack `vot2015.zip` into folder: `VOT2015/`
+-# To load data run:
+~~~
+./opencv/build/bin/example_datasets_track_vot -p=/home/user/path_to_unpacked_files/VOT2015/
+~~~
+@}
+
+*/
+
+namespace cv
+{
+namespace datasets
+{
+
+//! @addtogroup datasets
+//! @{
+
+struct Object
+{
+};
+
+class CV_EXPORTS Dataset
+{
+public:
+    Dataset() {}
+    virtual ~Dataset() {}
+
+    virtual void load(const std::string &path) = 0;
+
+    std::vector< Ptr<Object> >& getTrain(int splitNum = 0);
+    std::vector< Ptr<Object> >& getTest(int splitNum = 0);
+    std::vector< Ptr<Object> >& getValidation(int splitNum = 0);
+
+    int getNumSplits() const;
+
+protected:
+    std::vector< std::vector< Ptr<Object> > > train;
+    std::vector< std::vector< Ptr<Object> > > test;
+    std::vector< std::vector< Ptr<Object> > > validation;
+
+private:
+    std::vector< Ptr<Object> > empty;
+};
+
+//! @}
+
+}
+}
+
+#endif