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diff --git a/thirdparty1/linux/include/opencv2/datasets/dataset.hpp b/thirdparty1/linux/include/opencv2/datasets/dataset.hpp new file mode 100644 index 0000000..ccf2b66 --- /dev/null +++ b/thirdparty1/linux/include/opencv2/datasets/dataset.hpp @@ -0,0 +1,545 @@ +/*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 |