From 876716fb27c059e2723964ed81c6aec81bfc52f7 Mon Sep 17 00:00:00 2001 From: agam1994 Date: Fri, 8 Jul 2016 11:57:39 +0530 Subject: [PATCH 01/14] Update fetch_selective_search_data.sh --- data/scripts/fetch_selective_search_data.sh | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/data/scripts/fetch_selective_search_data.sh b/data/scripts/fetch_selective_search_data.sh index 84c0acaf5..d8222341c 100755 --- a/data/scripts/fetch_selective_search_data.sh +++ b/data/scripts/fetch_selective_search_data.sh @@ -4,8 +4,8 @@ DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )/../" && pwd )" cd $DIR FILE=selective_search_data.tgz -URL=http://www.cs.berkeley.edu/~rbg/fast-rcnn-data/$FILE -CHECKSUM=7078c1db87a7851b31966b96774cd9b9 +URL=ftp://ftp.cs.berkeley.edu/pub/projects/vision/$FILE +CHECKSUM=c341da31579e305292bf5ec08298b87c if [ -f $FILE ]; then echo "File already exists. Checking md5..." @@ -23,7 +23,7 @@ if [ -f $FILE ]; then fi fi -echo "Downloading precomputed selective search boxes (0.5G)..." +echo "Downloading Edge boxes for COCO train, val, and test-dev set..." wget $URL -O $FILE From 03a051ebcf4014acf2bb4697f4520dd1d13530c6 Mon Sep 17 00:00:00 2001 From: agam1994 Date: Fri, 8 Jul 2016 12:08:11 +0530 Subject: [PATCH 02/14] Update fetch_selective_search_data.sh --- data/scripts/fetch_selective_search_data.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data/scripts/fetch_selective_search_data.sh b/data/scripts/fetch_selective_search_data.sh index d8222341c..dd3204e49 100755 --- a/data/scripts/fetch_selective_search_data.sh +++ b/data/scripts/fetch_selective_search_data.sh @@ -3,7 +3,7 @@ DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )/../" && pwd )" cd $DIR -FILE=selective_search_data.tgz +FILE=edge_box_data.tgz URL=ftp://ftp.cs.berkeley.edu/pub/projects/vision/$FILE CHECKSUM=c341da31579e305292bf5ec08298b87c From 601f27634fd97aa0d1a32c19901eab9abbc88035 Mon Sep 17 00:00:00 2001 From: agam1994 Date: Fri, 8 Jul 2016 12:10:26 +0530 Subject: [PATCH 03/14] Rename fetch_selective_search_data.sh to fetch_edge_box_data.sh --- .../{fetch_selective_search_data.sh => fetch_edge_box_data.sh} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename data/scripts/{fetch_selective_search_data.sh => fetch_edge_box_data.sh} (100%) diff --git a/data/scripts/fetch_selective_search_data.sh b/data/scripts/fetch_edge_box_data.sh similarity index 100% rename from data/scripts/fetch_selective_search_data.sh rename to data/scripts/fetch_edge_box_data.sh From e933fe15d87265bd34f835006bb5d1ed48d350ff Mon Sep 17 00:00:00 2001 From: agam1994 Date: Fri, 8 Jul 2016 12:16:05 +0530 Subject: [PATCH 04/14] Update imdb.py --- lib/datasets/imdb.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lib/datasets/imdb.py b/lib/datasets/imdb.py index b56bf0a80..1679aba84 100644 --- a/lib/datasets/imdb.py +++ b/lib/datasets/imdb.py @@ -21,7 +21,7 @@ def __init__(self, name): self._num_classes = 0 self._classes = [] self._image_index = [] - self._obj_proposer = 'selective_search' + self._obj_proposer = 'edge_box' self._roidb = None self._roidb_handler = self.default_roidb # Use this dict for storing dataset specific config options From 5f8feb2478cfb30aa3a39608fa9bd44110e7a0ec Mon Sep 17 00:00:00 2001 From: agam1994 Date: Fri, 8 Jul 2016 12:21:57 +0530 Subject: [PATCH 05/14] Update pascal_voc.py --- lib/datasets/pascal_voc.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/lib/datasets/pascal_voc.py b/lib/datasets/pascal_voc.py index b55f2f6b2..3d63373e9 100644 --- a/lib/datasets/pascal_voc.py +++ b/lib/datasets/pascal_voc.py @@ -37,7 +37,7 @@ def __init__(self, image_set, year, devkit_path=None): self._image_ext = '.jpg' self._image_index = self._load_image_set_index() # Default to roidb handler - self._roidb_handler = self.selective_search_roidb + self._roidb_handler = self.edge_box_roidb self._salt = str(uuid.uuid4()) self._comp_id = 'comp4' @@ -111,15 +111,15 @@ def gt_roidb(self): return gt_roidb - def selective_search_roidb(self): + def edge_box_roidb(self): """ - Return the database of selective search regions of interest. + Return the database of edge box regions of interest. Ground-truth ROIs are also included. This function loads/saves from/to a cache file to speed up future calls. """ cache_file = os.path.join(self.cache_path, - self.name + '_selective_search_roidb.pkl') + self.name + '_edge_box_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: @@ -129,10 +129,10 @@ def selective_search_roidb(self): if int(self._year) == 2007 or self._image_set != 'test': gt_roidb = self.gt_roidb() - ss_roidb = self._load_selective_search_roidb(gt_roidb) + ss_roidb = self._load_edge_box_roidb(gt_roidb) roidb = imdb.merge_roidbs(gt_roidb, ss_roidb) else: - roidb = self._load_selective_search_roidb(None) + roidb = self._load_edge_box_roidb(None) with open(cache_file, 'wb') as fid: cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote ss roidb to {}'.format(cache_file) @@ -158,12 +158,12 @@ def _load_rpn_roidb(self, gt_roidb): box_list = cPickle.load(f) return self.create_roidb_from_box_list(box_list, gt_roidb) - def _load_selective_search_roidb(self, gt_roidb): + def _load_edge_box_roidb(self, gt_roidb): filename = os.path.abspath(os.path.join(cfg.DATA_DIR, - 'selective_search_data', + 'edge_box_data', self.name + '.mat')) assert os.path.exists(filename), \ - 'Selective search data not found at: {}'.format(filename) + 'edge data not found at: {}'.format(filename) raw_data = sio.loadmat(filename)['boxes'].ravel() box_list = [] From e355af039a7014063f35472828f72f4f3f0931f4 Mon Sep 17 00:00:00 2001 From: agam1994 Date: Fri, 8 Jul 2016 12:32:15 +0530 Subject: [PATCH 06/14] Update config.py --- lib/fast_rcnn/config.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lib/fast_rcnn/config.py b/lib/fast_rcnn/config.py index 253017369..c3db065e7 100644 --- a/lib/fast_rcnn/config.py +++ b/lib/fast_rcnn/config.py @@ -89,7 +89,7 @@ __C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2) # Train using these proposals -__C.TRAIN.PROPOSAL_METHOD = 'selective_search' +__C.TRAIN.PROPOSAL_METHOD = 'edge_box' # Make minibatches from images that have similar aspect ratios (i.e. both # tall and thin or both short and wide) in order to avoid wasting computation @@ -152,7 +152,7 @@ __C.TEST.HAS_RPN = False # Test using these proposals -__C.TEST.PROPOSAL_METHOD = 'selective_search' +__C.TEST.PROPOSAL_METHOD = 'edge_box' ## NMS threshold used on RPN proposals __C.TEST.RPN_NMS_THRESH = 0.7 From c54dd43101bd2b430658b4f66af669d65ba5c99a Mon Sep 17 00:00:00 2001 From: agam1994 Date: Fri, 8 Jul 2016 12:42:32 +0530 Subject: [PATCH 07/14] Update eval_recall.py --- tools/eval_recall.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/eval_recall.py b/tools/eval_recall.py index b1a59dc27..9ebc6ddb6 100755 --- a/tools/eval_recall.py +++ b/tools/eval_recall.py @@ -17,7 +17,7 @@ def parse_args(): default='voc_2007_test', type=str) parser.add_argument('--method', dest='method', help='proposal method', - default='selective_search', type=str) + default='edge_box', type=str) parser.add_argument('--rpn-file', dest='rpn_file', default=None, type=str) From 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zBbK}E5&NWnV*Oi~DrAARK@ey}&s-IG`+s2nftXzKNPLRtlRgoF0RxU_xmC*rT+@mb=eaQn#rnub5AH5ANWiSz{el{mjjMJO1Rc@gmcX+V4K8fd|P1sMTv znK=D<`DK7Q_E)ieB0lNw#9yg8KmJ0TRUY_MIMALGxBffvAG0PSC&482*z`&jS1pT!pen?o*tfR8a;3I2i|yQNVu=7yy6;0C Date: Thu, 14 Jul 2016 11:39:50 +0530 Subject: [PATCH 09/14] Add files via upload --- Bing/LICENSE | 27 + Bing/README.md | 63 + Bing/Src/CMakeLists.txt | 30 + Bing/Src/CMakeLists.txt.user | 176 ++ Bing/Src/CmFile.cpp | 173 ++ Bing/Src/CmFile.h | 75 + Bing/Src/CmShow.cpp | 82 + Bing/Src/CmShow.h | 9 + Bing/Src/CmTimer.h | 89 + Bing/Src/DataSetVOC.cpp | 224 ++ Bing/Src/DataSetVOC.h | 71 + Bing/Src/FilterTIG.cpp | 78 + Bing/Src/FilterTIG.h | 45 + Bing/Src/ImgContrastBB.h | 62 + Bing/Src/LibLinear/LibLinear.vcxproj | 94 + Bing/Src/LibLinear/README.1.93.txt | 531 +++++ Bing/Src/LibLinear/blas/Makefile | 22 + Bing/Src/LibLinear/blas/blas.h | 25 + Bing/Src/LibLinear/blas/blasp.h | 430 ++++ Bing/Src/LibLinear/blas/daxpy.c | 49 + Bing/Src/LibLinear/blas/ddot.c | 50 + Bing/Src/LibLinear/blas/dnrm2.c | 62 + Bing/Src/LibLinear/blas/dscal.c | 44 + Bing/Src/LibLinear/linear.cpp | 2811 ++++++++++++++++++++++++++ Bing/Src/LibLinear/linear.h | 74 + Bing/Src/LibLinear/train.c | 401 ++++ Bing/Src/LibLinear/tron.cpp | 235 +++ Bing/Src/LibLinear/tron.h | 36 + Bing/Src/Objectness.cpp | 1118 ++++++++++ Bing/Src/Objectness.h | 116 ++ Bing/Src/ReadMe.txt | 49 + Bing/Src/ValStructVec.h | 72 + Bing/Src/kyheader.cpp | 5 + Bing/Src/kyheader.h | 110 + Bing/Src/main.cpp | 55 + Bing/Src/xml2yaml.m | 22 + Bing/Src/yml.m | 17 + 37 files changed, 7632 insertions(+) create mode 100644 Bing/LICENSE create mode 100644 Bing/README.md create mode 100644 Bing/Src/CMakeLists.txt create mode 100644 Bing/Src/CMakeLists.txt.user create mode 100644 Bing/Src/CmFile.cpp create mode 100644 Bing/Src/CmFile.h create mode 100644 Bing/Src/CmShow.cpp create mode 100644 Bing/Src/CmShow.h create mode 100644 Bing/Src/CmTimer.h create mode 100644 Bing/Src/DataSetVOC.cpp create mode 100644 Bing/Src/DataSetVOC.h create mode 100644 Bing/Src/FilterTIG.cpp create mode 100644 Bing/Src/FilterTIG.h create mode 100644 Bing/Src/ImgContrastBB.h create mode 100644 Bing/Src/LibLinear/LibLinear.vcxproj create mode 100644 Bing/Src/LibLinear/README.1.93.txt create mode 100644 Bing/Src/LibLinear/blas/Makefile create mode 100644 Bing/Src/LibLinear/blas/blas.h create mode 100644 Bing/Src/LibLinear/blas/blasp.h create mode 100644 Bing/Src/LibLinear/blas/daxpy.c create mode 100644 Bing/Src/LibLinear/blas/ddot.c create mode 100644 Bing/Src/LibLinear/blas/dnrm2.c create mode 100644 Bing/Src/LibLinear/blas/dscal.c create mode 100644 Bing/Src/LibLinear/linear.cpp create mode 100644 Bing/Src/LibLinear/linear.h create mode 100644 Bing/Src/LibLinear/train.c create mode 100644 Bing/Src/LibLinear/tron.cpp create mode 100644 Bing/Src/LibLinear/tron.h create mode 100644 Bing/Src/Objectness.cpp create mode 100644 Bing/Src/Objectness.h create mode 100644 Bing/Src/ReadMe.txt create mode 100644 Bing/Src/ValStructVec.h create mode 100644 Bing/Src/kyheader.cpp create mode 100644 Bing/Src/kyheader.h create mode 100644 Bing/Src/main.cpp create mode 100644 Bing/Src/xml2yaml.m create mode 100644 Bing/Src/yml.m diff --git a/Bing/LICENSE b/Bing/LICENSE new file mode 100644 index 000000000..7f8edefa4 --- /dev/null +++ b/Bing/LICENSE @@ -0,0 +1,27 @@ +Copyright (c) 2014, Ming-Ming Cheng & Shuai Zheng +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions 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. + +* Neither the name of the {organization} nor the names of its + contributors may 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 COPYRIGHT HOLDER 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. diff --git a/Bing/README.md b/Bing/README.md new file mode 100644 index 000000000..7118cf0e6 --- /dev/null +++ b/Bing/README.md @@ -0,0 +1,63 @@ +Objectness Proposal Generator with BING +========== +BING Objectness proposal estimator Linux Ubuntu 14.04/Mac OSX Yosemite/iOS 8.1 version implementation, +runs at 1000 FPS at a Dell 7600 workstation with Linux Ubuntu 14.04. +## INSTALL +To make this program running fast, you need to enable release mode: +> cmake -DCMAKE_BUILD_TYPE=Release ../ + +## DEMO +If you run this in Ubuntu 14.04 or other similar Linux system, feel free to +change the path in main.cpp +> DataSetVOC voc2007("YOUR_PATH_TO_THE_VOC2007DATA"); + +Notice that WinRecall.m is generated by this code, you do not need them. If you +want to make this code working with other datasets, one simple solution is to +make the folders similar to the VOC 2007 one. + +## Introduction +This is the 1000 FPS BING objectness linux version library for efficient +objectness proposal estimator,We would appreciate if you could cite and refer to +the papers below. +``` +@inproceedings{BingObj2014, + title={{BING}: Binarized Normed Gradients for Objectness Estimation at 300fps}, + author={Ming-Ming Cheng and Ziming Zhang and Wen-Yan Lin and Philip H. S. Torr}, + booktitle={IEEE CVPR}, + year={2014}, +} +``` +``` +@inproceedings{depthobjectproposals_GCPR2015, +author = {Shuai Zheng and Victor Adrian Prisacariu and Melinos Averkiou and Ming-Ming Cheng and Niloy J. Mitra and Jamie Shotton and Philip H. S. Torr and Carsten Rother}, +title = {Object Proposal Estimation in Depth Images using Compact 3D Shape Manifolds}, +booktitle = {German Conference on Pattern Recognition (GCPR)}, +year = {2015} +} +``` +The original author Ming-Ming Cheng has already released the source code for +windows 64-bit platform. In this library, we intend to release the code for the +linux/mac/iOS users. You can maintain the code with Qt Creator IDE. + +Please find the original windows code / FAQ / Paper from this link: +http://mmcheng.net/bing/ + +## FAQ +In order to make the code running as the original version in windows, you need +to download the images/annotations PASCAL VOC 2007 data from the website. +(http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/#testdata) + +We have tested the code, it produces the same accuracy results as the original windows +version, while it runs at 1111 FPS(frame per second) at Ubuntu 12.04 with a Dell T7600 +workstation computer, which has two Intel Xeon E5-2687W (3.1GHz, 1600MHz) and 64 GB +1600MHz DDR3 Memory. + +## Author Info +Author: Ming-Ming Cheng removethisifyouarehuman-cmm.thu@gmail.com +Linux Author: Shuai Zheng (Kyle) removethisifyouarehuman-szhengcvpr@gmail.com +Please find more information from http://kylezheng.org/objectproposal/ +Date: 19, February + +## License +BSD license. + diff --git a/Bing/Src/CMakeLists.txt b/Bing/Src/CMakeLists.txt new file mode 100644 index 000000000..9c1af52de --- /dev/null +++ b/Bing/Src/CMakeLists.txt @@ -0,0 +1,30 @@ +project(BING_linux) +cmake_minimum_required(VERSION 2.8) + +find_package(OpenMP REQUIRED) + +# compile LibLinear +include_directories("LibLinear") +file(GLOB SOURCES "LibLinear/*.cpp" "LibLinear/blas/*.c") +add_library(LibLinear STATIC ${SOURCES}) + +#OPENCV +#include_directories(/usr/local/include) +#link_directories(/usr/local/lib) +##if this does not work, then try to uncomment the things below. +find_package( OpenCV REQUIRED ) +if(OpenCV_FOUND) + include_directories( ${OpenCV_INCLUDE_DIRS} ) +endif( OpenCV_FOUND ) +list( APPEND CMAKE_CXX_FLAGS "-std=c++0x ${CMAKE_CXX_FLAGS} -fopenmp -ftree-vectorize") +#list( APPEND CMAKE_CXX_FLAGS "-std=c++0x ${CMAKE_CXX_FLAGS} -fopenmp -ftest-coverage -fprofile-arcs") + +# compile BING +file(GLOB SOURCES "*.cpp") +add_library(BING STATIC ${SOURCES}) + +add_executable(${PROJECT_NAME} main.cpp) + +set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -Wall") +set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -Wall") +target_link_libraries(${PROJECT_NAME} opencv_core opencv_imgproc opencv_highgui opencv_imgcodecs ${EXTERNAL_LIBS} BING LibLinear) diff --git a/Bing/Src/CMakeLists.txt.user b/Bing/Src/CMakeLists.txt.user new file mode 100644 index 000000000..685b8403b --- /dev/null +++ b/Bing/Src/CMakeLists.txt.user @@ -0,0 +1,176 @@ + + + + + + ProjectExplorer.Project.ActiveTarget + 0 + + + ProjectExplorer.Project.EditorSettings + + true + false + true + + Cpp + + CppGlobal + + + + QmlJS + + QmlJSGlobal + + + 2 + System + false + 4 + false + true + 1 + true + 0 + true + 0 + 8 + true + 1 + true + true + true + false + + + + ProjectExplorer.Project.PluginSettings + + + + ProjectExplorer.Project.Target.0 + + Desktop + + CMakeProjectManager.DefaultCMakeTarget + 0 + 0 + 0 + + /home/bittnt/BING/BING_beta3/build + ProjectExplorer.ToolChain.Gcc:{966d53c9-fe6c-4bac-8571-0e5a33bebf05} + ProjectExplorer.ToolChain.Gcc:{966d53c9-fe6c-4bac-8571-0e5a33bebf05} + + + + + false + true + Make + + CMakeProjectManager.MakeStep + + 1 + Build + + ProjectExplorer.BuildSteps.Build + + + + clean + + true + true + Make + + CMakeProjectManager.MakeStep + + 1 + Clean + + ProjectExplorer.BuildSteps.Clean + + 2 + false + + all + + CMakeProjectManager.CMakeBuildConfiguration + + 1 + + + 0 + Deploy + + ProjectExplorer.BuildSteps.Deploy + + 1 + No deployment + + ProjectExplorer.DefaultDeployConfiguration + + 1 + + true + + false + false + false + false + true + 0.01 + 10 + true + 25 + + true + /usr/bin/valgrind + + 0 + 1 + 2 + 3 + 4 + 5 + 6 + 7 + 8 + 9 + 10 + 11 + 12 + 13 + 14 + + 2 + BING_linux + + false + + + BING_linux + + CMakeProjectManager.CMakeRunConfiguration. + 3768 + true + false + true + + 1 + + + + ProjectExplorer.Project.TargetCount + 1 + + + ProjectExplorer.Project.Updater.EnvironmentId + {1c480e06-4a57-40ca-8868-29ff19bb1281} + + + ProjectExplorer.Project.Updater.FileVersion + 11 + + diff --git a/Bing/Src/CmFile.cpp b/Bing/Src/CmFile.cpp new file mode 100644 index 000000000..804c2ec3b --- /dev/null +++ b/Bing/Src/CmFile.cpp @@ -0,0 +1,173 @@ +#include "kyheader.h" + + +#ifdef _WIN32 +#include +#include +#include +#include +#else +#include +#include +#include +#include +#endif + + +// Get image names from a wildcard. Eg: GetNames("D:\\*.jpg", imgNames); +int CmFile::GetNames(CStr &_nameW, vecS &_names) +{ + string _dir = GetFolder(_nameW); + _names.clear(); + + DIR *dir; + struct dirent *ent; + if((dir = opendir(_dir.c_str()))!=NULL){ + //print all the files and directories within directory + while((ent = readdir(dir))!=NULL){ + if(ent->d_name[0] == '.') + continue; + if(ent->d_type ==4) + continue; + _names.push_back(ent->d_name); + } + closedir(dir); + } else { + perror(""); + return EXIT_FAILURE; + } + return (int)_names.size(); +} +int CmFile::GetNames(CStr &_nameW, vecS &_names, string &_dir) +{ + _dir = GetFolder(_nameW); + _names.clear(); + + DIR *dir; + struct dirent *ent; + if((dir = opendir(_dir.c_str()))!=NULL){ + //print all the files and directories within directory + while((ent = readdir(dir))!=NULL){ + if(ent->d_name[0] == '.') + continue; + if(ent->d_type ==4) + continue; + _names.push_back(ent->d_name); + } + closedir(dir); + } else { + perror(""); + return EXIT_FAILURE; + } + return (int)_names.size(); +} +int CmFile::GetSubFolders(CStr &folder, vecS &subFolders) +{ + subFolders.clear(); + string nameWC = GetFolder(folder);//folder + "/*"; + + DIR *dir; + struct dirent *ent; + if((dir = opendir(nameWC.c_str()))!=NULL){ + while((ent = readdir(dir))!=NULL){ + if(ent->d_name[0] == '.') + continue; + if(ent->d_type == 4){ + subFolders.push_back(ent->d_name); + } + } + closedir(dir); + } else { + perror(""); + return EXIT_FAILURE; + } + return (int)subFolders.size(); +} +int CmFile::GetNames(CStr& rootFolder, CStr &fileW, vecS &names) +{ + GetNames(rootFolder + fileW, names); + vecS subFolders, tmpNames; + int subNum = CmFile::GetSubFolders(rootFolder, subFolders);// + for (int i = 0; i < subNum; i++){ + subFolders[i] += "/"; + int subNum = GetNames(rootFolder + subFolders[i], fileW, tmpNames); + for (int j = 0; j < subNum; j++) + names.push_back(subFolders[i] + tmpNames[j]); + } + return (int)names.size(); +} +int CmFile::GetNamesNE(CStr& nameWC, vecS &names) +{ + string dir = string(); + string ext = string(); + int fNum = GetNames(nameWC, names, dir); + ext = GetExtention(nameWC); + for (int i = 0; i < fNum; i++) + names[i] = GetNameNE(names[i]); + return fNum; +} +int CmFile::GetNamesNE(CStr& nameWC, vecS &names, string &dir, string &ext) +{ + int fNum = GetNames(nameWC, names, dir); + ext = GetExtention(nameWC); + for (int i = 0; i < fNum; i++) + names[i] = GetNameNE(names[i]); + return fNum; +} +int CmFile::GetNamesNE(CStr& rootFolder, CStr &fileW, vecS &names) +{ + int fNum = GetNames(rootFolder, fileW, names); + int extS = GetExtention(fileW).size(); + for (int i = 0; i < fNum; i++) + names[i].resize(names[i].size() - extS); + return fNum; +} +bool CmFile::MkDir(CStr &_path) +{ + if(_path.size() == 0) + return false; + static char buffer[1024]; + strcpy(buffer, _S(_path)); +#ifdef _WIN32 + for (int i = 0; buffer[i] != 0; i ++) { + if (buffer[i] == '\\' || buffer[i] == '/') { + buffer[i] = '\0'; + CreateDirectoryA(buffer, 0); + buffer[i] = '/'; + } + } + return CreateDirectoryA(_S(_path), 0); +#else + for (int i = 0; buffer[i] != 0; i ++) { + if (buffer[i] == '\\' || buffer[i] == '/') { + buffer[i] = '\0'; + mkdir(buffer, 0755); + buffer[i] = '/'; + } + } + return mkdir(_S(_path), 0755); +#endif +} +vecS CmFile::loadStrList(CStr &fName) +{ + ifstream fIn(fName); + string line; + vecS strs; + while(getline(fIn, line) && line.size()){ + unsigned sz = line.size(); + line.resize(sz - 1); //Please use script to convert the VOC format data into the OpenCV format data + //line.resize(sz); + strs.push_back(line); + } + return strs; +} +bool CmFile::writeStrList(CStr &fName, const vecS &strs) +{ + FILE *f = fopen(_S(fName), "w"); + if (f == NULL) + return false; + for (size_t i = 0; i < strs.size(); i++) + fprintf(f, "%s\n", _S(strs[i])); + fclose(f); + return true; +} diff --git a/Bing/Src/CmFile.h b/Bing/Src/CmFile.h new file mode 100644 index 000000000..ca6330fd2 --- /dev/null +++ b/Bing/Src/CmFile.h @@ -0,0 +1,75 @@ +#pragma once +#ifdef _WIN32 +#include +#else +#include +#include +#include +#include +#endif +struct CmFile +{ + static inline string GetFolder(CStr& path); + static inline string GetName(CStr& path); + static inline string GetNameNE(CStr& path); + static inline string GetPathNE(CStr& path); + + // Get file names from a wildcard. Eg: GetNames("D:\\*.jpg", imgNames); + static int GetNames(CStr &nameW, vecS &names, string &_dir); + static int GetNames(CStr &nameW, vecS &names); + static int GetNames(CStr& rootFolder, CStr &fileW, vecS &names); + static int GetNamesNE(CStr& nameWC, vecS &names, string &dir, string &ext); + static int GetNamesNE(CStr& nameWC, vecS &names); + static int GetNamesNE(CStr& rootFolder, CStr &fileW, vecS &names); + static inline string GetExtention(CStr name); + + static int GetSubFolders(CStr& folder, vecS& subFolders); + + static inline string GetWkDir(); + + static bool MkDir(CStr& path); + static vecS loadStrList(CStr &fName); + static bool writeStrList(CStr &fName, const vecS &strs); +}; + +/************************************************************************/ +/* Implementation of inline functions */ +/************************************************************************/ +string CmFile::GetFolder(CStr& path) +{ + return path.substr(0, path.find_last_of("\\/")+1); +} + +string CmFile::GetName(CStr& path) +{ + int start = path.find_last_of("\\/")+1; + int end = path.find_last_not_of(' ')+1; + return path.substr(start, end - start); +} + +string CmFile::GetNameNE(CStr& path) +{ + int start = path.find_last_of("\\/")+1; + int end = path.find_last_of('.'); + if (end >= 0) + return path.substr(start, end - start); + else + return path.substr(start, path.find_last_not_of(' ')+1 - start); +} + +string CmFile::GetPathNE(CStr& path) +{ + int end = path.find_last_of('.'); + if (end >= 0) + return path.substr(0, end); + else + return path.substr(0, path.find_last_not_of(' ') + 1); +} + +string CmFile::GetExtention(CStr name) +{ + return name.substr(name.find_last_of('.')); +} +/************************************************************************/ +/* Implementations */ +/************************************************************************/ diff --git a/Bing/Src/CmShow.cpp b/Bing/Src/CmShow.cpp new file mode 100644 index 000000000..7c7c593b0 --- /dev/null +++ b/Bing/Src/CmShow.cpp @@ -0,0 +1,82 @@ +#include "kyheader.h" +#include "CmShow.h" + + + +typedef pair CostiIdx; +Mat CmShow::HistBins(CMat& color3f, CMat& val, CStr& title, bool descendShow, CMat &with) +{ + // Prepare data + int H = 300, spaceH = 6, barH = 10, n = color3f.cols; + CV_Assert(color3f.size() == val.size() && color3f.rows == 1); + Mat binVal1i, binColor3b, width1i; + if (with.size() == val.size()) + with.convertTo(width1i, CV_32S, 400/sum(with).val[0]); // Default shown width + else + width1i = Mat(1, n, CV_32S, Scalar(10)); // Default bin width = 10 + int W = cvRound(sum(width1i).val[0]); + color3f.convertTo(binColor3b, CV_8UC3, 255); + double maxVal, minVal; + minMaxLoc(val, &minVal, &maxVal); + printf("%g\n", H/max(maxVal, -minVal)); + val.convertTo(binVal1i, CV_32S, 20000); + Size szShow(W, H + spaceH + barH); + szShow.height += minVal < 0 && !descendShow ? H + spaceH : 0; + Mat showImg3b(szShow, CV_8UC3, Scalar(255, 255, 255)); + int* binH = (int*)(binVal1i.data); + Vec3b* binColor = (Vec3b*)(binColor3b.data); + int* binW = (int*)(width1i.data); + vector costIdx(n); + if (descendShow){ + for (int i = 0; i < n; i++) + costIdx[i] = make_pair(binH[i], i); + sort(costIdx.begin(), costIdx.end(), std::greater()); + } + + // Show image + for (int i = 0, x = 0; i < n; i++){ + int idx = descendShow ? costIdx[i].second : i; + int h = descendShow ? abs(binH[idx]) : binH[idx]; + Scalar color(binColor[idx]); + Rect reg(x, H + spaceH, binW[idx], barH); + showImg3b(reg) = color; // Draw bar + rectangle(showImg3b, reg, Scalar(0)); + + reg.height = abs(h); + reg.y = h >= 0 ? H - h : H + 2 * spaceH + barH; + showImg3b(reg) = color; + rectangle(showImg3b, reg, Scalar(0)); + + x += binW[idx]; + } + imshow(title, showImg3b); + return showImg3b; +} + +void CmShow::showTinyMat(CStr &title, CMat &m) +{ + int scale = 50, sz = m.rows * m.cols; + while (sz > 200){ + scale /= 2; + sz /= 4; + } + + Mat img; + resize(m, img, Size(), scale, scale, CV_INTER_NN); + if (img.channels() == 3) + cvtColor(img, img, CV_RGB2BGR); + SaveShow(img, title); +} + +void CmShow::SaveShow(CMat& img, CStr& title) +{ + if (title.size() == 0) + return; + + int mDepth = CV_MAT_DEPTH(img.type()); + double scale = (mDepth == CV_32F || mDepth == CV_64F ? 255 : 1); + if (title.size() > 4 && title[title.size() - 4] == '.') + imwrite(title, img*scale); + else if (title.size()) + imshow(title, img); +} diff --git a/Bing/Src/CmShow.h b/Bing/Src/CmShow.h new file mode 100644 index 000000000..11d99a046 --- /dev/null +++ b/Bing/Src/CmShow.h @@ -0,0 +1,9 @@ +#pragma once +class CmShow +{ +public: + static Mat HistBins(CMat& color3f, CMat& val, CStr& title, bool descendShow = false, CMat &with = Mat()); + static void showTinyMat(CStr &title, CMat &m); + static inline void SaveShow(CMat& img, CStr& title); +}; + diff --git a/Bing/Src/CmTimer.h b/Bing/Src/CmTimer.h new file mode 100644 index 000000000..7883567f9 --- /dev/null +++ b/Bing/Src/CmTimer.h @@ -0,0 +1,89 @@ +#pragma once +#include +class CmTimer +{ +public: + CmTimer(CStr t):title(t) { is_started = false; gettimeofday(&start_clock,NULL); gettimeofday(&end_clock,NULL); n_starts = 0; } + + ~CmTimer(){ if (is_started) printf("CmTimer '%s' is started and is being destroyed.\n", title.c_str()); } + + inline void Start(); + inline void Stop(); + inline void Reset(); + + inline bool Report(); + inline bool StopAndReport() { Stop(); return Report(); } + inline float TimeInSeconds(); + +private: + CStr title; + + bool is_started; + struct timeval start_clock, end_clock; + //clock_t start_clock; + //clock_t cumulative_clock; + unsigned int n_starts; +}; + +/************************************************************************/ +/* Implementations */ +/************************************************************************/ + +void CmTimer::Start() +{ + if (is_started){ + printf("CmTimer '%s' is already started. Nothing done.\n", title.c_str()); + return; + } + + is_started = true; + n_starts++; + //start_clock = clock(); + gettimeofday(&start_clock,NULL); +} + +void CmTimer::Stop() +{ + if (!is_started){ + printf("CmTimer '%s' is started. Nothing done\n", title.c_str()); + return; + } + gettimeofday(&end_clock,NULL); + //cumulative_clock += clock() - start_clock; + is_started = false; +} + +void CmTimer::Reset() +{ + if (is_started) { + printf("CmTimer '%s'is started during reset request.\n Only reset cumulative time.\n"); + return; + } + gettimeofday(&start_clock,NULL); + gettimeofday(&end_clock,NULL); + //cumulative_clock = 0; +} + +bool CmTimer::Report() +{ + if (is_started){ + printf("CmTimer '%s' is started.\n Cannot provide a time report.", title.c_str()); + return false; + } + + float timeUsed = TimeInSeconds(); + printf("[%s] CumuTime: %gs, #run: %d, AvgTime: %gs\n", title.c_str(), timeUsed, n_starts, timeUsed/n_starts); + return true; +} + +float CmTimer::TimeInSeconds() +{ + if (is_started){ + printf("CmTimer '%s' is started. Nothing done\n", title.c_str()); + return 0; + } + return double((end_clock.tv_sec - start_clock.tv_sec) * 1000000u + + end_clock.tv_usec - start_clock.tv_usec) / 1.e6; + //return float(cumulative_clock) / CLOCKS_PER_SEC; +} + diff --git a/Bing/Src/DataSetVOC.cpp b/Bing/Src/DataSetVOC.cpp new file mode 100644 index 000000000..bb99a7de0 --- /dev/null +++ b/Bing/Src/DataSetVOC.cpp @@ -0,0 +1,224 @@ +#include "kyheader.h" +#include "DataSetVOC.h" + + +DataSetVOC::DataSetVOC(CStr &_wkDir) +{ + wkDir = _wkDir; + resDir = wkDir + "Results/"; + localDir = wkDir + "Local/"; + imgPathW = wkDir + "JPEGImages/%s.jpg"; + annoPathW = wkDir + "Annotations/%s.yml"; + CmFile::MkDir(resDir); + CmFile::MkDir(localDir); + + trainSet = CmFile::loadStrList(wkDir + "ImageSets/Main/train.txt"); + testSet = CmFile::loadStrList(wkDir + "ImageSets/Main/test.txt"); + classNames = CmFile::loadStrList(wkDir + "ImageSets/Main/class.txt"); + + // testSet.insert(testSet.end(), trainSet.begin(), trainSet.end()); + // testSet.resize(min(1000, (int)testSet.size())); + + trainNum = trainSet.size(); + testNum = testSet.size(); +} + + +Vec4i getMaskRange(CMat &mask1u, int ext = 0) +{ + int maxX = INT_MIN, maxY = INT_MIN, minX = INT_MAX, minY = INT_MAX, rows = mask1u.rows, cols = mask1u.cols; + for (int r = 0; r < rows; r++) { + const byte* data = mask1u.ptr(r); + for (int c = 0; c < cols; c++) + if (data[c] > 10) { + maxX = max(maxX, c); + minX = min(minX, c); + maxY = max(maxY, r); + minY = min(minY, r); + } + } + + maxX = maxX + ext + 1 < cols ? maxX + ext + 1 : cols; + maxY = maxY + ext + 1 < rows ? maxY + ext + 1 : rows; + minX = minX - ext > 0 ? minX - ext : 0; + minY = minY - ext > 0 ? minY - ext : 0; + + return Vec4i(minX + 1, minY + 1, maxX, maxY); // Rect(minX, minY, maxX - minX, maxY - minY); +} + + +DataSetVOC::~DataSetVOC(void) +{ +} + +void DataSetVOC::loadAnnotations() +{ + gtTrainBoxes.resize(trainNum); + gtTrainClsIdx.resize(trainNum); + for (int i = 0; i < trainNum; i++) + if (!loadBBoxes(trainSet[i], gtTrainBoxes[i], gtTrainClsIdx[i])) + return; + + gtTestBoxes.resize(testNum); + gtTestClsIdx.resize(testNum); + for (int i = 0; i < testNum; i++) + if(!loadBBoxes(testSet[i], gtTestBoxes[i], gtTestClsIdx[i])) + return; + printf("Load annotations finished\n"); +} + +void DataSetVOC::loadDataGenericOverCls() +{ + vecS allSet = trainSet; + allSet.insert(allSet.end(), testSet.begin(), testSet.end()); + int imgN = (int)allSet.size(); + trainSet.clear(), testSet.clear(); + trainSet.reserve(imgN), testSet.reserve(imgN); + vector> gtBoxes(imgN); + vector gtClsIdx(imgN); + for (int i = 0; i < imgN; i++){ + if (!loadBBoxes(allSet[i], gtBoxes[i], gtClsIdx[i])) + return; + vector trainBoxes, testBoxes; + vecI trainIdx, testIdx; + for (size_t j = 0; j < gtBoxes[i].size(); j++) + if (gtClsIdx[i][j] < 6){ + trainBoxes.push_back(gtBoxes[i][j]); + trainIdx.push_back(gtClsIdx[i][j]); + } + else{ + testBoxes.push_back(gtBoxes[i][j]); + testIdx.push_back(gtClsIdx[i][j]); + } + if (trainBoxes.size()){ + trainSet.push_back(allSet[i]); + gtTrainBoxes.push_back(trainBoxes); + gtTrainClsIdx.push_back(trainIdx); + } + else{ + testSet.push_back(allSet[i]); + gtTestBoxes.push_back(testBoxes); + gtTestClsIdx.push_back(testIdx); + } + } + trainNum = trainSet.size(); + testNum = testSet.size(); + printf("Load annotations (generic over classes) finished\n"); +} + +void DataSetVOC::loadBox(const FileNode &fn, vector &boxes, vecI &clsIdx){ + string isDifficult; + fn["difficult"]>>isDifficult; + if (isDifficult == "1") + return; + + string strXmin, strYmin, strXmax, strYmax; + fn["bndbox"]["xmin"] >> strXmin; + fn["bndbox"]["ymin"] >> strYmin; + fn["bndbox"]["xmax"] >> strXmax; + fn["bndbox"]["ymax"] >> strYmax; + boxes.push_back(Vec4i(atoi(_S(strXmin)), atoi(_S(strYmin)), atoi(_S(strXmax)), atoi(_S(strYmax)))); + + string clsName; + fn["name"]>>clsName; + clsIdx.push_back(findFromList(clsName, classNames)); + CV_Assert_(clsIdx[clsIdx.size() - 1] >= 0, ("Invalidate class name\n")); +} + +bool DataSetVOC::loadBBoxes(CStr &nameNE, vector &boxes, vecI &clsIdx) +{ + string fName = format(_S(annoPathW), _S(nameNE)); + FileStorage fs(fName, FileStorage::READ); + FileNode fn = fs["annotation"]["object"]; + boxes.clear(); + clsIdx.clear(); + if (fn.isSeq()){ + for (FileNodeIterator it = fn.begin(), it_end = fn.end(); it != it_end; it++){ + loadBox(*it, boxes, clsIdx); + } + } + else + loadBox(fn, boxes, clsIdx); + return true; +} + +// Needs to call yml.m in this solution before running this function. +bool DataSetVOC::cvt2OpenCVYml(CStr &annoDir) +{ + vecS namesNE; + int imgNum = CmFile::GetNamesNE(annoDir + "*.yaml", namesNE); + printf("Converting annotations to OpenCV yml format:\n"); + for (int i = 0; i < imgNum; i++){ + printf("%d/%d %s.yaml\r", i, imgNum, _S(namesNE[i])); + string fPath = annoDir + namesNE[i]; + cvt2OpenCVYml(fPath + ".yaml", fPath + ".yml"); + } + return true; +} + +// Needs to call yml.m in this solution before running this function. +bool DataSetVOC::cvt2OpenCVYml(CStr &yamlName, CStr &ymlName) +{ + ifstream f(yamlName); + FILE *fO = fopen(_S(ymlName), "w"); + if (!f.is_open() && fO == NULL) + return false; + fprintf(fO, "%s\n", "%YAML:1.0\n"); + string line; + + int addIdent = 0; + while(getline(f, line)){ + if (line.substr(0, 12) == " filename: ") + line = " filename: \"" + line.substr(12) + "\""; + int tmp = line.find_first_of('-'); + if (tmp != string::npos){ + bool allSpace = true; + for (int k = 0; k < tmp; k++) + if (line[k] != ' ') + allSpace = false; + if (allSpace) + addIdent = tmp; + } + for (int k = 0; k < addIdent; k++) + fprintf(fO, " "); + fprintf(fO, "%s\n", _S(line)); + } + fclose(fO); + + FileStorage fs(ymlName, FileStorage::READ); + string tmp; + fs["annotation"]["folder"]>>tmp; + return true; +} + + +// Get training and testing for demonstrating the generative of the objectness over classes +void DataSetVOC::getTrainTest() +{ + const int TRAIN_CLS_NUM = 6; + string trainCls[TRAIN_CLS_NUM] = {"bird", "car", "cat", "cow", "dog", "sheep"}; + +} + +void DataSetVOC::getXmlStrVOC(CStr &fName, string &buf) +{ + ifstream fin(fName); + string strLine; + buf.clear(); + buf.reserve(100000); + buf += "\n\n"; + while (getline(fin, strLine) && strLine.size()) { + int startP = strLine.find_first_of(">") + 1; + int endP = strLine.find_last_of("<"); + if (endP > startP){ + string val = keepXmlChar(strLine.substr(startP, endP - startP)); + if (val.size() < endP - startP) + strLine = strLine.substr(0, startP) + val + strLine.substr(endP); + } + buf += strLine + "\n"; + } + buf += "\n"; + //FileStorage fs(buf, FileStorage::READ + FileStorage::MEMORY); + ofstream fout("D:/t.xml"); + fout<< buf; +} diff --git a/Bing/Src/DataSetVOC.h b/Bing/Src/DataSetVOC.h new file mode 100644 index 000000000..4c1ec38af --- /dev/null +++ b/Bing/Src/DataSetVOC.h @@ -0,0 +1,71 @@ +#pragma once + +struct DataSetVOC +{ + DataSetVOC(CStr &wkDir); + ~DataSetVOC(void); + + // Organization structure data for the dataset + string wkDir; // Root working directory, all other directories are relative to this one + string resDir, localDir; // Directory for saving results and local data + string imgPathW, annoPathW; // Image and annotation path + + // Information for training and testing + int trainNum, testNum; + vecS trainSet, testSet; // File names (NE) for training and testing images + vecS classNames; // Object class names + vector> gtTrainBoxes, gtTestBoxes; // Ground truth bounding boxes for training and testing images + vector gtTrainClsIdx, gtTestClsIdx; // Object class indexes + + + // Load annotations + void loadAnnotations(); + + static bool cvt2OpenCVYml(CStr &annoDir); // Needs to call yml.m in this solution before running this function. + + static inline double interUnio(const Vec4i &box1, const Vec4i &box2); + + // Get training and testing for demonstrating the generative of the objectness over classes + void getTrainTest(); + +public: // Used for testing the ability of generic over classes + void loadDataGenericOverCls(); + +private: + void loadBox(const FileNode &fn, vector &boxes, vecI &clsIdx); + bool loadBBoxes(CStr &nameNE, vector &boxes, vecI &clsIdx); + static void getXmlStrVOC(CStr &fName, string &buf); + static inline string keepXmlChar(CStr &str); + static bool cvt2OpenCVYml(CStr &yamlName, CStr &ymlName); // Needs to call yml.m in this solution before running this function. +}; + +string DataSetVOC::keepXmlChar(CStr &_str) +{ + string str = _str; + int sz = (int)str.size(), count = 0; + for (int i = 0; i < sz; i++){ + char c = str[i]; + if ((c >= 'a' && c <= 'z') || (c >= 'A' && c <= 'Z') || (c >= '0' && c <= '9') || c == ' ' || c == '.') + str[count++] = str[i]; + } + str.resize(count); + return str; +} + +double DataSetVOC::interUnio(const Vec4i &bb, const Vec4i &bbgt) +{ + int bi[4]; + bi[0] = max(bb[0], bbgt[0]); + bi[1] = max(bb[1], bbgt[1]); + bi[2] = min(bb[2], bbgt[2]); + bi[3] = min(bb[3], bbgt[3]); + + double iw = bi[2] - bi[0] + 1; + double ih = bi[3] - bi[1] + 1; + double ov = 0; + if (iw>0 && ih>0){ + double ua = (bb[2]-bb[0]+1)*(bb[3]-bb[1]+1)+(bbgt[2]-bbgt[0]+1)*(bbgt[3]-bbgt[1]+1)-iw*ih; + ov = iw*ih/ua; + } + return ov; +} diff --git a/Bing/Src/FilterTIG.cpp b/Bing/Src/FilterTIG.cpp new file mode 100644 index 000000000..8643011e7 --- /dev/null +++ b/Bing/Src/FilterTIG.cpp @@ -0,0 +1,78 @@ +#include "kyheader.h" +#include "FilterTIG.h" +#include "CmShow.h" + + +void FilterTIG::update(CMat &w1f){ + CV_Assert(w1f.cols * w1f.rows == D && w1f.type() == CV_32F && w1f.isContinuous()); + float b[D], residuals[D]; + memcpy(residuals, w1f.data, sizeof(float)*D); + for (int i = 0; i < NUM_COMP; i++){ + float avg = 0; + for (int j = 0; j < D; j++){ + b[j] = residuals[j] >= 0.0f ? 1.0f : -1.0f; + avg += residuals[j] * b[j]; + } + avg /= D; + _coeffs1[i] = avg, _coeffs2[i] = avg*2, _coeffs4[i] = avg*4, _coeffs8[i] = avg*8; + for (int j = 0; j < D; j++) + residuals[j] -= avg*b[j]; + UINT64 tig = 0; + for (int j = 0; j < D; j++) + tig = (tig << 1) | (b[j] > 0 ? 1 : 0); + _bTIGs[i] = tig; + } +} + +void FilterTIG::reconstruct(Mat &w1f){ + w1f = Mat::zeros(8, 8, CV_32F); + float *weight = (float*)w1f.data; + for (int i = 0; i < NUM_COMP; i++){ + UINT64 tig = _bTIGs[i]; + for (int j = 0; j < D; j++) + weight[j] += _coeffs1[i] * (((tig >> (63-j)) & 1) ? 1 : -1); + } +} + +// For a W by H gradient magnitude map, find a W-7 by H-7 CV_32F matching score map +// Please refer to my paper for definition of the variables used in this function +Mat FilterTIG::matchTemplate(const Mat &mag1u){ + const int H = mag1u.rows, W = mag1u.cols; + const Size sz(W+1, H+1); // Expand original size to avoid dealing with boundary conditions + Mat_ Tig1 = Mat_::zeros(sz), Tig2 = Mat_::zeros(sz); + Mat_ Tig4 = Mat_::zeros(sz), Tig8 = Mat_::zeros(sz); + Mat_ Row1 = Mat_::zeros(sz), Row2 = Mat_::zeros(sz); + Mat_ Row4 = Mat_::zeros(sz), Row8 = Mat_::zeros(sz); + Mat_ scores(sz); + for(int y = 1; y <= H; y++){ + const byte* G = mag1u.ptr(y-1); + INT64* T1 = Tig1.ptr(y); // Binary TIG of current row + INT64* T2 = Tig2.ptr(y); + INT64* T4 = Tig4.ptr(y); + INT64* T8 = Tig8.ptr(y); + INT64* Tu1 = Tig1.ptr(y-1); // Binary TIG of upper row + INT64* Tu2 = Tig2.ptr(y-1); + INT64* Tu4 = Tig4.ptr(y-1); + INT64* Tu8 = Tig8.ptr(y-1); + byte* R1 = Row1.ptr(y); + byte* R2 = Row2.ptr(y); + byte* R4 = Row4.ptr(y); + byte* R8 = Row8.ptr(y); + float *s = scores.ptr(y); + for (int x = 1; x <= W; x++) { + byte g = G[x-1]; + R1[x] = (R1[x-1] << 1) | ((g >> 4) & 1); + R2[x] = (R2[x-1] << 1) | ((g >> 5) & 1); + R4[x] = (R4[x-1] << 1) | ((g >> 6) & 1); + R8[x] = (R8[x-1] << 1) | ((g >> 7) & 1); + T1[x] = (Tu1[x] << 8) | R1[x]; + T2[x] = (Tu2[x] << 8) | R2[x]; + T4[x] = (Tu4[x] << 8) | R4[x]; + T8[x] = (Tu8[x] << 8) | R8[x]; + s[x] = dot(T1[x], T2[x], T4[x], T8[x]); + } + } + Mat matchCost1f; + scores(Rect(8, 8, W-7, H-7)).copyTo(matchCost1f); + return matchCost1f; +} diff --git a/Bing/Src/FilterTIG.h b/Bing/Src/FilterTIG.h new file mode 100644 index 000000000..d8b3a2a7f --- /dev/null +++ b/Bing/Src/FilterTIG.h @@ -0,0 +1,45 @@ +#pragma once + +class FilterTIG +{ +public: + void update(CMat &w); + + // For a W by H gradient magnitude map, find a W-7 by H-7 CV_32F matching score map + Mat matchTemplate(const Mat &mag1u); + + inline float dot(const INT64 tig1, const INT64 tig2, const INT64 tig4, const INT64 tig8); + +public: + void reconstruct(Mat &w); // For illustration purpose + +private: + static const int NUM_COMP = 2; // Number of components + static const int D = 64; // Dimension of TIG + INT64 _bTIGs[NUM_COMP]; // Binary TIG features + float _coeffs1[NUM_COMP]; // Coefficients of binary TIG features + + // For efficiently deals with different bits in CV_8U gradient map + float _coeffs2[NUM_COMP], _coeffs4[NUM_COMP], _coeffs8[NUM_COMP]; +}; + + +inline float FilterTIG::dot(const INT64 tig1, const INT64 tig2, const INT64 tig4, const INT64 tig8) +{ + INT64 bcT1 = __builtin_popcountll(tig1); + INT64 bcT2 = __builtin_popcountll(tig2); + INT64 bcT4 = __builtin_popcountll(tig4); + INT64 bcT8 = __builtin_popcountll(tig8); + + INT64 bc01 = (__builtin_popcountll(_bTIGs[0] & tig1) << 1) - bcT1; + INT64 bc02 = ((__builtin_popcountll(_bTIGs[0] & tig2) << 1) - bcT2) << 1; + INT64 bc04 = ((__builtin_popcountll(_bTIGs[0] & tig4) << 1) - bcT4) << 2; + INT64 bc08 = ((__builtin_popcountll(_bTIGs[0] & tig8) << 1) - bcT8) << 3; + + INT64 bc11 = (__builtin_popcountll(_bTIGs[1] & tig1) << 1) - bcT1; + INT64 bc12 = ((__builtin_popcountll(_bTIGs[1] & tig2) << 1) - bcT2) << 1; + INT64 bc14 = ((__builtin_popcountll(_bTIGs[1] & tig4) << 1) - bcT4) << 2; + INT64 bc18 = ((__builtin_popcountll(_bTIGs[1] & tig8) << 1) - bcT8) << 3; + + return _coeffs1[0] * (bc01 + bc02 + bc04 + bc08) + _coeffs1[1] * (bc11 + bc12 + bc14 + bc18); +} diff --git a/Bing/Src/ImgContrastBB.h b/Bing/Src/ImgContrastBB.h new file mode 100644 index 000000000..71c5fb5e9 --- /dev/null +++ b/Bing/Src/ImgContrastBB.h @@ -0,0 +1,62 @@ +#pragma once + +struct ImgContrastBB +{ + ImgContrastBB(CStr &imgPath); + ImgContrastBB(CMat &img3u); + + inline float contrastVal(Vec4i ¢er); + inline int regSum(Vec4i &box, Vec3i &sumColor); // Return region size and sum color + +private: + Mat iImg; + int _w, _h; + inline void assertBBox(Vec4i ¢er, CStr &name); +}; + +ImgContrastBB::ImgContrastBB(CStr &imgPath) +{ + Mat img3u = imread(imgPath); + integral(img3u, iImg, CV_32SC3); + _w = img3u.cols; + _h = img3u.rows; +} + +ImgContrastBB::ImgContrastBB(CMat &img3u) +{ + integral(img3u, iImg, CV_32SC3); + _w = img3u.cols; + _h = img3u.rows; +} + +int ImgContrastBB::regSum(Vec4i &box, Vec3i &sumColor) +{ + int x1 = box[0] - 1, y1 = box[1] - 1, x2 = box[2] - 1, y2 = box[3] - 1; + sumColor = iImg.at(y2, x2) + iImg.at(y1, x1) - iImg.at(y1, x2) - iImg.at(y2, x1); + return (x2 - x1)*(y2 - y1); +} + + +float ImgContrastBB::contrastVal(Vec4i ¢er) +{ + int wd = (center[2] - center[0])/2, hd = (center[3] - center[1])/2; + Vec4i surround(max(center[0] - wd, 1), max(center[1] - hd, 1), min(center[2] + wd, _w), min(center[3] + hd, _h)); + Vec3i cColr, sColr; + + assertBBox(center, "Center"); + assertBBox(center, "Surround"); + int cSz = regSum(center, cColr); + int sSz = regSum(surround, sColr); + + sColr -= cColr; + sSz -= cSz; + sColr /= sSz; + cColr /= cSz; + return sqrtf((float)(sqr(sColr[0] - cColr[0]) + sqr(sColr[1] - cColr[1]) + sqr(sColr[2] - cColr[2])))/100.0f; +} + +void ImgContrastBB::assertBBox(Vec4i ¢er, CStr &name) +{ + if (center[0] < 1 || center[1] < 1 || center[2] > _w || center[3] > _h) + printf("%s: (%d, %d, %d, %d), (%d, %d)\n", _S(name), center[0], center[1], center[2], center[3], _w, _h); +} \ No newline at end of file diff --git a/Bing/Src/LibLinear/LibLinear.vcxproj b/Bing/Src/LibLinear/LibLinear.vcxproj new file mode 100644 index 000000000..62d1e650e --- /dev/null +++ b/Bing/Src/LibLinear/LibLinear.vcxproj @@ -0,0 +1,94 @@ + + + + + Debug + x64 + + + Release + x64 + + + + {86266F16-8B7E-4666-A12F-96E351579ADA} + Win32Proj + LibLinear + + + + StaticLibrary + true + MultiByte + v110 + + + StaticLibrary + false + true + MultiByte + v110 + + + + + + + + + + + + + + + + + Level3 + Disabled + WIN32;_DEBUG;_LIB;%(PreprocessorDefinitions) + + + Console + true + + + + + Level3 + + + MaxSpeed + true + true + WIN32;NDEBUG;_LIB;%(PreprocessorDefinitions) + + + Console + true + true + true + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/Bing/Src/LibLinear/README.1.93.txt b/Bing/Src/LibLinear/README.1.93.txt new file mode 100644 index 000000000..3a659e080 --- /dev/null +++ b/Bing/Src/LibLinear/README.1.93.txt @@ -0,0 +1,531 @@ +LIBLINEAR is a simple package for solving large-scale regularized linear +classification and regression. It currently supports +- L2-regularized logistic regression/L2-loss support vector classification/L1-loss support vector classification +- L1-regularized L2-loss support vector classification/L1-regularized logistic regression +- L2-regularized L2-loss support vector regression/L1-loss support vector regression. +This document explains the usage of LIBLINEAR. + +To get started, please read the ``Quick Start'' section first. +For developers, please check the ``Library Usage'' section to learn +how to integrate LIBLINEAR in your software. + +Table of Contents +================= + +- When to use LIBLINEAR but not LIBSVM +- Quick Start +- Installation +- `train' Usage +- `predict' Usage +- Examples +- Library Usage +- Building Windows Binaries +- Additional Information +- MATLAB/OCTAVE interface +- PYTHON interface + +When to use LIBLINEAR but not LIBSVM +==================================== + +There are some large data for which with/without nonlinear mappings +gives similar performances. Without using kernels, one can +efficiently train a much larger set via linear classification/regression. +These data usually have a large number of features. Document classification +is an example. + +Warning: While generally liblinear is very fast, its default solver +may be slow under certain situations (e.g., data not scaled or C is +large). See Appendix B of our SVM guide about how to handle such +cases. +http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf + +Warning: If you are a beginner and your data sets are not large, you +should consider LIBSVM first. + +LIBSVM page: +http://www.csie.ntu.edu.tw/~cjlin/libsvm + + +Quick Start +=========== + +See the section ``Installation'' for installing LIBLINEAR. + +After installation, there are programs `train' and `predict' for +training and testing, respectively. + +About the data format, please check the README file of LIBSVM. Note +that feature index must start from 1 (but not 0). + +A sample classification data included in this package is `heart_scale'. + +Type `train heart_scale', and the program will read the training +data and output the model file `heart_scale.model'. If you have a test +set called heart_scale.t, then type `predict heart_scale.t +heart_scale.model output' to see the prediction accuracy. The `output' +file contains the predicted class labels. + +For more information about `train' and `predict', see the sections +`train' Usage and `predict' Usage. + +To obtain good performances, sometimes one needs to scale the +data. Please check the program `svm-scale' of LIBSVM. For large and +sparse data, use `-l 0' to keep the sparsity. + +Installation +============ + +On Unix systems, type `make' to build the `train' and `predict' +programs. Run them without arguments to show the usages. + +On other systems, consult `Makefile' to build them (e.g., see +'Building Windows binaries' in this file) or use the pre-built +binaries (Windows binaries are in the directory `windows'). + +This software uses some level-1 BLAS subroutines. The needed functions are +included in this package. If a BLAS library is available on your +machine, you may use it by modifying the Makefile: Unmark the following line + + #LIBS ?= -lblas + +and mark + + LIBS ?= blas/blas.a + +`train' Usage +============= + +Usage: train [options] training_set_file [model_file] +options: +-s type : set type of solver (default 1) + for multi-class classification + 0 -- L2-regularized logistic regression (primal) + 1 -- L2-regularized L2-loss support vector classification (dual) + 2 -- L2-regularized L2-loss support vector classification (primal) + 3 -- L2-regularized L1-loss support vector classification (dual) + 4 -- support vector classification by Crammer and Singer + 5 -- L1-regularized L2-loss support vector classification + 6 -- L1-regularized logistic regression + 7 -- L2-regularized logistic regression (dual) + for regression + 11 -- L2-regularized L2-loss support vector regression (primal) + 12 -- L2-regularized L2-loss support vector regression (dual) + 13 -- L2-regularized L1-loss support vector regression (dual) +-c cost : set the parameter C (default 1) +-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1) +-e epsilon : set tolerance of termination criterion + -s 0 and 2 + |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2, + where f is the primal function and pos/neg are # of + positive/negative data (default 0.01) + -s 11 + |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001) + -s 1, 3, 4 and 7 + Dual maximal violation <= eps; similar to libsvm (default 0.1) + -s 5 and 6 + |f'(w)|_inf <= eps*min(pos,neg)/l*|f'(w0)|_inf, + where f is the primal function (default 0.01) + -s 12 and 13\n" + |f'(alpha)|_1 <= eps |f'(alpha0)|, + where f is the dual function (default 0.1) +-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1) +-wi weight: weights adjust the parameter C of different classes (see README for details) +-v n: n-fold cross validation mode +-q : quiet mode (no outputs) + +Option -v randomly splits the data into n parts and calculates cross +validation accuracy on them. + +Formulations: + +For L2-regularized logistic regression (-s 0), we solve + +min_w w^Tw/2 + C \sum log(1 + exp(-y_i w^Tx_i)) + +For L2-regularized L2-loss SVC dual (-s 1), we solve + +min_alpha 0.5(alpha^T (Q + I/2/C) alpha) - e^T alpha + s.t. 0 <= alpha_i, + +For L2-regularized L2-loss SVC (-s 2), we solve + +min_w w^Tw/2 + C \sum max(0, 1- y_i w^Tx_i)^2 + +For L2-regularized L1-loss SVC dual (-s 3), we solve + +min_alpha 0.5(alpha^T Q alpha) - e^T alpha + s.t. 0 <= alpha_i <= C, + +For L1-regularized L2-loss SVC (-s 5), we solve + +min_w \sum |w_j| + C \sum max(0, 1- y_i w^Tx_i)^2 + +For L1-regularized logistic regression (-s 6), we solve + +min_w \sum |w_j| + C \sum log(1 + exp(-y_i w^Tx_i)) + +For L2-regularized logistic regression (-s 7), we solve + +min_alpha 0.5(alpha^T Q alpha) + \sum alpha_i*log(alpha_i) + \sum (C-alpha_i)*log(C-alpha_i) - a constant + s.t. 0 <= alpha_i <= C, + +where + +Q is a matrix with Q_ij = y_i y_j x_i^T x_j. + +For L2-regularized L2-loss SVR (-s 11), we solve + +min_w w^Tw/2 + C \sum max(0, |y_i-w^Tx_i|-epsilon)^2 + +For L2-regularized L2-loss SVR dual (-s 12), we solve + +min_beta 0.5(beta^T (Q + lambda I/2/C) beta) - y^T beta + \sum |beta_i| + +For L2-regularized L1-loss SVR dual (-s 13), we solve + +min_beta 0.5(beta^T Q beta) - y^T beta + \sum |beta_i| + s.t. -C <= beta_i <= C, + +where + +Q is a matrix with Q_ij = x_i^T x_j. + +If bias >= 0, w becomes [w; w_{n+1}] and x becomes [x; bias]. + +The primal-dual relationship implies that -s 1 and -s 2 give the same +model, -s 0 and -s 7 give the same, and -s 11 and -s 12 give the same. + +We implement 1-vs-the rest multi-class strategy for classification. +In training i vs. non_i, their C parameters are (weight from -wi)*C +and C, respectively. If there are only two classes, we train only one +model. Thus weight1*C vs. weight2*C is used. See examples below. + +We also implement multi-class SVM by Crammer and Singer (-s 4): + +min_{w_m, \xi_i} 0.5 \sum_m ||w_m||^2 + C \sum_i \xi_i + s.t. w^T_{y_i} x_i - w^T_m x_i >= \e^m_i - \xi_i \forall m,i + +where e^m_i = 0 if y_i = m, + e^m_i = 1 if y_i != m, + +Here we solve the dual problem: + +min_{\alpha} 0.5 \sum_m ||w_m(\alpha)||^2 + \sum_i \sum_m e^m_i alpha^m_i + s.t. \alpha^m_i <= C^m_i \forall m,i , \sum_m \alpha^m_i=0 \forall i + +where w_m(\alpha) = \sum_i \alpha^m_i x_i, +and C^m_i = C if m = y_i, + C^m_i = 0 if m != y_i. + +`predict' Usage +=============== + +Usage: predict [options] test_file model_file output_file +options: +-b probability_estimates: whether to output probability estimates, 0 or 1 (default 0); currently for logistic regression only +-q : quiet mode (no outputs) + +Note that -b is only needed in the prediction phase. This is different +from the setting of LIBSVM. + +Examples +======== + +> train data_file + +Train linear SVM with L2-loss function. + +> train -s 0 data_file + +Train a logistic regression model. + +> train -v 5 -e 0.001 data_file + +Do five-fold cross-validation using L2-loss svm. +Use a smaller stopping tolerance 0.001 than the default +0.1 if you want more accurate solutions. + +> train -c 10 -w1 2 -w2 5 -w3 2 four_class_data_file + +Train four classifiers: +positive negative Cp Cn +class 1 class 2,3,4. 20 10 +class 2 class 1,3,4. 50 10 +class 3 class 1,2,4. 20 10 +class 4 class 1,2,3. 10 10 + +> train -c 10 -w3 1 -w2 5 two_class_data_file + +If there are only two classes, we train ONE model. +The C values for the two classes are 10 and 50. + +> predict -b 1 test_file data_file.model output_file + +Output probability estimates (for logistic regression only). + +Library Usage +============= + +- Function: model* train(const struct problem *prob, + const struct parameter *param); + + This function constructs and returns a linear classification + or regression model according to the given training data and + parameters. + + struct problem describes the problem: + + struct problem + { + int l, n; + int *y; + struct feature_node **x; + double bias; + }; + + where `l' is the number of training data. If bias >= 0, we assume + that one additional feature is added to the end of each data + instance. `n' is the number of feature (including the bias feature + if bias >= 0). `y' is an array containing the target values. (integers + in classification, real numbers in regression) And `x' is an array + of pointers, each of which points to a sparse representation (array + of feature_node) of one training vector. + + For example, if we have the following training data: + + LABEL ATTR1 ATTR2 ATTR3 ATTR4 ATTR5 + ----- ----- ----- ----- ----- ----- + 1 0 0.1 0.2 0 0 + 2 0 0.1 0.3 -1.2 0 + 1 0.4 0 0 0 0 + 2 0 0.1 0 1.4 0.5 + 3 -0.1 -0.2 0.1 1.1 0.1 + + and bias = 1, then the components of problem are: + + l = 5 + n = 6 + + y -> 1 2 1 2 3 + + x -> [ ] -> (2,0.1) (3,0.2) (6,1) (-1,?) + [ ] -> (2,0.1) (3,0.3) (4,-1.2) (6,1) (-1,?) + [ ] -> (1,0.4) (6,1) (-1,?) + [ ] -> (2,0.1) (4,1.4) (5,0.5) (6,1) (-1,?) + [ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (6,1) (-1,?) + + struct parameter describes the parameters of a linear classification + or regression model: + + struct parameter + { + int solver_type; + + /* these are for training only */ + double eps; /* stopping criteria */ + double C; + int nr_weight; + int *weight_label; + double* weight; + double p; + }; + + solver_type can be one of L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L1R_L2LOSS_SVC, L1R_LR, L2R_LR_DUAL, L2R_L2LOSS_SVR, L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL. + for classification + L2R_LR L2-regularized logistic regression (primal) + L2R_L2LOSS_SVC_DUAL L2-regularized L2-loss support vector classification (dual) + L2R_L2LOSS_SVC L2-regularized L2-loss support vector classification (primal) + L2R_L1LOSS_SVC_DUAL L2-regularized L1-loss support vector classification (dual) + MCSVM_CS support vector classification by Crammer and Singer + L1R_L2LOSS_SVC L1-regularized L2-loss support vector classification + L1R_LR L1-regularized logistic regression + L2R_LR_DUAL L2-regularized logistic regression (dual) + for regression + L2R_L2LOSS_SVR L2-regularized L2-loss support vector regression (primal) + L2R_L2LOSS_SVR_DUAL L2-regularized L2-loss support vector regression (dual) + L2R_L1LOSS_SVR_DUAL L2-regularized L1-loss support vector regression (dual) + + C is the cost of constraints violation. + p is the sensitiveness of loss of support vector regression. + eps is the stopping criterion. + + nr_weight, weight_label, and weight are used to change the penalty + for some classes (If the weight for a class is not changed, it is + set to 1). This is useful for training classifier using unbalanced + input data or with asymmetric misclassification cost. + + nr_weight is the number of elements in the array weight_label and + weight. Each weight[i] corresponds to weight_label[i], meaning that + the penalty of class weight_label[i] is scaled by a factor of weight[i]. + + If you do not want to change penalty for any of the classes, + just set nr_weight to 0. + + *NOTE* To avoid wrong parameters, check_parameter() should be + called before train(). + + struct model stores the model obtained from the training procedure: + + struct model + { + struct parameter param; + int nr_class; /* number of classes */ + int nr_feature; + double *w; + int *label; /* label of each class */ + double bias; + }; + + param describes the parameters used to obtain the model. + + nr_class and nr_feature are the number of classes and features, + respectively. nr_class = 2 for regression. + + The nr_feature*nr_class array w gives feature weights. We use one + against the rest for multi-class classification, so each feature + index corresponds to nr_class weight values. Weights are + organized in the following way + + +------------------+------------------+------------+ + | nr_class weights | nr_class weights | ... + | for 1st feature | for 2nd feature | + +------------------+------------------+------------+ + + If bias >= 0, x becomes [x; bias]. The number of features is + increased by one, so w is a (nr_feature+1)*nr_class array. The + value of bias is stored in the variable bias. + + The array label stores class labels. + +- Function: void cross_validation(const problem *prob, const parameter *param, int nr_fold, double *target); + + This function conducts cross validation. Data are separated to + nr_fold folds. Under given parameters, sequentially each fold is + validated using the model from training the remaining. Predicted + labels in the validation process are stored in the array called + target. + + The format of prob is same as that for train(). + +- Function: double predict(const model *model_, const feature_node *x); + + For a classification model, the predicted class for x is returned. + For a regression model, the function value of x calculated using + the model is returned. + +- Function: double predict_values(const struct model *model_, + const struct feature_node *x, double* dec_values); + + This function gives nr_w decision values in the array dec_values. + nr_w=1 if regression is applied or the number of classes is two. An exception is + multi-class svm by Crammer and Singer (-s 4), where nr_w = 2 if there are two classes. For all other situations, nr_w is the + number of classes. + + We implement one-vs-the rest multi-class strategy (-s 0,1,2,3,5,6,7) + and multi-class svm by Crammer and Singer (-s 4) for multi-class SVM. + The class with the highest decision value is returned. + +- Function: double predict_probability(const struct model *model_, + const struct feature_node *x, double* prob_estimates); + + This function gives nr_class probability estimates in the array + prob_estimates. nr_class can be obtained from the function + get_nr_class. The class with the highest probability is + returned. Currently, we support only the probability outputs of + logistic regression. + +- Function: int get_nr_feature(const model *model_); + + The function gives the number of attributes of the model. + +- Function: int get_nr_class(const model *model_); + + The function gives the number of classes of the model. + For a regression model, 2 is returned. + +- Function: void get_labels(const model *model_, int* label); + + This function outputs the name of labels into an array called label. + For a regression model, label is unchanged. + +- Function: const char *check_parameter(const struct problem *prob, + const struct parameter *param); + + This function checks whether the parameters are within the feasible + range of the problem. This function should be called before calling + train() and cross_validation(). It returns NULL if the + parameters are feasible, otherwise an error message is returned. + +- Function: int save_model(const char *model_file_name, + const struct model *model_); + + This function saves a model to a file; returns 0 on success, or -1 + if an error occurs. + +- Function: struct model *load_model(const char *model_file_name); + + This function returns a pointer to the model read from the file, + or a null pointer if the model could not be loaded. + +- Function: void free_model_content(struct model *model_ptr); + + This function frees the memory used by the entries in a model structure. + +- Function: void free_and_destroy_model(struct model **model_ptr_ptr); + + This function frees the memory used by a model and destroys the model + structure. + +- Function: void destroy_param(struct parameter *param); + + This function frees the memory used by a parameter set. + +- Function: void set_print_string_function(void (*print_func)(const char *)); + + Users can specify their output format by a function. Use + set_print_string_function(NULL); + for default printing to stdout. + +Building Windows Binaries +========================= + +Windows binaries are in the directory `windows'. To build them via +Visual C++, use the following steps: + +1. Open a dos command box and change to liblinear directory. If +environment variables of VC++ have not been set, type + +"C:\Program Files\Microsoft Visual Studio 10.0\VC\bin\vcvars32.bat" + +You may have to modify the above command according which version of +VC++ or where it is installed. + +2. Type + +nmake -f Makefile.win clean all + + +MATLAB/OCTAVE Interface +======================= + +Please check the file README in the directory `matlab'. + +PYTHON Interface +================ + +Please check the file README in the directory `python'. + +Additional Information +====================== + +If you find LIBLINEAR helpful, please cite it as + +R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. +LIBLINEAR: A Library for Large Linear Classification, Journal of +Machine Learning Research 9(2008), 1871-1874. Software available at +http://www.csie.ntu.edu.tw/~cjlin/liblinear + +For any questions and comments, please send your email to +cjlin@csie.ntu.edu.tw + + diff --git a/Bing/Src/LibLinear/blas/Makefile b/Bing/Src/LibLinear/blas/Makefile new file mode 100644 index 000000000..895fd244c --- /dev/null +++ b/Bing/Src/LibLinear/blas/Makefile @@ -0,0 +1,22 @@ +AR = ar rcv +RANLIB = ranlib + +HEADERS = blas.h blasp.h +FILES = dnrm2.o daxpy.o ddot.o dscal.o + +CFLAGS = $(OPTFLAGS) +FFLAGS = $(OPTFLAGS) + +blas: $(FILES) $(HEADERS) + $(AR) blas.a $(FILES) + $(RANLIB) blas.a + +clean: + - rm -f *.o + - rm -f *.a + - rm -f *~ + +.c.o: + $(CC) $(CFLAGS) -c $*.c + + diff --git a/Bing/Src/LibLinear/blas/blas.h b/Bing/Src/LibLinear/blas/blas.h new file mode 100644 index 000000000..558893a04 --- /dev/null +++ b/Bing/Src/LibLinear/blas/blas.h @@ -0,0 +1,25 @@ +/* blas.h -- C header file for BLAS Ver 1.0 */ +/* Jesse Bennett March 23, 2000 */ + +/** barf [ba:rf] 2. "He suggested using FORTRAN, and everybody barfed." + + - From The Shogakukan DICTIONARY OF NEW ENGLISH (Second edition) */ + +#ifndef BLAS_INCLUDE +#define BLAS_INCLUDE + +/* Data types specific to BLAS implementation */ +typedef struct { float r, i; } fcomplex; +typedef struct { double r, i; } dcomplex; +typedef int blasbool; + +#include "blasp.h" /* Prototypes for all BLAS functions */ + +#define FALSE 0 +#define TRUE 1 + +/* Macro functions */ +#define MIN(a,b) ((a) <= (b) ? (a) : (b)) +#define MAX(a,b) ((a) >= (b) ? (a) : (b)) + +#endif diff --git a/Bing/Src/LibLinear/blas/blasp.h b/Bing/Src/LibLinear/blas/blasp.h new file mode 100644 index 000000000..745836db8 --- /dev/null +++ b/Bing/Src/LibLinear/blas/blasp.h @@ -0,0 +1,430 @@ +/* blasp.h -- C prototypes for BLAS Ver 1.0 */ +/* Jesse Bennett March 23, 2000 */ + +/* Functions listed in alphabetical order */ + +#ifdef F2C_COMPAT + +void cdotc_(fcomplex *dotval, int *n, fcomplex *cx, int *incx, + fcomplex *cy, int *incy); + +void cdotu_(fcomplex *dotval, int *n, fcomplex *cx, int *incx, + fcomplex *cy, int *incy); + +double sasum_(int *n, float *sx, int *incx); + +double scasum_(int *n, fcomplex *cx, int *incx); + +double scnrm2_(int *n, fcomplex *x, int *incx); + +double sdot_(int *n, float *sx, int *incx, float *sy, int *incy); + +double snrm2_(int *n, float *x, int *incx); + +void zdotc_(dcomplex *dotval, int *n, dcomplex *cx, int *incx, + dcomplex *cy, int *incy); + +void zdotu_(dcomplex *dotval, int *n, dcomplex *cx, int *incx, + dcomplex *cy, int *incy); + +#else + +fcomplex cdotc_(int *n, fcomplex *cx, int *incx, fcomplex *cy, int *incy); + +fcomplex cdotu_(int *n, fcomplex *cx, int *incx, fcomplex *cy, int *incy); + +float sasum_(int *n, float *sx, int *incx); + +float scasum_(int *n, fcomplex *cx, int *incx); + +float scnrm2_(int *n, fcomplex *x, int *incx); + +float sdot_(int *n, float *sx, int *incx, float *sy, int *incy); + +float snrm2_(int *n, float *x, int *incx); + +dcomplex zdotc_(int *n, dcomplex *cx, int *incx, dcomplex *cy, int *incy); + +dcomplex zdotu_(int *n, dcomplex *cx, int *incx, dcomplex *cy, int *incy); + +#endif + +/* Remaining functions listed in alphabetical order */ + +int caxpy_(int *n, fcomplex *ca, fcomplex *cx, int *incx, fcomplex *cy, + int *incy); + +int ccopy_(int *n, fcomplex *cx, int *incx, fcomplex *cy, int *incy); + +int cgbmv_(char *trans, int *m, int *n, int *kl, int *ku, + fcomplex *alpha, fcomplex *a, int *lda, fcomplex *x, int *incx, + fcomplex *beta, fcomplex *y, int *incy); + +int cgemm_(char *transa, char *transb, int *m, int *n, int *k, + fcomplex *alpha, fcomplex *a, int *lda, fcomplex *b, int *ldb, + fcomplex *beta, fcomplex *c, int *ldc); + +int cgemv_(char *trans, int *m, int *n, fcomplex *alpha, fcomplex *a, + int *lda, fcomplex *x, int *incx, fcomplex *beta, fcomplex *y, + int *incy); + +int cgerc_(int *m, int *n, fcomplex *alpha, fcomplex *x, int *incx, + fcomplex *y, int *incy, fcomplex *a, int *lda); + +int cgeru_(int *m, int *n, fcomplex *alpha, fcomplex *x, int *incx, + fcomplex *y, int *incy, fcomplex *a, int *lda); + +int chbmv_(char *uplo, int *n, int *k, fcomplex *alpha, fcomplex *a, + int *lda, fcomplex *x, int *incx, fcomplex *beta, fcomplex *y, + int *incy); + +int chemm_(char *side, char *uplo, int *m, int *n, fcomplex *alpha, + fcomplex *a, int *lda, fcomplex *b, int *ldb, fcomplex *beta, + fcomplex *c, int *ldc); + +int chemv_(char *uplo, int *n, fcomplex *alpha, fcomplex *a, int *lda, + fcomplex *x, int *incx, fcomplex *beta, fcomplex *y, int *incy); + +int cher_(char *uplo, int *n, float *alpha, fcomplex *x, int *incx, + fcomplex *a, int *lda); + +int cher2_(char *uplo, int *n, fcomplex *alpha, fcomplex *x, int *incx, + fcomplex *y, int *incy, fcomplex *a, int *lda); + +int cher2k_(char *uplo, char *trans, int *n, int *k, fcomplex *alpha, + fcomplex *a, int *lda, fcomplex *b, int *ldb, float *beta, + fcomplex *c, int *ldc); + +int cherk_(char *uplo, char *trans, int *n, int *k, float *alpha, + fcomplex *a, int *lda, float *beta, fcomplex *c, int *ldc); + +int chpmv_(char *uplo, int *n, fcomplex *alpha, fcomplex *ap, fcomplex *x, + int *incx, fcomplex *beta, fcomplex *y, int *incy); + +int chpr_(char *uplo, int *n, float *alpha, fcomplex *x, int *incx, + fcomplex *ap); + +int chpr2_(char *uplo, int *n, fcomplex *alpha, fcomplex *x, int *incx, + fcomplex *y, int *incy, fcomplex *ap); + +int crotg_(fcomplex *ca, fcomplex *cb, float *c, fcomplex *s); + +int cscal_(int *n, fcomplex *ca, fcomplex *cx, int *incx); + +int csscal_(int *n, float *sa, fcomplex *cx, int *incx); + +int cswap_(int *n, fcomplex *cx, int *incx, fcomplex *cy, int *incy); + +int csymm_(char *side, char *uplo, int *m, int *n, fcomplex *alpha, + fcomplex *a, int *lda, fcomplex *b, int *ldb, fcomplex *beta, + fcomplex *c, int *ldc); + +int csyr2k_(char *uplo, char *trans, int *n, int *k, fcomplex *alpha, + fcomplex *a, int *lda, fcomplex *b, int *ldb, fcomplex *beta, + fcomplex *c, int *ldc); + +int csyrk_(char *uplo, char *trans, int *n, int *k, fcomplex *alpha, + fcomplex *a, int *lda, fcomplex *beta, fcomplex *c, int *ldc); + +int ctbmv_(char *uplo, char *trans, char *diag, int *n, int *k, + fcomplex *a, int *lda, fcomplex *x, int *incx); + +int ctbsv_(char *uplo, char *trans, char *diag, int *n, int *k, + fcomplex *a, int *lda, fcomplex *x, int *incx); + +int ctpmv_(char *uplo, char *trans, char *diag, int *n, fcomplex *ap, + fcomplex *x, int *incx); + +int ctpsv_(char *uplo, char *trans, char *diag, int *n, fcomplex *ap, + fcomplex *x, int *incx); + +int ctrmm_(char *side, char *uplo, char *transa, char *diag, int *m, + int *n, fcomplex *alpha, fcomplex *a, int *lda, fcomplex *b, + int *ldb); + +int ctrmv_(char *uplo, char *trans, char *diag, int *n, fcomplex *a, + int *lda, fcomplex *x, int *incx); + +int ctrsm_(char *side, char *uplo, char *transa, char *diag, int *m, + int *n, fcomplex *alpha, fcomplex *a, int *lda, fcomplex *b, + int *ldb); + +int ctrsv_(char *uplo, char *trans, char *diag, int *n, fcomplex *a, + int *lda, fcomplex *x, int *incx); + +int daxpy_(int *n, double *sa, double *sx, int *incx, double *sy, + int *incy); + +int dcopy_(int *n, double *sx, int *incx, double *sy, int *incy); + +int dgbmv_(char *trans, int *m, int *n, int *kl, int *ku, + double *alpha, double *a, int *lda, double *x, int *incx, + double *beta, double *y, int *incy); + +int dgemm_(char *transa, char *transb, int *m, int *n, int *k, + double *alpha, double *a, int *lda, double *b, int *ldb, + double *beta, double *c, int *ldc); + +int dgemv_(char *trans, int *m, int *n, double *alpha, double *a, + int *lda, double *x, int *incx, double *beta, double *y, + int *incy); + +int dger_(int *m, int *n, double *alpha, double *x, int *incx, + double *y, int *incy, double *a, int *lda); + +int drot_(int *n, double *sx, int *incx, double *sy, int *incy, + double *c, double *s); + +int drotg_(double *sa, double *sb, double *c, double *s); + +int dsbmv_(char *uplo, int *n, int *k, double *alpha, double *a, + int *lda, double *x, int *incx, double *beta, double *y, + int *incy); + +int dscal_(int *n, double *sa, double *sx, int *incx); + +int dspmv_(char *uplo, int *n, double *alpha, double *ap, double *x, + int *incx, double *beta, double *y, int *incy); + +int dspr_(char *uplo, int *n, double *alpha, double *x, int *incx, + double *ap); + +int dspr2_(char *uplo, int *n, double *alpha, double *x, int *incx, + double *y, int *incy, double *ap); + +int dswap_(int *n, double *sx, int *incx, double *sy, int *incy); + +int dsymm_(char *side, char *uplo, int *m, int *n, double *alpha, + double *a, int *lda, double *b, int *ldb, double *beta, + double *c, int *ldc); + +int dsymv_(char *uplo, int *n, double *alpha, double *a, int *lda, + double *x, int *incx, double *beta, double *y, int *incy); + +int dsyr_(char *uplo, int *n, double *alpha, double *x, int *incx, + double *a, int *lda); + +int dsyr2_(char *uplo, int *n, double *alpha, double *x, int *incx, + double *y, int *incy, double *a, int *lda); + +int dsyr2k_(char *uplo, char *trans, int *n, int *k, double *alpha, + double *a, int *lda, double *b, int *ldb, double *beta, + double *c, int *ldc); + +int dsyrk_(char *uplo, char *trans, int *n, int *k, double *alpha, + double *a, int *lda, double *beta, double *c, int *ldc); + +int dtbmv_(char *uplo, char *trans, char *diag, int *n, int *k, + double *a, int *lda, double *x, int *incx); + +int dtbsv_(char *uplo, char *trans, char *diag, int *n, int *k, + double *a, int *lda, double *x, int *incx); + +int dtpmv_(char *uplo, char *trans, char *diag, int *n, double *ap, + double *x, int *incx); + +int dtpsv_(char *uplo, char *trans, char *diag, int *n, double *ap, + double *x, int *incx); + +int dtrmm_(char *side, char *uplo, char *transa, char *diag, int *m, + int *n, double *alpha, double *a, int *lda, double *b, + int *ldb); + +int dtrmv_(char *uplo, char *trans, char *diag, int *n, double *a, + int *lda, double *x, int *incx); + +int dtrsm_(char *side, char *uplo, char *transa, char *diag, int *m, + int *n, double *alpha, double *a, int *lda, double *b, + int *ldb); + +int dtrsv_(char *uplo, char *trans, char *diag, int *n, double *a, + int *lda, double *x, int *incx); + + +int saxpy_(int *n, float *sa, float *sx, int *incx, float *sy, int *incy); + +int scopy_(int *n, float *sx, int *incx, float *sy, int *incy); + +int sgbmv_(char *trans, int *m, int *n, int *kl, int *ku, + float *alpha, float *a, int *lda, float *x, int *incx, + float *beta, float *y, int *incy); + +int sgemm_(char *transa, char *transb, int *m, int *n, int *k, + float *alpha, float *a, int *lda, float *b, int *ldb, + float *beta, float *c, int *ldc); + +int sgemv_(char *trans, int *m, int *n, float *alpha, float *a, + int *lda, float *x, int *incx, float *beta, float *y, + int *incy); + +int sger_(int *m, int *n, float *alpha, float *x, int *incx, + float *y, int *incy, float *a, int *lda); + +int srot_(int *n, float *sx, int *incx, float *sy, int *incy, + float *c, float *s); + +int srotg_(float *sa, float *sb, float *c, float *s); + +int ssbmv_(char *uplo, int *n, int *k, float *alpha, float *a, + int *lda, float *x, int *incx, float *beta, float *y, + int *incy); + +int sscal_(int *n, float *sa, float *sx, int *incx); + +int sspmv_(char *uplo, int *n, float *alpha, float *ap, float *x, + int *incx, float *beta, float *y, int *incy); + +int sspr_(char *uplo, int *n, float *alpha, float *x, int *incx, + float *ap); + +int sspr2_(char *uplo, int *n, float *alpha, float *x, int *incx, + float *y, int *incy, float *ap); + +int sswap_(int *n, float *sx, int *incx, float *sy, int *incy); + +int ssymm_(char *side, char *uplo, int *m, int *n, float *alpha, + float *a, int *lda, float *b, int *ldb, float *beta, + float *c, int *ldc); + +int ssymv_(char *uplo, int *n, float *alpha, float *a, int *lda, + float *x, int *incx, float *beta, float *y, int *incy); + +int ssyr_(char *uplo, int *n, float *alpha, float *x, int *incx, + float *a, int *lda); + +int ssyr2_(char *uplo, int *n, float *alpha, float *x, int *incx, + float *y, int *incy, float *a, int *lda); + +int ssyr2k_(char *uplo, char *trans, int *n, int *k, float *alpha, + float *a, int *lda, float *b, int *ldb, float *beta, + float *c, int *ldc); + +int ssyrk_(char *uplo, char *trans, int *n, int *k, float *alpha, + float *a, int *lda, float *beta, float *c, int *ldc); + +int stbmv_(char *uplo, char *trans, char *diag, int *n, int *k, + float *a, int *lda, float *x, int *incx); + +int stbsv_(char *uplo, char *trans, char *diag, int *n, int *k, + float *a, int *lda, float *x, int *incx); + +int stpmv_(char *uplo, char *trans, char *diag, int *n, float *ap, + float *x, int *incx); + +int stpsv_(char *uplo, char *trans, char *diag, int *n, float *ap, + float *x, int *incx); + +int strmm_(char *side, char *uplo, char *transa, char *diag, int *m, + int *n, float *alpha, float *a, int *lda, float *b, + int *ldb); + +int strmv_(char *uplo, char *trans, char *diag, int *n, float *a, + int *lda, float *x, int *incx); + +int strsm_(char *side, char *uplo, char *transa, char *diag, int *m, + int *n, float *alpha, float *a, int *lda, float *b, + int *ldb); + +int strsv_(char *uplo, char *trans, char *diag, int *n, float *a, + int *lda, float *x, int *incx); + +int zaxpy_(int *n, dcomplex *ca, dcomplex *cx, int *incx, dcomplex *cy, + int *incy); + +int zcopy_(int *n, dcomplex *cx, int *incx, dcomplex *cy, int *incy); + +int zdscal_(int *n, double *sa, dcomplex *cx, int *incx); + +int zgbmv_(char *trans, int *m, int *n, int *kl, int *ku, + dcomplex *alpha, dcomplex *a, int *lda, dcomplex *x, int *incx, + dcomplex *beta, dcomplex *y, int *incy); + +int zgemm_(char *transa, char *transb, int *m, int *n, int *k, + dcomplex *alpha, dcomplex *a, int *lda, dcomplex *b, int *ldb, + dcomplex *beta, dcomplex *c, int *ldc); + +int zgemv_(char *trans, int *m, int *n, dcomplex *alpha, dcomplex *a, + int *lda, dcomplex *x, int *incx, dcomplex *beta, dcomplex *y, + int *incy); + +int zgerc_(int *m, int *n, dcomplex *alpha, dcomplex *x, int *incx, + dcomplex *y, int *incy, dcomplex *a, int *lda); + +int zgeru_(int *m, int *n, dcomplex *alpha, dcomplex *x, int *incx, + dcomplex *y, int *incy, dcomplex *a, int *lda); + +int zhbmv_(char *uplo, int *n, int *k, dcomplex *alpha, dcomplex *a, + int *lda, dcomplex *x, int *incx, dcomplex *beta, dcomplex *y, + int *incy); + +int zhemm_(char *side, char *uplo, int *m, int *n, dcomplex *alpha, + dcomplex *a, int *lda, dcomplex *b, int *ldb, dcomplex *beta, + dcomplex *c, int *ldc); + +int zhemv_(char *uplo, int *n, dcomplex *alpha, dcomplex *a, int *lda, + dcomplex *x, int *incx, dcomplex *beta, dcomplex *y, int *incy); + +int zher_(char *uplo, int *n, double *alpha, dcomplex *x, int *incx, + dcomplex *a, int *lda); + +int zher2_(char *uplo, int *n, dcomplex *alpha, dcomplex *x, int *incx, + dcomplex *y, int *incy, dcomplex *a, int *lda); + +int zher2k_(char *uplo, char *trans, int *n, int *k, dcomplex *alpha, + dcomplex *a, int *lda, dcomplex *b, int *ldb, double *beta, + dcomplex *c, int *ldc); + +int zherk_(char *uplo, char *trans, int *n, int *k, double *alpha, + dcomplex *a, int *lda, double *beta, dcomplex *c, int *ldc); + +int zhpmv_(char *uplo, int *n, dcomplex *alpha, dcomplex *ap, dcomplex *x, + int *incx, dcomplex *beta, dcomplex *y, int *incy); + +int zhpr_(char *uplo, int *n, double *alpha, dcomplex *x, int *incx, + dcomplex *ap); + +int zhpr2_(char *uplo, int *n, dcomplex *alpha, dcomplex *x, int *incx, + dcomplex *y, int *incy, dcomplex *ap); + +int zrotg_(dcomplex *ca, dcomplex *cb, double *c, dcomplex *s); + +int zscal_(int *n, dcomplex *ca, dcomplex *cx, int *incx); + +int zswap_(int *n, dcomplex *cx, int *incx, dcomplex *cy, int *incy); + +int zsymm_(char *side, char *uplo, int *m, int *n, dcomplex *alpha, + dcomplex *a, int *lda, dcomplex *b, int *ldb, dcomplex *beta, + dcomplex *c, int *ldc); + +int zsyr2k_(char *uplo, char *trans, int *n, int *k, dcomplex *alpha, + dcomplex *a, int *lda, dcomplex *b, int *ldb, dcomplex *beta, + dcomplex *c, int *ldc); + +int zsyrk_(char *uplo, char *trans, int *n, int *k, dcomplex *alpha, + dcomplex *a, int *lda, dcomplex *beta, dcomplex *c, int *ldc); + +int ztbmv_(char *uplo, char *trans, char *diag, int *n, int *k, + dcomplex *a, int *lda, dcomplex *x, int *incx); + +int ztbsv_(char *uplo, char *trans, char *diag, int *n, int *k, + dcomplex *a, int *lda, dcomplex *x, int *incx); + +int ztpmv_(char *uplo, char *trans, char *diag, int *n, dcomplex *ap, + dcomplex *x, int *incx); + +int ztpsv_(char *uplo, char *trans, char *diag, int *n, dcomplex *ap, + dcomplex *x, int *incx); + +int ztrmm_(char *side, char *uplo, char *transa, char *diag, int *m, + int *n, dcomplex *alpha, dcomplex *a, int *lda, dcomplex *b, + int *ldb); + +int ztrmv_(char *uplo, char *trans, char *diag, int *n, dcomplex *a, + int *lda, dcomplex *x, int *incx); + +int ztrsm_(char *side, char *uplo, char *transa, char *diag, int *m, + int *n, dcomplex *alpha, dcomplex *a, int *lda, dcomplex *b, + int *ldb); + +int ztrsv_(char *uplo, char *trans, char *diag, int *n, dcomplex *a, + int *lda, dcomplex *x, int *incx); diff --git a/Bing/Src/LibLinear/blas/daxpy.c b/Bing/Src/LibLinear/blas/daxpy.c new file mode 100644 index 000000000..58f345a53 --- /dev/null +++ b/Bing/Src/LibLinear/blas/daxpy.c @@ -0,0 +1,49 @@ +#include "blas.h" + +int daxpy_(int *n, double *sa, double *sx, int *incx, double *sy, + int *incy) +{ + long int i, m, ix, iy, nn, iincx, iincy; + register double ssa; + + /* constant times a vector plus a vector. + uses unrolled loop for increments equal to one. + jack dongarra, linpack, 3/11/78. + modified 12/3/93, array(1) declarations changed to array(*) */ + + /* Dereference inputs */ + nn = *n; + ssa = *sa; + iincx = *incx; + iincy = *incy; + + if( nn > 0 && ssa != 0.0 ) + { + if (iincx == 1 && iincy == 1) /* code for both increments equal to 1 */ + { + m = nn-3; + for (i = 0; i < m; i += 4) + { + sy[i] += ssa * sx[i]; + sy[i+1] += ssa * sx[i+1]; + sy[i+2] += ssa * sx[i+2]; + sy[i+3] += ssa * sx[i+3]; + } + for ( ; i < nn; ++i) /* clean-up loop */ + sy[i] += ssa * sx[i]; + } + else /* code for unequal increments or equal increments not equal to 1 */ + { + ix = iincx >= 0 ? 0 : (1 - nn) * iincx; + iy = iincy >= 0 ? 0 : (1 - nn) * iincy; + for (i = 0; i < nn; i++) + { + sy[iy] += ssa * sx[ix]; + ix += iincx; + iy += iincy; + } + } + } + + return 0; +} /* daxpy_ */ diff --git a/Bing/Src/LibLinear/blas/ddot.c b/Bing/Src/LibLinear/blas/ddot.c new file mode 100644 index 000000000..a64a2808f --- /dev/null +++ b/Bing/Src/LibLinear/blas/ddot.c @@ -0,0 +1,50 @@ +#include "blas.h" + +double ddot_(int *n, double *sx, int *incx, double *sy, int *incy) +{ + long int i, m, nn, iincx, iincy; + double stemp; + long int ix, iy; + + /* forms the dot product of two vectors. + uses unrolled loops for increments equal to one. + jack dongarra, linpack, 3/11/78. + modified 12/3/93, array(1) declarations changed to array(*) */ + + /* Dereference inputs */ + nn = *n; + iincx = *incx; + iincy = *incy; + + stemp = 0.0; + if (nn > 0) + { + if (iincx == 1 && iincy == 1) /* code for both increments equal to 1 */ + { + m = nn-4; + for (i = 0; i < m; i += 5) + stemp += sx[i] * sy[i] + sx[i+1] * sy[i+1] + sx[i+2] * sy[i+2] + + sx[i+3] * sy[i+3] + sx[i+4] * sy[i+4]; + + for ( ; i < nn; i++) /* clean-up loop */ + stemp += sx[i] * sy[i]; + } + else /* code for unequal increments or equal increments not equal to 1 */ + { + ix = 0; + iy = 0; + if (iincx < 0) + ix = (1 - nn) * iincx; + if (iincy < 0) + iy = (1 - nn) * iincy; + for (i = 0; i < nn; i++) + { + stemp += sx[ix] * sy[iy]; + ix += iincx; + iy += iincy; + } + } + } + + return stemp; +} /* ddot_ */ diff --git a/Bing/Src/LibLinear/blas/dnrm2.c b/Bing/Src/LibLinear/blas/dnrm2.c new file mode 100644 index 000000000..e50cdf777 --- /dev/null +++ b/Bing/Src/LibLinear/blas/dnrm2.c @@ -0,0 +1,62 @@ +#include /* Needed for fabs() and sqrt() */ +#include "blas.h" + +double dnrm2_(int *n, double *x, int *incx) +{ + long int ix, nn, iincx; + double norm, scale, absxi, ssq, temp; + +/* DNRM2 returns the euclidean norm of a vector via the function + name, so that + + DNRM2 := sqrt( x'*x ) + + -- This version written on 25-October-1982. + Modified on 14-October-1993 to inline the call to SLASSQ. + Sven Hammarling, Nag Ltd. */ + + /* Dereference inputs */ + nn = *n; + iincx = *incx; + + if( nn > 0 && iincx > 0 ) + { + if (nn == 1) + { + norm = fabs(x[0]); + } + else + { + scale = 0.0; + ssq = 1.0; + + /* The following loop is equivalent to this call to the LAPACK + auxiliary routine: CALL SLASSQ( N, X, INCX, SCALE, SSQ ) */ + + for (ix=(nn-1)*iincx; ix>=0; ix-=iincx) + { + if (x[ix] != 0.0) + { + absxi = fabs(x[ix]); + if (scale < absxi) + { + temp = scale / absxi; + ssq = ssq * (temp * temp) + 1.0; + scale = absxi; + } + else + { + temp = absxi / scale; + ssq += temp * temp; + } + } + } + norm = scale * sqrt(ssq); + } + } + else + norm = 0.0; + + return norm; + +} /* dnrm2_ */ diff --git a/Bing/Src/LibLinear/blas/dscal.c b/Bing/Src/LibLinear/blas/dscal.c new file mode 100644 index 000000000..a0eca0c39 --- /dev/null +++ b/Bing/Src/LibLinear/blas/dscal.c @@ -0,0 +1,44 @@ +#include "blas.h" + +int dscal_(int *n, double *sa, double *sx, int *incx) +{ + long int i, m, nincx, nn, iincx; + double ssa; + + /* scales a vector by a constant. + uses unrolled loops for increment equal to 1. + jack dongarra, linpack, 3/11/78. + modified 3/93 to return if incx .le. 0. + modified 12/3/93, array(1) declarations changed to array(*) */ + + /* Dereference inputs */ + nn = *n; + iincx = *incx; + ssa = *sa; + + if (nn > 0 && iincx > 0) + { + if (iincx == 1) /* code for increment equal to 1 */ + { + m = nn-4; + for (i = 0; i < m; i += 5) + { + sx[i] = ssa * sx[i]; + sx[i+1] = ssa * sx[i+1]; + sx[i+2] = ssa * sx[i+2]; + sx[i+3] = ssa * sx[i+3]; + sx[i+4] = ssa * sx[i+4]; + } + for ( ; i < nn; ++i) /* clean-up loop */ + sx[i] = ssa * sx[i]; + } + else /* code for increment not equal to 1 */ + { + nincx = nn * iincx; + for (i = 0; i < nincx; i += iincx) + sx[i] = ssa * sx[i]; + } + } + + return 0; +} /* dscal_ */ diff --git a/Bing/Src/LibLinear/linear.cpp b/Bing/Src/LibLinear/linear.cpp new file mode 100644 index 000000000..1dc8056f1 --- /dev/null +++ b/Bing/Src/LibLinear/linear.cpp @@ -0,0 +1,2811 @@ +#include +#include +#include +#include +#include +#include +#include "linear.h" +#include "tron.h" +typedef signed char schar; +template static inline void swap(T& x, T& y) { T t=x; x=y; y=t; } +#ifndef min +template static inline T min(T x,T y) { return (x static inline T max(T x,T y) { return (x>y)?x:y; } +#endif +template static inline void clone(T*& dst, S* src, int n) +{ + dst = new T[n]; + memcpy((void *)dst,(void *)src,sizeof(T)*n); +} +#define Malloc(type,n) (type *)malloc((n)*sizeof(type)) +#define INF HUGE_VAL + +static void print_string_stdout(const char *s) +{ + fputs(s,stdout); + fflush(stdout); +} + +static void (*liblinear_print_string) (const char *) = &print_string_stdout; + +#if 1 +static void info(const char *fmt,...) +{ + char buf[BUFSIZ]; + va_list ap; + va_start(ap,fmt); + vsprintf(buf,fmt,ap); + va_end(ap); + (*liblinear_print_string)(buf); +} +#else +static void info(const char *fmt,...) {} +#endif + +class l2r_lr_fun: public function +{ +public: + l2r_lr_fun(const problem *prob, double *C); + ~l2r_lr_fun(); + + double fun(double *w); + void grad(double *w, double *g); + void Hv(double *s, double *Hs); + + int get_nr_variable(void); + +private: + void Xv(double *v, double *Xv); + void XTv(double *v, double *XTv); + + double *C; + double *z; + double *D; + const problem *prob; +}; + +l2r_lr_fun::l2r_lr_fun(const problem *prob, double *C) +{ + int l=prob->l; + + this->prob = prob; + + z = new double[l]; + D = new double[l]; + this->C = C; +} + +l2r_lr_fun::~l2r_lr_fun() +{ + delete[] z; + delete[] D; +} + + +double l2r_lr_fun::fun(double *w) +{ + int i; + double f=0; + double *y=prob->y; + int l=prob->l; + int w_size=get_nr_variable(); + + Xv(w, z); + + for(i=0;i= 0) + f += C[i]*log(1 + exp(-yz)); + else + f += C[i]*(-yz+log(1 + exp(yz))); + } + + return(f); +} + +void l2r_lr_fun::grad(double *w, double *g) +{ + int i; + double *y=prob->y; + int l=prob->l; + int w_size=get_nr_variable(); + + for(i=0;in; +} + +void l2r_lr_fun::Hv(double *s, double *Hs) +{ + int i; + int l=prob->l; + int w_size=get_nr_variable(); + double *wa = new double[l]; + + Xv(s, wa); + for(i=0;il; + feature_node **x=prob->x; + + for(i=0;iindex!=-1) + { + Xv[i]+=v[s->index-1]*s->value; + s++; + } + } +} + +void l2r_lr_fun::XTv(double *v, double *XTv) +{ + int i; + int l=prob->l; + int w_size=get_nr_variable(); + feature_node **x=prob->x; + + for(i=0;iindex!=-1) + { + XTv[s->index-1]+=v[i]*s->value; + s++; + } + } +} + +class l2r_l2_svc_fun: public function +{ +public: + l2r_l2_svc_fun(const problem *prob, double *C); + ~l2r_l2_svc_fun(); + + double fun(double *w); + void grad(double *w, double *g); + void Hv(double *s, double *Hs); + + int get_nr_variable(void); + +protected: + void Xv(double *v, double *Xv); + void subXv(double *v, double *Xv); + void subXTv(double *v, double *XTv); + + double *C; + double *z; + double *D; + int *I; + int sizeI; + const problem *prob; +}; + +l2r_l2_svc_fun::l2r_l2_svc_fun(const problem *prob, double *C) +{ + int l=prob->l; + + this->prob = prob; + + z = new double[l]; + D = new double[l]; + I = new int[l]; + this->C = C; +} + +l2r_l2_svc_fun::~l2r_l2_svc_fun() +{ + delete[] z; + delete[] D; + delete[] I; +} + +double l2r_l2_svc_fun::fun(double *w) +{ + int i; + double f=0; + double *y=prob->y; + int l=prob->l; + int w_size=get_nr_variable(); + + Xv(w, z); + + for(i=0;i 0) + f += C[i]*d*d; + } + + return(f); +} + +void l2r_l2_svc_fun::grad(double *w, double *g) +{ + int i; + double *y=prob->y; + int l=prob->l; + int w_size=get_nr_variable(); + + sizeI = 0; + for (i=0;in; +} + +void l2r_l2_svc_fun::Hv(double *s, double *Hs) +{ + int i; + int w_size=get_nr_variable(); + double *wa = new double[sizeI]; + + subXv(s, wa); + for(i=0;il; + feature_node **x=prob->x; + + for(i=0;iindex!=-1) + { + Xv[i]+=v[s->index-1]*s->value; + s++; + } + } +} + +void l2r_l2_svc_fun::subXv(double *v, double *Xv) +{ + int i; + feature_node **x=prob->x; + + for(i=0;iindex!=-1) + { + Xv[i]+=v[s->index-1]*s->value; + s++; + } + } +} + +void l2r_l2_svc_fun::subXTv(double *v, double *XTv) +{ + int i; + int w_size=get_nr_variable(); + feature_node **x=prob->x; + + for(i=0;iindex!=-1) + { + XTv[s->index-1]+=v[i]*s->value; + s++; + } + } +} + +class l2r_l2_svr_fun: public l2r_l2_svc_fun +{ +public: + l2r_l2_svr_fun(const problem *prob, double *C, double p); + + double fun(double *w); + void grad(double *w, double *g); + +private: + double p; +}; + +l2r_l2_svr_fun::l2r_l2_svr_fun(const problem *prob, double *C, double p): + l2r_l2_svc_fun(prob, C) +{ + this->p = p; +} + +double l2r_l2_svr_fun::fun(double *w) +{ + int i; + double f=0; + double *y=prob->y; + int l=prob->l; + int w_size=get_nr_variable(); + double d; + + Xv(w, z); + + for(i=0;i p) + f += C[i]*(d-p)*(d-p); + } + + return(f); +} + +void l2r_l2_svr_fun::grad(double *w, double *g) +{ + int i; + double *y=prob->y; + int l=prob->l; + int w_size=get_nr_variable(); + double d; + + sizeI = 0; + for(i=0;i p) + { + z[sizeI] = C[i]*(d-p); + I[sizeI] = i; + sizeI++; + } + + } + subXTv(z, g); + + for(i=0;iy[i]) +// To support weights for instances, use GETI(i) (i) + +class Solver_MCSVM_CS +{ + public: + Solver_MCSVM_CS(const problem *prob, int nr_class, double *C, double eps=0.1, int max_iter=100000); + ~Solver_MCSVM_CS(); + void Solve(double *w); + private: + void solve_sub_problem(double A_i, int yi, double C_yi, int active_i, double *alpha_new); + bool be_shrunk(int i, int m, int yi, double alpha_i, double minG); + double *B, *C, *G; + int w_size, l; + int nr_class; + int max_iter; + double eps; + const problem *prob; +}; + +Solver_MCSVM_CS::Solver_MCSVM_CS(const problem *prob, int nr_class, double *weighted_C, double eps, int max_iter) +{ + this->w_size = prob->n; + this->l = prob->l; + this->nr_class = nr_class; + this->eps = eps; + this->max_iter = max_iter; + this->prob = prob; + this->B = new double[nr_class]; + this->G = new double[nr_class]; + this->C = weighted_C; +} + +Solver_MCSVM_CS::~Solver_MCSVM_CS() +{ + delete[] B; + delete[] G; +} + +int compare_double(const void *a, const void *b) +{ + if(*(double *)a > *(double *)b) + return -1; + if(*(double *)a < *(double *)b) + return 1; + return 0; +} + +void Solver_MCSVM_CS::solve_sub_problem(double A_i, int yi, double C_yi, int active_i, double *alpha_new) +{ + int r; + double *D; + + clone(D, B, active_i); + if(yi < active_i) + D[yi] += A_i*C_yi; + qsort(D, active_i, sizeof(double), compare_double); + + double beta = D[0] - A_i*C_yi; + for(r=1;ry[i] == m + // alpha[i*nr_class+m] <= 0 if prob->y[i] != m + // If initial alpha isn't zero, uncomment the for loop below to initialize w + for(i=0;ix[i]; + QD[i] = 0; + while(xi->index != -1) + { + double val = xi->value; + QD[i] += val*val; + + // Uncomment the for loop if initial alpha isn't zero + // for(m=0; mindex-1)*nr_class+m] += alpha[i*nr_class+m]*val; + xi++; + } + active_size_i[i] = nr_class; + y_index[i] = (int)prob->y[i]; + index[i] = i; + } + + while(iter < max_iter) + { + double stopping = -INF; + for(i=0;i 0) + { + for(m=0;mx[i]; + while(xi->index!= -1) + { + double *w_i = &w[(xi->index-1)*nr_class]; + for(m=0;mvalue); + xi++; + } + + double minG = INF; + double maxG = -INF; + for(m=0;m maxG) + maxG = G[m]; + } + if(y_index[i] < active_size_i[i]) + if(alpha_i[(int) prob->y[i]] < C[GETI(i)] && G[y_index[i]] < minG) + minG = G[y_index[i]]; + + for(m=0;mm) + { + if(!be_shrunk(i, active_size_i[i], y_index[i], + alpha_i[alpha_index_i[active_size_i[i]]], minG)) + { + swap(alpha_index_i[m], alpha_index_i[active_size_i[i]]); + swap(G[m], G[active_size_i[i]]); + if(y_index[i] == active_size_i[i]) + y_index[i] = m; + else if(y_index[i] == m) + y_index[i] = active_size_i[i]; + break; + } + active_size_i[i]--; + } + } + } + + if(active_size_i[i] <= 1) + { + active_size--; + swap(index[s], index[active_size]); + s--; + continue; + } + + if(maxG-minG <= 1e-12) + continue; + else + stopping = max(maxG - minG, stopping); + + for(m=0;m= 1e-12) + { + d_ind[nz_d] = alpha_index_i[m]; + d_val[nz_d] = d; + nz_d++; + } + } + + xi = prob->x[i]; + while(xi->index != -1) + { + double *w_i = &w[(xi->index-1)*nr_class]; + for(m=0;mvalue; + xi++; + } + } + } + + iter++; + if(iter % 10 == 0) + { + info("."); + } + + if(stopping < eps_shrink) + { + if(stopping < eps && start_from_all == true) + break; + else + { + active_size = l; + for(i=0;i= max_iter) + info("\nWARNING: reaching max number of iterations\n"); + + // calculate objective value + double v = 0; + int nSV = 0; + for(i=0;i 0) + nSV++; + } + for(i=0;iy[i]]; + info("Objective value = %lf\n",v); + info("nSV = %d\n",nSV); + + delete [] alpha; + delete [] alpha_new; + delete [] index; + delete [] QD; + delete [] d_ind; + delete [] d_val; + delete [] alpha_index; + delete [] y_index; + delete [] active_size_i; +} + +// A coordinate descent algorithm for +// L1-loss and L2-loss SVM dual problems +// +// min_\alpha 0.5(\alpha^T (Q + D)\alpha) - e^T \alpha, +// s.t. 0 <= \alpha_i <= upper_bound_i, +// +// where Qij = yi yj xi^T xj and +// D is a diagonal matrix +// +// In L1-SVM case: +// upper_bound_i = Cp if y_i = 1 +// upper_bound_i = Cn if y_i = -1 +// D_ii = 0 +// In L2-SVM case: +// upper_bound_i = INF +// D_ii = 1/(2*Cp) if y_i = 1 +// D_ii = 1/(2*Cn) if y_i = -1 +// +// Given: +// x, y, Cp, Cn +// eps is the stopping tolerance +// +// solution will be put in w +// +// See Algorithm 3 of Hsieh et al., ICML 2008 + +#undef GETI +#define GETI(i) (y[i]+1) +// To support weights for instances, use GETI(i) (i) + +static void solve_l2r_l1l2_svc( + const problem *prob, double *w, double eps, + double Cp, double Cn, int solver_type) +{ + int l = prob->l; + int w_size = prob->n; + int i, s, iter = 0; + double C, d, G; + double *QD = new double[l]; + int max_iter = 1000; + int *index = new int[l]; + double *alpha = new double[l]; + schar *y = new schar[l]; + int active_size = l; + + // PG: projected gradient, for shrinking and stopping + double PG; + double PGmax_old = INF; + double PGmin_old = -INF; + double PGmax_new, PGmin_new; + + // default solver_type: L2R_L2LOSS_SVC_DUAL + double diag[3] = {0.5/Cn, 0, 0.5/Cp}; + double upper_bound[3] = {INF, 0, INF}; + if(solver_type == L2R_L1LOSS_SVC_DUAL) + { + diag[0] = 0; + diag[2] = 0; + upper_bound[0] = Cn; + upper_bound[2] = Cp; + } + + for(i=0; iy[i] > 0) + { + y[i] = +1; + } + else + { + y[i] = -1; + } + } + + // Initial alpha can be set here. Note that + // 0 <= alpha[i] <= upper_bound[GETI(i)] + for(i=0; ix[i]; + while (xi->index != -1) + { + double val = xi->value; + QD[i] += val*val; + w[xi->index-1] += y[i]*alpha[i]*val; + xi++; + } + index[i] = i; + } + + while (iter < max_iter) + { + PGmax_new = -INF; + PGmin_new = INF; + + for (i=0; ix[i]; + while(xi->index!= -1) + { + G += w[xi->index-1]*(xi->value); + xi++; + } + G = G*yi-1; + + C = upper_bound[GETI(i)]; + G += alpha[i]*diag[GETI(i)]; + + PG = 0; + if (alpha[i] == 0) + { + if (G > PGmax_old) + { + active_size--; + swap(index[s], index[active_size]); + s--; + continue; + } + else if (G < 0) + PG = G; + } + else if (alpha[i] == C) + { + if (G < PGmin_old) + { + active_size--; + swap(index[s], index[active_size]); + s--; + continue; + } + else if (G > 0) + PG = G; + } + else + PG = G; + + PGmax_new = max(PGmax_new, PG); + PGmin_new = min(PGmin_new, PG); + + if(fabs(PG) > 1.0e-12) + { + double alpha_old = alpha[i]; + alpha[i] = min(max(alpha[i] - G/QD[i], 0.0), C); + d = (alpha[i] - alpha_old)*yi; + xi = prob->x[i]; + while (xi->index != -1) + { + w[xi->index-1] += d*xi->value; + xi++; + } + } + } + + iter++; + if(iter % 10 == 0) + info("."); + + if(PGmax_new - PGmin_new <= eps) + { + if(active_size == l) + break; + else + { + active_size = l; + info("*"); + PGmax_old = INF; + PGmin_old = -INF; + continue; + } + } + PGmax_old = PGmax_new; + PGmin_old = PGmin_new; + if (PGmax_old <= 0) + PGmax_old = INF; + if (PGmin_old >= 0) + PGmin_old = -INF; + } + + info("\noptimization finished, #iter = %d\n",iter); + if (iter >= max_iter) + info("\nWARNING: reaching max number of iterations\nUsing -s 2 may be faster (also see FAQ)\n\n"); + + // calculate objective value + + double v = 0; + int nSV = 0; + for(i=0; i 0) + ++nSV; + } + info("Objective value = %lf\n",v/2); + info("nSV = %d\n",nSV); + + delete [] QD; + delete [] alpha; + delete [] y; + delete [] index; +} + + +// A coordinate descent algorithm for +// L1-loss and L2-loss epsilon-SVR dual problem +// +// min_\beta 0.5\beta^T (Q + diag(lambda)) \beta - p \sum_{i=1}^l|\beta_i| + \sum_{i=1}^l yi\beta_i, +// s.t. -upper_bound_i <= \beta_i <= upper_bound_i, +// +// where Qij = xi^T xj and +// D is a diagonal matrix +// +// In L1-SVM case: +// upper_bound_i = C +// lambda_i = 0 +// In L2-SVM case: +// upper_bound_i = INF +// lambda_i = 1/(2*C) +// +// Given: +// x, y, p, C +// eps is the stopping tolerance +// +// solution will be put in w +// +// See Algorithm 4 of Ho and Lin, 2012 + +#undef GETI +#define GETI(i) (0) +// To support weights for instances, use GETI(i) (i) + +static void solve_l2r_l1l2_svr( + const problem *prob, double *w, const parameter *param, + int solver_type) +{ + int l = prob->l; + double C = param->C; + double p = param->p; + int w_size = prob->n; + double eps = param->eps; + int i, s, iter = 0; + int max_iter = 1000; + int active_size = l; + int *index = new int[l]; + + double d, G, H; + double Gmax_old = INF; + double Gmax_new, Gnorm1_new; + double Gnorm1_init; + double *beta = new double[l]; + double *QD = new double[l]; + double *y = prob->y; + + // L2R_L2LOSS_SVR_DUAL + double lambda[1], upper_bound[1]; + lambda[0] = 0.5/C; + upper_bound[0] = INF; + + if(solver_type == L2R_L1LOSS_SVR_DUAL) + { + lambda[0] = 0; + upper_bound[0] = C; + } + + // Initial beta can be set here. Note that + // -upper_bound <= beta[i] <= upper_bound + for(i=0; ix[i]; + while(xi->index != -1) + { + double val = xi->value; + QD[i] += val*val; + w[xi->index-1] += beta[i]*val; + xi++; + } + + index[i] = i; + } + + + while(iter < max_iter) + { + Gmax_new = 0; + Gnorm1_new = 0; + + for(i=0; ix[i]; + while(xi->index != -1) + { + int ind = xi->index-1; + double val = xi->value; + G += val*w[ind]; + xi++; + } + + double Gp = G+p; + double Gn = G-p; + double violation = 0; + if(beta[i] == 0) + { + if(Gp < 0) + violation = -Gp; + else if(Gn > 0) + violation = Gn; + else if(Gp>Gmax_old && Gn<-Gmax_old) + { + active_size--; + swap(index[s], index[active_size]); + s--; + continue; + } + } + else if(beta[i] >= upper_bound[GETI(i)]) + { + if(Gp > 0) + violation = Gp; + else if(Gp < -Gmax_old) + { + active_size--; + swap(index[s], index[active_size]); + s--; + continue; + } + } + else if(beta[i] <= -upper_bound[GETI(i)]) + { + if(Gn < 0) + violation = -Gn; + else if(Gn > Gmax_old) + { + active_size--; + swap(index[s], index[active_size]); + s--; + continue; + } + } + else if(beta[i] > 0) + violation = fabs(Gp); + else + violation = fabs(Gn); + + Gmax_new = max(Gmax_new, violation); + Gnorm1_new += violation; + + // obtain Newton direction d + if(Gp < H*beta[i]) + d = -Gp/H; + else if(Gn > H*beta[i]) + d = -Gn/H; + else + d = -beta[i]; + + if(fabs(d) < 1.0e-12) + continue; + + double beta_old = beta[i]; + beta[i] = min(max(beta[i]+d, -upper_bound[GETI(i)]), upper_bound[GETI(i)]); + d = beta[i]-beta_old; + + if(d != 0) + { + xi = prob->x[i]; + while(xi->index != -1) + { + w[xi->index-1] += d*xi->value; + xi++; + } + } + } + + if(iter == 0) + Gnorm1_init = Gnorm1_new; + iter++; + if(iter % 10 == 0) + info("."); + + if(Gnorm1_new <= eps*Gnorm1_init) + { + if(active_size == l) + break; + else + { + active_size = l; + info("*"); + Gmax_old = INF; + continue; + } + } + + Gmax_old = Gmax_new; + } + + info("\noptimization finished, #iter = %d\n", iter); + if(iter >= max_iter) + info("\nWARNING: reaching max number of iterations\nUsing -s 11 may be faster\n\n"); + + // calculate objective value + double v = 0; + int nSV = 0; + for(i=0; il; + int w_size = prob->n; + int i, s, iter = 0; + double *xTx = new double[l]; + int max_iter = 1000; + int *index = new int[l]; + double *alpha = new double[2*l]; // store alpha and C - alpha + schar *y = new schar[l]; + int max_inner_iter = 100; // for inner Newton + double innereps = 1e-2; + double innereps_min = min(1e-8, eps); + double upper_bound[3] = {Cn, 0, Cp}; + + for(i=0; iy[i] > 0) + { + y[i] = +1; + } + else + { + y[i] = -1; + } + } + + // Initial alpha can be set here. Note that + // 0 < alpha[i] < upper_bound[GETI(i)] + // alpha[2*i] + alpha[2*i+1] = upper_bound[GETI(i)] + for(i=0; ix[i]; + while (xi->index != -1) + { + double val = xi->value; + xTx[i] += val*val; + w[xi->index-1] += y[i]*alpha[2*i]*val; + xi++; + } + index[i] = i; + } + + while (iter < max_iter) + { + for (i=0; ix[i]; + while (xi->index != -1) + { + ywTx += w[xi->index-1]*xi->value; + xi++; + } + ywTx *= y[i]; + double a = xisq, b = ywTx; + + // Decide to minimize g_1(z) or g_2(z) + int ind1 = 2*i, ind2 = 2*i+1, sign = 1; + if(0.5*a*(alpha[ind2]-alpha[ind1])+b < 0) + { + ind1 = 2*i+1; + ind2 = 2*i; + sign = -1; + } + + // g_t(z) = z*log(z) + (C-z)*log(C-z) + 0.5a(z-alpha_old)^2 + sign*b(z-alpha_old) + double alpha_old = alpha[ind1]; + double z = alpha_old; + if(C - z < 0.5 * C) + z = 0.1*z; + double gp = a*(z-alpha_old)+sign*b+log(z/(C-z)); + Gmax = max(Gmax, fabs(gp)); + + // Newton method on the sub-problem + const double eta = 0.1; // xi in the paper + int inner_iter = 0; + while (inner_iter <= max_inner_iter) + { + if(fabs(gp) < innereps) + break; + double gpp = a + C/(C-z)/z; + double tmpz = z - gp/gpp; + if(tmpz <= 0) + z *= eta; + else // tmpz in (0, C) + z = tmpz; + gp = a*(z-alpha_old)+sign*b+log(z/(C-z)); + newton_iter++; + inner_iter++; + } + + if(inner_iter > 0) // update w + { + alpha[ind1] = z; + alpha[ind2] = C-z; + xi = prob->x[i]; + while (xi->index != -1) + { + w[xi->index-1] += sign*(z-alpha_old)*yi*xi->value; + xi++; + } + } + } + + iter++; + if(iter % 10 == 0) + info("."); + + if(Gmax < eps) + break; + + if(newton_iter <= l/10) + innereps = max(innereps_min, 0.1*innereps); + + } + + info("\noptimization finished, #iter = %d\n",iter); + if (iter >= max_iter) + info("\nWARNING: reaching max number of iterations\nUsing -s 0 may be faster (also see FAQ)\n\n"); + + // calculate objective value + + double v = 0; + for(i=0; il; + int w_size = prob_col->n; + int j, s, iter = 0; + int max_iter = 1000; + int active_size = w_size; + int max_num_linesearch = 20; + + double sigma = 0.01; + double d, G_loss, G, H; + double Gmax_old = INF; + double Gmax_new, Gnorm1_new; + double Gnorm1_init; + double d_old, d_diff; + double loss_old, loss_new; + double appxcond, cond; + + int *index = new int[w_size]; + schar *y = new schar[l]; + double *b = new double[l]; // b = 1-ywTx + double *xj_sq = new double[w_size]; + feature_node *x; + + double C[3] = {Cn,0,Cp}; + + // Initial w can be set here. + for(j=0; jy[j] > 0) + y[j] = 1; + else + y[j] = -1; + } + for(j=0; jx[j]; + while(x->index != -1) + { + int ind = x->index-1; + x->value *= y[ind]; // x->value stores yi*xij + double val = x->value; + b[ind] -= w[j]*val; + xj_sq[j] += C[GETI(ind)]*val*val; + x++; + } + } + + while(iter < max_iter) + { + Gmax_new = 0; + Gnorm1_new = 0; + + for(j=0; jx[j]; + while(x->index != -1) + { + int ind = x->index-1; + if(b[ind] > 0) + { + double val = x->value; + double tmp = C[GETI(ind)]*val; + G_loss -= tmp*b[ind]; + H += tmp*val; + } + x++; + } + G_loss *= 2; + + G = G_loss; + H *= 2; + H = max(H, 1e-12); + + double Gp = G+1; + double Gn = G-1; + double violation = 0; + if(w[j] == 0) + { + if(Gp < 0) + violation = -Gp; + else if(Gn > 0) + violation = Gn; + else if(Gp>Gmax_old/l && Gn<-Gmax_old/l) + { + active_size--; + swap(index[s], index[active_size]); + s--; + continue; + } + } + else if(w[j] > 0) + violation = fabs(Gp); + else + violation = fabs(Gn); + + Gmax_new = max(Gmax_new, violation); + Gnorm1_new += violation; + + // obtain Newton direction d + if(Gp < H*w[j]) + d = -Gp/H; + else if(Gn > H*w[j]) + d = -Gn/H; + else + d = -w[j]; + + if(fabs(d) < 1.0e-12) + continue; + + double delta = fabs(w[j]+d)-fabs(w[j]) + G*d; + d_old = 0; + int num_linesearch; + for(num_linesearch=0; num_linesearch < max_num_linesearch; num_linesearch++) + { + d_diff = d_old - d; + cond = fabs(w[j]+d)-fabs(w[j]) - sigma*delta; + + appxcond = xj_sq[j]*d*d + G_loss*d + cond; + if(appxcond <= 0) + { + x = prob_col->x[j]; + while(x->index != -1) + { + b[x->index-1] += d_diff*x->value; + x++; + } + break; + } + + if(num_linesearch == 0) + { + loss_old = 0; + loss_new = 0; + x = prob_col->x[j]; + while(x->index != -1) + { + int ind = x->index-1; + if(b[ind] > 0) + loss_old += C[GETI(ind)]*b[ind]*b[ind]; + double b_new = b[ind] + d_diff*x->value; + b[ind] = b_new; + if(b_new > 0) + loss_new += C[GETI(ind)]*b_new*b_new; + x++; + } + } + else + { + loss_new = 0; + x = prob_col->x[j]; + while(x->index != -1) + { + int ind = x->index-1; + double b_new = b[ind] + d_diff*x->value; + b[ind] = b_new; + if(b_new > 0) + loss_new += C[GETI(ind)]*b_new*b_new; + x++; + } + } + + cond = cond + loss_new - loss_old; + if(cond <= 0) + break; + else + { + d_old = d; + d *= 0.5; + delta *= 0.5; + } + } + + w[j] += d; + + // recompute b[] if line search takes too many steps + if(num_linesearch >= max_num_linesearch) + { + info("#"); + for(int i=0; ix[i]; + while(x->index != -1) + { + b[x->index-1] -= w[i]*x->value; + x++; + } + } + } + } + + if(iter == 0) + Gnorm1_init = Gnorm1_new; + iter++; + if(iter % 10 == 0) + info("."); + + if(Gnorm1_new <= eps*Gnorm1_init) + { + if(active_size == w_size) + break; + else + { + active_size = w_size; + info("*"); + Gmax_old = INF; + continue; + } + } + + Gmax_old = Gmax_new; + } + + info("\noptimization finished, #iter = %d\n", iter); + if(iter >= max_iter) + info("\nWARNING: reaching max number of iterations\n"); + + // calculate objective value + + double v = 0; + int nnz = 0; + for(j=0; jx[j]; + while(x->index != -1) + { + x->value *= prob_col->y[x->index-1]; // restore x->value + x++; + } + if(w[j] != 0) + { + v += fabs(w[j]); + nnz++; + } + } + for(j=0; j 0) + v += C[GETI(j)]*b[j]*b[j]; + + info("Objective value = %lf\n", v); + info("#nonzeros/#features = %d/%d\n", nnz, w_size); + + delete [] index; + delete [] y; + delete [] b; + delete [] xj_sq; +} + +// A coordinate descent algorithm for +// L1-regularized logistic regression problems +// +// min_w \sum |wj| + C \sum log(1+exp(-yi w^T xi)), +// +// Given: +// x, y, Cp, Cn +// eps is the stopping tolerance +// +// solution will be put in w +// +// See Yuan et al. (2011) and appendix of LIBLINEAR paper, Fan et al. (2008) + +#undef GETI +#define GETI(i) (y[i]+1) +// To support weights for instances, use GETI(i) (i) + +static void solve_l1r_lr( + const problem *prob_col, double *w, double eps, + double Cp, double Cn) +{ + int l = prob_col->l; + int w_size = prob_col->n; + int j, s, newton_iter=0, iter=0; + int max_newton_iter = 100; + int max_iter = 1000; + int max_num_linesearch = 20; + int active_size; + int QP_active_size; + + double nu = 1e-12; + double inner_eps = 1; + double sigma = 0.01; + double w_norm, w_norm_new; + double z, G, H; + double Gnorm1_init; + double Gmax_old = INF; + double Gmax_new, Gnorm1_new; + double QP_Gmax_old = INF; + double QP_Gmax_new, QP_Gnorm1_new; + double delta, negsum_xTd, cond; + + int *index = new int[w_size]; + schar *y = new schar[l]; + double *Hdiag = new double[w_size]; + double *Grad = new double[w_size]; + double *wpd = new double[w_size]; + double *xjneg_sum = new double[w_size]; + double *xTd = new double[l]; + double *exp_wTx = new double[l]; + double *exp_wTx_new = new double[l]; + double *tau = new double[l]; + double *D = new double[l]; + feature_node *x; + + double C[3] = {Cn,0,Cp}; + + // Initial w can be set here. + for(j=0; jy[j] > 0) + y[j] = 1; + else + y[j] = -1; + + exp_wTx[j] = 0; + } + + w_norm = 0; + for(j=0; jx[j]; + while(x->index != -1) + { + int ind = x->index-1; + double val = x->value; + exp_wTx[ind] += w[j]*val; + if(y[ind] == -1) + xjneg_sum[j] += C[GETI(ind)]*val; + x++; + } + } + for(j=0; jx[j]; + while(x->index != -1) + { + int ind = x->index-1; + Hdiag[j] += x->value*x->value*D[ind]; + tmp += x->value*tau[ind]; + x++; + } + Grad[j] = -tmp + xjneg_sum[j]; + + double Gp = Grad[j]+1; + double Gn = Grad[j]-1; + double violation = 0; + if(w[j] == 0) + { + if(Gp < 0) + violation = -Gp; + else if(Gn > 0) + violation = Gn; + //outer-level shrinking + else if(Gp>Gmax_old/l && Gn<-Gmax_old/l) + { + active_size--; + swap(index[s], index[active_size]); + s--; + continue; + } + } + else if(w[j] > 0) + violation = fabs(Gp); + else + violation = fabs(Gn); + + Gmax_new = max(Gmax_new, violation); + Gnorm1_new += violation; + } + + if(newton_iter == 0) + Gnorm1_init = Gnorm1_new; + + if(Gnorm1_new <= eps*Gnorm1_init) + break; + + iter = 0; + QP_Gmax_old = INF; + QP_active_size = active_size; + + for(int i=0; ix[j]; + G = Grad[j] + (wpd[j]-w[j])*nu; + while(x->index != -1) + { + int ind = x->index-1; + G += x->value*D[ind]*xTd[ind]; + x++; + } + + double Gp = G+1; + double Gn = G-1; + double violation = 0; + if(wpd[j] == 0) + { + if(Gp < 0) + violation = -Gp; + else if(Gn > 0) + violation = Gn; + //inner-level shrinking + else if(Gp>QP_Gmax_old/l && Gn<-QP_Gmax_old/l) + { + QP_active_size--; + swap(index[s], index[QP_active_size]); + s--; + continue; + } + } + else if(wpd[j] > 0) + violation = fabs(Gp); + else + violation = fabs(Gn); + + QP_Gmax_new = max(QP_Gmax_new, violation); + QP_Gnorm1_new += violation; + + // obtain solution of one-variable problem + if(Gp < H*wpd[j]) + z = -Gp/H; + else if(Gn > H*wpd[j]) + z = -Gn/H; + else + z = -wpd[j]; + + if(fabs(z) < 1.0e-12) + continue; + z = min(max(z,-10.0),10.0); + + wpd[j] += z; + + x = prob_col->x[j]; + while(x->index != -1) + { + int ind = x->index-1; + xTd[ind] += x->value*z; + x++; + } + } + + iter++; + + if(QP_Gnorm1_new <= inner_eps*Gnorm1_init) + { + //inner stopping + if(QP_active_size == active_size) + break; + //active set reactivation + else + { + QP_active_size = active_size; + QP_Gmax_old = INF; + continue; + } + } + + QP_Gmax_old = QP_Gmax_new; + } + + if(iter >= max_iter) + info("WARNING: reaching max number of inner iterations\n"); + + delta = 0; + w_norm_new = 0; + for(j=0; j= max_num_linesearch) + { + for(int i=0; ix[i]; + while(x->index != -1) + { + exp_wTx[x->index-1] += w[i]*x->value; + x++; + } + } + + for(int i=0; i= max_newton_iter) + info("WARNING: reaching max number of iterations\n"); + + // calculate objective value + + double v = 0; + int nnz = 0; + for(j=0; jl; + int n = prob->n; + int nnz = 0; + int *col_ptr = new int[n+1]; + feature_node *x_space; + prob_col->l = l; + prob_col->n = n; + prob_col->y = new double[l]; + prob_col->x = new feature_node*[n]; + + for(i=0; iy[i] = prob->y[i]; + + for(i=0; ix[i]; + while(x->index != -1) + { + nnz++; + col_ptr[x->index]++; + x++; + } + } + for(i=1; ix[i] = &x_space[col_ptr[i]]; + + for(i=0; ix[i]; + while(x->index != -1) + { + int ind = x->index-1; + x_space[col_ptr[ind]].index = i+1; // starts from 1 + x_space[col_ptr[ind]].value = x->value; + col_ptr[ind]++; + x++; + } + } + for(i=0; il; + int max_nr_class = 16; + int nr_class = 0; + int *label = Malloc(int,max_nr_class); + int *count = Malloc(int,max_nr_class); + int *data_label = Malloc(int,l); + int i; + + for(i=0;iy[i]; + int j; + for(j=0;jeps; + int pos = 0; + int neg = 0; + for(int i=0;il;i++) + if(prob->y[i] > 0) + pos++; + neg = prob->l - pos; + + double primal_solver_tol = eps*max(min(pos,neg), 1)/prob->l; + + function *fun_obj=NULL; + switch(param->solver_type) + { + case L2R_LR: + { + double *C = new double[prob->l]; + for(int i = 0; i < prob->l; i++) + { + if(prob->y[i] > 0) + C[i] = Cp; + else + C[i] = Cn; + } + fun_obj=new l2r_lr_fun(prob, C); + TRON tron_obj(fun_obj, primal_solver_tol); + tron_obj.set_print_string(liblinear_print_string); + tron_obj.tron(w); + delete fun_obj; + delete C; + break; + } + case L2R_L2LOSS_SVC: + { + double *C = new double[prob->l]; + for(int i = 0; i < prob->l; i++) + { + if(prob->y[i] > 0) + C[i] = Cp; + else + C[i] = Cn; + } + fun_obj=new l2r_l2_svc_fun(prob, C); + TRON tron_obj(fun_obj, primal_solver_tol); + tron_obj.set_print_string(liblinear_print_string); + tron_obj.tron(w); + delete fun_obj; + delete C; + break; + } + case L2R_L2LOSS_SVC_DUAL: + solve_l2r_l1l2_svc(prob, w, eps, Cp, Cn, L2R_L2LOSS_SVC_DUAL); + break; + case L2R_L1LOSS_SVC_DUAL: + solve_l2r_l1l2_svc(prob, w, eps, Cp, Cn, L2R_L1LOSS_SVC_DUAL); + break; + case L1R_L2LOSS_SVC: + { + problem prob_col; + feature_node *x_space = NULL; + transpose(prob, &x_space ,&prob_col); + solve_l1r_l2_svc(&prob_col, w, primal_solver_tol, Cp, Cn); + delete [] prob_col.y; + delete [] prob_col.x; + delete [] x_space; + break; + } + case L1R_LR: + { + problem prob_col; + feature_node *x_space = NULL; + transpose(prob, &x_space ,&prob_col); + solve_l1r_lr(&prob_col, w, primal_solver_tol, Cp, Cn); + delete [] prob_col.y; + delete [] prob_col.x; + delete [] x_space; + break; + } + case L2R_LR_DUAL: + solve_l2r_lr_dual(prob, w, eps, Cp, Cn); + break; + case L2R_L2LOSS_SVR: + { + double *C = new double[prob->l]; + for(int i = 0; i < prob->l; i++) + C[i] = param->C; + + fun_obj=new l2r_l2_svr_fun(prob, C, param->p); + TRON tron_obj(fun_obj, param->eps); + tron_obj.set_print_string(liblinear_print_string); + tron_obj.tron(w); + delete fun_obj; + delete C; + break; + + } + case L2R_L1LOSS_SVR_DUAL: + solve_l2r_l1l2_svr(prob, w, param, L2R_L1LOSS_SVR_DUAL); + break; + case L2R_L2LOSS_SVR_DUAL: + solve_l2r_l1l2_svr(prob, w, param, L2R_L2LOSS_SVR_DUAL); + break; + default: + fprintf(stderr, "ERROR: unknown solver_type\n"); + break; + } +} + +// +// Interface functions +// +model* train(const problem *prob, const parameter *param) +{ + int i,j; + int l = prob->l; + int n = prob->n; + int w_size = prob->n; + model *model_ = Malloc(model,1); + + if(prob->bias>=0) + model_->nr_feature=n-1; + else + model_->nr_feature=n; + model_->param = *param; + model_->bias = prob->bias; + + if(param->solver_type == L2R_L2LOSS_SVR || + param->solver_type == L2R_L1LOSS_SVR_DUAL || + param->solver_type == L2R_L2LOSS_SVR_DUAL) + { + model_->w = Malloc(double, w_size); + model_->nr_class = 2; + model_->label = NULL; + train_one(prob, param, &model_->w[0], 0, 0); + } + else + { + int nr_class; + int *label = NULL; + int *start = NULL; + int *count = NULL; + int *perm = Malloc(int,l); + + // group training data of the same class + group_classes(prob,&nr_class,&label,&start,&count,perm); + + model_->nr_class=nr_class; + model_->label = Malloc(int,nr_class); + for(i=0;ilabel[i] = label[i]; + + // calculate weighted C + double *weighted_C = Malloc(double, nr_class); + for(i=0;iC; + for(i=0;inr_weight;i++) + { + for(j=0;jweight_label[i] == label[j]) + break; + if(j == nr_class) + fprintf(stderr,"WARNING: class label %d specified in weight is not found\n", param->weight_label[i]); + else + weighted_C[j] *= param->weight[i]; + } + + // constructing the subproblem + feature_node **x = Malloc(feature_node *,l); + for(i=0;ix[perm[i]]; + + int k; + problem sub_prob; + sub_prob.l = l; + sub_prob.n = n; + sub_prob.x = Malloc(feature_node *,sub_prob.l); + sub_prob.y = Malloc(double,sub_prob.l); + + for(k=0; ksolver_type == MCSVM_CS) + { + model_->w=Malloc(double, n*nr_class); + for(i=0;ieps); + Solver.Solve(model_->w); + } + else + { + if(nr_class == 2) + { + model_->w=Malloc(double, w_size); + + int e0 = start[0]+count[0]; + k=0; + for(; kw[0], weighted_C[0], weighted_C[1]); + } + else + { + model_->w=Malloc(double, w_size*nr_class); + double *w=Malloc(double, w_size); + for(i=0;iC); + + for(int j=0;jw[j*nr_class+i] = w[j]; + } + free(w); + } + + } + + free(x); + free(label); + free(start); + free(count); + free(perm); + free(sub_prob.x); + free(sub_prob.y); + free(weighted_C); + } + return model_; +} + +void cross_validation(const problem *prob, const parameter *param, int nr_fold, double *target) +{ + int i; + int *fold_start = Malloc(int,nr_fold+1); + int l = prob->l; + int *perm = Malloc(int,l); + + for(i=0;ibias; + subprob.n = prob->n; + subprob.l = l-(end-begin); + subprob.x = Malloc(struct feature_node*,subprob.l); + subprob.y = Malloc(double,subprob.l); + + k=0; + for(j=0;jx[perm[j]]; + subprob.y[k] = prob->y[perm[j]]; + ++k; + } + for(j=end;jx[perm[j]]; + subprob.y[k] = prob->y[perm[j]]; + ++k; + } + struct model *submodel = train(&subprob,param); + for(j=begin;jx[perm[j]]); + free_and_destroy_model(&submodel); + free(subprob.x); + free(subprob.y); + } + free(fold_start); + free(perm); +} + +double predict_values(const struct model *model_, const struct feature_node *x, double *dec_values) +{ + int idx; + int n; + if(model_->bias>=0) + n=model_->nr_feature+1; + else + n=model_->nr_feature; + double *w=model_->w; + int nr_class=model_->nr_class; + int i; + int nr_w; + if(nr_class==2 && model_->param.solver_type != MCSVM_CS) + nr_w = 1; + else + nr_w = nr_class; + + const feature_node *lx=x; + for(i=0;iindex)!=-1; lx++) + { + // the dimension of testing data may exceed that of training + if(idx<=n) + for(i=0;ivalue; + } + + if(nr_class==2) + { + if(model_->param.solver_type == L2R_L2LOSS_SVR || + model_->param.solver_type == L2R_L1LOSS_SVR_DUAL || + model_->param.solver_type == L2R_L2LOSS_SVR_DUAL) + return dec_values[0]; + else + return (dec_values[0]>0)?model_->label[0]:model_->label[1]; + } + else + { + int dec_max_idx = 0; + for(i=1;i dec_values[dec_max_idx]) + dec_max_idx = i; + } + return model_->label[dec_max_idx]; + } +} + +double predict(const model *model_, const feature_node *x) +{ + double *dec_values = Malloc(double, model_->nr_class); + double label=predict_values(model_, x, dec_values); + free(dec_values); + return label; +} + +double predict_probability(const struct model *model_, const struct feature_node *x, double* prob_estimates) +{ + if(check_probability_model(model_)) + { + int i; + int nr_class=model_->nr_class; + int nr_w; + if(nr_class==2) + nr_w = 1; + else + nr_w = nr_class; + + double label=predict_values(model_, x, prob_estimates); + for(i=0;inr_feature; + int n; + const parameter& param = model_->param; + + if(model_->bias>=0) + n=nr_feature+1; + else + n=nr_feature; + int w_size = n; + FILE *fp = fopen(model_file_name,"w"); + if(fp==NULL) return -1; + + char *old_locale = strdup(setlocale(LC_ALL, NULL)); + setlocale(LC_ALL, "C"); + + int nr_w; + if(model_->nr_class==2 && model_->param.solver_type != MCSVM_CS) + nr_w=1; + else + nr_w=model_->nr_class; + + fprintf(fp, "solver_type %s\n", solver_type_table[param.solver_type]); + fprintf(fp, "nr_class %d\n", model_->nr_class); + + if(model_->label) + { + fprintf(fp, "label"); + for(i=0; inr_class; i++) + fprintf(fp, " %d", model_->label[i]); + fprintf(fp, "\n"); + } + + fprintf(fp, "nr_feature %d\n", nr_feature); + + fprintf(fp, "bias %.16g\n", model_->bias); + + fprintf(fp, "w\n"); + for(i=0; iw[i*nr_w+j]); + fprintf(fp, "\n"); + } + + setlocale(LC_ALL, old_locale); + free(old_locale); + + if (ferror(fp) != 0 || fclose(fp) != 0) return -1; + else return 0; +} + +struct model *load_model(const char *model_file_name) +{ + FILE *fp = fopen(model_file_name,"r"); + if(fp==NULL) return NULL; + + int i; + int nr_feature; + int n; + int nr_class; + double bias; + model *model_ = Malloc(model,1); + parameter& param = model_->param; + + model_->label = NULL; + + char *old_locale = strdup(setlocale(LC_ALL, NULL)); + setlocale(LC_ALL, "C"); + + char cmd[81]; + while(1) + { + fscanf(fp,"%80s",cmd); + if(strcmp(cmd,"solver_type")==0) + { + fscanf(fp,"%80s",cmd); + int i; + for(i=0;solver_type_table[i];i++) + { + if(strcmp(solver_type_table[i],cmd)==0) + { + param.solver_type=i; + break; + } + } + if(solver_type_table[i] == NULL) + { + fprintf(stderr,"unknown solver type.\n"); + + setlocale(LC_ALL, old_locale); + free(model_->label); + free(model_); + free(old_locale); + return NULL; + } + } + else if(strcmp(cmd,"nr_class")==0) + { + fscanf(fp,"%d",&nr_class); + model_->nr_class=nr_class; + } + else if(strcmp(cmd,"nr_feature")==0) + { + fscanf(fp,"%d",&nr_feature); + model_->nr_feature=nr_feature; + } + else if(strcmp(cmd,"bias")==0) + { + fscanf(fp,"%lf",&bias); + model_->bias=bias; + } + else if(strcmp(cmd,"w")==0) + { + break; + } + else if(strcmp(cmd,"label")==0) + { + int nr_class = model_->nr_class; + model_->label = Malloc(int,nr_class); + for(int i=0;ilabel[i]); + } + else + { + fprintf(stderr,"unknown text in model file: [%s]\n",cmd); + setlocale(LC_ALL, old_locale); + free(model_->label); + free(model_); + free(old_locale); + return NULL; + } + } + + nr_feature=model_->nr_feature; + if(model_->bias>=0) + n=nr_feature+1; + else + n=nr_feature; + int w_size = n; + int nr_w; + if(nr_class==2 && param.solver_type != MCSVM_CS) + nr_w = 1; + else + nr_w = nr_class; + + model_->w=Malloc(double, w_size*nr_w); + for(i=0; iw[i*nr_w+j]); + fscanf(fp, "\n"); + } + + setlocale(LC_ALL, old_locale); + free(old_locale); + + if (ferror(fp) != 0 || fclose(fp) != 0) return NULL; + + return model_; +} + +int get_nr_feature(const model *model_) +{ + return model_->nr_feature; +} + +int get_nr_class(const model *model_) +{ + return model_->nr_class; +} + +void get_labels(const model *model_, int* label) +{ + if (model_->label != NULL) + for(int i=0;inr_class;i++) + label[i] = model_->label[i]; +} + +void free_model_content(struct model *model_ptr) +{ + if(model_ptr->w != NULL) + free(model_ptr->w); + if(model_ptr->label != NULL) + free(model_ptr->label); +} + +void free_and_destroy_model(struct model **model_ptr_ptr) +{ + struct model *model_ptr = *model_ptr_ptr; + if(model_ptr != NULL) + { + free_model_content(model_ptr); + free(model_ptr); + } +} + +void destroy_param(parameter* param) +{ + if(param->weight_label != NULL) + free(param->weight_label); + if(param->weight != NULL) + free(param->weight); +} + +const char *check_parameter(const problem *prob, const parameter *param) +{ + if(param->eps <= 0) + return "eps <= 0"; + + if(param->C <= 0) + return "C <= 0"; + + if(param->p < 0) + return "p < 0"; + + if(param->solver_type != L2R_LR + && param->solver_type != L2R_L2LOSS_SVC_DUAL + && param->solver_type != L2R_L2LOSS_SVC + && param->solver_type != L2R_L1LOSS_SVC_DUAL + && param->solver_type != MCSVM_CS + && param->solver_type != L1R_L2LOSS_SVC + && param->solver_type != L1R_LR + && param->solver_type != L2R_LR_DUAL + && param->solver_type != L2R_L2LOSS_SVR + && param->solver_type != L2R_L2LOSS_SVR_DUAL + && param->solver_type != L2R_L1LOSS_SVR_DUAL) + return "unknown solver type"; + + return NULL; +} + +int check_probability_model(const struct model *model_) +{ + return (model_->param.solver_type==L2R_LR || + model_->param.solver_type==L2R_LR_DUAL || + model_->param.solver_type==L1R_LR); +} + +void set_print_string_function(void (*print_func)(const char*)) +{ + if (print_func == NULL) + liblinear_print_string = &print_string_stdout; + else + liblinear_print_string = print_func; +} + diff --git a/Bing/Src/LibLinear/linear.h b/Bing/Src/LibLinear/linear.h new file mode 100644 index 000000000..22a356743 --- /dev/null +++ b/Bing/Src/LibLinear/linear.h @@ -0,0 +1,74 @@ +#ifndef _LIBLINEAR_H +#define _LIBLINEAR_H + +#ifdef __cplusplus +extern "C" { +#endif + +struct feature_node +{ + int index; + double value; +}; + +struct problem +{ + int l, n; + double *y; + struct feature_node **x; + double bias; /* < 0 if no bias term */ +}; + +enum { L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L1R_L2LOSS_SVC, L1R_LR, L2R_LR_DUAL, L2R_L2LOSS_SVR = 11, L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL }; /* solver_type */ + +struct parameter +{ + int solver_type; + + /* these are for training only */ + double eps; /* stopping criteria */ + double C; + int nr_weight; + int *weight_label; + double* weight; + double p; +}; + +struct model +{ + struct parameter param; + int nr_class; /* number of classes */ + int nr_feature; + double *w; + int *label; /* label of each class */ + double bias; +}; + +struct model* train(const struct problem *prob, const struct parameter *param); +void cross_validation(const struct problem *prob, const struct parameter *param, int nr_fold, double *target); + +double predict_values(const struct model *model_, const struct feature_node *x, double* dec_values); +double predict(const struct model *model_, const struct feature_node *x); +double predict_probability(const struct model *model_, const struct feature_node *x, double* prob_estimates); + +int save_model(const char *model_file_name, const struct model *model_); +struct model *load_model(const char *model_file_name); + +int get_nr_feature(const struct model *model_); +int get_nr_class(const struct model *model_); +void get_labels(const struct model *model_, int* label); + +void free_model_content(struct model *model_ptr); +void free_and_destroy_model(struct model **model_ptr_ptr); +void destroy_param(struct parameter *param); + +const char *check_parameter(const struct problem *prob, const struct parameter *param); +int check_probability_model(const struct model *model); +void set_print_string_function(void (*print_func) (const char*)); + +#ifdef __cplusplus +} +#endif + +#endif /* _LIBLINEAR_H */ + diff --git a/Bing/Src/LibLinear/train.c b/Bing/Src/LibLinear/train.c new file mode 100644 index 000000000..858229dab --- /dev/null +++ b/Bing/Src/LibLinear/train.c @@ -0,0 +1,401 @@ +#include +#include +#include +#include +#include +#include +#include "linear.h" +#define Malloc(type,n) (type *)malloc((n)*sizeof(type)) +#define INF HUGE_VAL + +#pragma warning(disable:4996) + +void print_null(const char *s) {} + +void exit_with_help() +{ + printf( + "Usage: train [options] training_set_file [model_file]\n" + "options:\n" + "-s type : set type of solver (default 1)\n" + " for multi-class classification\n" + " 0 -- L2-regularized logistic regression (primal)\n" + " 1 -- L2-regularized L2-loss support vector classification (dual)\n" + " 2 -- L2-regularized L2-loss support vector classification (primal)\n" + " 3 -- L2-regularized L1-loss support vector classification (dual)\n" + " 4 -- support vector classification by Crammer and Singer\n" + " 5 -- L1-regularized L2-loss support vector classification\n" + " 6 -- L1-regularized logistic regression\n" + " 7 -- L2-regularized logistic regression (dual)\n" + " for regression\n" + " 11 -- L2-regularized L2-loss support vector regression (primal)\n" + " 12 -- L2-regularized L2-loss support vector regression (dual)\n" + " 13 -- L2-regularized L1-loss support vector regression (dual)\n" + "-c cost : set the parameter C (default 1)\n" + "-p epsilon : set the epsilon in loss function of SVR (default 0.1)\n" + "-e epsilon : set tolerance of termination criterion\n" + " -s 0 and 2\n" + " |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,\n" + " where f is the primal function and pos/neg are # of\n" + " positive/negative data (default 0.01)\n" + " -s 11\n" + " |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001)\n" + " -s 1, 3, 4, and 7\n" + " Dual maximal violation <= eps; similar to libsvm (default 0.1)\n" + " -s 5 and 6\n" + " |f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,\n" + " where f is the primal function (default 0.01)\n" + " -s 12 and 13\n" + " |f'(alpha)|_1 <= eps |f'(alpha0)|,\n" + " where f is the dual function (default 0.1)\n" + "-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)\n" + "-wi weight: weights adjust the parameter C of different classes (see README for details)\n" + "-v n: n-fold cross validation mode\n" + "-q : quiet mode (no outputs)\n" + ); + exit(1); +} + +void exit_input_error(int line_num) +{ + fprintf(stderr,"Wrong input format at line %d\n", line_num); + exit(1); +} + +static char *line = NULL; +static int max_line_len; + +static char* readline(FILE *input) +{ + int len; + + if(fgets(line,max_line_len,input) == NULL) + return NULL; + + while(strrchr(line,'\n') == NULL) + { + max_line_len *= 2; + line = (char *) realloc(line,max_line_len); + len = (int) strlen(line); + if(fgets(line+len,max_line_len-len,input) == NULL) + break; + } + return line; +} + +void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name); +void read_problem(const char *filename); +void do_cross_validation(); + +struct feature_node *x_space; +struct parameter param; +struct problem prob; +struct model* model_; +int flag_cross_validation; +int nr_fold; +double bias; + +int main(int argc, char **argv) +{ + char input_file_name[1024]; + char model_file_name[1024]; + const char *error_msg; + + parse_command_line(argc, argv, input_file_name, model_file_name); + read_problem(input_file_name); + error_msg = check_parameter(&prob,¶m); + + if(error_msg) + { + fprintf(stderr,"ERROR: %s\n",error_msg); + exit(1); + } + + if(flag_cross_validation) + { + do_cross_validation(); + } + else + { + model_=train(&prob, ¶m); + if(save_model(model_file_name, model_)) + { + fprintf(stderr,"can't save model to file %s\n",model_file_name); + exit(1); + } + free_and_destroy_model(&model_); + } + destroy_param(¶m); + free(prob.y); + free(prob.x); + free(x_space); + free(line); + + return 0; +} + +void do_cross_validation() +{ + int i; + int total_correct = 0; + double total_error = 0; + double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0; + double *target = Malloc(double, prob.l); + + cross_validation(&prob,¶m,nr_fold,target); + if(param.solver_type == L2R_L2LOSS_SVR || + param.solver_type == L2R_L1LOSS_SVR_DUAL || + param.solver_type == L2R_L2LOSS_SVR_DUAL) + { + for(i=0;i=argc) + exit_with_help(); + switch(argv[i-1][1]) + { + case 's': + param.solver_type = atoi(argv[i]); + break; + + case 'c': + param.C = atof(argv[i]); + break; + + case 'p': + param.p = atof(argv[i]); + break; + + case 'e': + param.eps = atof(argv[i]); + break; + + case 'B': + bias = atof(argv[i]); + break; + + case 'w': + ++param.nr_weight; + param.weight_label = (int *) realloc(param.weight_label,sizeof(int)*param.nr_weight); + param.weight = (double *) realloc(param.weight,sizeof(double)*param.nr_weight); + param.weight_label[param.nr_weight-1] = atoi(&argv[i-1][2]); + param.weight[param.nr_weight-1] = atof(argv[i]); + break; + + case 'v': + flag_cross_validation = 1; + nr_fold = atoi(argv[i]); + if(nr_fold < 2) + { + fprintf(stderr,"n-fold cross validation: n must >= 2\n"); + exit_with_help(); + } + break; + + case 'q': + print_func = &print_null; + i--; + break; + + default: + fprintf(stderr,"unknown option: -%c\n", argv[i-1][1]); + exit_with_help(); + break; + } + } + + set_print_string_function(print_func); + + // determine filenames + if(i>=argc) + exit_with_help(); + + strcpy(input_file_name, argv[i]); + + if(i max_index) + max_index = inst_max_index; + + if(prob.bias >= 0) + x_space[j++].value = prob.bias; + + x_space[j++].index = -1; + } + + if(prob.bias >= 0) + { + prob.n=max_index+1; + for(i=1;iindex = prob.n; + x_space[j-2].index = prob.n; + } + else + prob.n=max_index; + + fclose(fp); +} diff --git a/Bing/Src/LibLinear/tron.cpp b/Bing/Src/LibLinear/tron.cpp new file mode 100644 index 000000000..b54bedf18 --- /dev/null +++ b/Bing/Src/LibLinear/tron.cpp @@ -0,0 +1,235 @@ +#include +#include +#include +#include +#include "tron.h" + +#ifndef min +template static inline T min(T x,T y) { return (x static inline T max(T x,T y) { return (x>y)?x:y; } +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +extern double dnrm2_(int *, double *, int *); +extern double ddot_(int *, double *, int *, double *, int *); +extern int daxpy_(int *, double *, double *, int *, double *, int *); +extern int dscal_(int *, double *, double *, int *); + +#ifdef __cplusplus +} +#endif + +static void default_print(const char *buf) +{ + fputs(buf,stdout); + fflush(stdout); +} + +void TRON::info(const char *fmt,...) +{ + char buf[BUFSIZ]; + va_list ap; + va_start(ap,fmt); + vsprintf(buf,fmt,ap); + va_end(ap); + (*tron_print_string)(buf); +} + +TRON::TRON(const function *fun_obj, double eps, int max_iter) +{ + this->fun_obj=const_cast(fun_obj); + this->eps=eps; + this->max_iter=max_iter; + tron_print_string = default_print; +} + +TRON::~TRON() +{ +} + +void TRON::tron(double *w) +{ + // Parameters for updating the iterates. + double eta0 = 1e-4, eta1 = 0.25, eta2 = 0.75; + + // Parameters for updating the trust region size delta. + double sigma1 = 0.25, sigma2 = 0.5, sigma3 = 4; + + int n = fun_obj->get_nr_variable(); + int i, cg_iter; + double delta, snorm, one=1.0; + double alpha, f, fnew, prered, actred, gs; + int search = 1, iter = 1, inc = 1; + double *s = new double[n]; + double *r = new double[n]; + double *w_new = new double[n]; + double *g = new double[n]; + + for (i=0; ifun(w); + fun_obj->grad(w, g); + delta = dnrm2_(&n, g, &inc); + double gnorm1 = delta; + double gnorm = gnorm1; + + if (gnorm <= eps*gnorm1) + search = 0; + + iter = 1; + + while (iter <= max_iter && search) + { + cg_iter = trcg(delta, g, s, r); + + memcpy(w_new, w, sizeof(double)*n); + daxpy_(&n, &one, s, &inc, w_new, &inc); + + gs = ddot_(&n, g, &inc, s, &inc); + prered = -0.5*(gs-ddot_(&n, s, &inc, r, &inc)); + fnew = fun_obj->fun(w_new); + + // Compute the actual reduction. + actred = f - fnew; + + // On the first iteration, adjust the initial step bound. + snorm = dnrm2_(&n, s, &inc); + if (iter == 1) + delta = min(delta, snorm); + + // Compute prediction alpha*snorm of the step. + if (fnew - f - gs <= 0) + alpha = sigma3; + else + alpha = max(sigma1, -0.5*(gs/(fnew - f - gs))); + + // Update the trust region bound according to the ratio of actual to predicted reduction. + if (actred < eta0*prered) + delta = min(max(alpha, sigma1)*snorm, sigma2*delta); + else if (actred < eta1*prered) + delta = max(sigma1*delta, min(alpha*snorm, sigma2*delta)); + else if (actred < eta2*prered) + delta = max(sigma1*delta, min(alpha*snorm, sigma3*delta)); + else + delta = max(delta, min(alpha*snorm, sigma3*delta)); + + info("iter %2d act %5.3e pre %5.3e delta %5.3e f %5.3e |g| %5.3e CG %3d\n", iter, actred, prered, delta, f, gnorm, cg_iter); + + if (actred > eta0*prered) + { + iter++; + memcpy(w, w_new, sizeof(double)*n); + f = fnew; + fun_obj->grad(w, g); + + gnorm = dnrm2_(&n, g, &inc); + if (gnorm <= eps*gnorm1) + break; + } + if (f < -1.0e+32) + { + info("WARNING: f < -1.0e+32\n"); + break; + } + if (fabs(actred) <= 0 && prered <= 0) + { + info("WARNING: actred and prered <= 0\n"); + break; + } + if (fabs(actred) <= 1.0e-12*fabs(f) && + fabs(prered) <= 1.0e-12*fabs(f)) + { + info("WARNING: actred and prered too small\n"); + break; + } + } + + delete[] g; + delete[] r; + delete[] w_new; + delete[] s; +} + +int TRON::trcg(double delta, double *g, double *s, double *r) +{ + int i, inc = 1; + int n = fun_obj->get_nr_variable(); + double one = 1; + double *d = new double[n]; + double *Hd = new double[n]; + double rTr, rnewTrnew, alpha, beta, cgtol; + + for (i=0; iHv(d, Hd); + + alpha = rTr/ddot_(&n, d, &inc, Hd, &inc); + daxpy_(&n, &alpha, d, &inc, s, &inc); + if (dnrm2_(&n, s, &inc) > delta) + { + info("cg reaches trust region boundary\n"); + alpha = -alpha; + daxpy_(&n, &alpha, d, &inc, s, &inc); + + double std = ddot_(&n, s, &inc, d, &inc); + double sts = ddot_(&n, s, &inc, s, &inc); + double dtd = ddot_(&n, d, &inc, d, &inc); + double dsq = delta*delta; + double rad = sqrt(std*std + dtd*(dsq-sts)); + if (std >= 0) + alpha = (dsq - sts)/(std + rad); + else + alpha = (rad - std)/dtd; + daxpy_(&n, &alpha, d, &inc, s, &inc); + alpha = -alpha; + daxpy_(&n, &alpha, Hd, &inc, r, &inc); + break; + } + alpha = -alpha; + daxpy_(&n, &alpha, Hd, &inc, r, &inc); + rnewTrnew = ddot_(&n, r, &inc, r, &inc); + beta = rnewTrnew/rTr; + dscal_(&n, &beta, d, &inc); + daxpy_(&n, &one, r, &inc, d, &inc); + rTr = rnewTrnew; + } + + delete[] d; + delete[] Hd; + + return(cg_iter); +} + +double TRON::norm_inf(int n, double *x) +{ + double dmax = fabs(x[0]); + for (int i=1; i= dmax) + dmax = fabs(x[i]); + return(dmax); +} + +void TRON::set_print_string(void (*print_string) (const char *buf)) +{ + tron_print_string = print_string; +} diff --git a/Bing/Src/LibLinear/tron.h b/Bing/Src/LibLinear/tron.h new file mode 100644 index 000000000..4fe34a5c6 --- /dev/null +++ b/Bing/Src/LibLinear/tron.h @@ -0,0 +1,36 @@ +#ifndef _TRON_H +#define _TRON_H + +#pragma warning(disable:4996) + +class function +{ +public: + virtual double fun(double *w) = 0 ; + virtual void grad(double *w, double *g) = 0 ; + virtual void Hv(double *s, double *Hs) = 0 ; + + virtual int get_nr_variable(void) = 0 ; + virtual ~function(void){} +}; + +class TRON +{ +public: + TRON(const function *fun_obj, double eps = 0.1, int max_iter = 1000); + ~TRON(); + + void tron(double *w); + void set_print_string(void (*i_print) (const char *buf)); + +private: + int trcg(double delta, double *g, double *s, double *r); + double norm_inf(int n, double *x); + + double eps; + int max_iter; + function *fun_obj; + void info(const char *fmt,...); + void (*tron_print_string)(const char *buf); +}; +#endif diff --git a/Bing/Src/Objectness.cpp b/Bing/Src/Objectness.cpp new file mode 100644 index 000000000..01cbc9541 --- /dev/null +++ b/Bing/Src/Objectness.cpp @@ -0,0 +1,1118 @@ +#include "kyheader.h" +#include "Objectness.h" +#include "CmShow.h" + +#define Malloc(type,n) (type *)malloc((n)*sizeof(type)) +void print_null(const char *s) {} +const char* Objectness::_clrName[3] = {"MAXBGR", "HSV", "I"}; +const int CN = 21; // Color Number +const char* COLORs[CN] = {"'k'", "'b'", "'g'", "'r'", "'c'", "'m'", "'y'", + "':k'", "':b'", "':g'", "':r'", "':c'", "':m'", "':y'", + "'--k'", "'--b'", "'--g'", "'--r'", "'--c'", "'--m'", "'--y'" +}; + + +// base for window size quantization, R orientation channels, and feature window size (_W, _W) +Objectness::Objectness(DataSetVOC &voc, double base, int W, int NSS) + : _voc(voc) + , _base(base) + , _W(W) + , _NSS(NSS) + , _logBase(log(_base)) + , _minT(cvCeil(log(10.)/_logBase)) + , _maxT(cvCeil(log(500.)/_logBase)) + , _numT(_maxT - _minT + 1) + , _Clr(MAXBGR) +{ + setColorSpace(_Clr); +} + +Objectness::~Objectness(void) +{ +} + +void Objectness::setColorSpace(int clr) +{ + _Clr = clr; + _modelName = _voc.resDir + format("ObjNessB%gW%d%s", _base, _W, _clrName[_Clr]); + _trainDirSI = _voc.localDir + format("TrainS1B%gW%d%s/", _base, _W, _clrName[_Clr]); + _bbResDir = _voc.resDir + format("BBoxesB%gW%d%s/", _base, _W, _clrName[_Clr]); +} + +int Objectness::loadTrainedModel(string modelName) // Return -1, 0, or 1 if partial, none, or all loaded +{ + if (modelName.size() == 0) + modelName = _modelName; + CStr s1 = modelName + ".wS1", s2 = modelName + ".wS2", sI = modelName + ".idx"; + Mat filters1f, reW1f, idx1i, show3u; + if (!matRead(s1, filters1f) || !matRead(sI, idx1i)){ + printf("Can't load model: %s or %s\n", _S(s1), _S(sI)); + return 0; + } + + //filters1f = aFilter(0.8f, 8); + //normalize(filters1f, filters1f, p, 1, NORM_MINMAX); + + normalize(filters1f, show3u, 1, 255, NORM_MINMAX, CV_8U); + CmShow::showTinyMat(_voc.resDir + "Filter.png", show3u); + _tigF.update(filters1f); + _tigF.reconstruct(filters1f); + + _svmSzIdxs = idx1i; + CV_Assert(_svmSzIdxs.size() > 1 && filters1f.size() == Size(_W, _W) && filters1f.type() == CV_32F); + _svmFilter = filters1f; + + if (!matRead(s2, _svmReW1f) || _svmReW1f.size() != Size(2, _svmSzIdxs.size())){ + _svmReW1f = Mat(); + return -1; + } + return 1; +} + +void Objectness::predictBBoxSI(CMat &img3u, ValStructVec &valBoxes, vecI &sz, int NUM_WIN_PSZ, bool fast) +{ + const int numSz = _svmSzIdxs.size(); + const int imgW = img3u.cols, imgH = img3u.rows; + valBoxes.reserve(10000); + sz.clear(); sz.reserve(10000); + for (int ir = numSz - 1; ir >= 0; ir--){ + int r = _svmSzIdxs[ir]; + int height = cvRound(pow(_base, r/_numT + _minT)), width = cvRound(pow(_base, r%_numT + _minT)); + if (height > imgH * _base || width > imgW * _base) + continue; + + height = min(height, imgH), width = min(width, imgW); + Mat im3u, matchCost1f, mag1u; + resize(img3u, im3u, Size(cvRound(_W*imgW*1.0/width), cvRound(_W*imgH*1.0/height))); + gradientMag(im3u, mag1u); + + //imwrite(_voc.localDir + format("%d.png", r), mag1u); + //Mat mag1f; + //mag1u.convertTo(mag1f, CV_32F); + //matchTemplate(mag1f, _svmFilter, matchCost1f, CV_TM_CCORR); + + matchCost1f = _tigF.matchTemplate(mag1u); + + ValStructVec matchCost; + nonMaxSup(matchCost1f, matchCost, _NSS, NUM_WIN_PSZ, fast); + + // Find true locations and match values + double ratioX = width/_W, ratioY = height/_W; + int iMax = min(matchCost.size(), NUM_WIN_PSZ); + for (int i = 0; i < iMax; i++){ + float mVal = matchCost(i); + Point pnt = matchCost[i]; + Vec4i box(cvRound(pnt.x * ratioX), cvRound(pnt.y*ratioY)); + box[2] = cvRound(min(box[0] + width, imgW)); + box[3] = cvRound(min(box[1] + height, imgH)); + box[0] ++; + box[1] ++; + valBoxes.pushBack(mVal, box); + sz.push_back(ir); + } + } + //exit(0); +} + +void Objectness::predictBBoxSII(ValStructVec &valBoxes, const vecI &sz) +{ + int numI = valBoxes.size(); + for (int i = 0; i < numI; i++){ + const float* svmIIw = _svmReW1f.ptr(sz[i]); + valBoxes(i) = valBoxes(i) * svmIIw[0] + svmIIw[1]; + } + valBoxes.sort(); +} + +// Get potential bounding boxes, each of which is represented by a Vec4i for (minX, minY, maxX, maxY). +// The trained model should be prepared before calling this function: loadTrainedModel() or trainStageI() + trainStageII(). +// Use numDet to control the final number of proposed bounding boxes, and number of per size (scale and aspect ratio) +void Objectness::getObjBndBoxes(CMat &img3u, ValStructVec &valBoxes, int numDetPerSize) +{ + CV_Assert_(filtersLoaded() , ("SVM filters should be initialized before getting object proposals\n")); + vecI sz; + predictBBoxSI(img3u, valBoxes, sz, numDetPerSize, false); + predictBBoxSII(valBoxes, sz); + return; +} + +void Objectness::nonMaxSup(CMat &matchCost1f, ValStructVec &matchCost, int NSS, int maxPoint, bool fast) +{ + const int _h = matchCost1f.rows, _w = matchCost1f.cols; + Mat isMax1u = Mat::ones(_h, _w, CV_8U), costSmooth1f; + ValStructVec valPnt; + matchCost.reserve(_h * _w); + valPnt.reserve(_h * _w); + if (fast){ + blur(matchCost1f, costSmooth1f, Size(3, 3)); + for (int r = 0; r < _h; r++){ + const float* d = matchCost1f.ptr(r); + const float* ds = costSmooth1f.ptr(r); + for (int c = 0; c < _w; c++) + if (d[c] >= ds[c]) + valPnt.pushBack(d[c], Point(c, r)); + } + } + else{ + for (int r = 0; r < _h; r++){ + const float* d = matchCost1f.ptr(r); + for (int c = 0; c < _w; c++) + valPnt.pushBack(d[c], Point(c, r)); + } + } + + valPnt.sort(); + for (int i = 0; i < valPnt.size(); i++){ + Point &pnt = valPnt[i]; + if (isMax1u.at(pnt)){ + matchCost.pushBack(valPnt(i), pnt); + for (int dy = -NSS; dy <= NSS; dy++) for (int dx = -NSS; dx <= NSS; dx++){ + Point neighbor = pnt + Point(dx, dy); + if (!CHK_IND(neighbor)) + continue; + isMax1u.at(neighbor) = false; + } + } + if (matchCost.size() >= maxPoint) + return; + } +} + +void Objectness::gradientMag(CMat &imgBGR3u, Mat &mag1u) +{ + switch (_Clr){ + case MAXBGR: + gradientRGB(imgBGR3u, mag1u); break; + case G: + gradientGray(imgBGR3u, mag1u); break; + case HSV: + gradientHSV(imgBGR3u, mag1u); break; + default: + printf("Error: not recognized color space\n"); + } +} + +void Objectness::gradientRGB(CMat &bgr3u, Mat &mag1u) +{ + const int H = bgr3u.rows, W = bgr3u.cols; + Mat Ix(H, W, CV_32S), Iy(H, W, CV_32S); + + // Left/right most column Ix + for (int y = 0; y < H; y++){ + Ix.at(y, 0) = bgrMaxDist(bgr3u.at(y, 1), bgr3u.at(y, 0))*2; + Ix.at(y, W-1) = bgrMaxDist(bgr3u.at(y, W-1), bgr3u.at(y, W-2))*2; + } + + // Top/bottom most column Iy + for (int x = 0; x < W; x++) { + Iy.at(0, x) = bgrMaxDist(bgr3u.at(1, x), bgr3u.at(0, x))*2; + Iy.at(H-1, x) = bgrMaxDist(bgr3u.at(H-1, x), bgr3u.at(H-2, x))*2; + } + + // Find the gradient for inner regions + for (int y = 0; y < H; y++){ + const Vec3b *dataP = bgr3u.ptr(y); + for (int x = 2; x < W; x++) + Ix.at(y, x-1) = bgrMaxDist(dataP[x-2], dataP[x]); // bgr3u.at(y, x+1), bgr3u.at(y, x-1)); + } + for (int y = 1; y < H-1; y++){ + const Vec3b *tP = bgr3u.ptr(y-1); + const Vec3b *bP = bgr3u.ptr(y+1); + for (int x = 0; x < W; x++) + Iy.at(y, x) = bgrMaxDist(tP[x], bP[x]); + } + gradientXY(Ix, Iy, mag1u); +} + +void Objectness::gradientGray(CMat &bgr3u, Mat &mag1u) +{ + Mat g1u; + cvtColor(bgr3u, g1u, CV_BGR2GRAY); + const int H = g1u.rows, W = g1u.cols; + Mat Ix(H, W, CV_32S), Iy(H, W, CV_32S); + + // Left/right most column Ix + for (int y = 0; y < H; y++){ + Ix.at(y, 0) = abs(g1u.at(y, 1) - g1u.at(y, 0)) * 2; + Ix.at(y, W-1) = abs(g1u.at(y, W-1) - g1u.at(y, W-2)) * 2; + } + + // Top/bottom most column Iy + for (int x = 0; x < W; x++) { + Iy.at(0, x) = abs(g1u.at(1, x) - g1u.at(0, x)) * 2; + Iy.at(H-1, x) = abs(g1u.at(H-1, x) - g1u.at(H-2, x)) * 2; + } + + // Find the gradient for inner regions + for (int y = 0; y < H; y++) + for (int x = 1; x < W-1; x++) + Ix.at(y, x) = abs(g1u.at(y, x+1) - g1u.at(y, x-1)); + for (int y = 1; y < H-1; y++) + for (int x = 0; x < W; x++) + Iy.at(y, x) = abs(g1u.at(y+1, x) - g1u.at(y-1, x)); + + gradientXY(Ix, Iy, mag1u); +} + + +void Objectness::gradientHSV(CMat &bgr3u, Mat &mag1u) +{ + Mat hsv3u; + cvtColor(bgr3u, hsv3u, CV_BGR2HSV); + const int H = hsv3u.rows, W = hsv3u.cols; + Mat Ix(H, W, CV_32S), Iy(H, W, CV_32S); + + // Left/right most column Ix + for (int y = 0; y < H; y++){ + Ix.at(y, 0) = vecDist3b(hsv3u.at(y, 1), hsv3u.at(y, 0)); + Ix.at(y, W-1) = vecDist3b(hsv3u.at(y, W-1), hsv3u.at(y, W-2)); + } + + // Top/bottom most column Iy + for (int x = 0; x < W; x++) { + Iy.at(0, x) = vecDist3b(hsv3u.at(1, x), hsv3u.at(0, x)); + Iy.at(H-1, x) = vecDist3b(hsv3u.at(H-1, x), hsv3u.at(H-2, x)); + } + + // Find the gradient for inner regions + for (int y = 0; y < H; y++) + for (int x = 1; x < W-1; x++) + Ix.at(y, x) = vecDist3b(hsv3u.at(y, x+1), hsv3u.at(y, x-1))/2; + for (int y = 1; y < H-1; y++) + for (int x = 0; x < W; x++) + Iy.at(y, x) = vecDist3b(hsv3u.at(y+1, x), hsv3u.at(y-1, x))/2; + + gradientXY(Ix, Iy, mag1u); +} + +void Objectness::gradientXY(CMat &x1i, CMat &y1i, Mat &mag1u) +{ + const int H = x1i.rows, W = x1i.cols; + mag1u.create(H, W, CV_8U); + for (int r = 0; r < H; r++){ + const int *x = x1i.ptr(r), *y = y1i.ptr(r); + byte* m = mag1u.ptr(r); + for (int c = 0; c < W; c++) + m[c] = min(x[c] + y[c], 255); //((int)sqrt(sqr(x[c]) + sqr(y[c])), 255); + } +} + +void Objectness::trainObjectness(int numDetPerSize) +{ + CmTimer tm1("Train1"), tm2("Train 2"); + + //* Learning stage I + generateTrianData(); + tm1.Start(); + trainStageI(); + tm1.Stop(); + printf("Learning stage I takes %g seconds... \n", tm1.TimeInSeconds()); //*/ + + //* Learning stage II + tm2.Start(); + trainStateII(numDetPerSize); + tm2.Stop(); + printf("Learning stage II takes %g seconds... \n", tm2.TimeInSeconds()); //*/ + return; +} + +void Objectness::generateTrianData() +{ + const int NUM_TRAIN = _voc.trainNum; + const int FILTER_SZ = _W*_W; + vector> xTrainP(NUM_TRAIN), xTrainN(NUM_TRAIN); + vector szTrainP(NUM_TRAIN); // Corresponding size index. + const int NUM_NEG_BOX = 100; // Number of negative windows sampled from each image + +#pragma omp parallel for + for (int i = 0; i < NUM_TRAIN; i++) { + const int NUM_GT_BOX = (int)_voc.gtTrainBoxes[i].size(); + vector &xP = xTrainP[i], &xN = xTrainN[i]; + vecI &szP = szTrainP[i]; + xP.reserve(NUM_GT_BOX*4), szP.reserve(NUM_GT_BOX*4), xN.reserve(NUM_NEG_BOX); + Mat im3u = imread(format(_S(_voc.imgPathW), _S(_voc.trainSet[i]))); + + // Get positive training data + for (int k = 0; k < NUM_GT_BOX; k++){ + const Vec4i& bbgt = _voc.gtTrainBoxes[i][k]; + vector bbs; // bounding boxes; + vecI bbR; // Bounding box ratios + int nS = gtBndBoxSampling(bbgt, bbs, bbR); + for (int j = 0; j < nS; j++){ + bbs[j][2] = min(bbs[j][2], im3u.cols); + bbs[j][3] = min(bbs[j][3], im3u.rows); + Mat mag1f = getFeature(im3u, bbs[j]), magF1f; + flip(mag1f, magF1f, CV_FLIP_HORIZONTAL); + xP.push_back(mag1f); + xP.push_back(magF1f); + szP.push_back(bbR[j]); + szP.push_back(bbR[j]); + } + } + // Get negative training data + for (int k = 0; k < NUM_NEG_BOX; k++){ + int x1 = rand() % im3u.cols + 1, x2 = rand() % im3u.cols + 1; + int y1 = rand() % im3u.rows + 1, y2 = rand() % im3u.rows + 1; + Vec4i bb(min(x1, x2), min(y1, y2), max(x1, x2), max(y1, y2)); + if (maxIntUnion(bb, _voc.gtTrainBoxes[i]) < 0.5) + xN.push_back(getFeature(im3u, bb)); + } + } + + const int NUM_R = _numT * _numT + 1; + vecI szCount(NUM_R); // Object counts of each size (combination of scale and aspect ratio) + int numP = 0, numN = 0, iP = 0, iN = 0; + for (int i = 0; i < NUM_TRAIN; i++){ + numP += xTrainP[i].size(); + numN += xTrainN[i].size(); + const vecI &rP = szTrainP[i]; + for (size_t j = 0; j < rP.size(); j++) + szCount[rP[j]]++; + } + vecI szActive; // Indexes of active size + for (int r = 1; r < NUM_R; r++){ + if (szCount[r] > 50) // If only 50- positive samples at this size, ignore it. + szActive.push_back(r-1); + } + matWrite(_modelName + ".idx", Mat(szActive)); + + Mat xP1f(numP, FILTER_SZ, CV_32F), xN1f(numN, FILTER_SZ, CV_32F); + for (int i = 0; i < NUM_TRAIN; i++) { + vector &xP = xTrainP[i], &xN = xTrainN[i]; + for (size_t j = 0; j < xP.size(); j++) + memcpy(xP1f.ptr(iP++), xP[j].data, FILTER_SZ*sizeof(float)); + for (size_t j = 0; j < xN.size(); j++) + memcpy(xN1f.ptr(iN++), xN[j].data, FILTER_SZ*sizeof(float)); + } + CV_Assert(numP == iP && numN == iN); + matWrite(_modelName + ".xP", xP1f); + matWrite(_modelName + ".xN", xN1f); +} + +Mat Objectness::getFeature(CMat &img3u, const Vec4i &bb) +{ + int x = bb[0] - 1, y = bb[1] - 1; + Rect reg(x, y, bb[2] - x, bb[3] - y); + Mat subImg3u, mag1f, mag1u; + resize(img3u(reg), subImg3u, Size(_W, _W)); + gradientMag(subImg3u, mag1u); + mag1u.convertTo(mag1f, CV_32F); + return mag1f; +} + +int Objectness::gtBndBoxSampling(const Vec4i &bbgt, vector &samples, vecI &bbR) +{ + double wVal = bbgt[2] - bbgt[0] + 1, hVal = (bbgt[3] - bbgt[1]) + 1; + wVal = log(wVal)/_logBase, hVal = log(hVal)/_logBase; + int wMin = max((int)(wVal - 0.5), _minT), wMax = min((int)(wVal + 1.5), _maxT); + int hMin = max((int)(hVal - 0.5), _minT), hMax = min((int)(hVal + 1.5), _maxT); + for (int h = hMin; h <= hMax; h++) for (int w = wMin; w <= wMax; w++){ + int wT = tLen(w) - 1, hT = tLen(h) - 1; + Vec4i bb(bbgt[0], bbgt[1], bbgt[0] + wT, bbgt[1] + hT); + if (DataSetVOC::interUnio(bb, bbgt) >= 0.5){ + samples.push_back(bb); + bbR.push_back(sz2idx(w, h)); + //if (bbgt[3] > hT){ + // bb = Vec4i(bbgt[0], bbgt[3] - hT, bbgt[0] + wT, bbgt[3]); + // CV_Assert(DataSetVOC::interUnio(bb, bbgt) >= 0.5); + // samples.push_back(bb); + // bbR.push_back(sz2idx(w, h)); + //} + //if (bbgt[2] > wT){ + // bb = Vec4i(bbgt[2] - wT, bbgt[1], bbgt[2], bbgt[1] + hT); + // CV_Assert(DataSetVOC::interUnio(bb, bbgt) >= 0.5); + // samples.push_back(bb); + // bbR.push_back(sz2idx(w, h)); + //} + //if (bbgt[2] > wT && bbgt[3] > hT){ + // bb = Vec4i(bbgt[2] - wT, bbgt[3] - hT, bbgt[2], bbgt[3]); + // CV_Assert(DataSetVOC::interUnio(bb, bbgt) >= 0.5); + // samples.push_back(bb); + // bbR.push_back(sz2idx(w, h)); + //} + } + } + return samples.size(); +} + +void Objectness::trainStateII(int numPerSz) +{ + loadTrainedModel(); + const int NUM_TRAIN = _voc.trainNum; + vector SZ(NUM_TRAIN), Y(NUM_TRAIN); + vector VAL(NUM_TRAIN); + +#pragma omp parallel for + for (int i = 0; i < _voc.trainNum; i++) { + const vector &bbgts = _voc.gtTrainBoxes[i]; + ValStructVec valBoxes; + vecI &sz = SZ[i], &y = Y[i]; + vecF &val = VAL[i]; + CStr imgPath = format(_S(_voc.imgPathW), _S(_voc.trainSet[i])); + predictBBoxSI(imread(imgPath), valBoxes, sz, numPerSz, false); + const int num = valBoxes.size(); + CV_Assert(sz.size() == num); + y.resize(num), val.resize(num); + for (int j = 0; j < num; j++){ + Vec4i bb = valBoxes[j]; + val[j] = valBoxes(j); + y[j] = maxIntUnion(bb, bbgts) >= 0.5 ? 1 : -1; + } + } + + const int NUM_SZ = _svmSzIdxs.size(); + const int maxTrainNum = 100000; + vector rXP(NUM_SZ), rXN(NUM_SZ); + for (int r = 0; r < NUM_SZ; r++){ + rXP[r].reserve(maxTrainNum); + rXN[r].reserve(1000000); + } + for (int i = 0; i < NUM_TRAIN; i++){ + const vecI &sz = SZ[i], &y = Y[i]; + vecF &val = VAL[i]; + int num = sz.size(); + for (int j = 0; j < num; j++){ + int r = sz[j]; + CV_Assert(r >= 0 && r < NUM_SZ); + if (y[j] == 1) + rXP[r].push_back(Mat(1, 1, CV_32F, &val[j])); + else + rXN[r].push_back(Mat(1, 1, CV_32F, &val[j])); + } + } + + Mat wMat(NUM_SZ, 2, CV_32F); + for (int i = 0; i < NUM_SZ; i++){ + const vecM &xP = rXP[i], &xN = rXN[i]; + if (xP.size() < 10 || xN.size() < 10) + printf("Warning %s:%d not enough training sample for r[%d] = %d. P = %d, N = %d\n", __FILE__, __LINE__, i, _svmSzIdxs[i], xP.size(), xN.size()); + for (size_t k = 0; k < xP.size(); k++) + CV_Assert(xP[k].size() == Size(1, 1) && xP[k].type() == CV_32F); + + Mat wr = trainSVM(xP, xN, L1R_L2LOSS_SVC, 100, 1); + CV_Assert(wr.size() == Size(2, 1)); + wr.copyTo(wMat.row(i)); + } + matWrite(_modelName + ".wS2", wMat); + _svmReW1f = wMat; +} + +void Objectness::meanStdDev(CMat &data1f, Mat &mean1f, Mat &stdDev1f) +{ + const int DIM = data1f.cols, NUM = data1f.rows; + mean1f = Mat::zeros(1, DIM, CV_32F), stdDev1f = Mat::zeros(1, DIM, CV_32F); + for (int i = 0; i < NUM; i++) + mean1f += data1f.row(i); + mean1f /= NUM; + for (int i = 0; i < NUM; i++){ + Mat tmp; + pow(data1f.row(i) - mean1f, 2, tmp); + stdDev1f += tmp; + } + pow(stdDev1f/NUM, 0.5, stdDev1f); +} + +vecD Objectness::getVector(const Mat &_t1f) +{ + Mat t1f; + _t1f.convertTo(t1f, CV_64F); + return (vecD)(t1f.reshape(1, 1)); +} + +void Objectness::illustrate() +{ + Mat xP1f, xN1f; + CV_Assert(matRead(_modelName + ".xP", xP1f) && matRead(_modelName + ".xN", xN1f)); + CV_Assert(xP1f.cols == xN1f.cols && xP1f.cols == _W*_W && xP1f.type() == CV_32F && xN1f.type() == CV_32F); + Mat meanP, meanN, stdDevP, stdDevN; + meanStdDev(xP1f, meanP, stdDevP); + meanStdDev(xN1f, meanN, stdDevN); + Mat meanV(_W, _W*2, CV_32F), stdDev(_W, _W*2, CV_32F); + meanP.reshape(1, _W).copyTo(meanV.colRange(0, _W)); + meanN.reshape(1, _W).copyTo(meanV.colRange(_W, _W*2)); + stdDevP.reshape(1, _W).copyTo(stdDev.colRange(0, _W)); + stdDevN.reshape(1, _W).copyTo(stdDev.colRange(_W, _W*2)); + normalize(meanV, meanV, 0, 255, NORM_MINMAX, CV_8U); + CmShow::showTinyMat(_voc.resDir + "PosNeg.png", meanV); + + FILE* f = fopen(_S(_voc.resDir + "PosNeg.m"), "w"); + CV_Assert(f != NULL); + fprintf(f, "figure(1);\n\n"); + PrintVector(f, getVector(meanP), "MeanP"); + PrintVector(f, getVector(meanN), "MeanN"); + PrintVector(f, getVector(stdDevP), "StdDevP"); + PrintVector(f, getVector(stdDevN), "StdDevN"); + PrintVector(f, getVector(_svmFilter), "Filter"); + fprintf(f, "hold on;\nerrorbar(MeanP, StdDevP, 'r');\nerrorbar(MeanN, StdDevN, 'g');\nhold off;"); + fclose(f); +} + +void Objectness::trainStageI() +{ + vecM pX, nX; + pX.reserve(200000), nX.reserve(200000); + Mat xP1f, xN1f; + CV_Assert(matRead(_modelName + ".xP", xP1f) && matRead(_modelName + ".xN", xN1f)); + for (int r = 0; r < xP1f.rows; r++) + pX.push_back(xP1f.row(r)); + for (int r = 0; r < xN1f.rows; r++) + nX.push_back(xN1f.row(r)); + Mat crntW = trainSVM(pX, nX, L1R_L2LOSS_SVC, 10, 1); + crntW = crntW.colRange(0, crntW.cols - 1).reshape(1, _W); + CV_Assert(crntW.size() == Size(_W, _W)); + matWrite(_modelName + ".wS1", crntW); +} + +// Training SVM with feature vector X and label Y. +// Each row of X is a feature vector, with corresponding label in Y. +// Return a CV_32F weight Mat +Mat Objectness::trainSVM(CMat &X1f, const vecI &Y, int sT, double C, double bias, double eps) +{ + // Set SVM parameters + parameter param; { + param.solver_type = sT; // L2R_L2LOSS_SVC_DUAL; + param.C = C; + param.eps = eps; // see setting below + param.p = 0.1; + param.nr_weight = 0; + param.weight_label = NULL; + param.weight = NULL; + set_print_string_function(print_null); + CV_Assert(X1f.rows == Y.size() && X1f.type() == CV_32F); + } + + // Initialize a problem + feature_node *x_space = NULL; + problem prob;{ + prob.l = X1f.rows; + prob.bias = bias; + prob.y = Malloc(double, prob.l); + prob.x = Malloc(feature_node*, prob.l); + const int DIM_FEA = X1f.cols; + prob.n = DIM_FEA + (bias >= 0 ? 1 : 0); + x_space = Malloc(feature_node, (prob.n + 1) * prob.l); + int j = 0; + for (int i = 0; i < prob.l; i++){ + prob.y[i] = Y[i]; + prob.x[i] = &x_space[j]; + const float* xData = X1f.ptr(i); + for (int k = 0; k < DIM_FEA; k++){ + x_space[j].index = k + 1; + x_space[j++].value = xData[k]; + } + if (bias >= 0){ + x_space[j].index = prob.n; + x_space[j++].value = bias; + } + x_space[j++].index = -1; + } + CV_Assert(j == (prob.n + 1) * prob.l); + } + + // Training SVM for current problem + const char* error_msg = check_parameter(&prob, ¶m); + if(error_msg){ + fprintf(stderr,"ERROR: %s\n",error_msg); + exit(1); + } + model *svmModel = train(&prob, ¶m); + Mat wMat(1, prob.n, CV_64F, svmModel->w); + wMat.convertTo(wMat, CV_32F); + free_and_destroy_model(&svmModel); + destroy_param(¶m); + free(prob.y); + free(prob.x); + free(x_space); + return wMat; +} + +// pX1f, nX1f are positive and negative training samples, each is a row vector +Mat Objectness::trainSVM(const vector &pX1f, const vector &nX1f, int sT, double C, double bias, double eps, int maxTrainNum) +{ + vecI ind(nX1f.size()); + for (size_t i = 0; i < ind.size(); i++) + ind[i] = i; + int numP = pX1f.size(), feaDim = pX1f[0].cols; + int totalSample = numP + nX1f.size(); + if (totalSample > maxTrainNum) + random_shuffle(ind.begin(), ind.end()); + totalSample = min(totalSample, maxTrainNum); + Mat X1f(totalSample, feaDim, CV_32F); + vecI Y(totalSample); + for(int i = 0; i < numP; i++){ + pX1f[i].copyTo(X1f.row(i)); + Y[i] = 1; + } + for (int i = numP; i < totalSample; i++){ + nX1f[ind[i - numP]].copyTo(X1f.row(i)); + Y[i] = -1; + } + return trainSVM(X1f, Y, sT, C, bias, eps); +} + +// Get potential bounding boxes for all test images +void Objectness::getObjBndBoxesForTests(vector> &_boxesTests, int numDetPerSize) +{ + const int TestNum = _voc.testSet.size(); + vecM imgs3u(TestNum); + vector> boxesTests; + boxesTests.resize(TestNum); + +#pragma omp parallel for + for (int i = 0; i < TestNum; i++){ + imgs3u[i] = imread(format(_S(_voc.imgPathW), _S(_voc.testSet[i]))); + boxesTests[i].reserve(10000); + } + + int scales[3] = {1, 3, 5}; + for (int clr = MAXBGR; clr <= G; clr++){ + setColorSpace(clr); + trainObjectness(numDetPerSize); + loadTrainedModel(); + CmTimer tm("Predict"); + tm.Start(); + +#pragma omp parallel for + for (int i = 0; i < TestNum; i++){ + ValStructVec boxes; + getObjBndBoxes(imgs3u[i], boxes, numDetPerSize); + boxesTests[i].append(boxes, scales[clr]); + } + + tm.Stop(); + printf("Average time for predicting an image (%s) is %gs\n", _clrName[_Clr], tm.TimeInSeconds()/TestNum); + } + + _boxesTests.resize(TestNum); + CmFile::MkDir(_bbResDir); +#pragma omp parallel for + for (int i = 0; i < TestNum; i++){ + CStr fName = _bbResDir + _voc.testSet[i]; + ValStructVec &boxes = boxesTests[i]; + FILE *f = fopen(_S(fName + ".txt"), "w"); + fprintf(f, "%d\n", boxes.size()); + for (size_t k = 0; k < boxes.size(); k++) + fprintf(f, "%g, %s\n", boxes(k), _S(strVec4i(boxes[k]))); + fclose(f); + + _boxesTests[i].resize(boxesTests[i].size()); + for (int j = 0; j < boxesTests[i].size(); j++) + _boxesTests[i][j] = boxesTests[i][j]; + } + + evaluatePerImgRecall(_boxesTests, "PerImgAllNS.m", 5000); + +#pragma omp parallel for + for (int i = 0; i < TestNum; i++){ + boxesTests[i].sort(false); + for (int j = 0; j < boxesTests[i].size(); j++) + _boxesTests[i][j] = boxesTests[i][j]; + } + evaluatePerImgRecall(_boxesTests, "PerImgAllS.m", 5000); +} + + +// Get potential bounding boxes for all test images +void Objectness::getObjBndBoxesForTestsFast(vector> &_boxesTests, int numDetPerSize) +{ + //setColorSpace(HSV); + trainObjectness(numDetPerSize); + loadTrainedModel(); + illustrate(); + + + const int TestNum = _voc.testSet.size(); + vecM imgs3u(TestNum); + vector> boxesTests; + boxesTests.resize(TestNum); + +#pragma omp parallel for + for (int i = 0; i < TestNum; i++){ + imgs3u[i] = imread(format(_S(_voc.imgPathW), _S(_voc.testSet[i]))); + boxesTests[i].reserve(10000); + } + + printf("Start predicting\n"); + CmTimer tm("Predict"); + tm.Start(); + +#pragma omp parallel for + for (int i = 0; i < TestNum; i++) + getObjBndBoxes(imgs3u[i], boxesTests[i], numDetPerSize); + + tm.Stop(); + printf("Average time for predicting an image (%s) is %gs\n", _clrName[_Clr], tm.TimeInSeconds()/TestNum); + + _boxesTests.resize(TestNum); + CmFile::MkDir(_bbResDir); + +#pragma omp parallel for + for (int i = 0; i < TestNum; i++){ + CStr fName = _bbResDir + _voc.testSet[i]; + ValStructVec &boxes = boxesTests[i]; + FILE *f = fopen(_S(fName + ".txt"), "w"); + fprintf(f, "%d\n", boxes.size()); + for (size_t k = 0; k < boxes.size(); k++) + fprintf(f, "%g, %s\n", boxes(k), _S(strVec4i(boxes[k]))); + fclose(f); + + _boxesTests[i].resize(boxesTests[i].size()); + for (int j = 0; j < boxesTests[i].size(); j++) + _boxesTests[i][j] = boxesTests[i][j]; + } + + evaluatePerImgRecall(_boxesTests, "PerImgAll.m", 5000); +} + + +void Objectness::getRandomBoxes(vector> &boxesTests, int num) +{ + const int TestNum = _voc.testSet.size(); + boxesTests.resize(TestNum); +#pragma omp parallel for + for (int i = 0; i < TestNum; i++){ + Mat imgs3u = imread(format(_S(_voc.imgPathW), _S(_voc.testSet[i]))); + int H = imgs3u.cols, W = imgs3u.rows; + boxesTests[i].reserve(num); + for (int k = 0; k < num; k++){ + int x1 = rand()%W + 1, x2 = rand()%W + 1; + int y1 = rand()%H + 1, y2 = rand()%H + 1; + boxesTests[i].push_back(Vec4i(min(x1, x2), min(y1, y2), max(x1, x2), max(y1, y2))); + } + } + evaluatePerImgRecall(boxesTests, "PerImgAll.m", num); +} + +void Objectness::evaluatePerImgRecall(const vector> &boxesTests, CStr &saveName, const int NUM_WIN) +{ + vecD recalls(NUM_WIN); + vecD avgScore(NUM_WIN); + const int TEST_NUM = _voc.testSet.size(); + for (int i = 0; i < TEST_NUM; i++){ + const vector &boxesGT = _voc.gtTestBoxes[i]; + const vector &boxes = boxesTests[i]; + const int gtNumCrnt = boxesGT.size(); + vecI detected(gtNumCrnt); + vecD score(gtNumCrnt); + double sumDetected = 0, abo = 0; + for (int j = 0; j < NUM_WIN; j++){ + if (j >= (int)boxes.size()){ + recalls[j] += sumDetected/gtNumCrnt; + avgScore[j] += abo/gtNumCrnt; + continue; + } + + for (int k = 0; k < gtNumCrnt; k++) { + double s = DataSetVOC::interUnio(boxes[j], boxesGT[k]); + score[k] = max(score[k], s); + detected[k] = score[k] >= 0.5 ? 1 : 0; + } + sumDetected = 0, abo = 0; + for (int k = 0; k < gtNumCrnt; k++) + sumDetected += detected[k], abo += score[k]; + recalls[j] += sumDetected/gtNumCrnt; + avgScore[j] += abo/gtNumCrnt; + } + } + + for (int i = 0; i < NUM_WIN; i++){ + recalls[i] /= TEST_NUM; + avgScore[i] /= TEST_NUM; + } + + int idx[8] = {1, 10, 100, 1000, 2000, 3000, 4000, 5000}; + for (int i = 0; i < 8; i++){ + if (idx[i] > NUM_WIN) + continue; + printf("%d:%.3g,%.3g\t", idx[i], recalls[idx[i] - 1], avgScore[idx[i] - 1]); + } + printf("\n"); + + FILE* f = fopen(_S(_voc.resDir + saveName), "w"); + CV_Assert(f != NULL); + fprintf(f, "figure(1);\n\n"); + PrintVector(f, recalls, "DR"); + PrintVector(f, avgScore, "MABO"); + fprintf(f, "semilogx(1:%d, DR(1:%d));\nhold on;\nsemilogx(1:%d, DR(1:%d));\naxis([1, 5000, 0, 1]);\nhold off;\n", NUM_WIN, NUM_WIN, NUM_WIN, NUM_WIN); + fclose(f); +} + +void Objectness::illuTestReults(const vector> &boxesTests) +{ + CStr resDir = _voc.localDir + "ResIlu/"; + CmFile::MkDir(resDir); + const int TEST_NUM = _voc.testSet.size(); + for (int i = 0; i < TEST_NUM; i++){ + const vector &boxesGT = _voc.gtTestBoxes[i]; + const vector &boxes = boxesTests[i]; + const int gtNumCrnt = boxesGT.size(); + CStr imgPath = format(_S(_voc.imgPathW), _S(_voc.testSet[i])); + CStr resNameNE = CmFile::GetNameNE(imgPath); + Mat img = imread(imgPath); + Mat bboxMatchImg = Mat::zeros(img.size(), CV_32F); + + vecD score(gtNumCrnt); + vector bboxMatch(gtNumCrnt); + for (int j = 0; j < boxes.size(); j++){ + const Vec4i &bb = boxes[j]; + for (int k = 0; k < gtNumCrnt; k++) { + double mVal = DataSetVOC::interUnio(boxes[j], boxesGT[k]); + if (mVal < score[k]) + continue; + score[k] = mVal; + bboxMatch[k] = boxes[j]; + } + } + + for (int k = 0; k < gtNumCrnt; k++){ + const Vec4i &bb = bboxMatch[k]; + rectangle(img, Point(bb[0], bb[1]), Point(bb[2], bb[3]), Scalar(0), 3); + rectangle(img, Point(bb[0], bb[1]), Point(bb[2], bb[3]), Scalar(255, 255, 255), 2); + rectangle(img, Point(bb[0], bb[1]), Point(bb[2], bb[3]), Scalar(0, 0, 255), 1); + } + + imwrite(resDir + resNameNE + "_Match.jpg", img); + } +} + +void Objectness::evaluatePerClassRecall(vector> &boxesTests, CStr &saveName, const int WIN_NUM) +{ + const int TEST_NUM = _voc.testSet.size(), CLS_NUM = _voc.classNames.size(); + if (boxesTests.size() != TEST_NUM){ + boxesTests.resize(TEST_NUM); + for (int i = 0; i < TEST_NUM; i++){ + Mat boxes; + matRead(_voc.localDir + _voc.testSet[i] + ".dat", boxes); + Vec4i* d = (Vec4i*)boxes.data; + boxesTests[i].resize(boxes.rows, WIN_NUM); + memcpy(&boxesTests[i][0], boxes.data, sizeof(Vec4i)*boxes.rows); + } + } + + for (int i = 0; i < TEST_NUM; i++) + if ((int)boxesTests[i].size() < WIN_NUM){ + printf("%s.dat: %d, %d\n", _S(_voc.testSet[i]), boxesTests[i].size(), WIN_NUM); + boxesTests[i].resize(WIN_NUM); + } + + + // #class by #win matrix for saving correct detection number and gt number + Mat crNum1i = Mat::zeros(CLS_NUM, WIN_NUM, CV_32S); + vecD gtNums(CLS_NUM); { + for (int i = 0; i < TEST_NUM; i++){ + const vector &boxes = boxesTests[i]; + const vector &boxesGT = _voc.gtTestBoxes[i]; + const vecI &clsGT = _voc.gtTestClsIdx[i]; + CV_Assert((int)boxes.size() >= WIN_NUM); + const int gtNumCrnt = boxesGT.size(); + for (int j = 0; j < gtNumCrnt; j++){ + gtNums[clsGT[j]]++; + double maxIntUni = 0; + int* crNum = crNum1i.ptr(clsGT[j]); + for (int k = 0; k < WIN_NUM; k++) { + double val = DataSetVOC::interUnio(boxes[k], boxesGT[j]); + maxIntUni = max(maxIntUni, val); + crNum[k] += maxIntUni >= 0.5 ? 1 : 0; + } + } + } + } + + FILE* f = fopen(_S(_voc.resDir + saveName), "w"); { + CV_Assert(f != NULL); + fprintf(f, "figure(1);\nhold on;\n\n\n"); + vecD val(WIN_NUM), recallObjs(WIN_NUM), recallClss(WIN_NUM); + for (int i = 0; i < WIN_NUM; i++) + val[i] = i; + PrintVector(f, gtNums, "GtNum"); + PrintVector(f, val, "WinNum"); + fprintf(f, "\n"); + string leglendStr("legend("); + double sumObjs = 0; + for (int c = 0; c < CLS_NUM; c++){ + sumObjs += gtNums[c]; + memset(&val[0], 0, sizeof(double)*WIN_NUM); + int* crNum = crNum1i.ptr(c); + for (int i = 0; i < WIN_NUM; i++){ + val[i] = crNum[i]/(gtNums[c] + 1e-200); + recallClss[i] += val[i]; + recallObjs[i] += crNum[i]; + } + CStr className = _voc.classNames[c]; + PrintVector(f, val, className); + fprintf(f, "plot(WinNum, %s, %s, 'linewidth', 2);\n", _S(className), COLORs[c % CN]); + leglendStr += format("'%s', ", _S(className)); + } + for (int i = 0; i < WIN_NUM; i++){ + recallClss[i] /= CLS_NUM; + recallObjs[i] /= sumObjs; + } + PrintVector(f, recallClss, "class"); + fprintf(f, "plot(WinNum, %s, %s, 'linewidth', 2);\n", "class", COLORs[CLS_NUM % CN]); + leglendStr += format("'%s', ", "class"); + PrintVector(f, recallObjs, "objects"); + fprintf(f, "plot(WinNum, %s, %s, 'linewidth', 2);\n", "objects", COLORs[(CLS_NUM+1) % CN]); + leglendStr += format("'%s', ", "objects"); + leglendStr.resize(leglendStr.size() - 2); + leglendStr += ");"; + fprintf(f, "%s\nhold off;\nxlabel('#WIN');\nylabel('Recall');\ngrid on;\naxis([0 %d 0 1]);\n", _S(leglendStr), WIN_NUM); + fprintf(f, "[class([1,10,100,1000]);objects([1,10,100,1000])]\ntitle('%s')\n", _S(saveName)); + fclose(f); + printf("%-70s\r", ""); + } + evaluatePerImgRecall(boxesTests, CmFile::GetNameNE(saveName) + "_PerI.m", WIN_NUM); +} + +void Objectness::PrintVector(FILE *f, const vecD &v, CStr &name) +{ + fprintf(f, "%s = [", name.c_str()); + for (size_t i = 0; i < v.size(); i++) + fprintf(f, "%g ", v[i]); + fprintf(f, "];\n"); +} + +// Write matrix to binary file +bool Objectness::matWrite(CStr& filename, CMat& _M){ + Mat M; + _M.copyTo(M); + FILE* file = fopen(_S(filename), "wb"); + if (file == NULL || M.empty()) + return false; + fwrite("CmMat", sizeof(char), 5, file); + int headData[3] = {M.cols, M.rows, M.type()}; + fwrite(headData, sizeof(int), 3, file); + fwrite(M.data, sizeof(char), M.step * M.rows, file); + fclose(file); + return true; +} + +// Read matrix from binary file +bool Objectness::matRead(const string& filename, Mat& _M){ + FILE* f = fopen(_S(filename), "rb"); + if (f == NULL) + return false; + char buf[8]; + int pre = fread(buf,sizeof(char), 5, f); + if (strncmp(buf, "CmMat", 5) != 0) { + printf("Invalidate CvMat data file %s\n", _S(filename)); + return false; + } + int headData[3]; // Width, height, type + fread(headData, sizeof(int), 3, f); + Mat M(headData[1], headData[0], headData[2]); + fread(M.data, sizeof(char), M.step * M.rows, f); + fclose(f); + M.copyTo(_M); + return true; +} + +void Objectness::evaluatePAMI12(CStr &saveName) +{ + const int TEST_NUM = _voc.testSet.size(); + vector> boxesTests(TEST_NUM); + CStr dir = _voc.wkDir + "PAMI12/"; + const int numDet = 1853; + for (int i = 0; i < TEST_NUM; i++){ + FILE *f = fopen(_S(dir + _voc.testSet[i] + ".txt"), "r"); + double score; + boxesTests[i].resize(numDet); + for (int j = 0; j < numDet; j++){ + Vec4i &v = boxesTests[i][j]; + fscanf(f, "%d %d %d %d %g\n", &v[0], &v[1], &v[2], &v[3], &score); + } + fclose(f); + } + printf("Load data finished\r"); + //evaluatePerImgRecall(boxesTests, saveName, numDet); + evaluatePerClassRecall(boxesTests, saveName, numDet); +} + +void Objectness::evaluateIJCV13(CStr &saveName) +{ + const int TEST_NUM = _voc.testSet.size(); + vector> boxesTests(TEST_NUM); + CStr dir = _voc.wkDir + "IJCV13/"; + const int numDet = 10000; + for (int i = 0; i < TEST_NUM; i++){ + FILE *f = fopen(_S(dir + _voc.testSet[i] + ".txt"), "r"); + boxesTests[i].resize(numDet); + for (int j = 0; j < numDet; j++){ + Vec4i &v = boxesTests[i][j]; + fscanf(f, "%d, %d, %d, %d\n", &v[1], &v[0], &v[3], &v[2]); + } + fclose(f); + } + printf("Load data finished\r"); + evaluatePerImgRecall(boxesTests, saveName, numDet); + //evaluate(boxesTests, saveName, numDet); +} +float distG(float d, float delta) {return exp(-d*d/(2*delta*delta));} + +Mat Objectness::aFilter(float delta, int sz) +{ + float dis = float(sz-1)/2.f; + Mat mat(sz, sz, CV_32F); + for (int r = 0; r < sz; r++) + for (int c = 0; c < sz; c++) + mat.at(r, c) = distG(sqrt(sqr(r-dis)+sqr(c-dis)) - dis, delta); + return mat; +} + +// +//// Calculate the image gradient: center option as in VLFeat +//void Objectness::gradientMag3(CMat &img3f, Mat &mag1f) +//{ +// const int H = img3f.rows, W = img3f.cols; +// Mat Ix(H, W, CV_32F), Iy(H, W, CV_32F); +// +// // Left/right most column Ix +// for (int y = 0; y < H; y++){ +// Ix.at(y, 0) = vecDist(img3f.at(y, 1), img3f.at(y, 0)); +// Ix.at(y, W-1) = vecDist(img3f.at(y, W-1), img3f.at(y, W-2)); +// } +// +// // Top/bottom most column Iy +// for (int x = 0; x < W; x++) { +// Iy.at(0, x) = vecDist(img3f.at(1, x), img3f.at(0, x)); +// Iy.at(H-1, x) = vecDist(img3f.at(H-1, x), img3f.at(H-2, x)); +// } +// +// // Find the gradient for inner regions +// for (int y = 0; y < H; y++) +// for (int x = 1; x < W-1; x++) +// Ix.at(y, x) = 0.5f * vecDist(img3f.at(y, x+1), img3f.at(y, x-1)); +// for (int y = 1; y < H-1; y++) +// for (int x = 0; x < W; x++) +// Iy.at(y, x) = 0.5f * vecDist(img3f.at(y+1, x), img3f.at(y-1, x)); +// mag1f = abs(Ix) + abs(Iy); +//} +// +//// +//// Calculate the image gradient: center option as in VLFeat +//void Objectness::gradientMag1(CMat &img1f, Mat &mag1f) +//{ +// CV_Assert(img1f.type() == CV_32F); +// const int H = img1f.rows, W = img1f.cols; +// Mat Ix(H, W, CV_32F), Iy(H, W, CV_32F); +// +// // Left/right most column Ix +// for (int y = 0; y < H; y++){ +// Ix.at(y, 0) = img1f.at(y, 1) - img1f.at(y, 0); +// Ix.at(y, W-1) = img1f.at(y, W-1) - img1f.at(y, W-2); +// } +// +// // Top/bottom most column Iy +// for (int x = 0; x < W; x++) { +// Iy.at(0, x) = img1f.at(1, x) - img1f.at(0, x); +// Iy.at(H-1, x) = img1f.at(H-1, x) - img1f.at(H-2, x); +// } +// +// // Find the gradient for inner regions +// for (int y = 0; y < H; y++) +// for (int x = 1; x < W-1; x++) +// Ix.at(y, x) = 0.5f * (img1f.at(y, x+1) - img1f.at(y, x-1)); +// for (int y = 1; y < H-1; y++) +// for (int x = 0; x < W; x++) +// Iy.at(y, x) = 0.5f * (img1f.at(y+1, x) - img1f.at(y-1, x)); +// mag1f = abs(Ix) + abs(Iy); +//} diff --git a/Bing/Src/Objectness.h b/Bing/Src/Objectness.h new file mode 100644 index 000000000..6d997f0a9 --- /dev/null +++ b/Bing/Src/Objectness.h @@ -0,0 +1,116 @@ +#pragma once +#include "DataSetVOC.h" +#include "ValStructVec.h" +#include "FilterTIG.h" +class Objectness +{ +public: + // base for window size quantization, feature window size (W, W), and non-maximal suppress size NSS + Objectness(DataSetVOC &voc, double base = 2, int W = 8, int NSS = 2); + ~Objectness(void); + + // Load trained model. + int loadTrainedModel(string modelName = ""); // Return -1, 0, or 1 if partial, none, or all loaded + + // Get potential bounding boxes, each of which is represented by a Vec4i for (minX, minY, maxX, maxY). + // The trained model should be prepared before calling this function: loadTrainedModel() or trainStageI() + trainStageII(). + // Use numDet to control the final number of proposed bounding boxes, and number of per size (scale and aspect ratio) + void getObjBndBoxes(CMat &img3u, ValStructVec &valBoxes, int numDetPerSize = 120); + + // Training and testing on the dataset + void trainObjectness(int numDetPerSize = 100); + void getObjBndBoxesForTests(vector> &boxesTests, int numDetPerSize = 100); + void getObjBndBoxesForTestsFast(vector> &boxesTests, int numDetPerSize = 100); + void getRandomBoxes(vector> &boxesTests, int numD = 10000); + void evaluatePAMI12(CStr &saveName = "PlotMAMI12.m"); + void evaluateIJCV13(CStr &saveName = "IJCV13.m"); + void evaluatePerClassRecall(vector> &boxesTests, CStr &saveName = "Plot.m", const int numDet = 1000); + void evaluatePerImgRecall(const vector> &boxesTests, CStr &saveName, const int numDet = 1000); + void illuTestReults(const vector> &boxesTests); + void setColorSpace(int clr = MAXBGR); + + // Training SVM with feature vector X and label Y. + // Each row of X is a feature vector, with corresponding label in Y. + // Return a CV_32F weight Mat + static Mat trainSVM(CMat &X1f, const vecI &Y, int sT, double C, double bias = -1, double eps = 0.01); + + // pX1f, nX1f are positive and negative training samples, each is a row vector + static Mat trainSVM(const vector &pX1f, const vector &nX1f, int sT, double C, double bias = -1, double eps = 0.01, int maxTrainNum = 100000); + + // Write matrix to binary file + static bool matWrite(CStr& filename, CMat& M); + + // Read matrix from binary file + static bool matRead( const string& filename, Mat& M); + + enum {MAXBGR, HSV, G}; + + static void meanStdDev(CMat &data1f, Mat &mean1f, Mat &stdDev1f); + + void illustrate(); + + inline static float LoG(float x, float y, float delta) {float d = -(x*x+y*y)/(2*delta*delta); return -1.0f/((float)(CV_PI)*pow(delta, 4)) * (1+d)*exp(d);} // Laplacian of Gaussian + static Mat aFilter(float delta, int sz); + +private: // Parameters + const double _base, _logBase; // base for window size quantization + const int _W; // As described in the paper: #Size, Size(_W, _H) of feature window. + const int _NSS; // Size for non-maximal suppress + const int _maxT, _minT, _numT; // The minimal and maximal dimensions of the template + + int _Clr; // + static const char* _clrName[3]; + + DataSetVOC &_voc; // The dataset for training, testing + string _modelName, _trainDirSI, _bbResDir; + + vecI _svmSzIdxs; // Indexes of active size. It's equal to _svmFilters.size() and _svmReW1f.rows + Mat _svmFilter; // Filters learned at stage I, each is a _H by _W CV_32F matrix + FilterTIG _tigF; // TIG filter + Mat _svmReW1f; // Re-weight parameters learned at stage II. + +private: // Help functions + + bool filtersLoaded() {int n = _svmSzIdxs.size(); return n > 0 && _svmReW1f.size() == Size(2, n) && _svmFilter.size() == Size(_W, _W);} + + int gtBndBoxSampling(const Vec4i &bbgt, vector &samples, vecI &bbR); + + Mat getFeature(CMat &img3u, const Vec4i &bb); // Return region feature + + inline double maxIntUnion(const Vec4i &bb, const vector &bbgts) {double maxV = 0; for(size_t i = 0; i < bbgts.size(); i++) maxV = max(maxV, DataSetVOC::interUnio(bb, bbgts[i])); return maxV; } + + // Convert VOC bounding box type to OpenCV Rect + inline Rect pnt2Rect(const Vec4i &bb){int x = bb[0] - 1, y = bb[1] - 1; return Rect(x, y, bb[2] - x, bb[3] - y);} + + // Template length at quantized scale t + inline int tLen(int t){return cvRound(pow(_base, t));} + + // Sub to quantization index + inline int sz2idx(int w, int h) {w -= _minT; h -= _minT; CV_Assert(w >= 0 && h >= 0 && w < _numT && h < _numT); return h * _numT + w + 1; } + inline string strVec4i(const Vec4i &v) const {return format("%d, %d, %d, %d", v[0], v[1], v[2], v[3]);} + + void generateTrianData(); + void trainStageI(); + void trainStateII(int numPerSz = 100); + void predictBBoxSI(CMat &mag3u, ValStructVec &valBoxes, vecI &sz, int NUM_WIN_PSZ = 100, bool fast = true); + void predictBBoxSII(ValStructVec &valBoxes, const vecI &sz); + + // Calculate the image gradient: center option as in VLFeat + void gradientMag(CMat &imgBGR3u, Mat &mag1u); + + static void gradientRGB(CMat &bgr3u, Mat &mag1u); + static void gradientGray(CMat &bgr3u, Mat &mag1u); + static void gradientHSV(CMat &bgr3u, Mat &mag1u); + static void gradientXY(CMat &x1i, CMat &y1i, Mat &mag1u); + + static inline int bgrMaxDist(const Vec3b &u, const Vec3b &v) {int b = abs(u[0]-v[0]), g = abs(u[1]-v[1]), r = abs(u[2]-v[2]); b = max(b,g); return max(b,r);} + static inline int vecDist3b(const Vec3b &u, const Vec3b &v) {return abs(u[0]-v[0]) + abs(u[1]-v[1]) + abs(u[2]-v[2]);} + + //Non-maximal suppress + static void nonMaxSup(CMat &matchCost1f, ValStructVec &matchCost, int NSS = 1, int maxPoint = 50, bool fast = true); + + static void PrintVector(FILE *f, const vecD &v, CStr &name); + + vecD getVector(CMat &t1f); +}; + diff --git a/Bing/Src/ReadMe.txt b/Bing/Src/ReadMe.txt new file mode 100644 index 000000000..67bf68e9f --- /dev/null +++ b/Bing/Src/ReadMe.txt @@ -0,0 +1,49 @@ +Objectness +========== +BING Objectness proposal estimator linux/mac/windows version implementation, +runs at 1000 FPS. This code is under BSD License. +Objectness Proposal estimator运行代码，已经在linux/mac/windows下测试成功， +执行速度超过1000帧每秒。 + + +This is the 1000 FPS BING objectness linux version library for efficient +objectness proposal estimator following the CVPR 2014 paper BING, please +consider to cite and refer to this paper. + +@inproceedings{BingObj2014, + title={{BING}: Binarized Normed Gradients for Objectness Estimation at 300fps}, + author={Ming-Ming Cheng and Ziming Zhang and Wen-Yan Lin and Philip H. S. Torr}, + booktitle={IEEE CVPR}, + year={2014}, +} + +The original author Ming-Ming Cheng has already released the source code for +windows 64-bit platform. In this library, we intend to release the code for the +linux/mac/windows users. You can maintain the code with Qt Creator IDE. + +Please find the original windows code / FAQ / Paper from this link: +http://mmcheng.net/bing/ + +In order to make the code running as the original version in windows, you need +to download the images/annotations PASCAL VOC 2007 data from the website. +(http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/#testdata) + +We have tested the code, it produces the same accuracy results as the original windows +version, while it runs at 1111 FPS(frame per second) at Ubuntu 12.04 with a Dell T7600 +workstation computer, which has two Intel Xeon E5-2687W (3.1GHz, 1600MHz) and 64 GB +1600MHz DDR3 Memory. + +FAQ +1. To run the code, you have to install OpenCV in the your ubuntu linux system. +We specify the dependencies on opencv at +" +include_directories(/usr/local/include) +link_directories(/usr/local/lib) +" +2. You can use/debug/change the code with Qt Creator IDE on ubuntu/mac. + + +Author: Ming-Ming Cheng(程明明) removethisifyouarehuman-cmm.thu@gmail.com +Linux Author: Shuai Zheng (Kyle,郑帅) removethisifyouarehuman-szhengcvpr@gmail.com +Please find more information from http://kylezheng.org/objectness/ +Date: 19, February diff --git a/Bing/Src/ValStructVec.h b/Bing/Src/ValStructVec.h new file mode 100644 index 000000000..077ca3513 --- /dev/null +++ b/Bing/Src/ValStructVec.h @@ -0,0 +1,72 @@ +#pragma once + +/************************************************************************/ +/* A value struct vector that supports efficient sorting */ +/************************************************************************/ + +template +struct ValStructVec +{ + ValStructVec(){clear();} + inline int size() const {return sz;} + inline void clear() {sz = 0; structVals.clear(); valIdxes.clear();} + inline void reserve(int resSz){clear(); structVals.reserve(resSz); valIdxes.reserve(resSz); } + inline void pushBack(const VT& val, const ST& structVal) {valIdxes.push_back(make_pair(val, sz)); structVals.push_back(structVal); sz++;} + + inline const VT& operator ()(int i) const {return valIdxes[i].first;} // Should be called after sort + inline const ST& operator [](int i) const {return structVals[valIdxes[i].second];} // Should be called after sort + inline VT& operator ()(int i) {return valIdxes[i].first;} // Should be called after sort + inline ST& operator [](int i) {return structVals[valIdxes[i].second];} // Should be called after sort + + void sort(bool descendOrder = true); + const vector &getSortedStructVal(); + void append(const ValStructVec &newVals, int startV = 0); + + vector structVals; // struct values + +private: + int sz; // size of the value struct vector + vector> valIdxes; // Indexes after sort + bool smaller() {return true;}; + vector sortedStructVals; +}; + +template +void ValStructVec::append(const ValStructVec &newVals, int startV) +{ + int sz = newVals.size(); + for (int i = 0; i < sz; i++) + pushBack((float)((i+300)*startV)/*newVals(i)*/, newVals[i]); +} + +template +void ValStructVec::sort(bool descendOrder /* = true */) +{ + if (descendOrder) + std::sort(valIdxes.begin(), valIdxes.end(), std::greater>()); + else + std::sort(valIdxes.begin(), valIdxes.end(), std::less>()); +} + +template +const vector& ValStructVec::getSortedStructVal() +{ + sortedStructVals.resize(sz); + for (int i = 0; i < sz; i++) + sortedStructVals[i] = structVals[valIdxes[i].second]; + return sortedStructVals; +} + +/* +void valStructVecDemo() +{ + ValStructVec sVals; + sVals.pushBack(3, "String 3"); + sVals.pushBack(5, "String 5"); + sVals.pushBack(4, "String 4"); + sVals.pushBack(1, "String 1"); + sVals.sort(false); + for (int i = 0; i < sVals.size(); i++) + printf("%d, %s\n", sVals(i), _S(sVals[i])); +} +*/ \ No newline at end of file diff --git a/Bing/Src/kyheader.cpp b/Bing/Src/kyheader.cpp new file mode 100644 index 000000000..40da9c6d7 --- /dev/null +++ b/Bing/Src/kyheader.cpp @@ -0,0 +1,5 @@ +// kyheader.cpp : source file that includes just the standard includes +// Objectness.pch will be the pre-compiled header +// stdafx.obj will contain the pre-compiled type information + +#include "kyheader.h" diff --git a/Bing/Src/kyheader.h b/Bing/Src/kyheader.h new file mode 100644 index 000000000..11f95f75a --- /dev/null +++ b/Bing/Src/kyheader.h @@ -0,0 +1,110 @@ +// stdafx.h : include file for standard system include files, +// or project specific include files that are used frequently, but +// are changed infrequently +// +#pragma once +#pragma warning(disable: 4996) +#pragma warning(disable: 4995) +#pragma warning(disable: 4805) +#pragma warning(disable: 4267) + +#define _CRTDBG_MAP_ALLOC +#include +//#include + +//#include +#include + + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +//#include +//#include +#include +#include +using namespace std; + +// TODO: reference additional headers your program requires here +#include "LibLinear/linear.h" +#include + +#define CV_VERSION_ID CVAUX_STR(CV_MAJOR_VERSION) CVAUX_STR(CV_MINOR_VERSION) CVAUX_STR(CV_SUBMINOR_VERSION) +#ifdef _DEBUG +#define cvLIB(name) "opencv_" name CV_VERSION_ID "d" +#else +#define cvLIB(name) "opencv_" name CV_VERSION_ID +#endif + +#pragma comment( lib, cvLIB("core")) +#pragma comment( lib, cvLIB("imgproc")) +#pragma comment( lib, cvLIB("highgui")) +using namespace cv; +#ifdef WIN32 +/* windows stuff */ +#else +typedef unsigned long DWORD; +typedef unsigned short WORD; +typedef unsigned int UNINT32; +typedef bool BOOL; +typedef void *HANDLE; +typedef unsigned char byte; +#endif +typedef vector vecI; +typedef const string CStr; +typedef const Mat CMat; +typedef vector vecS; +typedef vector vecM; +typedef vector vecF; +typedef vector vecD; + +enum{CV_FLIP_BOTH = -1, CV_FLIP_VERTICAL = 0, CV_FLIP_HORIZONTAL = 1}; +#define _S(str) ((str).c_str()) +#define CHK_IND(p) ((p).x >= 0 && (p).x < _w && (p).y >= 0 && (p).y < _h) +#define CV_Assert_(expr, args) \ +{\ + if(!(expr)) {\ + string msg = cv::format args; \ + printf("%s in %s:%d\n", msg.c_str(), __FILE__, __LINE__); \ + cv::error(cv::Exception(CV_StsAssert, msg, __FUNCTION__, __FILE__, __LINE__) ); }\ +} + +// Return -1 if not in the list +template +static inline int findFromList(const T &word, const vector &strList) {size_t idx = find(strList.begin(), strList.end(), word) - strList.begin(); return idx < strList.size() ? idx : -1;} +template inline T sqr(T x) { return x * x; } // out of range risk for T = byte, ... +template inline T vecSqrDist(const Vec &v1, const Vec &v2) {T s = 0; for (int i=0; i inline T vecDist(const Vec &v1, const Vec &v2) { return sqrt(vecSqrDist(v1, v2)); } // out of range risk for T = byte, ... + +inline Rect Vec4i2Rect(Vec4i &v){return Rect(Point(v[0] - 1, v[1] - 1), Point(v[2], v[3])); } +#ifdef __WIN32 + #define INT64 long long +#else + #define INT64 long + typedef unsigned long UINT64; +#endif + +#define __POPCNT__ +#include +#include +#ifdef __WIN32 +# include +# define POPCNT(x) __popcnt(x) +# define POPCNT64(x) __popcnt64(x) +#endif +#ifndef __WIN32 +# define POPCNT(x) __builtin_popcount(x) +# define POPCNT64(x) __builtin_popcountll(x) +#endif + +#include "CmFile.h" +#include "CmTimer.h" + diff --git a/Bing/Src/main.cpp b/Bing/Src/main.cpp new file mode 100644 index 000000000..244afe9b5 --- /dev/null +++ b/Bing/Src/main.cpp @@ -0,0 +1,55 @@ +#include "kyheader.h" +#include "Objectness.h" +#include "ValStructVec.h" +#include "CmShow.h" + + +void RunObjectness(CStr &resName, double base, int W, int NSS, int numPerSz); + +void illutrateLoG() +{ + for (float delta = 0.5f; delta < 1.1f; delta+=0.1f){ + Mat f = Objectness::aFilter(delta, 8); + normalize(f, f, 0, 1, NORM_MINMAX); + CmShow::showTinyMat(format("D=%g", delta), f); + } + waitKey(0); +} + + + +int main(int argc, char* argv[]) +{ + //CStr wkDir = "D:/WkDir/DetectionProposals/VOC2007/Local/"; + //illutrateLoG(); + //RunObjectness("WinRecall.m", 2, 8, 2, 130); + //RunObjectness("WinRecall.m", 2, 8, 2, 130); + RunObjectness("WinRecall.m", 2, 8, 2, 130); + //RunObjectness("WinRecall.m", 2, 8, 2, 130); + //RunObjectness("WinRecall.m", 2, 8, 2, 130); + + return 0; +} + +void RunObjectness(CStr &resName, double base, int W, int NSS, int numPerSz) +{ + srand((unsigned int)time(NULL)); + DataSetVOC voc2007("/home/bittnt/BING/BING_beta1/VOC/VOC2007/"); + voc2007.loadAnnotations(); + //voc2007.loadDataGenericOverCls(); + + printf("Dataset:`%s' with %d training and %d testing\n", _S(voc2007.wkDir), voc2007.trainNum, voc2007.testNum); + printf("%s Base = %g, W = %d, NSS = %d, perSz = %d\n", _S(resName), base, W, NSS, numPerSz); + + Objectness objNess(voc2007, base, W, NSS); + + vector> boxesTests; + //objNess.getObjBndBoxesForTests(boxesTests, 250); + objNess.getObjBndBoxesForTestsFast(boxesTests, numPerSz); + //objNess.getRandomBoxes(boxesTests); + + //objNess.evaluatePerClassRecall(boxesTests, resName, 1000); + //objNess.illuTestReults(boxesTests); + //objNess.evaluatePAMI12(); + //objNess.evaluateIJCV13(); +} diff --git a/Bing/Src/xml2yaml.m b/Bing/Src/xml2yaml.m new file mode 100644 index 000000000..b78616577 --- /dev/null +++ b/Bing/Src/xml2yaml.m @@ -0,0 +1,22 @@ +%% Convert the file type of opencv xml annotations to yaml thus it can be read by opencv +% This functions relies on http://code.google.com/p/yamlmatlab/ +% The results needs to be further refined to deal with indentation problem + +function xml2yaml(wkDir) + fNs = dir([wkDir '*.xml']); + fNum = length(fNs); + for i = 1:fNum + [~, nameNE, ~] = fileparts(fNs(i).name); + %fprintf('%d/%d: %s\n', i, fNum, [wkDir nameNE]); + fPathN = [wkDir nameNE '.yaml']; + x=VOCreadxml([wkDir nameNE '.xml']); + if isfield(x.annotation, 'owner') + x.annotation = rmfield(x.annotation, 'owner'); + end + names = fieldnames(x.annotation.object); + if (strcmp(names{end}, 'bndbox')) + x.annotation.object = orderfields(x.annotation.object, names([end, 1:end-1])); + end + WriteYaml(fPathN, x); + end +end \ No newline at end of file diff --git a/Bing/Src/yml.m b/Bing/Src/yml.m new file mode 100644 index 000000000..cf842575b --- /dev/null +++ b/Bing/Src/yml.m @@ -0,0 +1,17 @@ +wkDir = 'D:/WkDir/DetectionProposals/VOC2007/Annotations/'; +xml2yaml(wkDir); +wkDir = 'D:\WkDir\DetectionProposals\ImageNet\ILSVRC2012_bbox_val_v3\val\'; +xml2yaml(wkDir); + +ImgNetDir = 'D:\WkDir\DetectionProposals\ImageNet\ILSVRC2012_bbox_train_v2\'; +d = dir(ImgNetDir); +isub = [d(:).isdir]; %# returns logical vector +nameFolds = {d(isub).name}'; +nameFolds(ismember(nameFolds,{'.','..'})) = []; +for i=1:length(nameFolds) + wkDir = [ImgNetDir nameFolds{i} '\']; + fprintf('%d/%d: %s\n', i, length(nameFolds), wkDir); + xml2yaml(wkDir); +end + + From 2558c3b5e18c85e94e9563df74a55fbb15d51ba5 Mon Sep 17 00:00:00 2001 From: agam1994 Date: Thu, 14 Jul 2016 11:47:39 +0530 Subject: [PATCH 10/14] Create fetch_bing_data.sh --- data/scripts/fetch_bing_data.sh | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 data/scripts/fetch_bing_data.sh diff --git a/data/scripts/fetch_bing_data.sh b/data/scripts/fetch_bing_data.sh new file mode 100644 index 000000000..1c2e6e251 --- /dev/null +++ b/data/scripts/fetch_bing_data.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )/../" && pwd )" +cd $DIR + +FILE=edge_box_data.tgz +URL = "c:/users/FileLocation" + +if [ -f $FILE ]; then + echo "File already exists. Checking md5..." + os=`uname -s` + if [ "$os" = "Linux" ]; then + checksum=`md5sum $FILE | awk '{ print $1 }'` + elif [ "$os" = "Darwin" ]; then + checksum=`cat $FILE | md5` + fi + if [ "$checksum" = "$CHECKSUM" ]; then + echo "Checksum is correct. No need to download." + exit 0 + else + echo "Checksum is incorrect. Need to download again." + fi +fi + +echo "Downloading Edge boxes for COCO train, val, and test-dev set..." + +wget $URL -O $FILE + +echo "Unzipping..." + +tar zxvf $FILE + +echo "Done. Please run this command again to verify that checksum = $CHECKSUM." From a6612734de7406ac771eac690add367166994f57 Mon Sep 17 00:00:00 2001 From: agam1994 Date: Thu, 14 Jul 2016 11:52:16 +0530 Subject: [PATCH 11/14] Update imdb.py --- lib/datasets/imdb.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/lib/datasets/imdb.py b/lib/datasets/imdb.py index 1679aba84..d7c28e394 100644 --- a/lib/datasets/imdb.py +++ b/lib/datasets/imdb.py @@ -21,7 +21,8 @@ def __init__(self, name): self._num_classes = 0 self._classes = [] self._image_index = [] - self._obj_proposer = 'edge_box' + self._obj_proposer1 = 'edge_box' + self._obj_proposer2 = 'bing' self._roidb = None self._roidb_handler = self.default_roidb # Use this dict for storing dataset specific config options From 49ffa5b8517e97e7690d245f0c8b44f9ac0f14e5 Mon Sep 17 00:00:00 2001 From: agam1994 Date: Thu, 14 Jul 2016 12:00:45 +0530 Subject: [PATCH 12/14] Update pascal_voc.py --- lib/datasets/pascal_voc.py | 52 +++++++++++++++++++++++++++++++++++++- 1 file changed, 51 insertions(+), 1 deletion(-) diff --git a/lib/datasets/pascal_voc.py b/lib/datasets/pascal_voc.py index 3d63373e9..f1fc944ba 100644 --- a/lib/datasets/pascal_voc.py +++ b/lib/datasets/pascal_voc.py @@ -37,7 +37,8 @@ def __init__(self, image_set, year, devkit_path=None): self._image_ext = '.jpg' self._image_index = self._load_image_set_index() # Default to roidb handler - self._roidb_handler = self.edge_box_roidb + self._roidb_handler1 = self.edge_box_roidb + self._roib_handler2 = self.bing_roib self._salt = str(uuid.uuid4()) self._comp_id = 'comp4' @@ -138,6 +139,36 @@ def edge_box_roidb(self): print 'wrote ss roidb to {}'.format(cache_file) return roidb + + + def bing_roidb(self): + """ + Return the database of bing regions of interest. + Ground-truth ROIs are also included. + + This function loads/saves from/to a cache file to speed up future calls. + """ + cache_file = os.path.join(self.cache_path, + self.name + '_bing_roidb.pkl') + + if os.path.exists(cache_file): + with open(cache_file, 'rb') as fid: + roidb = cPickle.load(fid) + print '{} ss roidb loaded from {}'.format(self.name, cache_file) + return roidb + + if int(self._year) == 2007 or self._image_set != 'test': + gt_roidb = self.gt_roidb() + ss_roidb = self._load_bing_roidb(gt_roidb) + roidb = imdb.merge_roidbs(gt_roidb, ss_roidb) + else: + roidb = self._load_bing_roidb(None) + with open(cache_file, 'wb') as fid: + cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL) + print 'wrote ss roidb to {}'.format(cache_file) + + return roidb + def rpn_roidb(self): if int(self._year) == 2007 or self._image_set != 'test': @@ -177,6 +208,25 @@ def _load_edge_box_roidb(self, gt_roidb): return self.create_roidb_from_box_list(box_list, gt_roidb) + def _load_bing_roidb(self, gt_roidb): + filename = os.path.abspath(os.path.join(cfg.DATA_DIR, + 'bing_data', + self.name + '.mat')) + assert os.path.exists(filename), \ + 'bing data not found at: {}'.format(filename) + raw_data = sio.loadmat(filename)['boxes'].ravel() + + box_list = [] + for i in xrange(raw_data.shape[0]): + boxes = raw_data[i][:, (1, 0, 3, 2)] - 1 + keep = ds_utils.unique_boxes(boxes) + boxes = boxes[keep, :] + keep = ds_utils.filter_small_boxes(boxes, self.config['min_size']) + boxes = boxes[keep, :] + box_list.append(boxes) + + return self.create_roidb_from_box_list(box_list, gt_roidb) + def _load_pascal_annotation(self, index): """ Load image and bounding boxes info from XML file in the PASCAL VOC From cc5acccca1884f1753c0db42564138842f5a9ff5 Mon Sep 17 00:00:00 2001 From: agam1994 Date: Thu, 14 Jul 2016 12:02:57 +0530 Subject: [PATCH 13/14] Update config.py --- lib/fast_rcnn/config.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/lib/fast_rcnn/config.py b/lib/fast_rcnn/config.py index c3db065e7..7acdcb342 100644 --- a/lib/fast_rcnn/config.py +++ b/lib/fast_rcnn/config.py @@ -90,6 +90,7 @@ # Train using these proposals __C.TRAIN.PROPOSAL_METHOD = 'edge_box' +#_C.TRAIN>PROPASAL_METHOD = 'bing' # Make minibatches from images that have similar aspect ratios (i.e. both # tall and thin or both short and wide) in order to avoid wasting computation @@ -153,6 +154,7 @@ # Test using these proposals __C.TEST.PROPOSAL_METHOD = 'edge_box' +#_C.TRAIN>PROPASAL_METHOD = 'bing' ## NMS threshold used on RPN proposals __C.TEST.RPN_NMS_THRESH = 0.7 From a732e5b3d7e369017a06d43d413e91e959f3e817 Mon Sep 17 00:00:00 2001 From: agam1994 Date: Thu, 14 Jul 2016 12:04:52 +0530 Subject: [PATCH 14/14] Update eval_recall.py --- tools/eval_recall.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tools/eval_recall.py b/tools/eval_recall.py index 9ebc6ddb6..ad1273eab 100755 --- a/tools/eval_recall.py +++ b/tools/eval_recall.py @@ -18,6 +18,9 @@ def parse_args(): parser.add_argument('--method', dest='method', help='proposal method', default='edge_box', type=str) + parser.add_argument('--method', dest='method', + help='proposal method', + default='bing', type=str) parser.add_argument('--rpn-file', dest='rpn_file', default=None, type=str)