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values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | jorgepsmatos/cft-otb-master | vl_test_inthist.m | .m | cft-otb-master/vlfeat-0.9.20/toolbox/xtest/vl_test_inthist.m | 811 | utf_8 | 459027d0c54d8f197563a02ab66ef45d | function results = vl_test_inthist(varargin)
% VL_TEST_INTHIST
vl_test_init ;
function s = setup()
rand('state',0) ;
s.labels = uint32(8*rand(123, 76, 3)) ;
function test_basic(s)
l = 10 ;
hist = vl_inthist(s.labels, 'numlabels', l) ;
hist_ = inthist_slow(s.labels, l) ;
vl_assert_equal(double(hist),hist_) ;
function... |
github | jorgepsmatos/cft-otb-master | vl_test_imdisttf.m | .m | cft-otb-master/vlfeat-0.9.20/toolbox/xtest/vl_test_imdisttf.m | 1,885 | utf_8 | ae921197988abeb984cbcdf9eaf80e77 | function results = vl_test_imdisttf(varargin)
% VL_TEST_DISTTF
vl_test_init ;
function test_basic()
for conv = {@single, @double}
conv = conv{1} ;
I = conv([0 0 0 ; 0 -2 0 ; 0 0 0]) ;
D = vl_imdisttf(I);
assert(isequal(D, conv(- [0 1 0 ; 1 2 1 ; 0 1 0]))) ;
I(2,2) = -3 ;
[D,map] = vl_imdisttf(I) ;
asse... |
github | jorgepsmatos/cft-otb-master | vl_test_vlad.m | .m | cft-otb-master/vlfeat-0.9.20/toolbox/xtest/vl_test_vlad.m | 1,977 | utf_8 | d3797288d6edb1d445b890db3780c8ce | function results = vl_test_vlad(varargin)
% VL_TEST_VLAD
vl_test_init ;
function s = setup()
randn('state',0) ;
s.x = randn(128,256) ;
s.mu = randn(128,16) ;
assignments = rand(16, 256) ;
s.assignments = bsxfun(@times, assignments, 1 ./ sum(assignments,1)) ;
function test_basic (s)
x = [1, 2, 3] ;
mu = [0, 0, 0] ;
a... |
github | jorgepsmatos/cft-otb-master | vl_test_pr.m | .m | cft-otb-master/vlfeat-0.9.20/toolbox/xtest/vl_test_pr.m | 3,763 | utf_8 | 4d1da5ccda1a7df2bec35b8f12fdd620 | function results = vl_test_pr(varargin)
% VL_TEST_PR
vl_test_init ;
function s = setup()
s.scores0 = [5 4 3 2 1] ;
s.scores1 = [5 3 4 2 1] ;
s.labels = [1 1 -1 -1 -1] ;
function test_perfect_tptn(s)
[rc,pr] = vl_pr(s.labels,s.scores0) ;
vl_assert_almost_equal(pr, [1 1/1 2/2 2/3 2/4 2/5]) ;
vl_assert_almost_equal(rc, ... |
github | jorgepsmatos/cft-otb-master | vl_test_hog.m | .m | cft-otb-master/vlfeat-0.9.20/toolbox/xtest/vl_test_hog.m | 1,555 | utf_8 | eed7b2a116d142040587dc9c4eb7cd2e | function results = vl_test_hog(varargin)
% VL_TEST_HOG
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
[x,y]= meshgrid(linspace(-1,1,128)) ;
s.round = single(x.^2+y.^2);
s.imSmall = s.im(1:128,1:128,:) ;
s.imSmall = s.im ;
s.imSmallFlipped = s.imSmall(:,end:-1:1,:) ;
function test_basic... |
github | jorgepsmatos/cft-otb-master | vl_test_argparse.m | .m | cft-otb-master/vlfeat-0.9.20/toolbox/xtest/vl_test_argparse.m | 795 | utf_8 | e72185b27206d0ee1dfdc19fe77a5be6 | function results = vl_test_argparse(varargin)
% VL_TEST_ARGPARSE
vl_test_init ;
function test_basic()
opts.field1 = 1 ;
opts.field2 = 2 ;
opts.field3 = 3 ;
opts_ = opts ;
opts_.field1 = 3 ;
opts_.field2 = 10 ;
opts = vl_argparse(opts, {'field2', 10, 'field1', 3}) ;
assert(isequal(opts, opts_)) ;
opts_.field1 = 9 ;
... |
github | jorgepsmatos/cft-otb-master | vl_test_liop.m | .m | cft-otb-master/vlfeat-0.9.20/toolbox/xtest/vl_test_liop.m | 1,023 | utf_8 | a162be369073bed18e61210f44088cf3 | function results = vl_test_liop(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
randn('state',0) ;
s.patch = randn(65,'single') ;
xr = -32:32 ;
[x,y] = meshgrid(xr) ;
s.blob = - single(x.^2+y.^2) ;
function test_basic(s)
d = vl_liop(s.patch) ;
function test_blob(s)
% with a blob, all local intensity ord... |
github | jorgepsmatos/cft-otb-master | vl_test_binsearch.m | .m | cft-otb-master/vlfeat-0.9.20/toolbox/xtest/vl_test_binsearch.m | 1,339 | utf_8 | 85dc020adce3f228fe7dfb24cf3acc63 | function results = vl_test_binsearch(varargin)
% VL_TEST_BINSEARCH
vl_test_init ;
function test_inf_bins()
x = [-inf -1 0 1 +inf] ;
vl_assert_equal(vl_binsearch([], x), [0 0 0 0 0]) ;
vl_assert_equal(vl_binsearch([-inf 0], x), [1 1 2 2 2]) ;
vl_assert_equal(vl_binsearch([-inf], x), [1 1 1 1 1]) ;
vl_a... |
github | jorgepsmatos/cft-otb-master | vl_roc.m | .m | cft-otb-master/vlfeat-0.9.20/toolbox/plotop/vl_roc.m | 10,113 | utf_8 | 22fd8ff455ee62a96ffd94b9074eafeb | function [tpr,tnr,info] = vl_roc(labels, scores, varargin)
%VL_ROC ROC curve.
% [TPR,TNR] = VL_ROC(LABELS, SCORES) computes the Receiver Operating
% Characteristic (ROC) curve [1]. LABELS is a row vector of ground
% truth labels, greater than zero for a positive sample and smaller
% than zero for a negative o... |
github | jorgepsmatos/cft-otb-master | vl_click.m | .m | cft-otb-master/vlfeat-0.9.20/toolbox/plotop/vl_click.m | 2,661 | utf_8 | 6982e869cf80da57fdf68f5ebcd05a86 | function P = vl_click(N,varargin) ;
% VL_CLICK Click a point
% P=VL_CLICK() let the user click a point in the current figure and
% returns its coordinates in P. P is a two dimensiona vectors where
% P(1) is the point X-coordinate and P(2) the point Y-coordinate. The
% user can abort the operation by pressing any k... |
github | jorgepsmatos/cft-otb-master | vl_pr.m | .m | cft-otb-master/vlfeat-0.9.20/toolbox/plotop/vl_pr.m | 9,138 | utf_8 | c7fe6832d2b6b9917896810c52a05479 | function [recall, precision, info] = vl_pr(labels, scores, varargin)
%VL_PR Precision-recall curve.
% [RECALL, PRECISION] = VL_PR(LABELS, SCORES) computes the
% precision-recall (PR) curve. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative on... |
github | jorgepsmatos/cft-otb-master | vl_ubcread.m | .m | cft-otb-master/vlfeat-0.9.20/toolbox/sift/vl_ubcread.m | 3,015 | utf_8 | e8ddd3ecd87e76b6c738ba153fef050f | function [f,d] = vl_ubcread(file, varargin)
% SIFTREAD Read Lowe's SIFT implementation data files
% [F,D] = VL_UBCREAD(FILE) reads the frames F and the descriptors D
% from FILE in UBC (Lowe's original implementation of SIFT) format
% and returns F and D as defined by VL_SIFT().
%
% VL_UBCREAD(FILE, 'FORMAT', '... |
github | jorgepsmatos/cft-otb-master | vl_frame2oell.m | .m | cft-otb-master/vlfeat-0.9.20/toolbox/sift/vl_frame2oell.m | 2,806 | utf_8 | c93792632f630743485fa4c2cf12d647 | function eframes = vl_frame2oell(frames)
% VL_FRAMES2OELL Convert a geometric frame to an oriented ellipse
% EFRAME = VL_FRAME2OELL(FRAME) converts the generic FRAME to an
% oriented ellipses EFRAME. FRAME and EFRAME can be matrices, with
% one frame per column.
%
% A frame is either a point, a disc, an orien... |
github | jorgepsmatos/cft-otb-master | vl_plotsiftdescriptor.m | .m | cft-otb-master/vlfeat-0.9.20/toolbox/sift/vl_plotsiftdescriptor.m | 5,114 | utf_8 | a4e125a8916653f00143b61cceda2f23 | function h=vl_plotsiftdescriptor(d,f,varargin)
% VL_PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% VL_PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptor D. If D is a
% matrix, it plots one descriptor per column. D has the same format
% used by VL_SIFT().
%
% VL_PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warpe... |
github | jorgepsmatos/cft-otb-master | phow_caltech101.m | .m | cft-otb-master/vlfeat-0.9.20/apps/phow_caltech101.m | 11,594 | utf_8 | 7f4890a2e6844ca56debbfe23cca64f3 | function phow_caltech101()
% PHOW_CALTECH101 Image classification in the Caltech-101 dataset
% This program demonstrates how to use VLFeat to construct an image
% classifier on the Caltech-101 data. The classifier uses PHOW
% features (dense SIFT), spatial histograms of visual words, and a
% Chi2 SVM. To speedu... |
github | jorgepsmatos/cft-otb-master | sift_mosaic.m | .m | cft-otb-master/vlfeat-0.9.20/apps/sift_mosaic.m | 4,621 | utf_8 | 8fa3ad91b401b8f2400fb65944c79712 | function mosaic = sift_mosaic(im1, im2)
% SIFT_MOSAIC Demonstrates matching two images using SIFT and RANSAC
%
% SIFT_MOSAIC demonstrates matching two images based on SIFT
% features and RANSAC and computing their mosaic.
%
% SIFT_MOSAIC by itself runs the algorithm on two standard test
% images. Use SIFT_MOSAI... |
github | jorgepsmatos/cft-otb-master | encodeImage.m | .m | cft-otb-master/vlfeat-0.9.20/apps/recognition/encodeImage.m | 5,278 | utf_8 | 5d9dc6161995b8e10366b5649bf4fda4 | function descrs = encodeImage(encoder, im, varargin)
% ENCODEIMAGE Apply an encoder to an image
% DESCRS = ENCODEIMAGE(ENCODER, IM) applies the ENCODER
% to image IM, returning a corresponding code vector PSI.
%
% IM can be an image, the path to an image, or a cell array of
% the same, to operate on multiple ... |
github | jorgepsmatos/cft-otb-master | experiments.m | .m | cft-otb-master/vlfeat-0.9.20/apps/recognition/experiments.m | 6,905 | utf_8 | 1e4a4911eed4a451b9488b9e6cc9b39c | function experiments()
% EXPERIMENTS Run image classification experiments
% The experimens download a number of benchmark datasets in the
% 'data/' subfolder. Make sure that there are several GBs of
% space available.
%
% By default, experiments run with a lite option turned on. This
% quickly runs all... |
github | jorgepsmatos/cft-otb-master | getDenseSIFT.m | .m | cft-otb-master/vlfeat-0.9.20/apps/recognition/getDenseSIFT.m | 1,679 | utf_8 | 2059c0a2a4e762226d89121408c6e51c | function features = getDenseSIFT(im, varargin)
% GETDENSESIFT Extract dense SIFT features
% FEATURES = GETDENSESIFT(IM) extract dense SIFT features from
% image IM.
% Author: Andrea Vedaldi
% Copyright (C) 2013 Andrea Vedaldi
% All rights reserved.
%
% This file is part of the VLFeat library and is made availab... |
github | jorgepsmatos/cft-otb-master | parameters_to_projective_matrix.m | .m | cft-otb-master/rstEval/parameters_to_projective_matrix.m | 1,035 | utf_8 | dfee648fa200122674ad8c5578a60f94 | % Yigang Peng, Arvind Ganesh, November 2009.
% Questions? abalasu2@illinois.edu
%
% Copyright: Perception and Decision Laboratory, University of Illinois, Urbana-Champaign
% Microsoft Research Asia, Beijing
% Computes projective matrix based on input parameters and transformation
% type.
function T = para... |
github | jorgepsmatos/cft-otb-master | makeConfig.m | .m | cft-otb-master/trackers/MEEM/makeConfig.m | 3,812 | utf_8 | 252c738e6c80a3d5dd0fd0328af555d2 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | MEEMTrack.m | .m | cft-otb-master/trackers/MEEM/MEEMTrack.m | 7,219 | utf_8 | 1613bcd3d072e7c12f4c50474d971c6b | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | getLogLikelihoodEntropy.m | .m | cft-otb-master/trackers/MEEM/expert_ensemble/getLogLikelihoodEntropy.m | 1,526 | utf_8 | 999c7599b3ff406580b85afb947e1cda | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | expertsDo.m | .m | cft-otb-master/trackers/MEEM/expert_ensemble/expertsDo.m | 7,474 | utf_8 | 6b9235e07d36082e168d109642bbe955 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | updateTrackerExperts.m | .m | cft-otb-master/trackers/MEEM/expert_ensemble/updateTrackerExperts.m | 1,442 | utf_8 | a39e69f945312fc1cf91778493422bb7 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | updateSample.m | .m | cft-otb-master/trackers/MEEM/base_tracker/updateSample.m | 4,103 | utf_8 | 50b70fd5f4abba8cb2bf71ba437942b3 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | initSvmTracker.m | .m | cft-otb-master/trackers/MEEM/base_tracker/initSvmTracker.m | 2,771 | utf_8 | 60f01ee60b230dc9d178f5a37e47ec5f | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | resample.m | .m | cft-otb-master/trackers/MEEM/base_tracker/resample.m | 2,721 | utf_8 | f177be18c2835444f5afe2a73755ecc6 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | createSampler.m | .m | cft-otb-master/trackers/MEEM/base_tracker/createSampler.m | 1,185 | utf_8 | 65bf772fdc4290aa698d9d2982cb6cc2 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | updateSvmTracker.m | .m | cft-otb-master/trackers/MEEM/base_tracker/updateSvmTracker.m | 7,192 | utf_8 | 060062d0670481dc4cf232c96ad3e8c7 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | initSampler.m | .m | cft-otb-master/trackers/MEEM/base_tracker/initSampler.m | 1,771 | utf_8 | f4a646e15a3834aa0393ed88733c28e3 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | createSvmTracker.m | .m | cft-otb-master/trackers/MEEM/base_tracker/createSvmTracker.m | 1,669 | utf_8 | fbe5e892af83bc3afa1fb56b6621d9db | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | getFrame2Compute.m | .m | cft-otb-master/trackers/MEEM/utils/getFrame2Compute.m | 1,376 | utf_8 | cbfdf5bac2d455a5beacfb7298718f3e | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | rsz_rt.m | .m | cft-otb-master/trackers/MEEM/utils/rsz_rt.m | 2,008 | utf_8 | f0fd5cd62af45c1522fc811d27157650 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | getIOU.m | .m | cft-otb-master/trackers/MEEM/utils/getIOU.m | 1,394 | utf_8 | d93aa5275ea66fd3299b9272b249219d | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | getFeatureRep.m | .m | cft-otb-master/trackers/MEEM/utils/getFeatureRep.m | 2,256 | utf_8 | 54eed8fc72a068989c154467f8c1c166 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... |
github | jorgepsmatos/cft-otb-master | compile.m | .m | cft-otb-master/trackers/MEEM/mex/compile.m | 2,795 | utf_8 | 594a3606634fd6f685dd57abd2df046e | % make the mex file for Windows system
% Jianming Zhang
% 9/14/2013
function compile()
% set the values
opts.opencv_include_path = '/usr/local/include/opencv2'; % OpenCV include path
opts.opencv_lib_path = '/usr/local/lib'; % OpenCV lib path
opts.clean = false; % clean mode
opts.dryru... |
github | jorgepsmatos/cft-otb-master | run_tracker.m | .m | cft-otb-master/trackers/KCF/run_tracker.m | 6,526 | utf_8 | 4da9745fa140f8a4ef36fdd45bc7dcfa |
%
% High-Speed Tracking with Kernelized Correlation Filters
%
% Joao F. Henriques, 2014
% http://www.isr.uc.pt/~henriques/
%
% Main interface for Kernelized/Dual Correlation Filters (KCF/DCF).
% This function takes care of setting up parameters, loading video
% information and computing precisions. For the actua... |
github | jorgepsmatos/cft-otb-master | run_tracker.m | .m | cft-otb-master/trackers/CF2/run_tracker.m | 4,338 | utf_8 | 4fe4e379aea960b47bcd44ca42245fb1 | % RUN_TRACKER: process a specified video using CF2
%
% Input:
% - video: the name of the selected video
% - show_visualization: set to True for visualizing tracking results
% - show_plots: set to True for plotting quantitative results
% Output:
% - precision: precision thre... |
github | jorgepsmatos/cft-otb-master | tracker_ensemble.m | .m | cft-otb-master/trackers/CF2/tracker_ensemble.m | 7,947 | utf_8 | 610b3a9a2bd353121cef30792685d704 | % tracker_ensemble: Correlation filter tracking with convolutional features
%
% Input:
% - video_path: path to the image sequence
% - img_files: list of image names
% - pos: intialized center position of the target in (row, col)
% - target_sz: intialized target size ... |
github | jorgepsmatos/cft-otb-master | get_features.m | .m | cft-otb-master/trackers/CF2/utility/get_features.m | 970 | utf_8 | 4227d2da1c4a1f86818f58b3d5c8a71b | % GET_FEATURES: Extracting hierachical convolutional features
function feat = get_features(im, cos_window, layers)
global net
global enableGPU
if isempty(net)
initial_net();
end
% if ~existsOnGPU(net.layers{1,1}.biases) && enableGPU
% net = vl_simplenn_move(net, 'gpu');
% end
sz_window = size(cos_window);
... |
github | jorgepsmatos/cft-otb-master | get_subwindow.m | .m | cft-otb-master/trackers/CF2/utility/get_subwindow.m | 844 | utf_8 | 6d98ab20898d9f62b02062481caf2a0c | function out = get_subwindow(im, pos, sz)
%GET_SUBWINDOW Obtain sub-window from image, with replication-padding.
% Returns sub-window of image IM centered at POS ([y, x] coordinates),
% with size SZ ([height, width]). If any pixels are outside of the image,
% they will replicate the values at the borders.
%
% J... |
github | jorgepsmatos/cft-otb-master | p_getImageFilenamesFromDirectory.m | .m | cft-otb-master/trackers/CF2/external/tools/p_getImageFilenamesFromDirectory.m | 2,073 | utf_8 | 65a1bafeaa60531c73f5acc8148a7dd0 | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), ... |
github | jorgepsmatos/cft-otb-master | p_writeLog.m | .m | cft-otb-master/trackers/CF2/external/tools/p_writeLog.m | 1,741 | utf_8 | 58a8994d8d0440fb8411cabbcc221319 | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), ... |
github | jorgepsmatos/cft-otb-master | p_drawRect.m | .m | cft-otb-master/trackers/CF2/external/tools/p_drawRect.m | 1,863 | utf_8 | cf21589b84dd8763dbc3f35822455ee6 | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), to ... |
github | jorgepsmatos/cft-otb-master | p_readLog.m | .m | cft-otb-master/trackers/CF2/external/tools/p_readLog.m | 1,641 | utf_8 | 2a4ae33234fb76fd9a65fbec9d5138a8 | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), ... |
github | jorgepsmatos/cft-otb-master | p_computeCenterDistanceRect.m | .m | cft-otb-master/trackers/CF2/external/tools/p_computeCenterDistanceRect.m | 1,654 | utf_8 | 3e9490f8aba83c4e483380f9e1e4412f | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), ... |
github | jorgepsmatos/cft-otb-master | p_createVideoSequence.m | .m | cft-otb-master/trackers/CF2/external/tools/p_createVideoSequence.m | 3,243 | utf_8 | 25d1272582b6261f8ad50caa69313ac2 | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), ... |
github | jorgepsmatos/cft-otb-master | p_evalSequence.m | .m | cft-otb-master/trackers/CF2/external/tools/p_evalSequence.m | 3,336 | utf_8 | 782b0e2a79b65b10fb2c77816ee35169 | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), ... |
github | jorgepsmatos/cft-otb-master | p_computePascalScoreRect.m | .m | cft-otb-master/trackers/CF2/external/tools/p_computePascalScoreRect.m | 2,002 | utf_8 | 8a4a3e85f392e8fec2fccb17fba5771f | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), ... |
github | jorgepsmatos/cft-otb-master | cnn_cifar.m | .m | cft-otb-master/trackers/CF2/external/matconvnet/examples/cnn_cifar.m | 5,513 | utf_8 | 59f780e7e4c836f62caeede920e50afa | function cnn_cifar(varargin)
% CNN_CIFAR Demonstrates MatConvNet on CIFAR
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data','cifar') ;
opts.expDir = fullfile('data','cifar-baseline') ;
opts.imdbPath = fullfile(opts.expDir, 'imdb.mat');
opts.train.... |
github | jorgepsmatos/cft-otb-master | cnn_imagenet.m | .m | cft-otb-master/trackers/CF2/external/matconvnet/examples/cnn_imagenet.m | 9,564 | utf_8 | c367d3378c58c3fcd280ecb7fe40237d | function cnn_imagenet(varargin)
% CNN_IMAGENET Demonstrates training a CNN on ImageNet
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data','imagenet12') ;
opts.expDir = fullfile('data','imagenet12-baseline') ;
[opts, varargin] = vl_argparse(opts, va... |
github | jorgepsmatos/cft-otb-master | cnn_mnist.m | .m | cft-otb-master/trackers/CF2/external/matconvnet/examples/cnn_mnist.m | 4,802 | utf_8 | de07039a31bdf53e479516978eb49b40 | function [net, info] = cnn_mnist(varargin)
% CNN_MNIST Demonstrated MatConNet on MNIST
run(fullfile(fileparts(mfilename('fullpath')),'..','matlab','vl_setupnn.m')) ;
opts.dataDir = fullfile('data','mnist') ;
opts.expDir = fullfile('data','mnist-baseline') ;
opts.imdbPath = fullfile(opts.expDir, 'imdb.mat');
opts.tra... |
github | jorgepsmatos/cft-otb-master | cnn_train.m | .m | cft-otb-master/trackers/CF2/external/matconvnet/examples/cnn_train.m | 9,795 | utf_8 | f7fea080c42f15bf429eda82b48b102f | function [net, info] = cnn_train(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a suitable
% g... |
github | jorgepsmatos/cft-otb-master | cnn_imagenet_evaluate.m | .m | cft-otb-master/trackers/CF2/external/matconvnet/examples/cnn_imagenet_evaluate.m | 2,726 | utf_8 | 2f3d1847fbe22a45f35687ae096a0376 | function info = cnn_imagenet_evaluate(varargin)
% CNN_IMAGENET_EVALUATE Evauate MatConvNet models on ImageNet
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data', 'imagenet12') ;
opts.expDir = fullfile('data', 'imagenet12-eval-vgg-f') ;
opts.imdbPat... |
github | jorgepsmatos/cft-otb-master | vl_compilenn.m | .m | cft-otb-master/trackers/CF2/external/matconvnet/matlab/vl_compilenn.m | 20,725 | utf_8 | bcbc23d4f290bcdbb238588847fc34de | function vl_compilenn( varargin )
% VL_COMPILENN Compile the MatConvNet toolbox
% The `vl_compilenn()` function compiles the MEX files in the
% MatConvNet toolbox. See below for the requirements for compiling
% CPU and GPU code, respectively.
%
% `vl_compilenn('OPTION', ARG, ...)` accepts the following opt... |
github | jorgepsmatos/cft-otb-master | vl_simplenn_display.m | .m | cft-otb-master/trackers/CF2/external/matconvnet/matlab/vl_simplenn_display.m | 5,109 | utf_8 | 8db9db2733c5d41b239d87806680a068 | function info = vl_simplenn_display(net, res)
% VL_SIMPLENN_DISPLAY Simple CNN statistics
% VL_SIMPLENN_DISPLAY(NET) prints statistics about the network NET.
% Copyright (C) 2014 Andrea Vedaldi.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD li... |
github | jorgepsmatos/cft-otb-master | mtimesx_build.m | .m | cft-otb-master/trackers/SRDCF/mtimesx_build.m | 16,405 | utf_8 | 838ce3d9c7bc33beb0d2f75546ead978 | % mtimesx_build compiles mtimesx.c with BLAS libraries
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_build
% Filename: mtimesx_build.m
% Programmer: James Tursa
% Version: 1.40
% Dat... |
github | jorgepsmatos/cft-otb-master | dft2dfs_matrix.m | .m | cft-otb-master/trackers/SRDCF/dft2dfs_matrix.m | 1,167 | utf_8 | 2ac7f57088541f2de90b9ec52ad524cd | % Constructs a sparse matrix that transforms the discrete fourier transform
% (DFT) to the real discrete fourier series (DFS), given the input and
% output index permutations.
function dfs_matrix = dft2dfs_matrix(dft_sym_ind, dft_pos_ind, dft_neg_ind, dfs_sym_ind, dfs_real_ind, dfs_imag_ind)
i_sym = dfs_sym_ind;
j_sy... |
github | jorgepsmatos/cft-otb-master | reflect_spectrum2.m | .m | cft-otb-master/trackers/SRDCF/reflect_spectrum2.m | 171 | utf_8 | dc0be80390bcf543980843d9cee0aee9 | % Do a reflection in the fourier domain for a 2-dimensional signal
function xf_reflected = reflect_spectrum2(xf)
xf_reflected = circshift(flip(flip(xf, 1), 2), [1 1 0]); |
github | jorgepsmatos/cft-otb-master | partition_spectrum2.m | .m | cft-otb-master/trackers/SRDCF/partition_spectrum2.m | 1,702 | utf_8 | 3d7c697a3ae271dc81a098736cf5e049 | % Partitions the spectrum of a 2-dimensional signal with dimensions dft_sz into
% the real part, a set of "positive" frequencies and the corresponding
% "negative" frequencies.
function [dft_sym_ind, dft_pos_ind, dft_neg_ind] = partition_spectrum2(dft_sz)
% construct the index vector for the half of the spectrum to b... |
github | jorgepsmatos/cft-otb-master | mtimesx_sparse.m | .m | cft-otb-master/trackers/SRDCF/mtimesx_sparse.m | 3,015 | utf_8 | eeb3eb2df4d70c69695b45188807e91c | % mtimesx_sparse does sparse matrix multiply of two inputs
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_sparse
% Filename: mtimesx_sparse.m
% Programmer: James Tursa
% Version: 1.00
... |
github | jorgepsmatos/cft-otb-master | run_SRDCF.m | .m | cft-otb-master/trackers/SRDCF/run_SRDCF.m | 3,078 | utf_8 | 3c195b92e109fbd2033e7e37bc4315bd | % This function runs the SRDCF tracker on the video specified in "seq".
% It can be integrated directly in the Online Tracking Benchmark (OTB).
% The parameters are set as in the ICCV 2015 paper.
function results=run_SRDCF(seq, res_path, bSaveImage, parameters)
% Default parameters used in the ICCV 2015 paper
% HOG ... |
github | jorgepsmatos/cft-otb-master | SRDCF_tracker.m | .m | cft-otb-master/trackers/SRDCF/SRDCF_tracker.m | 20,238 | utf_8 | 8f8bc547f9406377ac9fe6e84014af52 | % This function implements the SRDCF tracker.
function results = SRDCF_tracker(params)
% parameters
search_area_scale = params.search_area_scale;
output_sigma_factor = params.output_sigma_factor;
lambda = params.lambda;
learning_rate = params.learning_rate;
refinement_iterations = params.refinement_iterations;
filter... |
github | jorgepsmatos/cft-otb-master | run_tracker.m | .m | cft-otb-master/trackers/KCF_raw/run_tracker.m | 6,526 | utf_8 | 4da9745fa140f8a4ef36fdd45bc7dcfa |
%
% High-Speed Tracking with Kernelized Correlation Filters
%
% Joao F. Henriques, 2014
% http://www.isr.uc.pt/~henriques/
%
% Main interface for Kernelized/Dual Correlation Filters (KCF/DCF).
% This function takes care of setting up parameters, loading video
% information and computing precisions. For the actua... |
github | jorgepsmatos/cft-otb-master | TestMotionSaliencyBinWangApr2014.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/opencv_contrib/test/unit_tests/TestMotionSaliencyBinWangApr2014.m | 1,377 | utf_8 | 022f1ce293232dbb009d7ba6da77b804 | classdef TestMotionSaliencyBinWangApr2014
%TestMotionSaliencyBinWangApr2014
methods (Static)
function test_1
cap = cv.VideoCapture(get_pedestrian_video());
assert(cap.isOpened());
img = cap.read();
assert(~isempty(img));
saliency = cv.MotionS... |
github | jorgepsmatos/cft-otb-master | TestDPMDetector.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/opencv_contrib/test/unit_tests/TestDPMDetector.m | 2,820 | utf_8 | dc236d0356ee9e95494404ad6bcb2a62 | classdef TestDPMDetector
%TestDPMDetector
properties (Constant)
%xmlfile = fullfile(mexopencv.root(),'test','inriaperson.xml');
xmlfile = fullfile(mexopencv.root(),'test','cat.xml');
im = fullfile(mexopencv.root(),'test','cat.jpg');
end
methods (Static)
function test_cat... |
github | jorgepsmatos/cft-otb-master | TestObjectnessBING.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/opencv_contrib/test/unit_tests/TestObjectnessBING.m | 2,102 | utf_8 | 374fa348747233bc65948b40c8c828cf | classdef TestObjectnessBING
%TestObjectnessBING
properties (Constant)
im = fullfile(mexopencv.root(),'test','balloon.jpg');
end
methods (Static)
function test_1
saliency = cv.ObjectnessBING();
cname = saliency.getClassName();
validateattributes(cname,... |
github | jorgepsmatos/cft-otb-master | TestStructuredEdgeDetection.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/opencv_contrib/test/unit_tests/TestStructuredEdgeDetection.m | 1,658 | utf_8 | df1d59c85b1a093b5319c8fbb1d7ff07 | classdef TestStructuredEdgeDetection
%TestStructuredEdgeDetection
properties (Constant)
im = fullfile(mexopencv.root(),'test','balloon.jpg');
end
methods (Static)
function test_1
img = imread(TestStructuredEdgeDetection.im);
img = single(img) / 255.0;
... |
github | jorgepsmatos/cft-otb-master | delaunay2_demo.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/samples/delaunay2_demo.m | 4,256 | utf_8 | bd1df777ca02f64da7ddd544bb735924 | %% Delaunay triangulation
% This program demostrates iterative construction of
% delaunay triangulation and voronoi tesselation.
%
% It draws a random set of points in an image and then
% delaunay triangulates them.
%
% <https://github.com/Itseez/opencv/blob/master/samples/cpp/delaunay2.cpp>
%
function delaunay2_demo(... |
github | jorgepsmatos/cft-otb-master | gabor_filter_gui.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/samples/gabor_filter_gui.m | 4,925 | utf_8 | 763fe346e9dd72e663848de93b44c98a | %% Gabor Filter demo
%
% A GUI to interact with the 5 different Gabor filter parameters, while
% visualizing the resulting filter.
%
function varargout = gabor_filter_gui(ksize)
% create the UI
if nargin < 1, ksize = [121 121]; end
h = buildGUI(ksize);
if nargout > 1, varargout{1} = h; end
end
functio... |
github | jorgepsmatos/cft-otb-master | TestDenoise_TVL1.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestDenoise_TVL1.m | 2,921 | utf_8 | 970ea32ecb62a5985a3bbf2ea71a1a87 | classdef TestDenoise_TVL1
%TestDenoise_TVL1
properties (Constant)
im = fullfile(mexopencv.root(),'test','lena.jpg');
end
methods (Static)
function test_1
img = cv.imread(TestDenoise_TVL1.im, 'Grayscale',true);
images = repmat({img},1,5);
out = cv.deno... |
github | jorgepsmatos/cft-otb-master | TestStereoRectify.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestStereoRectify.m | 3,416 | utf_8 | b1a2a89cbdf1294861ee585b42c264b2 | classdef TestStereoRectify
%TestStereoRectify
properties (Constant)
fields = {'R1', 'R2', 'P1', 'P2', 'Q', 'roi1', 'roi2'};
end
methods (Static)
function test_1
[opts, ipts1, ipts2, imgSize] = getPointsPair();
SS = cv.stereoCalibrate(opts, ipts1, ipts2, imgSize);... |
github | jorgepsmatos/cft-otb-master | TestNormalize.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestNormalize.m | 3,811 | utf_8 | 141bdf50af25d18bbc0dcdf8136a2a3d | classdef TestNormalize
%TestNormalize
methods (Static)
function test_minmax_gray
img = imread(fullfile(mexopencv.root(),'test','left01.jpg'));
I = cv.normalize(img, 'NormType','MinMax', 'DType','int8', ...
'Alpha',-50, 'Beta',100);
J = my_normalize_mi... |
github | jorgepsmatos/cft-otb-master | TestGeneralizedHoughGuil.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestGeneralizedHoughGuil.m | 1,510 | utf_8 | 7176a7abae82392b460af519e181690a | classdef TestGeneralizedHoughGuil
%TestGeneralizedHoughGuil
methods (Static)
function test_1
hough = cv.GeneralizedHoughGuil();
[img, tmpl] = sample_data();
hough.setTemplate(tmpl);
[pos,votes] = hough.detect(img);
assert(iscell(pos) && iscell... |
github | jorgepsmatos/cft-otb-master | TestStereoCalibrate.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestStereoCalibrate.m | 4,942 | utf_8 | f2ca8a7c617343800233eebe6f259ec6 | classdef TestStereoCalibrate
%TestStereoCalibrate
properties (Constant)
fields = {'cameraMatrix1', 'distCoeffs1', ...
'cameraMatrix2', 'distCoeffs2', 'R', 'T', 'E', 'F', 'reprojErr'};
end
methods (Static)
function test_one_view
[opts, ipts1, ipts2, imgSize] = get... |
github | jorgepsmatos/cft-otb-master | TestSVM.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestSVM.m | 9,877 | utf_8 | 448b63cc2d0f20a8264e02bfcd42a35a | classdef TestSVM
%TestSVM
properties (Constant)
X = [randn(50,3)+1; randn(50,3)-1];
Y = int32([ones(50,1); -ones(50,1)]);
YReg = [ones(50,1); -ones(50,1)] + randn(100,1)*0.5;
end
methods (Static)
function test_classification1
% 2-class (binary)
mo... |
github | jorgepsmatos/cft-otb-master | TestDownhillSolver.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestDownhillSolver.m | 4,029 | utf_8 | ed19b7aa0406685f6f4af8d93484559f | classdef TestDownhillSolver
%TestDownhillSolver
methods (Static)
function test_rosenbrock
% skip test if external M-file is not found on the path
if ~exist('rosenbrockFcn.m', 'file')
disp('SKIPPED')
return
end
solver = cv.... |
github | jorgepsmatos/cft-otb-master | TestCalibrateCamera.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestCalibrateCamera.m | 4,029 | utf_8 | 1cd5fa9b1bf520414972e49eea7d7ea1 | classdef TestCalibrateCamera
%TestCalibrateCamera
methods (Static)
function test_one_view
[opts, ipts, imgSize] = getPoints();
[A, distCoeffs, err] = cv.calibrateCamera(opts, ipts, imgSize);
[A, distCoeffs, err, rvecs, tvecs] = cv.calibrateCamera(opts, ipts, imgSize)... |
github | jorgepsmatos/cft-otb-master | TestHOGDescriptor.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestHOGDescriptor.m | 7,317 | utf_8 | 1a933d0f1e33b0297ae65b344aa9a6fb | classdef TestHOGDescriptor
%TestHOGDescriptor
properties (Constant)
fields = {'scale', 'locations', 'confidences'}; % DetectionROI
end
methods (Static)
function test_class
hog = cv.HOGDescriptor('NBins',9);
hog.CellSize = [8 8];
evalc('disp(hog)');
... |
github | jorgepsmatos/cft-otb-master | TestRemap.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestRemap.m | 5,171 | utf_8 | c888469558bfb3cbab697156de87bd61 | classdef TestRemap
%TestRemap
properties (Constant)
im = fullfile(mexopencv.root(),'test','fruits.jpg');
end
methods (Static)
function test_1
src = [1 0 0; 0 0 0; 0 0 0];
X = [0 0 0; 0 0 0; 0 0 0];
Y = [0 0 0; 0 0 0; 0 0 0];
ref = [1 1... |
github | jorgepsmatos/cft-otb-master | TestReprojectImageTo3D.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestReprojectImageTo3D.m | 2,832 | utf_8 | 865559d9f603f226d996b38a62c67f79 | classdef TestReprojectImageTo3D
%TestReprojectImageTo3D
methods (Static)
function test_1
D = randi([-20 1000], [200 300], 'int16');
Q = [1 0 0 -rand()*100; 0 1 0 -rand()*100; 0 0 0 rand()*500; 0 0 rand() 0];
im3d = cv.reprojectImageTo3D(D, Q, ...
'Han... |
github | jorgepsmatos/cft-otb-master | TestGeneralizedHoughBallard.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestGeneralizedHoughBallard.m | 1,525 | utf_8 | 94648e2e6dbf04e90b20e303cad5d5dd | classdef TestGeneralizedHoughBallard
%TestGeneralizedHoughBallard
methods (Static)
function test_1
hough = cv.GeneralizedHoughBallard();
[img, tmpl] = sample_data();
hough.setTemplate(tmpl);
[pos,votes] = hough.detect(img);
assert(iscell(pos) ... |
github | jorgepsmatos/cft-otb-master | TestConjGradSolver.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestConjGradSolver.m | 5,619 | utf_8 | 6a688a8f329c1ea4761d8954d2293f8f | classdef TestConjGradSolver
%TestConjGradSolver
methods (Static)
function test_rosenbrock
% skip test if external M-files are not found on the path
if ~exist('rosenbrockFcn.m', 'file') || ~exist('rosenbrockGrad.m', 'file')
disp('SKIPPED')
return
... |
github | jorgepsmatos/cft-otb-master | TestCascadeClassifier.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestCascadeClassifier.m | 4,781 | utf_8 | f492746c4af71d71f83375c3d6920e23 | classdef TestCascadeClassifier
%TestCascadeClassifier
properties (Constant)
xmlfile = fullfile(mexopencv.root(),'test','haarcascade_frontalface_alt2.xml');
end
methods (Static)
function test_old_format
im = imread(fullfile(mexopencv.root(),'test','lena.jpg'));
cc... |
github | jorgepsmatos/cft-otb-master | TestStereoRectifyUncalibrated.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestStereoRectifyUncalibrated.m | 1,701 | utf_8 | 90db50b8e8966204b4aad67d576d05c4 | classdef TestStereoRectifyUncalibrated
%TestStereoRectifyUncalibrated
methods (Static)
function test_1
[~, ipts1, ipts2, imgSize] = getPointsPair();
F = cv.findFundamentalMat(ipts1, ipts2);
[H1, H2, success] = cv.stereoRectifyUncalibrated(...
ipts1, i... |
github | jorgepsmatos/cft-otb-master | TestSuperResolution.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestSuperResolution.m | 1,773 | utf_8 | 72ed95ad37046ec185d509b97b763dd9 | classdef TestSuperResolution
%TestSuperResolution
methods (Static)
function test_1
if true
filename = which('shuttle.avi'); % 512x288 ~ 120 frames
sz = [288 512 3];
else
filename = get_car_video();
end
if i... |
github | jorgepsmatos/cft-otb-master | TestComputeRecallPrecisionCurve.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/test/unit_tests/TestComputeRecallPrecisionCurve.m | 2,162 | utf_8 | e8915e5188c724a20d0918693058bf76 | classdef TestComputeRecallPrecisionCurve
%TestComputeRecallPrecisionCurve
methods (Static)
function test_1
H = [0.8 -0.04 50; -0.05 0.9 50; 1e-5 1e-4 1];
img1 = imread(fullfile(mexopencv.root(),'test','fruits.jpg'));
img2 = cv.warpPerspective(img1, H);
o... |
github | jorgepsmatos/cft-otb-master | make.m | .m | cft-otb-master/trackers/MUSTer/mexopencv/+mexopencv/make.m | 22,233 | utf_8 | 4f7a05a4fafab2ccd86c67006f320813 | function make(varargin)
%MAKE Compile MEX-functions
%
% mexopencv.make
% mexopencv.make('OptionName', optionValue, ...)
%
% Make builds mexopencv library. In Unix, this function invokes Makefile
% in the project root. In Windows, the function takes an optional argument
% to specify installed OpenCV path.
%
% ## ... |
github | jorgepsmatos/cft-otb-master | classification_demo.m | .m | cft-otb-master/trackers/MUSTer/mexopencv_old/samples/classification_demo.m | 1,748 | utf_8 | 3e0b46419f3d88c4367cbd411fed7fb2 | function classification_demo
%CLASSIFICATION_DEMO classification demo
%
% This demonstrates an example of machine learning algorithms in a simple
% classification problem. It compares different classifiers using the same
% data samples.
%
% Before start, addpath('/path/to/mexopencv');
%
% Prepare data: there are two ... |
github | jorgepsmatos/cft-otb-master | make.m | .m | cft-otb-master/trackers/MUSTer/mexopencv_old/+mexopencv/make.m | 12,279 | utf_8 | e3ba5f7de94c5ff92192b1ac48b51d29 | function make(varargin)
%MAKE Compile MEX-functions
%
% mexopencv.make
% mexopencv.make('OptionName', optionValue, ...)
%
% Make builds mexopencv library. In Unix, this function invokes Makefile
% in the project root. In Windows, the function takes an optional argument
% to specify installed OpenCV path.
%
% ## ... |
github | jorgepsmatos/cft-otb-master | st_get_features.m | .m | cft-otb-master/trackers/MUSTer/ICF/st_get_features.m | 1,987 | utf_8 | 188a68874ae59a00c71e788df33074de | function x = st_get_features(im, features, cell_size, cos_window)
%GET_FEATURES
% Extracts dense features from image.
%
% X = GET_FEATURES(IM, FEATURES, CELL_SIZE)
% Extracts features specified in struct FEATURES, from image IM. The
% features should be densely sampled, in cells or intervals of CELL_SIZE.
% T... |
github | jorgepsmatos/cft-otb-master | train_bbox_regressor.m | .m | cft-otb-master/trackers/MDNET/utils/train_bbox_regressor.m | 5,006 | utf_8 | 4172d555381b80ef02bb1adac4570cb4 | function bbox_reg = train_bbox_regressor(X, bbox, gt, varargin)
% bbox_reg = rcnn_train_bbox_regressor(imdb, rcnn_model, varargin)
% Trains a bounding box regressor on the image database imdb
% for use with the R-CNN model rcnn_model. The regressor is trained
% using ridge regression.
%
% Keys that can be passe... |
github | jorgepsmatos/cft-otb-master | vl_compilenn.m | .m | cft-otb-master/trackers/MDNET/matconvnet/matlab/vl_compilenn.m | 23,157 | utf_8 | 94e383af2503bf657c1f728b3d85a3f1 | function vl_compilenn( varargin )
% VL_COMPILENN Compile the MatConvNet toolbox
% The `vl_compilenn()` function compiles the MEX files in the
% MatConvNet toolbox. See below for the requirements for compiling
% CPU and GPU code, respectively.
%
% `vl_compilenn('OPTION', ARG, ...)` accepts the following opt... |
github | jorgepsmatos/cft-otb-master | vl_simplenn_display.m | .m | cft-otb-master/trackers/MDNET/matconvnet/matlab/vl_simplenn_display.m | 5,140 | utf_8 | d5d591422b0f719d77dc2b206fabef82 | function info = vl_simplenn_display(net, res)
% VL_SIMPLENN_DISPLAY Simple CNN statistics
% VL_SIMPLENN_DISPLAY(NET) prints statistics about the network NET.
% Copyright (C) 2014 Andrea Vedaldi.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD li... |
github | jorgepsmatos/cft-otb-master | seq2roidb.m | .m | cft-otb-master/trackers/MDNET/pretraining/seq2roidb.m | 3,021 | utf_8 | b7f210e5ff0c3f2d4f7ddda962aae79b | function [ roidb ] = seq2roidb(config, opts)
% SEQ2ROIDB
% Extract training bounding boxes from the sequence given by config,
% to construct a dataset of RoIs for training MDNet.
%
% Hyeonseob Nam, 2015
%
images = config.imgList;
gts = config.gt;
im = imread(images{1});
[h,w,~] = size(im);
imgSize = [h, w];
roidb ... |
github | jorgepsmatos/cft-otb-master | mdnet_pretrain.m | .m | cft-otb-master/trackers/MDNET/pretraining/mdnet_pretrain.m | 6,297 | utf_8 | 0eb1c6a5c500fd4b7f730c0f194b2599 | function [ ] = mdnet_pretrain( varargin )
% MDNET_PRETRAIN
% Pretrain MDNet from multiple tracking sequences.
%
% Modified from cnn_imagenet() in the MatConvNet library.
% Hyeonseob Nam, 2015
%
% The list of tracking sequences for training MDNet.
opts.seqsList = {struct('dataset','vot2013','list','pretraining/seqLis... |
github | jorgepsmatos/cft-otb-master | mdnet_train.m | .m | cft-otb-master/trackers/MDNET/pretraining/mdnet_train.m | 6,540 | utf_8 | c0fe2d9596e5f178f8c630f99946b440 | function [net, info] = mdnet_train(net, roidb, getBatch, varargin)
% MDNET_TRAIN
% Train a MDNet by a modified SGD.
%
% Modified from cnn_train() in the MatConvNet library.
% Hyeonseob Nam, 2015
%
opts.batch_frames = 8 ;
opts.batchSize = 128 ;
opts.batch_pos = 32;
opts.batch_neg = 96;
opts.numCycles = 10... |
github | zhangxaochen/peac-master | splitXYZ.m | .m | peac-master/matlab/splitXYZ.m | 1,338 | utf_8 | 37526e4bbc4e5ef1367a3bb9fc9fcf22 | %
% Copyright 2014 Mitsubishi Electric Research Laboratories All
% Rights Reserved.
%
% Permission to use, copy and modify this software and its
% documentation without fee for educational, research and non-profit
% purposes, is hereby granted, provided that the above copyright
% notice, this paragraph, and the followi... |
github | zhangxaochen/peac-master | setAHCPlaneFitterParams.m | .m | peac-master/matlab/setAHCPlaneFitterParams.m | 1,419 | utf_8 | 4f37643eba9e241bcd0b07c53c346778 | %
% Copyright 2014 Mitsubishi Electric Research Laboratories All
% Rights Reserved.
%
% Permission to use, copy and modify this software and its
% documentation without fee for educational, research and non-profit
% purposes, is hereby granted, provided that the above copyright
% notice, this paragraph, and the followi... |
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