plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | rising-turtle/slam_matlab-master | vl_demo_aib.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/demo/vl_demo_aib.m | 2,928 | utf_8 | 590c6db09451ea608d87bfd094662cac | function vl_demo_aib
% VL_DEMO_AIB Test Agglomerative Information Bottleneck (AIB)
D = 4 ;
K = 20 ;
randn('state',0) ;
rand('state',0) ;
X1 = randn(2,300) ; X1(1,:) = X1(1,:) + 2 ;
X2 = randn(2,300) ; X2(1,:) = X2(1,:) - 2 ;
X3 = randn(2,300) ; X3(2,:) = X3(2,:) + 2 ;
figure(1) ; clf ; hold on ;
vl_plotframe(X... |
github | rising-turtle/slam_matlab-master | vl_demo_alldist.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/demo/vl_demo_alldist.m | 5,460 | utf_8 | 6d008a64d93445b9d7199b55d58db7eb | function vl_demo_alldist
%
numRepetitions = 3 ;
numDimensions = 1000 ;
numSamplesRange = [300] ;
settingsRange = {{'alldist2', 'double', 'l2', }, ...
{'alldist', 'double', 'l2', 'nosimd'}, ...
{'alldist', 'double', 'l2' }, ...
{'alldist2', 's... |
github | rising-turtle/slam_matlab-master | vl_demo_svmpegasos.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/demo/vl_demo_svmpegasos.m | 1,304 | utf_8 | 5470b2cbce41c6323cb562dbcd37556b | % VL_DEMO_SVMPEGASOS Demo: SVMPEGASOS: 2D linear learning
function vl_demo_svmpegasos
% Set up training data
Np = 200 ;
Nn = 200 ;
Xp = diag([1 3])*randn(2, Np) ;
Xn = diag([1 3])*randn(2, Nn) ;
Xp(1,:) = Xp(1,:) + 2 ;
Xn(1,:) = Xn(1,:) - 2 ;
X = [Xp Xn] ;
y = [ones(1,Np) -ones(1,Nn)] ;
figure(1)
plot(Xn(1,:),Xn(... |
github | rising-turtle/slam_matlab-master | vl_demo_kdtree_sift.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/demo/vl_demo_kdtree_sift.m | 6,832 | utf_8 | e676f80ac330a351f0110533c6ebba89 | function vl_demo_kdtree_sift
% VL_DEMO_KDTREE_SIFT
% Demonstrates the use of a kd-tree forest to match SIFT
% features. If FLANN is present, this function runs a comparison
% against it.
% AUTORIGHS
rand('state',0) ;
randn('state',0);
do_median = 0 ;
do_mean = 1 ;
% try to setup flann
if ~exist('flann_search'... |
github | rising-turtle/slam_matlab-master | vl_impattern.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/imop/vl_impattern.m | 6,702 | utf_8 | 7f5d173ebd720f7b89eccfa416aa71d3 | function im = vl_impattern(varargin)
% VL_IMPATTERN Generate an image from a stock pattern
% IM=VLPATTERN(NAME) returns an instance of the specified
% pattern. These stock patterns are useful for testing algoirthms.
%
% All generated patterns are returned as an image of class
% DOUBLE. Both gray-scale and colou... |
github | rising-turtle/slam_matlab-master | vl_tpsu.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/imop/vl_tpsu.m | 1,755 | utf_8 | 09f36e1a707c069b375eb2817d0e5f13 | function [U,dU,delta]=vl_tpsu(X,Y)
% VL_TPSU Compute the U matrix of a thin-plate spline transformation
% U=VL_TPSU(X,Y) returns the matrix
%
% [ U(|X(:,1) - Y(:,1)|) ... U(|X(:,1) - Y(:,N)|) ]
% [ ]
% [ U(|X(:,M) - Y(:,1)|) ... U(|X(:,M) - Y(:,N)|) ]
%
% where X... |
github | rising-turtle/slam_matlab-master | vl_xyz2lab.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/imop/vl_xyz2lab.m | 1,570 | utf_8 | 09f95a6f9ae19c22486ec1157357f0e3 | function J=vl_xyz2lab(I,il)
% VL_XYZ2LAB Convert XYZ color space to LAB
% J = VL_XYZ2LAB(I) converts the image from XYZ format to LAB format.
%
% VL_XYZ2LAB(I,IL) uses one of the illuminants A, B, C, E, D50, D55,
% D65, D75, D93. The default illuminatn is E.
%
% See also: VL_XYZ2LUV(), VL_HELP().
% Copyright ... |
github | rising-turtle/slam_matlab-master | vl_test_twister.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_twister.m | 1,162 | utf_8 | 1ae9040a416db503ad73600f081d096b | function results = vl_test_twister(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_illegal_args()
vl_assert_exception(@() vl_twister(-1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister(1, -1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister([1, -1]), 'vl:invalidArgument') ;
function te... |
github | rising-turtle/slam_matlab-master | vl_test_kdtree.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_kdtree.m | 2,448 | utf_8 | 66f429ff8286089a34c193d7d3f9f016 | function results = vl_test_kdtree(varargin)
% VL_TEST_KDTREE
vl_test_init ;
function s = setup()
randn('state',0) ;
s.X = single(randn(10, 1000)) ;
s.Q = single(randn(10, 10)) ;
function test_nearest(s)
for tmethod = {'median', 'mean'}
for type = {@single, @double}
conv = type{1} ;
tmethod = char(tmethod) ;... |
github | rising-turtle/slam_matlab-master | vl_test_imwbackward.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_imwbackward.m | 514 | utf_8 | 33baa0784c8f6f785a2951d7f1b49199 | function results = vl_test_imwbackward(varargin)
% VL_TEST_IMWBACKWARD
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
function test_identity(s)
xr = 1:size(s.I,2) ;
yr = 1:size(s.I,1) ;
[x,y] = meshgrid(xr,yr) ;
vl_assert_almost_equal(s.I, vl_imwbackward(xr,yr,s.I,... |
github | rising-turtle/slam_matlab-master | vl_test_pegasos.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_pegasos.m | 5,428 | utf_8 | cc28a57ce6cf6ecba349d21698228e2e | function results = vl_test_pegasos(varargin)
% VL_TEST_KDTREE
vl_test_init ;
function s = setup()
randn('state',0) ;
s.biasMultiplier = 10 ;
s.lambda = 0.01 ;
Np = 10 ;
Nn = 10 ;
Xp = diag([1 3])*randn(2, Np) ;
Xn = diag([1 3])*randn(2, Nn) ;
Xp(1,:) = Xp(1,:) + 2 + 1 ;
Xn(1,:) = Xn(1,:) - 2 + 1 ;
s.X = [Xp Xn] ;
s... |
github | rising-turtle/slam_matlab-master | vl_test_alphanum.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_alphanum.m | 1,624 | utf_8 | 2da2b768c2d0f86d699b8f31614aa424 | function results = vl_test_alphanum(varargin)
% VL_TEST_ALPHANUM
vl_test_init ;
function s = setup()
s.strings = ...
{'1000X Radonius Maximus','10X Radonius','200X Radonius','20X Radonius','20X Radonius Prime','30X Radonius','40X Radonius','Allegia 50 Clasteron','Allegia 500 Clasteron','Allegia 50B Clasteron','Al... |
github | rising-turtle/slam_matlab-master | vl_test_svmpegasos.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_svmpegasos.m | 5,802 | utf_8 | dcd13a3246830b74817e8c44100db022 | function results = vl_test_svmpegasos(varargin)
% VL_TEST_KDTREE
vl_test_init ;
function s = setup()
randn('state',0) ;
s.biasMultiplier = 10 ;
s.lambda = 0.01 ;
Np = 10 ;
Nn = 10 ;
Xp = diag([1 3])*randn(2, Np) ;
Xn = diag([1 3])*randn(2, Nn) ;
Xp(1,:) = Xp(1,:) + 2 + 1 ;
Xn(1,:) = Xn(1,:) - 2 + 1 ;
s.X = [Xp Xn] ... |
github | rising-turtle/slam_matlab-master | vl_test_cummax.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_cummax.m | 762 | utf_8 | 3dddb5736dfffacdd94b156e67cb9c14 | function results = vl_test_cummax(varargin)
% VL_TEST_CUMMAX
vl_test_init ;
function test_basic()
vl_assert_almost_equal(...
vl_cummax(1), 1) ;
vl_assert_almost_equal(...
vl_cummax([1 2 3 4], 2), [1 2 3 4]) ;
function test_multidim()
a = [1 2 3 4 3 2 1] ;
b = [1 2 3 4 4 4 4] ;
for k=1:6
dims = ones(1,6) ;
dim... |
github | rising-turtle/slam_matlab-master | vl_test_imintegral.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_imintegral.m | 1,429 | utf_8 | 4750f04ab0ac9fc4f55df2c8583e5498 | function results = vl_test_imintegral(varargin)
% VL_TEST_IMINTEGRAL
vl_test_init ;
function state = setup()
state.I = ones(5,6) ;
state.correct = [ 1 2 3 4 5 6 ;
2 4 6 8 10 12 ;
3 6 9 12 15 18 ;
4 8 12 ... |
github | rising-turtle/slam_matlab-master | vl_test_sift.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_sift.m | 1,318 | utf_8 | 806c61f9db9f2ebb1d649c9bfcf3dc0a | function results = vl_test_sift(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
s.I = im2single(imread(fullfile(vl_root,'data','box.pgm'))) ;
[s.ubc.f, s.ubc.d] = ...
vl_ubcread(fullfile(vl_root,'data','box.sift')) ;
function test_ubc_descriptor(s)
err = [] ;
[f, d] = vl_sift(s.I,...
... |
github | rising-turtle/slam_matlab-master | vl_test_binsum.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_binsum.m | 1,301 | utf_8 | 5bbd389cbc4d997e413d809fe4efda6d | function results = vl_test_binsum(varargin)
% VL_TEST_BINSUM
vl_test_init ;
function test_three_args()
vl_assert_almost_equal(...
vl_binsum([0 0], 1, 2), [0 1]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, 1), [0 7]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, [1 2 2 2 2 2 2 2]), [0 0]) ;
function te... |
github | rising-turtle/slam_matlab-master | vl_test_lbp.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_lbp.m | 1,056 | utf_8 | 3b5cca50109af84014e56a4280a3352a | function results = vl_test_lbp(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_one_on()
I = {} ;
I{1} = [0 0 0 ; 0 0 1 ; 0 0 0] ;
I{2} = [0 0 0 ; 0 0 0 ; 0 0 1] ;
I{3} = [0 0 0 ; 0 0 0 ; 0 1 0] ;
I{4} = [0 0 0 ; 0 0 0 ; 1 0 0] ;
I{5} = [0 0 0 ; 1 0 0 ; 0 0 0] ;
I{6} = [1 0 0 ; 0 0 0 ; 0 0 0] ;
I{7} = [0 1 0 ;... |
github | rising-turtle/slam_matlab-master | vl_test_colsubset.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_colsubset.m | 828 | utf_8 | be0c080007445b36333b863326fb0f15 | function results = vl_test_colsubset(varargin)
% VL_TEST_COLSUBSET
vl_test_init ;
function s = setup()
s.x = [5 2 3 6 4 7 1 9 8 0] ;
function test_beginning(s)
vl_assert_equal(1:5, vl_colsubset(1:10, 5, 'beginning')) ;
vl_assert_equal(1:5, vl_colsubset(1:10, .5, 'beginning')) ;
function test_ending(s)
vl_assert_equa... |
github | rising-turtle/slam_matlab-master | vl_test_alldist.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_alldist.m | 2,373 | utf_8 | 9ea1a36c97fe715dfa2b8693876808ff | function results = vl_test_alldist(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist(zeros(15,12), zeros(15,0), 'kl2'), ...
zeros(12,0)) ;
vl_assert_equa... |
github | rising-turtle/slam_matlab-master | vl_test_ihashsum.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_ihashsum.m | 581 | utf_8 | edc283062469af62056b0782b171f5fc | function results = vl_test_ihashsum(varargin)
% VL_TEST_IHASHSUM
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(round(16*rand(2,100))) ;
sel = find(all(s.data==0)) ;
s.data(1,sel)=1 ;
function test_hash(s)
D = size(s.data,1) ;
K = 5 ;
h = zeros(1,K,'uint32') ;
id = zeros(D,K,'uint8');
next = zer... |
github | rising-turtle/slam_matlab-master | vl_test_grad.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_grad.m | 434 | utf_8 | 4d03eb33a6a4f68659f868da95930ffb | function results = vl_test_grad(varargin)
% VL_TEST_GRAD
vl_test_init ;
function s = setup()
s.I = rand(150,253) ;
s.I_small = rand(2,2) ;
function test_equiv(s)
vl_assert_equal(gradient(s.I), vl_grad(s.I)) ;
function test_equiv_small(s)
vl_assert_equal(gradient(s.I_small), vl_grad(s.I_small)) ;
function test_equiv... |
github | rising-turtle/slam_matlab-master | vl_test_whistc.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_whistc.m | 1,384 | utf_8 | 81c446d35c82957659840ab2a579ec2c | function results = vl_test_whistc(varargin)
% VL_TEST_WHISTC
vl_test_init ;
function test_acc()
x = ones(1, 10) ;
e = 1 ;
o = 1:10 ;
vl_assert_equal(vl_whistc(x, o, e), 55) ;
function test_basic()
x = 1:10 ;
e = 1:10 ;
o = ones(1, 10) ;
vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ;
x = linspace(-1,11,100) ;
o =... |
github | rising-turtle/slam_matlab-master | vl_test_roc.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_roc.m | 1,019 | utf_8 | 9b2ae71c9dc3eda0fc54c65d55054d0c | function results = vl_test_roc(varargin)
% VL_TEST_ROC
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)
[tpr,tnr] = vl_roc(s.labels,s.scores0) ;
vl_assert_almost_equal(tpr, [0 1 2 2 2 2] / 2) ;
vl_assert_almost_equal(tnr,... |
github | rising-turtle/slam_matlab-master | vl_test_dsift.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_dsift.m | 2,048 | utf_8 | fbbfb16d5a21936c1862d9551f657ccc | function results = vl_test_dsift(varargin)
% VL_TEST_DSIFT
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = rgb2gray(single(I)) ;
function test_fast_slow(s)
binSize = 4 ; % bin size in pixels
magnif = 3 ; % bin size / keypoint scale
scale = binSize... |
github | rising-turtle/slam_matlab-master | vl_test_alldist2.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_alldist2.m | 2,284 | utf_8 | 89a787e3d83516653ae8d99c808b9d67 | function results = vl_test_alldist2(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
% TODO: test integer classes
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist2(zeros(15,12), zeros(15,0), 'kl2'), ...
... |
github | rising-turtle/slam_matlab-master | vl_test_imsmooth.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_imsmooth.m | 1,837 | utf_8 | 718235242cad61c9804ba5e881c22f59 | function results = vl_test_imsmooth(varargin)
% VL_TEST_IMSMOOTH
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
I = max(min(vl_imdown(I),1),0) ;
s.I = single(I) ;
function test_pad_by_continuity(s)
% Convolving a constant signal padded with continuity does not change... |
github | rising-turtle/slam_matlab-master | vl_test_phow.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_phow.m | 549 | utf_8 | f761a3bb218af855986263c67b2da411 | function results = vl_test_phow(varargin)
% VL_TEST_PHOPW
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = single(s.I) ;
function test_gray(s)
[f,d] = vl_phow(s.I, 'color', 'gray') ;
assert(size(d,1) == 128) ;
function test_rgb(s)
[f,d] = vl_phow(s.I, 'color',... |
github | rising-turtle/slam_matlab-master | vl_test_kmeans.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_kmeans.m | 2,788 | utf_8 | 14374b7dbae832fc3509e02caf00cdf5 | function results = vl_test_kmeans(varargin)
% VL_TEST_KMEANS
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
vl_test_init ;
function s = setup()
randn('sta... |
github | rising-turtle/slam_matlab-master | vl_test_hikmeans.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_hikmeans.m | 463 | utf_8 | dc3b493646e66316184e86ff4e6138ab | function results = vl_test_hikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[tree, assign] = vl_hikmeans(s.data,3,100) ;
assign_ = vl_hikmeanspush(tree, s.data) ;
vl_assert_equal(assign,assign_) ;
function test_elka... |
github | rising-turtle/slam_matlab-master | vl_test_aib.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_aib.m | 1,277 | utf_8 | 78978ae54e7ebe991d136336ba4bf9c6 | function results = vl_test_aib(varargin)
% VL_TEST_AIB
vl_test_init ;
function s = setup()
s = [] ;
function test_basic(s)
Pcx = [.3 .3 0 0
0 0 .2 .2] ;
% This results in the AIB tree
%
% 1 - \
% 5 - \
% 2 - / \
% - 7
% 3 - \ /
% 6 - /
% 4 - /
%
% coded by the map [5 ... |
github | rising-turtle/slam_matlab-master | vl_test_imarray.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_imarray.m | 795 | utf_8 | c5e6a5aa8c2e63e248814f5bd89832a8 | function results = vl_test_imarray(varargin)
% VL_TEST_IMARRAY
vl_test_init ;
function test_movie_rgb(s)
A = rand(23,15,3,4) ;
B = vl_imarray(A,'movie',true) ;
function test_movie_indexed(s)
cmap = get(0,'DefaultFigureColormap') ;
A = uint8(size(cmap,1)*rand(23,15,4)) ;
A = min(A,size(cmap,1)-1) ;
B = vl_imarray(A,'m... |
github | rising-turtle/slam_matlab-master | vl_test_homkermap.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_homkermap.m | 1,903 | utf_8 | c157052bf4213793a961bde1f73fb307 | function results = vl_test_homkermap(varargin)
% VL_TEST_HOMKERMAP
vl_test_init ;
function check_ker(ker, n, window, period)
args = {n, ker, 'window', window} ;
if nargin > 3
args = {args{:}, 'period', period} ;
end
x = [-1 -.5 0 .5 1] ;
y = linspace(0,2,100) ;
for conv = {@single, @double}
x = feval(conv{1}, x) ;... |
github | rising-turtle/slam_matlab-master | vl_test_slic.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_slic.m | 211 | utf_8 | 9077cfa77eb7b8d43880ba62408291f8 | function results = vl_test_slic(varargin)
% VL_TEST_SLIC
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
function test_slic(s)
segmentation = vl_slic(s.im, 10, 0.1, 'verbose') ;
|
github | rising-turtle/slam_matlab-master | vl_test_ikmeans.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_ikmeans.m | 466 | utf_8 | 1ee2f647ac0035ed0d704a0cd615b040 | function results = vl_test_ikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[centers, assign] = vl_ikmeans(s.data,100) ;
assign_ = vl_ikmeanspush(s.data, centers) ;
vl_assert_equal(assign,assign_) ;
function test_elk... |
github | rising-turtle/slam_matlab-master | vl_test_mser.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_mser.m | 242 | utf_8 | 1ad33563b0c86542a2978ee94e0f4a39 | function results = vl_test_mser(varargin)
% VL_TEST_MSER
vl_test_init ;
function s = setup()
s.im = im2uint8(rgb2gray(vl_impattern('roofs1'))) ;
function test_mser(s)
[regions,frames] = vl_mser(s.im) ;
mask = vl_erfill(s.im, regions(1)) ;
|
github | rising-turtle/slam_matlab-master | vl_test_inthist.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/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 | rising-turtle/slam_matlab-master | vl_test_imdisttf.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/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 | rising-turtle/slam_matlab-master | vl_test_pr.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_pr.m | 2,950 | utf_8 | fbe44689dacb16970984e4dbcede0430 | 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 | rising-turtle/slam_matlab-master | vl_test_hog.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/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 | rising-turtle/slam_matlab-master | vl_test_argparse.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/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 | rising-turtle/slam_matlab-master | vl_test_binsearch.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/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 | rising-turtle/slam_matlab-master | vl_test_maketrainingset.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/xtest/vl_test_maketrainingset.m | 1,014 | utf_8 | 147ca63d80a18ed3659dac4a3efcf84e | function results = vl_test_maketrainingset(varargin)
% VL_TEST_KDTREE
vl_test_init ;
function s = setup()
randn('state',0) ;
s.biasMultiplier = 10 ;
s.lambda = 0.01 ;
Np = 10 ;
Nn = 10 ;
Xp = diag([1 3])*randn(2, Np) ;
Xn = diag([1 3])*randn(2, Nn) ;
Xp(1,:) = Xp(1,:) + 2 + 1 ;
Xn(1,:) = Xn(1,:) - 2 + 1 ;
s.X = [Xp... |
github | rising-turtle/slam_matlab-master | vl_plotframe.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/plotop/vl_plotframe.m | 5,397 | utf_8 | eb21148a33aae6a835f47faa0db311d6 | function h = vl_plotframe(frames,varargin)
% VL_PLOTFRAME Plot feature frame
% VL_PLOTFRAME(FRAME) plots the frames FRAME. Frames are attributed
% image regions (as, for example, extracted by a feature detector). A
% frame is a vector of D=2,3,..,6 real numbers, depending on its
% class. VL_PLOTFRAME() supports t... |
github | rising-turtle/slam_matlab-master | vl_roc.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/plotop/vl_roc.m | 8,743 | utf_8 | eb8acd02ccf91e98a933e49754da010a | 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. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative one. SCORES are... |
github | rising-turtle/slam_matlab-master | vl_click.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/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 | rising-turtle/slam_matlab-master | vl_pr.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/plotop/vl_pr.m | 8,131 | utf_8 | 089b4b895dac21402ff0f7fba75fb823 | 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 | rising-turtle/slam_matlab-master | vl_ubcread.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/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 | rising-turtle/slam_matlab-master | vl_plotsiftdescriptor.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/toolbox/sift/vl_plotsiftdescriptor.m | 4,348 | utf_8 | b9a98b0c298fa249fb5fcd1314762b88 | function h=vl_plotsiftdescriptor(d,f,varargin)
% VL_PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% VL_PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptors D, stored as
% columns of the matrix D. D has the same format used by VL_SIFT().
%
% VL_PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warped to
% the SIFT fram... |
github | rising-turtle/slam_matlab-master | phow_caltech101.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/apps/phow_caltech101.m | 11,301 | utf_8 | 8316095b4842a2c43cf3dfc91e313aee | 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 speedup ... |
github | rising-turtle/slam_matlab-master | sift_mosaic.m | .m | slam_matlab-master/SIFT/vlfeat-0.9.16/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 | rising-turtle/slam_matlab-master | gen_shapes.m | .m | slam_matlab-master/SIFT/vicinalboost-1.0/code/gen_shapes.m | 3,755 | utf_8 | 059f82692aa112d79d75cc2e74209581 | function gen_shapes
% GEN_SHAPES Generate synthetic shapes dataset
% Data will be saved to 'data/shapes.mat'.
% N = number of patches per type
% h = size of a patch in pixels
N = 1000 ;
h = 24 ;
% these functions generates the basic shapes
genf = { @genbox, @gentri, @gencirc, @genstar } ;
% x will store the pat... |
github | rising-turtle/slam_matlab-master | vicinalboost.m | .m | slam_matlab-master/SIFT/vicinalboost-1.0/code/vicinalboost.m | 18,197 | utf_8 | 2f86f963b5d08b16753b25e8ac24f903 | function rs = vicinalboost(cfg, data)
% VICINALBOOST
%
% RS = VICINALBOOST(CFG, DATA)
%
% DATA is a structure with the following fields:
%
% DATA.Y Training data labels
% DATA.X Training data samples
% DATA.DX Data tangent vectors (scaled)
% DATA.IS_TRAIN Mark training / te... |
github | rising-turtle/slam_matlab-master | plotexp.m | .m | slam_matlab-master/SIFT/vicinalboost-1.0/code/experiments/plotexp.m | 5,198 | utf_8 | 4cbdb4ceb5f1ff95ac8640af96a9cf85 | function plotexp(rs, aggr, split, print_path)
% PLOTEXP Plot experiment results
%
% PLOTEXP(RS, AGGR) plot the experiments RS. AGGR is a cell array of
% string listing the fileds of the RS structure that should be used
% to identify uniquely an experiment (experiments with the same
% values of this field are treat... |
github | rising-turtle/slam_matlab-master | plotmatches.m | .m | slam_matlab-master/SIFT/sift-0.9.19/sift/plotmatches.m | 10,144 | utf_8 | 4d7daa0d3265f0885ebc7f3310a47fc1 | function h=plotmatches(I1,I2,P1,P2,matches,varargin)
% PLOTMATCHES Plot keypoint matches
% PLOTMATCHES(I1,I2,P1,P2,MATCHES) plots the two images I1 and I2
% and lines connecting the frames (keypoints) P1 and P2 as specified
% by MATCHES.
%
% P1 and P2 specify two sets of frames, one per column. The first
% t... |
github | rising-turtle/slam_matlab-master | gaussianss.m | .m | slam_matlab-master/SIFT/sift-0.9.19/sift/gaussianss.m | 7,935 | utf_8 | ea953b78ba9dcf80cd10b1f4c599408e | function SS = gaussianss(I,sigman,O,S,omin,smin,smax,sigma0)
% GAUSSIANSS
% SS = GAUSSIANSS(I,SIGMAN,O,S,OMIN,SMIN,SMAX,SIGMA0) returns the
% Gaussian scale space of image I. Image I is assumed to be
% pre-smoothed at level SIGMAN. O,S,OMIN,SMIN,SMAX,SIGMA0 are the
% parameters of the scale space as explained i... |
github | rising-turtle/slam_matlab-master | plotsiftdescriptor.m | .m | slam_matlab-master/SIFT/sift-0.9.19/sift/plotsiftdescriptor.m | 5,461 | utf_8 | 4159397cc60b624656bb3372023a43e9 | function h=plotsiftdescriptor(d,f)
% PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptors D, stored as
% columns of the matrix D. D has the same format used by SIFT().
%
% PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warped to the
% SIFT frames F, specified as colum... |
github | rising-turtle/slam_matlab-master | plotmatches.m | .m | slam_matlab-master/SIFT/sift-0.9.19-bin/sift/plotmatches.m | 10,144 | utf_8 | 4d7daa0d3265f0885ebc7f3310a47fc1 | function h=plotmatches(I1,I2,P1,P2,matches,varargin)
% PLOTMATCHES Plot keypoint matches
% PLOTMATCHES(I1,I2,P1,P2,MATCHES) plots the two images I1 and I2
% and lines connecting the frames (keypoints) P1 and P2 as specified
% by MATCHES.
%
% P1 and P2 specify two sets of frames, one per column. The first
% t... |
github | rising-turtle/slam_matlab-master | gaussianss.m | .m | slam_matlab-master/SIFT/sift-0.9.19-bin/sift/gaussianss.m | 7,935 | utf_8 | ea953b78ba9dcf80cd10b1f4c599408e | function SS = gaussianss(I,sigman,O,S,omin,smin,smax,sigma0)
% GAUSSIANSS
% SS = GAUSSIANSS(I,SIGMAN,O,S,OMIN,SMIN,SMAX,SIGMA0) returns the
% Gaussian scale space of image I. Image I is assumed to be
% pre-smoothed at level SIGMAN. O,S,OMIN,SMIN,SMAX,SIGMA0 are the
% parameters of the scale space as explained i... |
github | rising-turtle/slam_matlab-master | plotsiftdescriptor.m | .m | slam_matlab-master/SIFT/sift-0.9.19-bin/sift/plotsiftdescriptor.m | 5,461 | utf_8 | 4159397cc60b624656bb3372023a43e9 | function h=plotsiftdescriptor(d,f)
% PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptors D, stored as
% columns of the matrix D. D has the same format used by SIFT().
%
% PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warped to the
% SIFT frames F, specified as colum... |
github | rising-turtle/slam_matlab-master | plotmatches.m | .m | slam_matlab-master/SIFT/sift-0.9.17/sift/plotmatches.m | 10,221 | utf_8 | a701b7d74819dd725219aa884d6f7f18 | function h=plotmatches(I1,I2,P1,P2,matches,varargin)
% PLOTMATCHES Plot keypoint matches
% PLOTMATCHES(I1,I2,P1,P2,MATCHES) plots the two images I1 and I2
% and lines connecting the frames (keypoints) P1 and P2 as specified
% by MATCHES.
%
% P1 and P2 specify two sets of frames, one per column. The first
% t... |
github | rising-turtle/slam_matlab-master | gaussianss.m | .m | slam_matlab-master/SIFT/sift-0.9.17/sift/gaussianss.m | 7,995 | utf_8 | 5ddc73695f19ef5411d4e19a24f9a860 | function SS = gaussianss(I,sigman,O,S,omin,smin,smax,sigma0)
% GAUSSIANSS
% SS = GAUSSIANSS(I,SIGMAN,O,S,OMIN,SMIN,SMAX,SIGMA0) returns the
% Gaussian scale space of image I. Image I is assumed to be
% pre-smoothed at level SIGMAN. O,S,OMIN,SMIN,SMAX,SIGMA0 are the
% parameters of the scale space as explained i... |
github | rising-turtle/slam_matlab-master | plotsiftdescriptor.m | .m | slam_matlab-master/SIFT/sift-0.9.17/sift/plotsiftdescriptor.m | 5,466 | utf_8 | 65f208762b6abd63cf2fa092410c1256 | function h=plotsiftdescriptor(d,f)
% PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptors D, stored as
% columns of the matrix D. D has the same format used by SIFT().
%
% PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warped to the
% SIFT frames F, specified as colum... |
github | rising-turtle/slam_matlab-master | syn_error_pos.m | .m | slam_matlab-master/torso_orien/syn_error_pos.m | 1,620 | utf_8 | 58cae15433fde58eb47ad2e429f1f1ae | function syn_error_pos()
%% after synchronize, compute the error of position
%
est = load('estimate_07.log'); % load('estimate_06.log');
gt = load('gt_orien_07.log'); % load('gt_orien_06.log');
st_est = 1539612735.666800; %
st_gt = 9.475; %
syn_gt_est = syn_yaw_with_gt(gt, est, st_gt, st_est);
%% find scale
s... |
github | rising-turtle/slam_matlab-master | syn_error.m | .m | slam_matlab-master/torso_orien/syn_error.m | 1,745 | utf_8 | fd003193838125b389c6b1332849a522 | function syn_error()
%% after synchronize, compute the error of orientation
%
est = load('estimate_05.log'); % load('estimate_06.log');
gt = load('gt_orien_05.log'); % load('gt_orien_06.log');
st_est = 1539117466.769190; % 1539117578.592901;
st_gt = 48.825; % 50.75;
syn_gt_est = syn_yaw_with_gt(gt, est, st_gt, s... |
github | rising-turtle/slam_matlab-master | syn_error_z.m | .m | slam_matlab-master/torso_orien/syn_error_z.m | 1,419 | utf_8 | d7ff4a4c9d65e80395717b4b96c7dca6 | function syn_error_z()
%% after synchronize, compute the depth error
%
est = load('estimate_07.log'); % load('estimate_06.log');
gt = load('gt_orien_07.log'); % load('gt_orien_06.log');
st_est = 1539612735.666800; %
st_gt = 9.475; %
syn_gt_est = syn_yaw_with_gt(gt, est, st_gt, st_est);
%% find scale
st = 30; ... |
github | rising-turtle/slam_matlab-master | find_orien_gt.m | .m | slam_matlab-master/torso_orien/find_orien_gt.m | 1,941 | utf_8 | b8d7bf0923f301abaea67a4802f61dfe | function find_orien_gt()
% Oct. 7 2018, He Zhang, hzhang8@vcu.edu
% read points tracked by motion capture and estimate the square model in
% the camera coordinate system,
% then compute the normal of the torsor
M = csvread('gt_seq_07.csv');
vt = M(:,1);
pts_T = M(:,2:13);
pts_S = M(:,14:25);
mean_pts_T = me... |
github | rising-turtle/slam_matlab-master | load_camera_frame.m | .m | slam_matlab-master/graph_slam/load_camera_frame.m | 2,106 | utf_8 | f61def678bbb3739023b59fd0fc7aa40 | function [img, frm, des, p, ld_err] = load_camera_frame(fid)
%
% David Z, March 3th, 2015
% load camera data:
% img, 2D pixels
% frm,
% p [x y z]; (width, height, 3)
% ld_err = 1, if not exist
global g_data_dir g_data_prefix g_data_suffix g_camera_type
global g_filter_type
ld_err = 0; % TODO: take the load data e... |
github | rising-turtle/slam_matlab-master | pre_check_dir.m | .m | slam_matlab-master/graph_slam/pre_check_dir.m | 547 | utf_8 | 8a7e9d1ce4f080306bd3b5ee748a06f2 | %
% David Z, Jan 22th, 2015
% pre-check the save dir, if not exist, create it
%
function pre_check_dir(dir_)
global g_feature_dir g_matched_dir g_pose_std_dir
feature_dir = sprintf('/%s', g_feature_dir);
match_dir = sprintf('/%s', g_matched_dir);
not_exist_then_create(strcat(dir_, feature_dir));
not_exist... |
github | rising-turtle/slam_matlab-master | LoadCreative_dat.m | .m | slam_matlab-master/graph_slam/LoadCreative_dat.m | 783 | utf_8 | f539355d95dcac539680013bc4ae091c | %
% David Z, Jan 22th, 2015
% Load Creative Data
%
function [img, x, y, z, c, time, err] = LoadCreative_dat(data_name, j)
[prefix, confidence_read] = get_sr4k_dataset_prefix(data_name);
img = []; x = []; y = []; z = []; c = [];
err = 0;
%% time elapse
t_pre = tic;
%% load data file
[file_name, err] = sprintf('%s... |
github | rising-turtle/slam_matlab-master | VRO (2).m | .m | slam_matlab-master/graph_slam/VRO (2).m | 9,711 | utf_8 | 3d1a277522b64445257bd5affbb1e382 | function [t, pose_std, e] = VRO(id1, id2, img1, img2, des1, frm1, p1, des2, frm2, p2)
%
% March 3th, 2015, David Z
% match two images and return the transformation between img1 and img2
% t : [ phi, theta, psi, trans];
% pose_std: pose covariance
% e : error
%
%% extract features, match img1 to img2
if ~exist('des... |
github | rising-turtle/slam_matlab-master | graphslam_addpath.m | .m | slam_matlab-master/graph_slam/graphslam_addpath.m | 1,409 | utf_8 | 503a943bb30b3483282203370b1e5f57 | % Add the path for graph slam
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 10/16/12
function graphslam_addpath
% addpath('D:\Soonhac\SW\gtsam-toolbox-2.3.0-win64\toolbox');
% addpath('D:\soonhac\SW\kdtree');
% addpath('D:\soonhac\SW\LevenbergMarquardt');
% addpath('D:\soonhac\SW\Localization');
% addpath('D:... |
github | rising-turtle/slam_matlab-master | plot_graph_trajectory.m | .m | slam_matlab-master/graph_slam/plot_graph_trajectory.m | 1,866 | utf_8 | 57a03921cd0e61d805de50b94592cd8c | function plot_graph_trajectory(gtsam_pose_initial, gtsam_pose_result)
%
% David Z, 3/6/2015
% draw the trajectory in the graph structure
%
import gtsam.*
plot_xyz_initial = [];
%% plot the initial pose trajectory : VRO result
keys = KeyVector(gtsam_pose_initial.keys);
initial_max_index = keys.size-1;
for i=0:int32(ini... |
github | rising-turtle/slam_matlab-master | img_preprocess.m | .m | slam_matlab-master/graph_slam/img_preprocess.m | 1,748 | utf_8 | b8de79b4d263155edea6f71fe59278e6 | function [ img ] = img_preprocess( data_name, old_file_version)
%IMG_PREPROCESS Summary of this function goes here
% Detailed explanation goes here
if nargin < 1
old_file_version = 1; % 1;
% data_name='/home/davidz/work/EmbMess/mesa/pcl_mesa/build/bin/sr_data/d1_0001.bdat';
data_name='/home/davidz/work... |
github | rising-turtle/slam_matlab-master | VRO.m | .m | slam_matlab-master/graph_slam/VRO.m | 7,883 | utf_8 | 375948226a012ecf3f66d09fec7af01c | function [t, pose_std, e] = VRO(id1, id2, img1, img2, des1, frm1, p1, des2, frm2, p2)
%
% March 3th, 2015, David Z
% match two images and return the transformation between img1 and img2
% t : [ phi, theta, psi, trans];
% pose_std: pose covariance
% e : error
%
%% extract features, match img1 to img2
if ~exist('des... |
github | rising-turtle/slam_matlab-master | dump_matrix_2_file.m | .m | slam_matlab-master/graph_slam/dump_matrix_2_file.m | 364 | utf_8 | ff92d9360b0b19252a256ed0ebd60fd3 | function dump_matrix_2_file(fname, m)
%
% David Z, Feb 19, 2015
% try to construct a function to dump every kind of matrix into a text file
%
dump_matrix_2_file_wf(fname, m)
end
function dump_matrix_2_file_wf(f, m)
f_id = fopen(f, 'w+');
for i=1:size(m,1)
fprintf(f_id, '%f ', m(i,:));
fprint... |
github | rising-turtle/slam_matlab-master | sampling_vro.m | .m | slam_matlab-master/Localization/sampling_vro.m | 1,168 | utf_8 | 6034c6abcaf471707991c8ae56168921 | % Sampling VRO with interval
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 12/13/13
function sampling_vro()
% Load data
file_name = '498_frame_abs_intensity_sift_i_r_s_i_t_t_c_i_a_c_c_featureidxfix_fast_fast_dist2_nobpc_20st_gaussian_0.dat';
vro = load(file_name);
% Sample VRO with interval
interval = 15;
ne... |
github | rising-turtle/slam_matlab-master | load_pose_std.m | .m | slam_matlab-master/Localization/load_pose_std.m | 1,367 | utf_8 | 329d9ca060491048ca17bf713699f70a | % Load matched points from a file
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 3/11/2013
function [pose_std] = load_pose_std(data_name, dm, first_cframe, second_cframe, isgframe, sequence_data)
[prefix, confidence_read] = get_sr4k_dataset_prefix(data_name, dm);
if sequence_data == true
if strcmp(data_name... |
github | rising-turtle/slam_matlab-master | compute_pose_std.m | .m | slam_matlab-master/Localization/compute_pose_std.m | 1,000 | utf_8 | 92a7ecfe7741a125b61cc27344fc802d | % Compute covariance of vro
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 3/10/11
%
% Reference : [cov_pose_shift,q_dpose,T_dpose] = bootstrap_cov_calc(idx1,idx2)
%
function [pose_std] = compute_pose_std(op_pset1,op_pset2,rot_mean, trans_mean)
nData = size(op_pset1,2);
sampleSize = min(40,floor(0.75*nData));
n... |
github | rising-turtle/slam_matlab-master | localization_sift_ransac_limit_cov_fast_fast_dist2_nobpc_a.m | .m | slam_matlab-master/Localization/localization_sift_ransac_limit_cov_fast_fast_dist2_nobpc_a.m | 20,536 | utf_8 | 73fe356fb62535fac468a5d25f2e87ca | % This function computes the pose of the sensor between two data set from
% SR400 using SIFT . The orignial function was vot.m in the ASEE/pitch_4_plot1.
%
% Parameters :
% dm : number of prefix of directory containing the first data set.
% inc : relative number of prefix of directory containing the second data s... |
github | rising-turtle/slam_matlab-master | get_swing_filename.m | .m | slam_matlab-master/Localization/get_swing_filename.m | 595 | utf_8 | 8aea150ea1b5e47239276537edf87afc | % Get directory name of motive datasets
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 11/20/13
function motive_filename_lists=get_swing_filename()
motive_filename_lists = {'forward1','forward2','forward3','forward4','forward5','forward6','forward7_10m','forward8_10m', 'forward9_10m','forward10_10m','forwar... |
github | rising-turtle/slam_matlab-master | LoadSR_no_bpc_wu.m | .m | slam_matlab-master/Localization/LoadSR_no_bpc_wu.m | 4,230 | utf_8 | 3481d9400dbded650f4cbb0568337a7a | % Load data from Swiss Ranger
%
% Parameters
% data_name : the directory name of data
% dm : index of directory of data
% j : index of frame
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 4/20/11
% No bad pixel compensation
function [img, x, y, z, c, rtime] = LoadSR_no_bpc_wu(data_name, filter_name, bo... |
github | rising-turtle/slam_matlab-master | localization_sift_ransac_limit_cov_fast_fast_dist2_nobpc_sr.m | .m | slam_matlab-master/Localization/localization_sift_ransac_limit_cov_fast_fast_dist2_nobpc_sr.m | 20,549 | utf_8 | 963d2b78711e06b32a8bb36c6f2362ae | % This function computes the pose of the sensor between two data set from
% SR400 using SIFT . The orignial function was vot.m in the ASEE/pitch_4_plot1.
%
% Parameters :
% dm : number of prefix of directory containing the first data set.
% inc : relative number of prefix of directory containing the second data s... |
github | rising-turtle/slam_matlab-master | localization_sift_ransac_limit_cov_fast_fast_dist2_nobpc_pm.m | .m | slam_matlab-master/Localization/localization_sift_ransac_limit_cov_fast_fast_dist2_nobpc_pm.m | 21,689 | utf_8 | bec6d7eeeab3f0472b13909cd21b1fea | % This function computes the pose of the sensor between two data set from
% SR400 using SIFT . The orignial function was vot.m in the ASEE/pitch_4_plot1.
%
% Parameters :
% dm : number of prefix of directory containing the first data set.
% inc : relative number of prefix of directory containing the second data s... |
github | rising-turtle/slam_matlab-master | save_visual_features.m | .m | slam_matlab-master/Localization/save_visual_features.m | 1,181 | utf_8 | ba22c81ae039ca48c4065d21de093e35 | % Save sift visual feature into a file
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 2/13/2013
function save_visual_features(data_name, dm, cframe, frm, des, elapsed_sift, img, x, y, z, c, elapsed_pre, sequence_data, image_name)
[prefix, confidence_read] = get_sr4k_dataset_prefix(data_name, dm);
if sequence_da... |
github | rising-turtle/slam_matlab-master | localization_sift_ransac_limit_cov_fast_fast_dist2_nobpc_c2.m | .m | slam_matlab-master/Localization/localization_sift_ransac_limit_cov_fast_fast_dist2_nobpc_c2.m | 17,776 | utf_8 | 577e5b47084e3aab212e2ee92834b933 | % This function computes the pose of the sensor between two data set from
% SR400 using SIFT . The orignial function was vot.m in the ASEE/pitch_4_plot1.
%
% Parameters :
% dm : number of prefix of directory containing the first data set.
% inc : relative number of prefix of directory containing the second data s... |
github | rising-turtle/slam_matlab-master | localization_sift_ransac_limit_cov_fast_fast.m | .m | slam_matlab-master/Localization/localization_sift_ransac_limit_cov_fast_fast.m | 16,231 | utf_8 | 84b24dd4aa8859f73260befb87e72c1e | % This function computes the pose of the sensor between two data set from
% SR400 using SIFT . The orignial function was vot.m in the ASEE/pitch_4_plot1.
%
% Parameters :
% dm : number of prefix of directory containing the first data set.
% inc : relative number of prefix of directory containing the second data s... |
github | rising-turtle/slam_matlab-master | LoadPrimesense_newmodel1.m | .m | slam_matlab-master/Localization/LoadPrimesense_newmodel1.m | 2,543 | utf_8 | 7ec641cc6b0375f05707697ba5146dc8 | % Load data from Kinect
%
% Parameters
% data_name : the directory name of data
% dm : index of directory of data
% j : index of frame
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 4/20/11
function [img, X, Y, Z] = LoadPrimesense_model(dm, file_index)
if file_index<11
z_file_name=sprintf('D:/image... |
github | rising-turtle/slam_matlab-master | scale_img.m | .m | slam_matlab-master/Localization/scale_img.m | 1,061 | utf_8 | 9c5217d62c210a4383e5c109eb4964c6 | % Scale and smoothing image
%
% Parameters
% img : input image
% fw : the size of median filter
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 4/21/11
function [img] = scale_img(img, fw, value_type, data_type)
[m, n, v] = find (img>65000); %????
imgt=img;
num=size(m,1);
for kk=1:num
imgt(m(kk), n(kk... |
github | rising-turtle/slam_matlab-master | compensate_badpixel.m | .m | slam_matlab-master/Localization/compensate_badpixel.m | 1,844 | utf_8 | f28d56efdbf5d1e06c25dd3f07945a90 | % Compenstate the bad pixel with low confidence by median filter
% Date : 3/13/12
% Author : Soonhac Hong (sxhong1@ualr.edu)
function [img, x, y, z, c] = compensate_badpixel(img, x, y, z, c, confidence_cut_off)
e_index = c < confidence_cut_off;
for i = 1:size(img,1) % row
for j=1:size(img,2) % column... |
github | rising-turtle/slam_matlab-master | check_stored_depth_feature.m | .m | slam_matlab-master/Localization/check_stored_depth_feature.m | 812 | utf_8 | a037ad501e066d47ebf04737a0196091 | % Check if there is the stored visual feature
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 2/13/2013
function [exist_flag] = check_stored_depth_feature(data_name, dm, cframe)
exist_flag = 0;
[prefix, confidence_read] = get_sr4k_dataset_prefix(data_name, dm);
dataset_dir = strrep(prefix, '/d1','');
dataset_di... |
github | rising-turtle/slam_matlab-master | localization_sift_ransac_limit_icp2_cov_fast.m | .m | slam_matlab-master/Localization/localization_sift_ransac_limit_icp2_cov_fast.m | 17,406 | utf_8 | b03f8f6fe4f24a2e9865f15785efe46f | % This function computes the pose of the sensor between two data set from
% SR400 using SIFT . The orignial function was vot.m in the ASEE/pitch_4_plot1.
%
% Parameters :
% dm : number of prefix of directory containing the first data set.
% inc : relative number of prefix of directory containing the second data s... |
github | rising-turtle/slam_matlab-master | LoadKinect_depthbased.m | .m | slam_matlab-master/Localization/LoadKinect_depthbased.m | 2,589 | utf_8 | eda775a4e73610c0ffdbdb3d7c327da6 | % Load data from Kinect
%
% Parameters
% data_name : the directory name of data
% dm : index of directory of data
% j : index of frame
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 4/20/11
function [img, X, Y, Z, rtime, depth_time_stamp] = LoadKinect_depthbased(dm, j)
t_load = tic;
dir_name_list = get... |
github | rising-turtle/slam_matlab-master | vro_icp_9_cov.m | .m | slam_matlab-master/Localization/vro_icp_9_cov.m | 10,624 | utf_8 | e5d049b0e1df183d4947e039b8a2e8bf | % This function compute the transformation of two 3D point clouds by ICP
%
% Parameters :
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 9/20/12
% ICP6 + convexhull = ICP9
function [phi_icp, theta_icp, psi_icp, trans_icp, match_rmse, match_num, elapsed_time, sta_icp, error, pose_std] = vro_icp_9_cov(op_pset1,... |
github | rising-turtle/slam_matlab-master | check_stored_visual_feature.m | .m | slam_matlab-master/Localization/check_stored_visual_feature.m | 1,230 | utf_8 | 08b710330ed346960fc4aa626de7a5d7 | % Check if there is the stored visual feature
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 2/13/2013
function [exist_flag] = check_stored_visual_feature(data_name, dm, cframe, sequence_data, image_name)
exist_flag = 0;
[prefix, confidence_read] = get_sr4k_dataset_prefix(data_name, dm);
if sequence_data == tr... |
github | rising-turtle/slam_matlab-master | localization_sift_ransac_limit_cov_fast_fast_dist2_nobpc_svde6.m | .m | slam_matlab-master/Localization/localization_sift_ransac_limit_cov_fast_fast_dist2_nobpc_svde6.m | 17,644 | utf_8 | 59d68df885879e92e4bb12165cf177a0 | % This function computes the pose of the sensor between two data set from
% SR400 using SIFT . The orignial function was vot.m in the ASEE/pitch_4_plot1.
%
% Parameters :
% dm : number of prefix of directory containing the first data set.
% inc : relative number of prefix of directory containing the second data s... |
github | rising-turtle/slam_matlab-master | check_feature_distance_icp.m | .m | slam_matlab-master/Localization/check_feature_distance_icp.m | 781 | utf_8 | 04b938571127b8ecb8e8139ba603a597 | % Check the distance of feaure points
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 4/5/13
function [op_pset1, op_pset2] = check_feature_distance_icp(op_pset1, op_pset2)
distance_min = 0.8;
distance_max = 5;
op_pset1_distance = sqrt(sum(op_pset1.^2));
op_pset2_distance = sqrt(sum(op_pset2.^2));
op_pset1_dis... |
github | rising-turtle/slam_matlab-master | load_visual_features.m | .m | slam_matlab-master/Localization/load_visual_features.m | 1,115 | utf_8 | b287b4545e4f246bec0e76880f2e3716 | % Load sift visual feature from a file
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 2/13/2013
function [frm, des, elapsed_sift, img, x, y, z, c, elapsed_pre] = load_visual_features(data_name, dm, cframe, sequence_data, image_name)
[prefix, confidence_read] = get_sr4k_dataset_prefix(data_name, dm);
if sequence... |
github | rising-turtle/slam_matlab-master | load_matched_points.m | .m | slam_matlab-master/Localization/load_matched_points.m | 950 | utf_8 | 1a99af336cb68c960b51337530b7259d | % Load matched points from a file
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 3/11/2013
function [match_num, ransac_iteration, op_pset1_image_index, op_pset2_image_index, op_pset_cnt, elapsed_match, elapsed_ransac, op_pset1, op_pset2] = load_matched_points(data_name, dm, first_cframe, second_cframe, isgframe... |
github | rising-turtle/slam_matlab-master | vro_icp_6_cov.m | .m | slam_matlab-master/Localization/vro_icp_6_cov.m | 10,511 | utf_8 | 641524042e16a232c87308d355249f26 | % This function compute the transformation of two 3D point clouds by ICP
%
% Parameters :
%
% Author : Soonhac Hong (sxhong1@ualr.edu)
% Date : 9/20/12
function [phi_icp, theta_icp, psi_icp, trans_icp, match_rmse, match_num, elapsed_time, sta_icp, error, pose_std] = vro_icp_6_cov(op_pset1, op_pset2, rot, trans, x1, ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.