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 | tp6vul3wj/PatchMatch-filter-master | vl_demo_kdtree_sift.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_impattern.m | .m | PatchMatch-filter-master/vlfeat/toolbox/imop/vl_impattern.m | 6,876 | utf_8 | 1716a4d107f0186be3d11c647bc628ce | 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 | tp6vul3wj/PatchMatch-filter-master | vl_tpsu.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_xyz2lab.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_gmm.m | .m | PatchMatch-filter-master/vlfeat/toolbox/xtest/vl_test_gmm.m | 1,332 | utf_8 | 76782cae6c98781c6c38d4cbf5549d94 | function results = vl_test_gmm(varargin)
% VL_TEST_GMM
% 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 ;
end
function s = setup()
randn('st... |
github | tp6vul3wj/PatchMatch-filter-master | vl_test_twister.m | .m | PatchMatch-filter-master/vlfeat/toolbox/xtest/vl_test_twister.m | 1,251 | utf_8 | 2bfb5a30cbd6df6ac80c66b73f8646da | 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 | tp6vul3wj/PatchMatch-filter-master | vl_test_kdtree.m | .m | PatchMatch-filter-master/vlfeat/toolbox/xtest/vl_test_kdtree.m | 2,449 | utf_8 | 9d7ad2b435a88c22084b38e5eb5f9eb9 | 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 | tp6vul3wj/PatchMatch-filter-master | vl_test_imwbackward.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_alphanum.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_printsize.m | .m | PatchMatch-filter-master/vlfeat/toolbox/xtest/vl_test_printsize.m | 1,447 | utf_8 | 0f0b6437c648b7a2e1310900262bd765 | function results = vl_test_printsize(varargin)
% VL_TEST_PRINTSIZE
vl_test_init ;
function s = setup()
s.fig = figure(1) ;
s.usletter = [8.5, 11] ; % inches
s.a4 = [8.26772, 11.6929] ;
clf(s.fig) ; plot(1:10) ;
function teardown(s)
close(s.fig) ;
function test_basic(s)
for sigma = [1 0.5 0.2]
vl_printsize(s.fig, s... |
github | tp6vul3wj/PatchMatch-filter-master | vl_test_cummax.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_imintegral.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_sift.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_binsum.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_lbp.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_colsubset.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_alldist.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_ihashsum.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_grad.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_whistc.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_roc.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_dsift.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_alldist2.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_fisher.m | .m | PatchMatch-filter-master/vlfeat/toolbox/xtest/vl_test_fisher.m | 1,703 | utf_8 | 41b28dce7f0d0ae5cb6abd942acbef56 | function results = vl_test_fisher(varargin)
% VL_TEST_FISHER
vl_test_init ;
function s = setup()
randn('state',0) ;
dimension = 5 ;
numData = 21 ;
numComponents = 3 ;
s.x = randn(dimension,numData) ;
s.mu = randn(dimension,numComponents) ;
s.sigma2 = ones(dimension,numComponents) ;
s.prior = ones(1,numComponents) ;
s... |
github | tp6vul3wj/PatchMatch-filter-master | vl_test_imsmooth.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_svmtrain.m | .m | PatchMatch-filter-master/vlfeat/toolbox/xtest/vl_test_svmtrain.m | 4,277 | utf_8 | 071b7c66191a22e8236fda16752b27aa | function results = vl_test_svmtrain(varargin)
% VL_TEST_SVMTRAIN
vl_test_init ;
end
function s = setup()
randn('state',0) ;
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.y = [ones(1,Np) ... |
github | tp6vul3wj/PatchMatch-filter-master | vl_test_phow.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_kmeans.m | .m | PatchMatch-filter-master/vlfeat/toolbox/xtest/vl_test_kmeans.m | 3,632 | utf_8 | 719f7fca81e19eed5cc45c2ca251aad0 | 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 | tp6vul3wj/PatchMatch-filter-master | vl_test_hikmeans.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_aib.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_plotbox.m | .m | PatchMatch-filter-master/vlfeat/toolbox/xtest/vl_test_plotbox.m | 414 | utf_8 | aa06ce4932a213fb933bbede6072b029 | function results = vl_test_plotbox(varargin)
% VL_TEST_PLOTBOX
vl_test_init ;
function test_basic(s)
figure(1) ; clf ;
vl_plotbox([-1 -1 1 1]') ;
xlim([-2 2]) ;
ylim([-2 2]) ;
close(1) ;
function test_multiple(s)
figure(1) ; clf ;
randn('state', 0) ;
vl_plotbox(randn(4,10)) ;
close(1) ;
function test_style(s)
figure... |
github | tp6vul3wj/PatchMatch-filter-master | vl_test_imarray.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_homkermap.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_slic.m | .m | PatchMatch-filter-master/vlfeat/toolbox/xtest/vl_test_slic.m | 200 | utf_8 | 12a6465e3ef5b4bcfd7303cd8a9229d4 | 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) ;
|
github | tp6vul3wj/PatchMatch-filter-master | vl_test_ikmeans.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_mser.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_inthist.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_imdisttf.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_vlad.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_pr.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_hog.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_argparse.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_liop.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_test_binsearch.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_roc.m | .m | PatchMatch-filter-master/vlfeat/toolbox/plotop/vl_roc.m | 8,747 | utf_8 | 6b8b4786c9242d5112ca90a616db507a | 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 | tp6vul3wj/PatchMatch-filter-master | vl_click.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_pr.m | .m | PatchMatch-filter-master/vlfeat/toolbox/plotop/vl_pr.m | 9,135 | utf_8 | c5d1b9d67f843d10c0b2c6b48fab3c53 | 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 | tp6vul3wj/PatchMatch-filter-master | vl_ubcread.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vl_frame2oell.m | .m | PatchMatch-filter-master/vlfeat/toolbox/sift/vl_frame2oell.m | 2,160 | utf_8 | 457c5f2e8b637108c8c1b2256396de13 | function eframes = vl_frame2oell(frames)
% FRAMES2OELL Convert generic feature frames to oriented ellipses
% EFRAMES = VL_FRAME2OELL(FRAMES) converts the specified FRAMES to
% the oriented ellipses EFRAMES.
%
% A frame is either a point, disc, oriented disc, ellipse, or
% oriented ellipse. These are represene... |
github | tp6vul3wj/PatchMatch-filter-master | vl_plotsiftdescriptor.m | .m | PatchMatch-filter-master/vlfeat/toolbox/sift/vl_plotsiftdescriptor.m | 4,725 | utf_8 | 395bf4e0d7417674401ddf34cc8a70da | 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 | tp6vul3wj/PatchMatch-filter-master | phow_caltech101.m | .m | PatchMatch-filter-master/vlfeat/apps/phow_caltech101.m | 11,595 | utf_8 | cdd4c2add2b7bbfe66a43831513f99fc | 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 | tp6vul3wj/PatchMatch-filter-master | sift_mosaic.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | encodeImage.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | experiments.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | getDenseSIFT.m | .m | PatchMatch-filter-master/vlfeat/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 | tp6vul3wj/PatchMatch-filter-master | vgg_kmiter.m | .m | PatchMatch-filter-master/features/global_selfsim_release1.0/supportfunctions/vgg/vgg_kmiter.m | 263 | utf_8 | af087d302e24450697c2aca6db07e2ba | %VGG_KMITER
% $Id: vgg_kmiter.m,v 1.1 2007/11/06 20:39:16 ojw Exp $
function varargout = vgg_kmiter(varargin)
funcName = mfilename;
sourceList = {[funcName '.cxx']}; % Cell array of source files
vgg_mexcompile_script; % Compilation happens in this script
return |
github | tp6vul3wj/PatchMatch-filter-master | vgg_matlab_root.m | .m | PatchMatch-filter-master/features/global_selfsim_release1.0/supportfunctions/vgg/vgg_matlab_root.m | 130 | utf_8 | 7a3bbe4b969f7e0b7903dba66818c3e6 | %VGG_MATLAB_ROOT Returns string with vgg_matlab root path.
function s = vgg_matlab_root
s = fileparts(which(mfilename));
return
|
github | tp6vul3wj/PatchMatch-filter-master | load_cubes.m | .m | PatchMatch-filter-master/features/mdaisy/load_cubes.m | 966 | utf_8 | bb93760cab952913274f8f972a348e60 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | load_gradient_layers.m | .m | PatchMatch-filter-master/features/mdaisy/load_gradient_layers.m | 818 | utf_8 | 56ba27604c8b8e243b8e0b323cda2dc5 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | smooth_layers.m | .m | PatchMatch-filter-master/features/mdaisy/smooth_layers.m | 948 | utf_8 | 8e4e07fe898568ee3895e5c277d0c8f0 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | normalize_sift.m | .m | PatchMatch-filter-master/features/mdaisy/normalize_sift.m | 999 | utf_8 | 3a6f6b3b78d9a9b2ced32cf0e0eb5a8e | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | init_daisy.m | .m | PatchMatch-filter-master/features/mdaisy/init_daisy.m | 5,099 | utf_8 | 22f9d601147f7bf3056c0b2dcb37eab1 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | display_descriptor.m | .m | PatchMatch-filter-master/features/mdaisy/display_descriptor.m | 874 | utf_8 | 7856c5daf584d34f670d5423c27881b2 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | normalize_full.m | .m | PatchMatch-filter-master/features/mdaisy/normalize_full.m | 817 | utf_8 | 3b1a9503de84a29510a54c475161189a | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | u_compute_descriptor_11.m | .m | PatchMatch-filter-master/features/mdaisy/u_compute_descriptor_11.m | 1,638 | utf_8 | 22d9a9d91c0c2b072347361ae80011c0 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | rgb_to_gray.m | .m | PatchMatch-filter-master/features/mdaisy/rgb_to_gray.m | 944 | utf_8 | 0ceeb906b6462c4a890558d04a8420f7 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | u_compute_descriptor_10.m | .m | PatchMatch-filter-master/features/mdaisy/u_compute_descriptor_10.m | 1,487 | utf_8 | 9b5898ac2e02f3ef03f9c7953610dbd3 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | filter_size.m | .m | PatchMatch-filter-master/features/mdaisy/filter_size.m | 816 | utf_8 | 26467375e11baa2934cefcab7d49a59a | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | u_compute_descriptor_01.m | .m | PatchMatch-filter-master/features/mdaisy/u_compute_descriptor_01.m | 1,362 | utf_8 | 232f65067b178366c55b6460958144f5 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | compute_descriptor.m | .m | PatchMatch-filter-master/features/mdaisy/compute_descriptor.m | 1,245 | utf_8 | 8360532d3913077706f687278150a74e | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | u_compute_descriptor_00.m | .m | PatchMatch-filter-master/features/mdaisy/u_compute_descriptor_00.m | 1,221 | utf_8 | da624997fc5d8300944ff10c246d0966 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | compute_daisy.m | .m | PatchMatch-filter-master/features/mdaisy/compute_daisy.m | 2,191 | utf_8 | 68be33cf07181114c625f8b452b65614 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | layered_gradient.m | .m | PatchMatch-filter-master/features/mdaisy/layered_gradient.m | 1,200 | utf_8 | cb4f5bd05db59d85b7c833b4c0a1eb7b | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | gaussian_1d.m | .m | PatchMatch-filter-master/features/mdaisy/gaussian_1d.m | 841 | utf_8 | 63e2b79ef06e6ad7e272bc2ec8700d60 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | normalize_partial.m | .m | PatchMatch-filter-master/features/mdaisy/normalize_partial.m | 840 | utf_8 | f743f08ee3ca474a130e668adc546b56 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | load_sgradient_layers.m | .m | PatchMatch-filter-master/features/mdaisy/load_sgradient_layers.m | 820 | utf_8 | fd4789cc27a24feb5cc4efc776be6dd6 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | tp6vul3wj/PatchMatch-filter-master | make_fb.m | .m | PatchMatch-filter-master/features/gb/feature_code/make_fb.m | 1,844 | utf_8 | 9df3976167da37c1e3ce2bdb0c05eddd | function [FB,oris] = make_fb( nori, aspect, scale, half_support)
% Demo code for matching roughly based on the procedure
% described in:
%
% "Shape Matching and Object Recognition using Low Distortion Correspondence"
% A. C. Berg, T. L. Berg, J. Malik
% CVPR 2005
%
% code Copyright 2005 Alex Berg
%
% questions -> Alex... |
github | dangweili/caffe_openblas-master | prepare_batch.m | .m | caffe_openblas-master/matlab/caffe/prepare_batch.m | 1,298 | utf_8 | 68088231982895c248aef25b4886eab0 | % ------------------------------------------------------------------------
function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
% ------------------------------------------------------------------------
if nargin < 2
d = load('ilsvrc_2012_mean');
IMAGE_MEAN = d.image_mean;
end
num_images = length... |
github | dangweili/caffe_openblas-master | matcaffe_demo_vgg.m | .m | caffe_openblas-master/matlab/caffe/matcaffe_demo_vgg.m | 3,036 | utf_8 | f836eefad26027ac1be6e24421b59543 | function scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
%
% Demo of the matlab wrapper using the networks described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional... |
github | dangweili/caffe_openblas-master | matcaffe_demo.m | .m | caffe_openblas-master/matlab/caffe/matcaffe_demo.m | 3,344 | utf_8 | 669622769508a684210d164ac749a614 | function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
% scores = matcaffe_demo(im, use_gpu)
%
% Demo of the matlab wrapper using the ILSVRC network.
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
%
% You m... |
github | dangweili/caffe_openblas-master | matcaffe_demo_vgg_mean_pix.m | .m | caffe_openblas-master/matlab/caffe/matcaffe_demo_vgg_mean_pix.m | 3,069 | utf_8 | 04b831d0f205ef0932c4f3cfa930d6f9 | function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
%
% Demo of the matlab wrapper based on the networks used for the "VGG" entry
% in the ILSVRC-2014 competition and described in the tech. report
% "Very Deep Convo... |
github | caxenie/corr-learn-som-quadrotor-master | visualize_runtime.m | .m | corr-learn-som-quadrotor-master/analysis/visualize_runtime.m | 5,920 | utf_8 | 6e7602c1048498b94fdcd99082c73a99 | % load the data from the file
function visualize_runtime(filein)
close all;
rdata = load_runtime_data(filein);
% % plot runtime and learning parameters of the network
% figure(1);
% set(gcf, 'color', 'white');
% subplot(2,2,1);
% plot(rdata.sim.alpha, '.k'); xlabel('in learning epochs'); ylabel('alpha');
% grid of... |
github | caxenie/corr-learn-som-quadrotor-master | plot_acc_LPF_and_FT.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_acc_LPF_and_FT.m | 2,720 | utf_8 | 51b93c54ee279f2fb4f322b72cd4d3a8 |
function [] = plot_acc_LPF_and_FT(ld)
imu = ld.imu;
acc = ld.acc;
r = 3;
c = 2;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
p(4).h = subplot(r,c,4);
p(5).h = subplot(r,c,5);
p(6).h = subplot(r,c,6);
k=1;
p(k).t{1} = imu.hrt.t;
p(k).t{2} = imu.hrt.t;
p(k).d{1} = acc.ax;
p(k).d{2} =... |
github | caxenie/corr-learn-som-quadrotor-master | add_acc_a_lin.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/add_acc_a_lin.m | 1,118 | utf_8 | d0533afdaaeb23b4ce73e17b4be057a4 | function [ld_out] = add_acc_a_lin(ld)
if(isfield(ld,'a_rot_ref') == 0 || isfield(ld,'acc') == 0)
ld_out = ld;
return;
end
%**************************************************************************
%% linear acceleration: accelerometer based
% with attitude / tracker based reference
%*********... |
github | caxenie/corr-learn-som-quadrotor-master | add_b_ref.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/add_b_ref.m | 1,652 | utf_8 | 3181da7e3363076e73e2e4a36169d0dc | function [ld_out] = add_b_ref(ld)
if(isfield(ld,'imu') == 0)
ld_out = ld;
return;
end
%**************************************************************************
%% earth magnetic field reference
% attitude / tracker based reference
%*************************************************************... |
github | caxenie/corr-learn-som-quadrotor-master | add_rigid_body_precalcs.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/add_rigid_body_precalcs.m | 2,191 | utf_8 | 0aee1c07f8e54887133db16aea7619fe | function [ld_out] = add_rigid_body_precalcs(ld)
if(isfield(ld,'rb') == 0)
ld_out = ld;
return;
end
ld.rb.pos = [ld.rb.x';ld.rb.y';ld.rb.z'];
% make sure that the rotations are valid
n = ld.rb.n;
for k=1:n
if(toDeg(ld.rb.roll(k)) > 90 && toDeg(ld.rb.pitch(k)) < -90)
ld.rb.roll(k) = -toRad(180) + ld.rb.... |
github | caxenie/corr-learn-som-quadrotor-master | add_optical_flow_precalcs.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/add_optical_flow_precalcs.m | 1,261 | utf_8 | ee0124a02eb8cf88eebb497fb8462f7e | function [ld_out] = add_optical_flow_precalcs(ld)
if(isfield(ld,'of') == 0)
ld_out = ld;
return;
end
quality = ld.of.quality / 255;
th = ones(size(quality));
% threshold at 90 %
th_p = 0.7;
th(quality < th_p) = 0;
ld.of.qth = th_p;
ld.of.i_quality_ok = (quality >= th_p);
ld.of.vx_th = ld.of.flow_comp_m_... |
github | caxenie/corr-learn-som-quadrotor-master | plot_time_analysis.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_time_analysis.m | 4,037 | utf_8 | d9b4b4eb9e00ae48faccefe53065e7f7 |
function [] = plot_time_analysis(ld)
imu = ld.imu;
att = ld.att;
of = ld.of;
rb = ld.rb;
r = 4;
c = 2;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
p(4).h = subplot(r,c,4);
p(5).h = subplot(r,c,5);
p(6).h = subplot(r,c,6);
p(7).h = subplot(r,c,7);
p(8).h = subplot(r,c,8);
k = 1;
... |
github | caxenie/corr-learn-som-quadrotor-master | main_mag_analysis.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/main_mag_analysis.m | 1,067 | utf_8 | 839181eabc50ae6c31d7f8fb90998a8c |
function [] = main_mag_analysis()
clear all
close all
phi = 0;
theta = 5;
psi = 30;
B = [0.25;0;0.39];
% test rotation
Bm = rot_euler(phi,theta,psi)*B;
% find out yaw angle
% undo roll and pitch rotations
B_hat = roty(-theta)*rotx(-phi)*Bm;
% get yaw rotation
yaw = toDeg(atan2(B_hat(2),B_hat(1)));
fprin... |
github | caxenie/corr-learn-som-quadrotor-master | plot_optical_flow.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_optical_flow.m | 1,273 | utf_8 | ac946044a54f68454f8c181396475d2f |
function [] = plot_optical_flow(of, tsmin)
ts = of.ts;
if nargin == 2
t = (ts - tsmin)*1e-6;
else
t = (ts - ts(1))*1e-6;
end
r = 3;
c = 2;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
p(4).h = subplot(r,c,4);
p(5).h = subplot(r,c,5);
p(6).h = subplot(r,c,6);
p(1).d = of... |
github | caxenie/corr-learn-som-quadrotor-master | plot_acc_a_rot.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_acc_a_rot.m | 1,957 | utf_8 | 099337d064e46b9b19a4aac14944bb6b |
function [] = plot_acc_a_rot(ld)
imu = ld.imu;
acc = ld.acc;
r = 3;
c = 1;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
k=1;
p(k).title = 'a_x [m/s^2]';
p(k).t{1} = imu.hrt.t;
p(k).t{2} = imu.hrt.t;
p(k).d{1} = ld.a_rot_ref(1,:);
p(k).d{2} = acc.a_rot_f(1,:);
k=2;
p(k).title = ... |
github | caxenie/corr-learn-som-quadrotor-master | add_acc_angle_diff.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/add_acc_angle_diff.m | 1,441 | utf_8 | df9a1679b74843a0dd51f58ab5fd0d67 | function [ld_out] = add_acc_angle_diff(ld)
if(isfield(ld,'imu') == 0)
ld_out = ld;
return;
end
roll = ld.acc.raw.roll;
pitch = ld.acc.raw.pitch;
Fs = ld.imu.hrt.freq_mean;
dt_mean = ld.imu.hrt.dt_mean;
% numerically differentiate
droll = num_diff(roll, dt_mean);
% low pass filter raw vel signal
% droll =... |
github | caxenie/corr-learn-som-quadrotor-master | plot_highres_imu.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_highres_imu.m | 1,829 | utf_8 | c8e8596e6c6d04b87d78ea2840860163 |
function [] = plot_highres_imu(imu, tsmin)
ts = imu.ts;
if nargin == 2
t = (ts - tsmin)*1e-6;
else
t = (ts - ts(1))*1e-6;
end
r = 3;
c = 3;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
p(4).h = subplot(r,c,4);
p(5).h = subplot(r,c,5);
p(6).h = subplot(... |
github | caxenie/corr-learn-som-quadrotor-master | plot_rpy.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_rpy.m | 2,960 | utf_8 | 1455cd152d6d107ceea267ad6ec3a887 |
function [] = plot_rpy(ld)
imu = ld.imu;
att = ld.att;
if(isfield(ld,'rb'))
rb = ld.rb;
else
rb.t = 0;
rb.roll = 0;
rb.pitch = 0;
rb.yaw = 0;
end
acc = ld.acc;
gyro = ld.gyro;
cm = colormap(jet(5));
r = 3;
c = 2;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = s... |
github | caxenie/corr-learn-som-quadrotor-master | plot_rpy_acc.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_rpy_acc.m | 2,238 | utf_8 | 811891480b0aafb1844457e6febd04a6 |
function [] = plot_rpy_acc(ld)
imu = ld.imu;
att = ld.att;
rb = ld.rb;
acc = ld.acc;
cm = colormap(jet(5));
r = 3;
c = 1;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
k=1;
p(k).title = 'roll';
p(k).t{1} = rb.t;
p(k).t{2} = att.t;
p(k).t{3} = imu.hrt.t;
% p(k).t{4} = imu.hrt.t(imu.a... |
github | caxenie/corr-learn-som-quadrotor-master | sphericalFilter.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/sphericalFilter.m | 370 | utf_8 | 9e2eda0b830c1cf02f8a1ef30d674196 | % quick and dirty way to ignore acc vectors > g
function [valid] = sphericalFilter(x,y,z,r,delta)
r_hat = x.*x + y.*y + z.*z;
d1 = r - delta;
d2 = r + delta;
sqdelta1 = d1*d1;
sqdelta2 = d2*d2;
valid = ones(size(x));
for k=1:length(x)
if(r_hat(k) < sqdelta1 || r_hat(k) > sqdelta2)
valid(k) = 0; ... |
github | caxenie/corr-learn-som-quadrotor-master | plot_rigidBody_lin_trans.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_rigidBody_lin_trans.m | 2,124 | utf_8 | e1dd34eaa16476e0da1cd7d6bc571353 |
function [] = plot_rigidBody_lin_trans(ld)
imu = ld.imu;
att = ld.att;
rb = ld.rb;
r = 3;
c = 3;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
p(4).h = subplot(r,c,4);
p(5).h = subplot(r,c,5);
p(6).h = subplot(r,c,6);
p(7).h = subplot(r,c,7);
p(8).h = subplot(r,c,8);
p(9).h = subplot(r... |
github | caxenie/corr-learn-som-quadrotor-master | plot_sys_status.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_sys_status.m | 883 | utf_8 | 44adea45e71487bda68a03f6119324db |
function [] = plot_sys_status(ss, tsmin)
ts = ss.ts;
if nargin == 2
t = (ts - tsmin)*1e-6;
else
t = (ts - ts(1))*1e-6;
end
r = 1;
c = 2;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
% p(3).h = subplot(r,c,3);
% p(4).h = subplot(r,c,4);
p(1).d = ss.load / 10; % system cpu load
p(2).d =... |
github | caxenie/corr-learn-som-quadrotor-master | plot_mag_b.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_mag_b.m | 1,500 | utf_8 | 565ca0422d327683e9c1badc6f027942 |
function [] = plot_mag_b(ld)
imu = ld.imu;
rb = ld.rb;
mag = ld.mag;
r = 3;
c = 1;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
k=1;
p(k).title = 'b_x [Gauss]';
p(k).t{1} = imu.hrt.t;
p(k).t{2} = imu.hrt.t;
p(k).d{1} = ld.b_ref(1,:);
p(k).d{2} = mag.bx;
k=2;
p(k).title = 'b_y ... |
github | caxenie/corr-learn-som-quadrotor-master | fix_singularities.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/fix_singularities.m | 160 | utf_8 | 6a9716b835c4180a52d93f3ff831ee63 | % fix singularities in the atan computation
function y = fix_singularities(in)
while(any(isnan(in)))
in(isnan(in)) = in(find(isnan(in))-1);
end
y = in;
end |
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