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 | ZhengyaoJiang/OLPS-master | algorithmAnalyserMenu.m | .m | OLPS-master/PGUI/algorithmAnalyserMenu.m | 10,455 | utf_8 | 8b15cea88cc7964ff0efff485e4db310 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This file is part of OLPS: http://OLPS.stevenhoi.org/
% Original authors: Doyen Sahoo
% Contributors: Bin LI, Steven C.H. Hoi
% Change log:
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ ] = algorit... |
github | ZhengyaoJiang/OLPS-master | displayTable2.m | .m | OLPS-master/PGUI/displayTable2.m | 4,083 | utf_8 | 1e752548fd342724e05a5976eef8443a | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This file is part of OLPS: http://OLPS.stevenhoi.org/
% Original authors: Doyen Sahoo
% Contributors: Bin LI, Steven C.H. Hoi
% Change log:
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [] = displayT... |
github | ZhengyaoJiang/OLPS-master | resultManager.m | .m | OLPS-master/PGUI/resultManager.m | 10,727 | utf_8 | 11866bb0964d14c8e55dc270891225fe | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This file is part of OLPS: http://OLPS.stevenhoi.org/
% Original authors: Doyen Sahoo
% Contributors: Bin LI, Steven C.H. Hoi
% Change log:
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [] = resultMa... |
github | ZhengyaoJiang/OLPS-master | configurationMenu.m | .m | OLPS-master/PGUI/configurationMenu.m | 4,629 | utf_8 | 5d57ae4ca749f9c9d44d6696dae14475 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This file is part of OLPS: http://OLPS.stevenhoi.org/
% Original authors: Doyen Sahoo
% Contributors: Bin LI, Steven C.H. Hoi
% Change log:
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ ] = configu... |
github | ZhengyaoJiang/OLPS-master | displayTable.m | .m | OLPS-master/PGUI/displayTable.m | 4,329 | utf_8 | e6121ec7857f1481b1ad8fee60a1bac1 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This file is part of OLPS: http://OLPS.stevenhoi.org/
% Original authors: Doyen Sahoo
% Contributors: Bin LI, Steven C.H. Hoi
% Change log:
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [] = displayT... |
github | susanmeerdink/Fractional-Cover-Image-Analysis-master | gui_image_analysis.m | .m | Fractional-Cover-Image-Analysis-master/gui_image_analysis.m | 20,649 | utf_8 | e41738c22a2cf34508f61bce977730b9 | function varargout = gui_image_analysis(varargin)
% GUI_IMAGE_ANALYSIS MATLAB code for gui_image_analysis.fig
% GUI_IMAGE_ANALYSIS, by itself, creates a new GUI_IMAGE_ANALYSIS or raises the existing
% singleton*.
%
% H = GUI_IMAGE_ANALYSIS returns the handle to a new GUI_IMAGE_ANALYSIS or the handle to
%... |
github | kaltwang/2015latent-master | graph_perclass.m | .m | 2015latent-master/+hpmm2/@graph_perclass/graph_perclass.m | 4,802 | utf_8 | 3a7919923f03220569c5f02dc88bdd6c |
classdef graph_perclass
properties
% Parameters
ind_class = [];
K_class = [];
graph = hpmm2.graph.empty();
graphs_perclass = hpmm2.graph.empty();
end
methods
% constructor
function obj = graph_perclass(varargin)
obj = set(obj,varargin{:});
end
... |
github | MaigoAkisame/VMSep-2010-master | extractPitch.m | .m | VMSep-2010-master/code/extractPitch.m | 1,790 | utf_8 | c433259d80a535bdc7da5e18466c2cb5 | function midi = extractPitch(configMfcc, configEsi, x, mfccHmm, esiHmm)
%EXTRACTPITCH Function for A/U/V decision and pitch tracking.
% Format: midi = extractPitch(configMfcc, configEsi, x, mfccHmm, esiHmm
% Inputs:
% configMfcc: Configuration for MFCC features.
% configEsi: Configuration for ESI fea... |
github | MaigoAkisame/VMSep-2010-master | configEsi.m | .m | VMSep-2010-master/code/configEsi.m | 1,943 | utf_8 | f5867afad70bfaa382f8a6773e7c684d | function config = configEsi
%CONFIGESI Configuration for ESI features.
% Basic parameters
config.fs = 16000;
config.frameLen = 640; % 40ms @ 16kHz
config.frameShift = 320; % 20ms @ 16kHz
config.fftSize = 1024; % minimum power of 2 >= frameLen
config.window ... |
github | MaigoAkisame/VMSep-2010-master | vmsep.m | .m | VMSep-2010-master/code/vmsep.m | 2,376 | utf_8 | 950cfe2d040031a5b66dee79714a9451 | function vmsep(fileMix, fileVoice, fileAccom)
%VMSEP Main function for voice/accompaniment. To compile into an exe file.
% Format: vmsep(fileMix, fileVoice, fileAccom)
% Inputs:
% fileMix: Filename of the mixed signal. The extension '.wav' may
% be omitted. The file must be mono-channel,... |
github | MaigoAkisame/VMSep-2010-master | separate.m | .m | VMSep-2010-master/code/separate.m | 3,124 | utf_8 | a6e2e80c4d0db734f0fbb0aba9a2089b | function [voice accom] = separate(config, x, midi)
%SEPARATE Separation step by soft masking.
% Format: [voice accom] = separate(config, x, midi)
% Inputs:
% config: Struct of configurations.
% x: Mixed signal.
% midi: Pitch contour, in midi number scale, one number per frame.
% Outputs... |
github | MaigoAkisame/VMSep-2010-master | configSep.m | .m | VMSep-2010-master/code/configSep.m | 1,680 | utf_8 | e8a1d9c82ab6aa12195476433bb11298 | function config = configSep
%CONFIGSEP Configuration for separation step.
% Basic parameters
config.fs = 16000;
config.frameLen = 640; % 40ms @ 16kHz
config.frameShift = 320; % 20ms @ 16kHz
config.fftSize = 640; % = frameLen for resynthesis
config.window ... |
github | affixalex/swig-master | member_pointer_runme.m | .m | swig-master/Examples/test-suite/octave/member_pointer_runme.m | 922 | utf_8 | 5d65cb03abdda4efc4dd89ee739e27b6 | # Example using pointers to member functions
member_pointer
function check(what,expected,actual)
if (expected != actual)
error ("Failed: %s, Expected: %f, Actual: %f",what,expected,actual);
endif
end
# Get the pointers
area_pt = areapt;
perim_pt = perimeterpt;
# Create some objects
s = Square(10);
# Do s... |
github | affixalex/swig-master | director_basic_runme.m | .m | swig-master/Examples/test-suite/octave/director_basic_runme.m | 1,734 | utf_8 | b6b47f50cd7b79dce886ca9a71659d86 | director_basic
function self=OctFoo()
global director_basic;
self=subclass(director_basic.Foo());
self.ping=@OctFoo_ping;
end
function string=OctFoo_ping(self)
string="OctFoo::ping()";
end
a = OctFoo();
if (!strcmp(a.ping(),"OctFoo::ping()"))
error(a.ping())
endif
if (!strcmp(a.pong(),"Foo::pong();OctFoo... |
github | affixalex/swig-master | director_string_runme.m | .m | swig-master/Examples/test-suite/octave/director_string_runme.m | 456 | utf_8 | 253061c50e9f69d1d7f90b0bf7c64b21 | director_string
function out=get_first(self)
out = strcat(self.A.get_first()," world!");
end
function process_text(self,string)
self.A.process_text(string);
self.smem = "hello";
end
B=@(string) subclass(A(string),'get_first',@get_first,'process_text',@process_text);
b = B("hello");
b.get(0);
if (!strcmp(b.ge... |
github | affixalex/swig-master | li_boost_shared_ptr_runme.m | .m | swig-master/Examples/test-suite/octave/li_boost_shared_ptr_runme.m | 15,557 | utf_8 | 416225787d84c7b540725c7558b887a7 | 1;
li_boost_shared_ptr;
function verifyValue(expected, got)
if (expected ~= got)
error("verify value failed.");% Expected: ", expected, " Got: ", got)
end
endfunction
function verifyCount(expected, k)
got = use_count(k);
if (expected ~= got)
error("verify use_count failed. Expected: %d Go... |
github | affixalex/swig-master | voidtest_runme.m | .m | swig-master/Examples/test-suite/octave/voidtest_runme.m | 489 | utf_8 | e8f18ed9dcf24d5fabce570c881dde59 | voidtest
voidtest.globalfunc();
f = voidtest.Foo();
f.memberfunc();
voidtest.Foo_staticmemberfunc();
function fvoid()
end
try
a = f.memberfunc();
catch
end_try_catch
try
a = fvoid();
catch
end_try_catch
v1 = voidtest.vfunc1(f);
v2 = voidtest.vfunc2(f);
if (swig_this(v1) != swig_this(v2))
error
endif
v3 =... |
github | affixalex/swig-master | director_detect_runme.m | .m | swig-master/Examples/test-suite/octave/director_detect_runme.m | 654 | utf_8 | b67110f81021223aa13bcfcf1a6db401 | director_detect
global MyBar=@(val=2) subclass(director_detect.Bar(),'val',val,@get_value,@get_class,@just_do_it,@clone);
function val=get_value(self)
self.val = self.val + 1;
val = self.val;
end
function ptr=get_class(self)
global director_detect;
self.val = self.val + 1;
ptr=director_detect.A();
end
... |
github | affixalex/swig-master | preproc_constants_runme.m | .m | swig-master/Examples/test-suite/octave/preproc_constants_runme.m | 533 | utf_8 | 917a1bdadc03d5ce92d7468e9c4c81b2 | preproc_constants
assert(CONST_INT1, 10)
assert(CONST_DOUBLE3, 12.3)
assert(CONST_BOOL1, true)
assert(CONST_CHAR, 'x')
assert(CONST_STRING1, "const string")
# Test global constants can be seen within functions
function test_global()
global CONST_INT1
global CONST_DOUBLE3
global CONST_BOOL1
global CO... |
github | affixalex/swig-master | director_classic_runme.m | .m | swig-master/Examples/test-suite/octave/director_classic_runme.m | 2,411 | utf_8 | 168e96aff7298695046a230adc01e911 | director_classic
TargetLangPerson=@() subclass(Person(),'id',@(self) "TargetLangPerson");
TargetLangChild=@() subclass(Child(),'id',@(self) "TargetLangChild");
TargetLangGrandChild=@() subclass(GrandChild(),'id',@(self) "TargetLangGrandChild");
# Semis - don't override id() in target language
TargetLangSemiPerson=@()... |
github | affixalex/swig-master | cpp11_strongly_typed_enumerations_runme.m | .m | swig-master/Examples/test-suite/octave/cpp11_strongly_typed_enumerations_runme.m | 9,095 | utf_8 | 79292c56985d5d809f0dd6ca56afc4f7 | cpp11_strongly_typed_enumerations
function newvalue = enumCheck(actual, expected)
if (actual != expected);
error("Enum value mismatch. Expected: %d Actual: %d", expected, actual);
endif
newvalue = expected + 1;
end
val = 0;
val = enumCheck(cpp11_strongly_typed_enumerations.Enum1_Val1, val);
val = enumCheck(... |
github | affixalex/swig-master | li_std_vector_enum_runme.m | .m | swig-master/Examples/test-suite/octave/li_std_vector_enum_runme.m | 376 | utf_8 | 3915e1d2851c46706f5ee2878da627c4 | li_std_vector_enum
function check(a, b)
if (a != b)
error("incorrect match");
endif
end
ev = EnumVector();
check(ev.nums(0), 10);
check(ev.nums(1), 20);
check(ev.nums(2), 30);
it = ev.nums.begin();
v = it.value();
check(v, 10);
it.next();
v = it.value();
check(v, 20);
#expected = 10
#ev.nums.each do|val|
... |
github | affixalex/swig-master | exception_order_runme.m | .m | swig-master/Examples/test-suite/octave/exception_order_runme.m | 898 | utf_8 | a9559c0998ed1178eef715d5314f32e1 | exception_order
function check_lasterror(expected)
if (!strcmp(lasterror.message, expected))
# Take account of older versions prefixing with "error: " and adding a newline at the end
if (!strcmp(regexprep(lasterror.message, 'error: (.*)\n$', '$1'), expected))
error(["Bad exception order. Expected: \"",... |
github | affixalex/swig-master | director_abstract_runme.m | .m | swig-master/Examples/test-suite/octave/director_abstract_runme.m | 993 | utf_8 | 010e2a8f3e12a660ec6f6b89f629f5fd | director_abstract
MyFoo=@() subclass(director_abstract.Foo(),@ping);
function out=ping(self)
out="MyFoo::ping()";
end
a = MyFoo();
if (!strcmp(a.ping(),"MyFoo::ping()"))
error(a.ping())
endif
if (!strcmp(a.pong(),"Foo::pong();MyFoo::ping()"))
error(a.pong())
endif
MyExample1=@() subclass(director_abstract.... |
github | affixalex/swig-master | runme.m | .m | swig-master/Examples/octave/class/runme.m | 1,017 | utf_8 | 402733d5ff5c5f0ec147a476914de00a | # file: runme.m
# This file illustrates the proxy class C++ interface generated
# by SWIG.
swigexample
# ----- Object creation -----
printf("Creating some objects:\n");
c = swigexample.Circle(10)
s = swigexample.Square(10)
# ----- Access a static member -----
printf("\nA total of %i shapes were created\n", swigex... |
github | affixalex/swig-master | runme.m | .m | swig-master/Examples/octave/module_load/runme.m | 1,590 | utf_8 | cb4e6741909a6a701307193e0f304b54 | # file: runme_args.m
# load module
clear all;
swigexample;
assert(cvar.ivar == ifunc);
assert(exist("swigexample","var"));
clear all
swigexample;
assert(cvar.ivar == ifunc);
assert(exist("swigexample","var"));
clear all
# load module in a function globally before base context
clear all;
function testme
swigexample;... |
github | irudeva/RWray-master | zfltr.m | .m | RWray-master/scripts/matlab/zfltr.m | 1,301 | utf_8 | d349176a9bbc199280bb100e562126e9 | %%%%% put in 1 for pole; cutoff and sampr are in time domain units.
%%%%% pole is the order of the butterworth filter
function Zl=zfltr(Zu,pole,cutoff,sampr)
% USE: Zl=zfltr(Zu,pole,cutoff,sampr)
% Run a time series through a lowpass butterworth filter.
% Zu can be an array of time series to be filtered.
%
% >> Zu ... |
github | irudeva/RWray-master | zfltr.m | .m | RWray-master/scripts/Ks/zfltr.m | 1,301 | utf_8 | d349176a9bbc199280bb100e562126e9 | %%%%% put in 1 for pole; cutoff and sampr are in time domain units.
%%%%% pole is the order of the butterworth filter
function Zl=zfltr(Zu,pole,cutoff,sampr)
% USE: Zl=zfltr(Zu,pole,cutoff,sampr)
% Run a time series through a lowpass butterworth filter.
% Zu can be an array of time series to be filtered.
%
% >> Zu ... |
github | martinResearch/MatlabAutoDiff-master | AutoDiffVerif.m | .m | MatlabAutoDiff-master/src/AutoDiffVerif.m | 13,424 | utf_8 | 6ca571cc34d151d2d88c3b9975dba50b | % License FreeBSD:
%
% Copyright (c) 2016 Martin de La Gorce
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% 1. Redistributions of source code must retain the above copyright notice, this
% ... |
github | martinResearch/MatlabAutoDiff-master | CheckAutoDiffJacobian.m | .m | MatlabAutoDiff-master/src/CheckAutoDiffJacobian.m | 632 | utf_8 | a29121a56e1a9b58743d5aaaa3313d0e | function CheckAutoDiffJacobian(func, x, tol)
% check the function is deterministic
f1=func(x);
f2=func(x);
assert (all(size(f1)==size(f2)));
if ~isempty(f1)
assert(maxabsdiff(f1, f2) == 0);
end
% check jacobian
[J2, f2] = AutoDiffJacobianFiniteDiff(func, x, 1:numel(x), []);
[J1, f1] = AutoDiffJacobianAutoDiff(func... |
github | martinResearch/MatlabAutoDiff-master | autodiff_troubleshoot.m | .m | MatlabAutoDiff-master/src/autodiff_troubleshoot.m | 1,337 | utf_8 | e2a4d4622d08145a4590d2552690a9e8 | % Error type 1 : assigning autodiff variables to double array parts
try
full(AutoDiffJacobianAutoDiff(@f2_fails, 5)) % this fails
catch error
fprintf(error.message)
end
try
full(AutoDiffJacobianAutoDiff(@f2_fails, [5, 6])) % this fails
catch error
fprintf(error.message)
end
%
full(AutoDiffJacobianAut... |
github | martinResearch/MatlabAutoDiff-master | AutoDiffFD.m | .m | MatlabAutoDiff-master/src/AutoDiffFD.m | 14,800 | utf_8 | 9efe05ae1b822b92acb5ef0f2c4559f0 | % License FreeBSD:
%
% Copyright (c) 2016 Martin de La Gorce
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% 1. Redistributions of source code must retain the above copyright notice, this
% ... |
github | alshedivat/nphmm-master | kernel.m | .m | nphmm-master/lib/kde/kernel.m | 1,272 | utf_8 | 146602cb647fe8a85eabc585d278c900 | function K = kernel(X, C, h, order)
% Returns the value of the kernel evaluated at the points X centred at C and
% with bandwidth h.
% Inputs
% X : nxd data matrix
% C : mxd centre matrix. If empty is initialized to zero(1, d)
% h : the bandwidth of the kernel
% order : order of the kernel
% Ouputs
% K : The nxm kernel... |
github | alshedivat/nphmm-master | kdePickBW.m | .m | nphmm-master/lib/kde/kdePickBW.m | 4,002 | utf_8 | ded2fdc06b143606db13cf86eaeb4146 | function [optBW, kdeFuncH] = kdePickBW(X, smoothness, params, bwLogBounds)
% This picks a bandwidth for the KDE. We use k-fold cross validation in the
% range specified by bwLogBounds.
% If params.getKdeFuncH is True, then it also returns a function handle for the
% kde with the optimal bandiwidth.
% prelims
numDa... |
github | alshedivat/nphmm-master | kdeLegendreKernel.m | .m | nphmm-master/lib/kde/kdeLegendreKernel.m | 1,283 | utf_8 | 5a4c2ff6b16ab3a41970ed6e72a09aeb | function K = kdeLegendreKernel(X, C, h, order)
% Returns the value of the kernel evaluated at the points X centred at C and
% with bandwidth h.
% Inputs
% X : nxd data matrix
% C : mxd centre matrix. If empty is initialized to zero(1, d)
% h : the bandwidth of the kernel
% order : order of the kernel
% Ouputs
% K : The... |
github | alshedivat/nphmm-master | kdeGivenBW.m | .m | nphmm-master/lib/kde/kdeGivenBW.m | 3,705 | utf_8 | 0e911abaf58c850fbb67bc6f7d19fde9 | function kde = kdeGivenBW(X, h, smoothness, params)
% Implements Kernel Density Estimator with kernels of order floor(smoothness)
% for the given bandwidth. You should cross validate h externally.
% Inputs
% X: the nxd data matrix
% h: bandwidth
% smoothness: If using a Gaussian Kernel this should be 'gaussian'. ... |
github | alshedivat/nphmm-master | genNPpdfs.m | .m | nphmm-master/lib/utils/genNPpdfs.m | 4,774 | utf_8 | 2b73925bab66c2ecf43cb4e5674ca1e0 | function [samplers, pdfs] = genNPpdfs(numPdfs, boundary)
% A script to generate some pdfs.
% Inputs:
% - numPdfs: the number of pdfs
% - boundary: a 1x2 array specifying the interval.
% Outputs:
% - samplers: a numPdfs x 1 cell where samplers{i}(n) will generate n samplers from
% the i^th pdf.
% - pdfs: A numPdf... |
github | alshedivat/nphmm-master | mixgaussPdfs.m | .m | nphmm-master/lib/utils/mixgaussPdfs.m | 372 | utf_8 | ea1364426bf02e0d3924b4e5c696e4f8 | function pdfs = mixgaussPdfs(mu, Sigma, mixmat)
m = size(mu, 2);
pdfs = cell(m, 1);
for j = 1:m
pdfs{j} = @(x) mixgaussFun(x, mu(:,j,:), Sigma(:,:,j,:), mixmat(j,:));
end
end
function pdf = mixgaussFun(x, mu, Sigma, mixmat)
K = size(mixmat, 2);
pdf = 0;
for k = 1:K
pdf = pdf + mixmat(k) .* gau... |
github | alshedivat/nphmm-master | npHMM_EM.m | .m | nphmm-master/lib/nphmm/npHMM_EM.m | 3,926 | utf_8 | 68783a55984633f20a2b4706ba7495b5 | function [hmm, ell] = npHMM_EM(X, m, obsBoundary, params)
% npHMM_EM Fits a nonparametric HMM using an EM-like iterative procedure.
%
% INPUTS:
% hmm - an NPHMM object (should be already initialized).
% X - the data matrix where every row is a sequence of observations.
% Set the default params
if ~exist('para... |
github | alshedivat/nphmm-master | computeObsRepr.m | .m | nphmm-master/lib/nphmm/computeObsRepr.m | 3,150 | utf_8 | 5ebd9cf86148133446eb9aa39978070e | function [obsHMM, b1, binf, Bx, U, Bxc] = ...
computeObsRepr(P1, P21, P321, m, obsBoundary, options)
% Compute the observable representation for the nonparametric HMM
% given the probabilities P1, P12 and P123.
% Inputs:
% P1: A chebfun handle for the marginals.
% P21: A chebfun2 handle for the pairs.
... |
github | dapengchen123/SCSP-master | S_feat_extraction_sepcolor.m | .m | SCSP-master/spatial_feat_extraction/process/S_feat_extraction_sepcolor.m | 8,380 | utf_8 | 9e46a83e13d423153438a5c29a0b0c05 | function [descriptors, output_index] = S_feat_extraction_sepcolor(oriImgs, options)
%
%
numScales = 3;
BH = 8;
StepH = 4;
BW = 16;
StepW = 8;
colorBins = [16, 16, 16];
RowStep = 6;
RowWidth = 31;
feattype = 'HSV';
i... |
github | dapengchen123/SCSP-master | feat_extraction_sepcolor.m | .m | SCSP-master/spatial_feat_extraction/process/feat_extraction_sepcolor.m | 5,123 | utf_8 | 844c41149dccb9340b55b3da931a3f9b | function descriptors = feat_extraction_sepcolor( oriImgs, options)
%
%
numScales = 3;
BH = 8;
StepH = 4;
BW = 16;
StepW = 8;
colorBins = [16, 16, 16];
feattype = 'HSV';
if nargin >=2
if isfield(options,'numScales')
nu... |
github | dapengchen123/SCSP-master | S_feat_extraction_sepcolor_N2_2.m | .m | SCSP-master/spatial_feat_extraction/process/S_feat_extraction_sepcolor_N2_2.m | 10,117 | utf_8 | 8d17514927b8de6051220459d5f7aebf |
function [descriptors, output_index] = S_feat_extraction_sepcolor_N2_2(oriImgs, options)
%
%
numScales = 3;
BH = 8;
StepH = 4;
BW = 16;
StepW = 8;
colorBins = [16, 16, 16];
RowStep = 6;
RowWidth = 31;
feattype = 'HSV';
... |
github | dapengchen123/SCSP-master | feat_extraction_texture.m | .m | SCSP-master/spatial_feat_extraction/process/feat_extraction_texture.m | 5,358 | utf_8 | fbb2e3f03ac50e4492cfd89ff84814db | function Descriptors = feat_extraction_texture(oriImgs, options)
%
% %%%%%%%%%%%%%%%
%
numScales = 3;
BH = 8;
StepH = 4;
BW = 16;
StepW = 8;
feattype = 'SILTHist';
tau = 0.3;
Rr = 5;
numPoints... |
github | dapengchen123/SCSP-master | S_feat_extraction_sepcolor_N2.m | .m | SCSP-master/spatial_feat_extraction/process/S_feat_extraction_sepcolor_N2.m | 8,549 | utf_8 | 980c749ad6a26114a3b3959b14e1b1df |
function [descriptors, output_index] = S_feat_extraction_sepcolor_N2(oriImgs, options)
%
%
numScales = 3;
BH = 8;
StepH = 4;
BW = 16;
StepW = 8;
colorBins = [16, 16, 16];
RowStep = 6;
RowWidth = 31;
feattype = 'HSV';
... |
github | dapengchen123/SCSP-master | S_feat_extraction_texture.m | .m | SCSP-master/spatial_feat_extraction/process/S_feat_extraction_texture.m | 8,381 | utf_8 | a9110b50365b2a1ee8001be5028560cd | function [descriptors, output_index] = S_feat_extraction_texture( oriImgs, options)
%
%
numScales = 3;
BH = 8;
StepH = 4;
BW = 16;
StepW = 8;
feattype = 'SILTHist';
tau = 0.3;
Rr = 5;
numPoints = 4;
%%%%%... |
github | knmcguire/EdgeFlow_matlab-master | ransac.m | .m | EdgeFlow_matlab-master/ransac.m | 2,808 | utf_8 | 6bd9c89f13ca257bade2c99033897a0d |
function px = ransac(pts,iterNum,inlier_threshold,inlier_ratio)
a_it = zeros(1,iterNum);
b_it = zeros(1,iterNum);
% inlier_threshold = 8;
% inlier_ratio = 0.5;
errors = zeros(1,iterNum);
for it = 1:iterNum
% ind = randsample(pts(1,:),2);
ind = datasample(pts(1,:),2,'Replace',false);
% while (... |
github | hossfeld/QoE-Metrics-master | QoEmetrics.m | .m | QoE-Metrics-master/QoEmetrics.m | 5,271 | utf_8 | bca5ae7701fbe4c00c1c6fa58b0d810d | % Computes QoE metrics for subjective user rating in y. The subjective data
% y can be passed as matrix or as vector.
% Parameters can be passed as key value pairs.
%
% Input: y - If y is a matrix, each row i represents the ratings all users
% for test condition i. Each column j represents the ratings
% ... |
github | giubatt/Projeto-IIR-master | criarFiltro.m | .m | Projeto-IIR-master/criarFiltro.m | 1,103 | utf_8 | 57565aee54ea0bc0ab362a3d67e87603 | %Projeto Filtro IIR
%Retorna zeros, polos e ganho do filtro especificado
%Nome: Lucas Fernandes e Giuseppe Batistella
%Data: 25/06/2016
%in:
%n = ordem do filtro
%Wn = O escalar ou vetor das correspondentes frequencias de corte
%Ap = Ripple na banda passante
%As = Atenuacao minima na banda de rejeicao
%tipoFiltro ... |
github | giubatt/Projeto-IIR-master | quantizar.m | .m | Projeto-IIR-master/quantizar.m | 662 | utf_8 | fce1e2d965cea4cf809c3dd83707f59a | % Projeto Filtro IIR
% Quantizador
% Quantiza a funcao de acordo com a quantidade de bits do parametro
%
% Autores: Lucas Fernandes e Giuseppe Bastitella
% Data: 25/06/2016
%
% bits = quantidade de bits da mantissa, vetor = vetor a ser quantizado;
function [vetor_quantizado] = quantizar(vetor, bits)
if(bits ==... |
github | giubatt/Projeto-IIR-master | cheb2Folga.m | .m | Projeto-IIR-master/cheb2Folga.m | 1,052 | utf_8 | c35ce5f52e63011d1f166623424176b2 | % Projeto Filtro IIR
% Ajusta a ordem do fitro Chebyshev 2
% Retorna ordem e valor do ripple na banda passente ajustado
% para o valor minimo na ordem gerada por cheb2ord()
%
% Autores: Lucas Fernandes e Giuseppe Batistella
% Data: 25/06/2016
%
% in:
% Wp = Frequencia de corte na banda passante
% Ws = Frequencia de... |
github | giubatt/Projeto-IIR-master | implementaIIR.m | .m | Projeto-IIR-master/implementaIIR.m | 2,498 | utf_8 | 7f7f5a1e0b7c322c8ca5bf08bded2c28 | % Projeto Filtro IIR
% Implementa o filtro na forma direta II
% Autores: Lucas Fernandes e Giuseppe Battistella
% Data: 25/06/2016
%in:
% oderm = ordem do filtro
% k = quantidade de zeros em x
% sos = Second-order-sections (retornado pela funcao sos();)
% escal = escalar de a0
% bits = quantidade de bits a ser quanti... |
github | giubatt/Projeto-IIR-master | preOtimizacao.m | .m | Projeto-IIR-master/preOtimizacao.m | 1,002 | utf_8 | 6a719faad7abfee0584dbee1bcdb736a | % Projeto Filtro IIR
% Pre-otimizacao
% Calcula o menor Ap que continue com a mesma ordem do filtro
%
% Autores: Lucas Fernandes e Giuseppe Battistella
% Data: 28/05/2016
%
% in:
% Wp = limites de banda passante
% Ws = limites de banda de rejeicao
% Ap = ripple na banda passante
% As = atenuacao minima na banda de re... |
github | giubatt/Projeto-IIR-master | ajusteSimetria.m | .m | Projeto-IIR-master/ajusteSimetria.m | 523 | utf_8 | 996713e52a2df48ef914c43fb4b7b3b3 | % Projeto Filtro IIR
% Teste de simetria
% Calcula ws e wp que satisfacam a simetria, sem modificar wp
%
% Autores: Lucas Fernandes e Giuseppe Battistella
% Data: 25/06/2016
%
% in:
% ws = frequencias de banda de rejeicao
% wp = frequencias de banda passante
%
% out:
% ws = freq de rejeicao simetricas em relacao a w... |
github | giubatt/Projeto-IIR-master | butterFolga.m | .m | Projeto-IIR-master/butterFolga.m | 1,048 | utf_8 | 2eb90a7d52c871a80e4e6a23f4488e2c | % Projeto Filtro IIR
% Ajusta a ordem do fitro Butterworth
% Retorna ordem e valor do ripple na banda passente ajustado
% para o valor minimo na ordem gerada por buttord()
%
% Autores: Lucas Fernandes e Giuseppe Batistella
% Data: 25/06/2016
%
% in:
% Wp = Frequencia de corte na banda passante
% Ws = Frequencia de ... |
github | giubatt/Projeto-IIR-master | elipticoFolga.m | .m | Projeto-IIR-master/elipticoFolga.m | 1,055 | utf_8 | 8c478d7ea91ea604f52bae4acec59d17 | % Projeto Filtro IIR
% Ajusta a ordem do fitro Eliptico
% Retorna ordem e valor do ripple na banda passente ajustado
% para o valor minimo na ordem gerada por ellipord()
%
% Autores: Lucas Fernandes e Giuseppe Batistella
% Data: 25/06/2016
%
% in:
% Wp = Frequencia de corte na banda passante
% Ws = Frequencia de c... |
github | giubatt/Projeto-IIR-master | cheb1Folga.m | .m | Projeto-IIR-master/cheb1Folga.m | 1,049 | utf_8 | eaac16f19d5d6ca21e078df320ef7cdf | % Projeto Filtro IIR
% Ajusta a ordem do fitro Chebyshev 1
% Retorna ordem e valor do ripple na banda passente ajustado
% para o valor minimo na ordem gerada por cheb1ord()
%
% Autores: Lucas Fernandes e Giuseppe Batistella
% Data: 25/06/2016
%
% in:
% Wp = Frequencia de corte na banda passante
% Ws = Frequencia de... |
github | a-bailly/nonLinearAdaptationTimeSeries-master | kema_xp.m | .m | nonLinearAdaptationTimeSeries-master/functions/kema_xp.m | 9,939 | utf_8 | 97b9ab613d01686a31c9961d1d963386 | % function [KE_A_u, KE_A_t, KE_A_ut, KE_B_u, KE_B_t, KE_B_ut] = kema_predict(trainA_labeled, labelsA, trainA_unlabeled, unlabelsA, testA, tlabelsA, trainB_labeled, labelsB, trainB_unlabeled, unlabelsB, testB, tlabelsB, options)
%
% Inputs:
% trainA_labeled: Labeled Time Series (LTS) from Domain A
% labelsA: Correspo... |
github | a-bailly/nonLinearAdaptationTimeSeries-master | ppc.m | .m | nonLinearAdaptationTimeSeries-master/functions/ppc.m | 2,431 | utf_8 | 8501cbb9ae315a1b92983c10987db3ec | % ppc: data splitting with a per-class criterion
%
% [Xtr Ytr Xts Yts indices] = ppc(X,Y,ppc)
%
% This function splits the data in X according to the labels in Y and
% returns train and test data, along with the indices in the original data
% vector.
%
% Inputs: - X: data vector
% - Y: labels vector... |
github | a-bailly/nonLinearAdaptationTimeSeries-master | run_xp.m | .m | nonLinearAdaptationTimeSeries-master/functions/run_xp.m | 6,637 | utf_8 | 83c1f739431c9441254bc80aa7f96481 | % function [] = run_xp(domA, lbA, domB, lbB, N)
%
% Inputs:
% domA: Time Series (LTS) from Domain A
% lbA: Corresponding Labels for Time Series from Domain A
% domB: Time Series (LTS) from Domain B
% lbB: Corresponding Labels for Time Series from Domain B
% N: Number of TS par class used as labeled example
%
% Output:... |
github | a-bailly/nonLinearAdaptationTimeSeries-master | ssma_xp.m | .m | nonLinearAdaptationTimeSeries-master/functions/ssma_xp.m | 9,841 | utf_8 | 73c0cabd89422379de40d9fe5fbe1099 | % function [SS_A_u, SS_A_t, SS_A_ut, SS_B_u, SS_B_t, SS_B_ut] = ssma_predict(trainA_labeled, labelsA, trainA_unlabeled, unlabelsA, testA, tlabelsA, trainB_labeled, labelsB, trainB_unlabeled, unlabelsB, testB, tlabelsB, options)
%
% Inputs:
% trainA_labeled: Labeled Time Series (LTS) from Domain A
% labelsA: Correspo... |
github | a-bailly/nonLinearAdaptationTimeSeries-master | kernelmatrix.m | .m | nonLinearAdaptationTimeSeries-master/functions/kernelmatrix.m | 4,025 | utf_8 | 469298a577cfcfb1345fc1aa8a0b3c44 | % function K = kernelmatrix(ker,X,X2,sigma)
%
% Inputs:
% ker: 'lin','poly','rbf','sam'
% X: data matrix with training samples in columns and features in rows
% X2: data matrix with test samples in columnsand features in rows
% sigma: width of the RBF kernel
% b: bias in the linear and polinomial kernel
% d: ... |
github | faustomilletari/3D-Caffe-master | classification_demo.m | .m | 3D-Caffe-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | andrekovac/reservoir-persistent-memory-master | generateResults.m | .m | reservoir-persistent-memory-master/src/simulation/generateResults.m | 4,293 | utf_8 | 3375d57dbf8234a8db5dd0c7779529f8 | function generateResults()
global d
design_matrix_tf = designMatrix( 'teacher_forcing' );
design_matrix_tr = designMatrix( 'training' );
design_matrix_vl = designMatrix( 'validation' );
% targets (for better readability)
targets_tf = d.task.feedback_targets( : , 1 + d.task.offset_tf ... |
github | andrekovac/reservoir-persistent-memory-master | linear_nonLinearTask_rand.m | .m | reservoir-persistent-memory-master/src/tasks/linear_nonLinearTask_rand.m | 8,493 | utf_8 | 77427f30486989ce11413cf2c585e792 | function [input, feedback_target, readout_targets] = linear_nonLinearTask_rand( N_tf, N_tr , varargin )
% %%%%%%%%%%%%%%%% INPUT %%%%%%%%%%%%%%%%%%%
% input 1 and 2 are cues
% input 3 and 4 are arbitrary signals
% input 5 is feedback signal
% %%%%%%%%%%%%%%%% READOUTS %%%%%%%%%%%%%%%%%%%
% readout 1 is switch between... |
github | andrekovac/reservoir-persistent-memory-master | fctTask.m | .m | reservoir-persistent-memory-master/src/tasks/fctTask.m | 6,656 | utf_8 | 3869b095619ae443e364a7cec8bfb7c2 | function [input, feedback_target, readout_targets] = fctTask(N_tf, N_tr, varargin)
% creates input and targets for a simple task that relies on long-term
% memory. The third readout-target is the important target.
% %%%%%%%%%%%%%%%% INPUT %%%%%%%%%%%%%%%%%%%
% input 1 and 2 are cues
% input 3 is sine function
% input ... |
github | andrekovac/reservoir-persistent-memory-master | rampingTask.m | .m | reservoir-persistent-memory-master/src/tasks/rampingTask.m | 9,837 | utf_8 | 729d468357f5a8b5013f856bba08a8cc | function [input, feedback_targets, readout_targets] = rampingTask( N_tf, N_tr, varargin )
% %%%%%%%%%%%%%%%% INPUT %%%%%%%%%%%%%%%%%%%
% input 1 is cue signal
% input 2 is arbitrary noise signal
% input 3 and 4 are two feedback signals
% %%%%%%%%%%%%%%%% FEEDBACK TARGETS %%%%%%%%%%%%%%%%%%%
% feedback target 1 is sho... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | learnCRFPotentials.m | .m | fundus-vessel-segmentation-tmbe-master/learnCRFPotentials.m | 5,288 | utf_8 | bdf2edef46f05907c8cc275f06c56a0d |
function [bestModel, qualityOverValidation, bestParam] = learnCRFPotentials(config, trainingdata, validationdata)
% Determine the type of metric to optimize during model selection
if (isfield(config,'modelSelectionMetric' ~= 1))
% By default, we optimize the parameters in terms of the MCC
conf... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | preprocessing.m | .m | fundus-vessel-segmentation-tmbe-master/Preprocessing/preprocessing.m | 672 | utf_8 | 849069d18191d400b9907e05287035dd |
function I = preprocessing(I, mask, options)
% preprocessing Preprocess the given image
% I = preprocessing(I, mask, options)
% OUTPUT: I: image preprocessed
% INPUT: I: image (it can be a RGB image)
% mask: a binary mask indicating the FOV
% options: a configuration structure containing the options
... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getConfiguration_CrossValidation_UNIX.m | .m | fundus-vessel-segmentation-tmbe-master/Configuration/getConfiguration_CrossValidation_UNIX.m | 3,394 | utf_8 | 733b20cd162a642d97dc2e519104924b |
function [config] = getConfiguration_CrossValidation_UNIX(datasetName, datasetPath, resultsPath, learnC, crfVersion, cValue)
% getConfiguration_CrossValidation_UNIX Get configuration structure for cross
% validation (for UNIX platforms)
% [config] = getConfiguration_CrossValidation_UNIX(datasetName, datasetPath, re... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getConfiguration_GenericDataset.m | .m | fundus-vessel-segmentation-tmbe-master/Configuration/getConfiguration_GenericDataset.m | 3,560 | utf_8 | 8011352621ded23cfdaba76dc8303c06 |
function [config] = getConfiguration_GenericDataset(datasetName, datasetPath, resultsPath, learnC, crfVersion, cValue)
% getConfiguration_GenericDataset Get a generic configuration structure
% [config] = getConfiguration_GenericDataset(datasetName, datasetPath, resultsPath, learnC, crfVersion, cValue)
% datasetN... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getConfiguration_CrossValidation.m | .m | fundus-vessel-segmentation-tmbe-master/Configuration/getConfiguration_CrossValidation.m | 3,345 | utf_8 | dd76749e8f5174c47b627447c65807c9 |
function [config] = getConfiguration_CrossValidation(datasetName, datasetPath, resultsPath, learnC, crfVersion, cValue)
% getConfiguration_CrossValidation Get configuration structure for cross
% validation
% [config] = getConfiguration_CrossValidation(datasetName, datasetPath, resultsPath, learnC, crfVersion, cValu... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | estimateScaleFactor.m | .m | fundus-vessel-segmentation-tmbe-master/Configuration/estimateScaleFactor.m | 1,899 | utf_8 | a9550045e1de90c821d0b7e2e35cf769 |
function [rho] = estimateScaleFactor(pretrained_path, newset_path)
% estimateScaleFactor Estimate the scale factor to adjust the parameters
% of the image features.
% rho = estimateScaleFactor(pretrained_path, newset_path) estimates the
% scale factor as the proportion between the average FOV width of the
% mas... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getConfiguration_GenericDataset_UNIX.m | .m | fundus-vessel-segmentation-tmbe-master/Configuration/getConfiguration_GenericDataset_UNIX.m | 3,814 | utf_8 | 8568600b195b126c50b66eeb40dd7ec5 |
function [config] = getConfiguration_GenericDataset_UNIX(datasetName, datasetPath, resultsPath, learnC, crfVersion, cValue)
% getConfiguration_GenericDataset_UNIX Get a generic configuration
% structure (for UNIX platforms)
% [config] = getConfiguration_GenericDataset_UNIX(datasetName, datasetPath, resultsPath, lea... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | sosvmCallback.m | .m | fundus-vessel-segmentation-tmbe-master/SOSVM/sosvmCallback.m | 2,173 | utf_8 | c224c5c2e5dc1f7041542ffe1f938e48 |
function [model, config, state] = sosvmCallback(config, trainingdata)
% sosvmCallback Configure the SOSVM and call it to learn the model
% [model, config, state] = sosvmCallback(config, trainingdata)
% OUTPUT: model: learned model
% config: configuration structure
% state: last state
% INPUT: config: c... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | bundler.m | .m | fundus-vessel-segmentation-tmbe-master/SOSVM/bundler.m | 3,497 | utf_8 | b36403af6e68cda730867196ff31a690 |
function state = bundler(state, a, b, soft)
% BUNDLER
%
% Solves the problem
%
% min_{w,xi} lambda/2 |w|^2 + xi, xi >= b_t - <a_t, w> for t = 1, ..., T
%
% Optionally, it also enforces additional hard constraints
%
% <a_p,w> >= b_p, p = 1, ..., P
%
% The algorithm uses the dual to do so. Introducing L... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | sosvm.m | .m | fundus-vessel-segmentation-tmbe-master/SOSVM/sosvm.m | 3,610 | utf_8 | e1b65bba00e56d5c46652d4222d26cb5 | function [model, config, state] = sosvm(config, patterns, labels, oldstate)
% sosvm Learn a model using a SOSVM
% [model, config, state] = sosvm(config, patterns, labels, oldstate)
% OUTPUT: model: learned model
% config: configuration structure, updated with learning
% information
% state: last... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | encodeTrainingData.m | .m | fundus-vessel-segmentation-tmbe-master/SOSVM/Util/encodeTrainingData.m | 1,321 | utf_8 | 396bb284143ff9fb98e015bea2459a67 |
function [patterns, labels] = encodeTrainingData(config, trainingdata)
% Preallocate memory for the patterns and labels arrays
patterns = cell(size(trainingdata.images));
labels = cell(size(trainingdata.labels));
% For each image in the training set
for i = 1:length(patterns)
... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | pairwisePotentials.m | .m | fundus-vessel-segmentation-tmbe-master/SOSVM/Util/pairwisePotentials.m | 505 | utf_8 | 6ca433a6a29495068eb2d087eb719a6b |
function [phi_p] = pairwisePotentials(config, x, y)
% Get the mask
mask = x{2};
% Get the pairwise features
pairwiseFeatures = x{4};
% Get the pairwises using the MEX implementation
phi_p = - pairwisePart(int32(size(mask, 2)), int32(size(mask, 1)), ...
int16(y), (single(pair... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getfeatures.m | .m | fundus-vessel-segmentation-tmbe-master/SOSVM/Util/getfeatures.m | 453 | utf_8 | 75e9a1395dd4c7454ff8cbfa72a98a06 |
function [phi] = getfeatures(x, y)
% Get the feature vectors
X = x{3};
% Compute the unary features
phi_u = zeros(size(X, 1), size(X, 2) * 2);
% Take the Kronecker product of the features with the corresponding
% binary vector, according to the given labeling y
phi_u(y==0, :) = k... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | constraintCB.m | .m | fundus-vessel-segmentation-tmbe-master/SOSVM/Callbacks/constraintCB.m | 2,592 | utf_8 | 22c05f50714a23743b05365bdea13a63 |
function [yhat] = constraintCB(config, model, x, y)
% constraintCB Compute the most violated constraint
% [yhat] = constraintCB(config, model, x, y)
% OUTPUT: yhat: estimated labelling
% INPUT: config: configuration structure
% model: learned model
% x: a cell array containing the FOV mask, the unary and... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | featureCB.m | .m | fundus-vessel-segmentation-tmbe-master/SOSVM/Callbacks/featureCB.m | 1,270 | utf_8 | 949ed2951b59851f9cb805b70d8f1ee8 |
function [phi] = featureCB(config, x, y)
% featureCB Compute the feature map.
% [phi] = featureCB(config, x, y)
% OUTPUT: phi: feature map
% INPUT: config: configuration structure
% x: cell-array with the training data
% y: cell-array with a labeling.
% Put both the unary and the pairwise features... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | lossCB.m | .m | fundus-vessel-segmentation-tmbe-master/SOSVM/Callbacks/lossCB.m | 275 | utf_8 | 755242e4097264e96f6e1ab8cd34e87f |
function [delta] = lossCB(param, y, tildey)
% lossCB Compute the loss
% [delta] = lossCB(param, y, tildey)
% OUTPUT: delta: loss
% INPUT: param: parameters
% y: ground truth labelling
% tildey: estimated labelling
delta = length(find(y~=tildey));
end |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | filterFileNames.m | .m | fundus-vessel-segmentation-tmbe-master/Util/filterFileNames.m | 368 | utf_8 | 82daf3aff6d39a6016b9f5e45150b562 |
function [filteredNames] = filterFileNames(names)
filteredNames = {};
for i = 1:length(names)
if (~strcmp(names{i},'..') && ~strcmp(names{i},'.'))
if (isempty(filteredNames))
filteredNames = names{i};
else
filteredNames = [filteredNames {names{i}... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getLabeledDataFilenames.m | .m | fundus-vessel-segmentation-tmbe-master/Util/getLabeledDataFilenames.m | 482 | utf_8 | 7df672f8dce25cf2cf49750132bac4e6 |
function [images, labels, masks] = getLabeledDataFilenames(folder)
imagesFolder = strcat(folder, filesep, 'images', filesep);
masksFolder = strcat(folder, filesep, 'masks', filesep);
labelsFolder = strcat(folder, filesep, 'labels', filesep);
% Open images, masks and labels for the training set
im... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | resizeImages.m | .m | fundus-vessel-segmentation-tmbe-master/Util/resizeImages.m | 141 | utf_8 | 4884bb2df4de4e37a204432a7fd01afd |
function [images] = resizeImages(images, scale)
for i = 1 : length(images)
images{i} = imresize(images{i}, scale);
end
end |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | encodeFileName.m | .m | fundus-vessel-segmentation-tmbe-master/Util/encodeFileName.m | 604 | utf_8 | 4a5eefc7243ab4df86c312f5a975eb48 |
function [filefullname] = encodeFileName(root, param, type)
%training_dataset_uUNARIES_pPAIRWISES
filefullname = strcat(root, filesep, type, '_', param.dataset, '_', ...
'u', num2str(featuresToNumber(param.unaryFeatures)), '_', ...
'p', num2str(featuresToNumber(param.pairwiseFeatures)), '.mat'... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | rgb2hsi.m | .m | fundus-vessel-segmentation-tmbe-master/Util/rgb2hsi.m | 340 | utf_8 | 03725a699c3c0878878944ff3e4f18cd |
function [hsi] = rgb2hsi(image)
image = double(image);
r=image(:,:,1);
g=image(:,:,2);
b=image(:,:,3);
th=acos((0.5*((r-g)+(r-b)))./((sqrt((r-g).^2+(r-b).*(g-b)))+eps));
H=th;
H(b>g)=2*pi-H(b>g);
H=H/(2*pi);
S=1-3.*(min(min(r,g),b))./(r+g+b+eps);
I=(r+g+b)/3;
hsi=cat(... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | rgb2cmyk.m | .m | fundus-vessel-segmentation-tmbe-master/Util/rgb2cmyk.m | 591 | utf_8 | 0c6ad4d8366003a567b1d97479d3f147 |
function [c] = rgb2cmyk(c)
s = size(c);
n = size(s,2);
m = s(2+(n==3));
if ~( isnumeric(c) && any( n == [ 2 3 ]) && ...
any( m == [ 3 4 ] ) )
error('Input must be a RGB or CMYK ColorMatrice.');
end
if isempty(c)
return
end
u8 = isa(c,'uint8');
if u8
c = double(c)/255;
end
sub = { ':' ':' }... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getMultipleImagesFileNames.m | .m | fundus-vessel-segmentation-tmbe-master/Util/getMultipleImagesFileNames.m | 299 | utf_8 | 6cce949558dbd9b837d7c64b024d0e05 | % Open multiple files from a given directory
function allNames = getMultipleImagesFileNames(directory)
% Get all file names
allFiles = dir(directory);
% Get only the names of the images inside the folder
allNames = cell({allFiles.name});
allNames = filterFileNames(allNames);
end |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | compareGivenSegmentations.m | .m | fundus-vessel-segmentation-tmbe-master/Util/Evaluation/compareGivenSegmentations.m | 2,526 | utf_8 | fb064aa71b529e2260d52ded8a6e0a2c |
function [qualityMeasures, averageQualityMeasures] = compareGivenSegmentations(segmentations, masks, groundtruth)
% compareGivenSegmentation Compare a list of given segmentations with
% respect to the ground truth labellings
% [qualityMeasures, averageQualityMeasures] = compareGivenSegmentations(segmentations, masks, ... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | evaluateOverTestData.m | .m | fundus-vessel-segmentation-tmbe-master/Util/Evaluation/evaluateOverTestData.m | 187 | utf_8 | 7c22334c7d46aa676adcd0e996719bb0 |
function [result] = evaluateOverTestData(param, model, testset)
% Get results
[result.segmentations, result.qualityMeasures] = getBunchSegmentations(param, testset, model);
end |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | compareSegmentations.m | .m | fundus-vessel-segmentation-tmbe-master/Util/Evaluation/compareSegmentations.m | 3,074 | utf_8 | 4719ac8515977e722549a2fc17201992 |
function [qualityMeasures, averageQualityMeasures] = compareSegmentations(segmentationRoot, groundtruthRoot, masksRoot)
% compareSegmentation Compare segmentations
% [qualityMeasures, averageQualityMeasures] = compareSegmentations(segmentationRoot, groundtruthRoot, masksRoot)
% OUTPUT: qualityMeasures: all the quality... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | computeAriasQualityMeasure.m | .m | fundus-vessel-segmentation-tmbe-master/Util/Evaluation/computeAriasQualityMeasure.m | 1,553 | utf_8 | 4dbe7edc2c2d0dfe0acc5215fe02a656 |
function qualityArias = computeAriasQualityMeasure(Sg, S, alpha, beta)
% Sg = reference image, gold standard segmentation
% S = segmentation to evaluate
Sg = logical(Sg);
S = logical(S);
if (nargin < 3)
alpha = 2;
beta = 2;
end
% *****************************... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getAverageMeasures.m | .m | fundus-vessel-segmentation-tmbe-master/Util/Evaluation/Metrics/getAverageMeasures.m | 536 | utf_8 | 0e099d6712a64bc70c0163345da0b2a1 |
function [averageQualityMeasures] = getAverageMeasures(qualityMeasures)
averageQualityMeasures.se = mean(qualityMeasures.se);
averageQualityMeasures.sp = mean(qualityMeasures.sp);
averageQualityMeasures.acc = mean(qualityMeasures.acc);
averageQualityMeasures.precision = mean(qualityMeasures.precis... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getQualityMeasures.m | .m | fundus-vessel-segmentation-tmbe-master/Util/Evaluation/Metrics/getQualityMeasures.m | 1,291 | utf_8 | b52730ee9908e795981be69f9f9ce7db |
function qualityMeasures = getQualityMeasures(yhat, y)
% getQualityMeasures Compute quality measures
% qualityMeasures = getQualityMeasures(yhat, y)
% OUTPUT: qualityMeasures: quality measures
% INPUT: yhat: estimated labelling
% y: ground truth labelling
% Get the confusion matrix
C = confusionmat(int... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getAverageMeasures2.m | .m | fundus-vessel-segmentation-tmbe-master/Util/Evaluation/Metrics/getAverageMeasures2.m | 3,056 | utf_8 | dd6f6427bfb32db02be33d975626ddd5 |
function [averageQualityMeasures] = getAverageMeasures2(qualityMeasures)
% getAverageMeasures2 Compute the average measures
% [averageQualityMeasures] = getAverageMeasures2(qualityMeasures)
% OUTPUT: averageQualityMeasures: average quality measures
% INPUT: qualityMeasures: struct with arrays for each specific quality... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | openTestData.m | .m | fundus-vessel-segmentation-tmbe-master/Util/Open/openTestData.m | 1,222 | utf_8 | edc7756c9b024da3a8cf7d724f261e69 |
function [testdata] = openTestData(testroot)
% Get all directories inside the root
allFiles = dir(testroot);
% Get only the names of the files and folders inside the folder
allNames = cell({allFiles.name});
% Filter only the folders
testDirs = {};
for i = 1:length(allNames)
... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | verifyExistence.m | .m | fundus-vessel-segmentation-tmbe-master/Util/Open/verifyExistence.m | 134 | utf_8 | da34030d5d3336137544afc9663d0a73 |
function []= verifyExistence(path)
% if the dir doesn't exists
if (exist(path,'dir') == 0)
mkdir(path);
end
end |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | openSingleImage.m | .m | fundus-vessel-segmentation-tmbe-master/Util/Open/openSingleImage.m | 830 | utf_8 | 88e6e26f01626de96d548dc86e17bcaf |
function [I, mask, label] = openSingleImage(folder, image_filename, label_filename, mask_filename, config)
% open image only if the name is provided
if ~strcmp(image_filename,'')
I = imread(strcat(folder, filesep, 'images', filesep, image_filename));
else
I = 0;
end
% open image on... |
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