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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
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github | chuhang/HouseCraft-master | toolboxGenDoc.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/toolboxGenDoc.m | 3,662 | utf_8 | 4934899922f2871c74276763da467c73 | function toolboxGenDoc
% Generate documentation, must run from dir toolbox.
%
% 1) Make sure to update and run toolboxUpdateHeader.m
% 2) Update history.txt appropriately, including w current version
% 3) Update overview.html file with the version/date/link to zip:
% edit external/m2html/templates/frame-piotr/overv... |
github | chuhang/HouseCraft-master | toolboxHeader.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/toolboxHeader.m | 2,452 | utf_8 | 1499bf2d4951622f88a62361afa57861 | function [y1,y2] = toolboxHeader( x1, x2, x3, prm )
% One line description of function (will appear in file summary).
%
% General commments explaining purpose of function [width is 75
% characters]. There may be multiple paragraphs. In special cases some or
% all of these guidelines may need to be broken.
%
% Next come... |
github | chuhang/HouseCraft-master | mdot.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/m2html/mdot.m | 2,516 | utf_8 | 34a14428c433e118d1810e23f5a6caf5 | function mdot(mmat, dotfile,f)
%MDOT - Export a dependency graph into DOT language
% MDOT(MMAT, DOTFILE) loads a .mat file generated by M2HTML using option
% ('save','on') and writes an ascii file using the DOT language that can
% be drawn using <dot> or <neato> .
% MDOT(MMAT, DOTFILE,F) builds the graph containing... |
github | chuhang/HouseCraft-master | m2html.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/m2html/m2html.m | 49,063 | utf_8 | 472047b4c36a4f8b162012840e31b59b | function m2html(varargin)
%M2HTML - Documentation Generator for Matlab M-files and Toolboxes in HTML
% M2HTML by itself generates an HTML documentation of the Matlab M-files found
% in the direct subdirectories of the current directory. HTML files are
% written in a 'doc' directory (created if necessary). All the o... |
github | chuhang/HouseCraft-master | doxysearch.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/m2html/private/doxysearch.m | 7,724 | utf_8 | 8331cde8495f34b86aef8c18656b37f2 | function result = doxysearch(query,filename)
%DOXYSEARCH Search a query in a 'search.idx' file
% RESULT = DOXYSEARCH(QUERY,FILENAME) looks for request QUERY
% in FILENAME (Doxygen search.idx format) and returns a list of
% files responding to the request in RESULT.
%
% See also DOXYREAD, DOXYWRITE
% Copyright (C)... |
github | chuhang/HouseCraft-master | doxywrite.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/m2html/private/doxywrite.m | 3,584 | utf_8 | 3255d8f824957ebc173dde374d0f78af | function doxywrite(filename, kw, statinfo, docinfo)
%DOXYWRITE Write a 'search.idx' file compatible with DOXYGEN
% DOXYWRITE(FILENAME, KW, STATINFO, DOCINFO) writes file FILENAME
% (Doxygen search.idx. format) using the cell array KW containing the
% word list, the sparse matrix (nbword x nbfile) with non-null value... |
github | chuhang/HouseCraft-master | doxyread.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/m2html/private/doxyread.m | 3,093 | utf_8 | 3152e7d26bf7ac64118be56f72832a20 | function [statlist, docinfo] = doxyread(filename)
%DOXYREAD Read a 'search.idx' file generated by DOXYGEN
% STATLIST = DOXYREAD(FILENAME) reads FILENAME (Doxygen search.idx
% format) and returns the list of keywords STATLIST as a cell array.
% [STATLIST, DOCINFO] = DOXYREAD(FILENAME) also returns a cell array
% con... |
github | chuhang/HouseCraft-master | imwrite2split.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/imwrite2split.m | 1,617 | utf_8 | 4222fd45df123e6dec9ef40ae793004f | % Writes/reads a large set of images into/from multiple directories.
%
% This is useful since certain OS handle very large directories (of say
% >20K images) rather poorly (I'm talking to you Bill). Thus, can take
% 100K images, and write into 5 separate directories, then read them back
% in.
%
% USAGE
% I = imwrite2... |
github | chuhang/HouseCraft-master | playmovies.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/playmovies.m | 1,935 | utf_8 | ef2eaad8a130936a1a281f1277ca0ea1 | % [4D] shows R videos simultaneously as a movie.
%
% Plays a movie.
%
% USAGE
% playmovies( I, [fps], [loop] )
%
% INPUTS
% I - MxNxTxR or MxNx1xTxR or MxNx3xTxR array (if MxNxT calls
% playmovie)
% fps - [100] maximum number of frames to display per second use
% fps==0 to introduce n... |
github | chuhang/HouseCraft-master | pca_apply_large.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/pca_apply_large.m | 2,062 | utf_8 | af84a2179b9d8042519bc6b378736a88 | % Wrapper for pca_apply that allows for application to large X.
%
% Wrapper for pca_apply that splits and processes X in parts, this may be
% useful if processing cannot be done fully in parallel because of memory
% constraints. See pca_apply for usage.
%
% USAGE
% same as pca_apply
%
% INPUTS
% same as pca_apply
%
%... |
github | chuhang/HouseCraft-master | montages2.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/montages2.m | 2,269 | utf_8 | 505e2be915d65fff8bfef8473875cc98 | % MONTAGES2 [4D] Used to display R sets of T images each.
%
% Displays one montage (see montage2) per row. Each of the R image sets is
% flattened to a single long image by concatenating the T images in the
% set. Alternative to montages.
%
% USAGE
% varargout = montages2( IS, [montage2prms], [padSiz] )
%
% INPUTS
% ... |
github | chuhang/HouseCraft-master | filter_gauss_1D.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/filter_gauss_1D.m | 1,137 | utf_8 | 94a453b82dcdeba67bd886e042d552d9 | % 1D Gaussian filter.
%
% Equivalent to (but faster then):
% f = fspecial('Gaussian',[2*r+1,1],sigma);
% f = filter_gauss_nD( 2*r+1, r+1, sigma^2 );
%
% USAGE
% f = filter_gauss_1D( r, sigma, [show] )
%
% INPUTS
% r - filter size=2r+1, if r=[] -> r=ceil(2.25*sigma)
% sigma - standard deviation of filter
% ... |
github | chuhang/HouseCraft-master | clfEcoc.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/clfEcoc.m | 1,493 | utf_8 | e77e1b4fd5469ed39f47dd6ed15f130f | function clf = clfEcoc(p,clfInit,clfparams,nclasses,use01targets)
% Wrapper for ecoc that makes ecoc compatible with nfoldxval.
%
% Requires the SVM toolbox by Anton Schwaighofer.
%
% USAGE
% clf = clfEcoc(p,clfInit,clfparams,nclasses,use01targets)
%
% INPUTS
% p - data dimension
% clfInit - bi... |
github | chuhang/HouseCraft-master | getargs.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/getargs.m | 3,455 | utf_8 | de2bab917fa6b9ba3099f1c6b6d68cf0 | % Utility to process parameter name/value pairs.
%
% DEPRECATED -- ONLY USED BY KMEANS2? SHOULD BE REMOVED.
% USE GETPARAMDEFAULTS INSTEAD.
%
% Based on code fromt Matlab Statistics Toolobox's "private/statgetargs.m"
%
% [EMSG,A,B,...]=GETARGS(PNAMES,DFLTS,'NAME1',VAL1,'NAME2',VAL2,...)
% accepts a cell array PNAMES o... |
github | chuhang/HouseCraft-master | normxcorrn_fg.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/normxcorrn_fg.m | 2,699 | utf_8 | e65c38d97efb3a624e0fa94a97f75eb6 | % Normalized n-dimensional cross-correlation with a mask.
%
% Similar to normxcorrn, except takes an additional argument that specifies
% a figure ground mask for the T. That is T_fg must be of the same
% dimensions as T, with each entry being 0 or 1, where zero specifies
% regions to ignore (the ground) and 1 specifi... |
github | chuhang/HouseCraft-master | makemovie.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/makemovie.m | 1,266 | utf_8 | 9a03d9a5227c4eaa86520f206ce283e7 | % [3D] Used to convert a stack of T images into a movie.
%
% To display same data statically use montage.
%
% USAGE
% M = makemovies( IS )
%
% INPUTS
% IS - MxNxT or MxNx1xT or MxNx3xT array of movies.
%
% OUTPUTS
% M - resulting movie
%
% EXAMPLE
% load( 'images.mat' );
% M = makemovie(... |
github | chuhang/HouseCraft-master | localsum_block.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/localsum_block.m | 815 | utf_8 | 1216b03a3bd44ff1fc3256de16a2f1c6 | % Calculates the sum in non-overlapping blocks of I of size dims.
%
% Similar to localsum except gets sum in non-overlapping windows.
% Equivalent to doing localsum, and then subsampling (except more
% efficient).
%
% USAGE
% I = localsum_block( I, dims )
%
% INPUTS
% I - matrix to compute sum over
% dims -... |
github | chuhang/HouseCraft-master | imrotate2.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/imrotate2.m | 1,326 | utf_8 | bb2ff6c3138ce5f53154d58d7ebc4f31 | % Custom version of imrotate that demonstrates use of apply_homography.
%
% Works exactly the same as imrotate. For usage see imrotate.
%
% USAGE
% IR = imrotate2( I, angle, [method], [bbox] )
%
% INPUTS
% I - 2D image [converted to double]
% angle - angle to rotate in degrees
% method - ['linear'] 'neare... |
github | chuhang/HouseCraft-master | imSubsResize.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/imSubsResize.m | 1,338 | utf_8 | cd7dedf790c015adfb1f2d620e9ed82f | % Resizes subs by resizVals.
%
% Resizes subs in subs/vals image representation by resizVals.
%
% This essentially replaces each sub by sub.*resizVals. The only subtlety
% is that in images the leftmost sub value is .5, so for example when
% resizing by a factor of 2, the first pixel is replaced by 2 pixels and so
% l... |
github | chuhang/HouseCraft-master | imtranslate.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/imtranslate.m | 1,183 | utf_8 | 054727fb31c105414b655c0f938b6ced | % Translate an image to subpixel accuracy.
%
% Note that for subplixel accuracy cannot use nearest neighbor interp.
%
% USAGE
% IR = imtranslate( I, dx, dy, [method], [bbox] )
%
% INPUTS
% I - 2D image [converted to double]
% dx - x translation (right)
% dy - y translation (up)
% method - ['linear... |
github | chuhang/HouseCraft-master | randperm2.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/randperm2.m | 1,398 | utf_8 | 5007722f3d5f5ba7c0f83f32ef8a3a2c | % Returns a random permutation of integers.
%
% randperm2(n) is a random permutation of the integers from 1 to n. For
% example, randperm2(6) might be [2 4 5 6 1 3]. randperm2(n,k) is only
% returns the first k elements of the permuation, so for example
% randperm2(6) might be [2 4]. This is a faster version of randp... |
github | chuhang/HouseCraft-master | apply_homography.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/apply_homography.m | 3,582 | utf_8 | 9c3ed72d35b1145f41114e6e6135b44f | % Applies the homography defined by H on the image I.
%
% Takes the center of the image as the origin, not the top left corner.
% Also, the coordinate system is row/ column format, so H must be also.
%
% The bounding box of the image is set by the BBOX argument, a string that
% can be 'loose' (default) or 'crop'. When ... |
github | chuhang/HouseCraft-master | pca_apply.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/pca_apply.m | 2,427 | utf_8 | 0831befb6057f8502bc492227455019a | % Companion function to pca.
%
% Use pca to retrieve the principal components U and the mean mu from a
% set fo vectors X1 via [U,mu,vars] = pca(X1). Then given a new
% vector x, use y = pca_apply( x, U, mu, vars, k ) to get the first k
% coefficients of x in the space spanned by the columns of U. See pca for
% genera... |
github | chuhang/HouseCraft-master | mode2.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/deprecated/mode2.m | 731 | utf_8 | 5c9321ef4b610b4f4a2d43902a68838e | % Returns the mode of a vector.
%
% Was mode not part of Matlab before?
%
% USAGE
% y = mode2( x )
%
% INPUTS
% x - vector of integers
%
% OUTPUTS
% y - mode
%
% EXAMPLE
% x = randint2( 1, 10, [1 3] )
% mode(x), mode2( x )
%
% See also MODE
% Piotr's Image&Video Toolbox Version 1.5
% Written and maintain... |
github | chuhang/HouseCraft-master | savefig.m | .m | HouseCraft-master/asset_detector/edges/toolbox/external/other/savefig.m | 13,374 | utf_8 | b8aace9bef044800b8fc876fde5bf33f | function savefig(fname, varargin)
% Usage: savefig(filename, fighdl, options)
%
% Saves a pdf, eps, png, jpeg, and/or tiff of the contents of the fighandle's (or current) figure.
% It saves an eps of the figure and the uses Ghostscript to convert to the other formats.
% The result is a cropped, clean picture. There a... |
github | chuhang/HouseCraft-master | dirSynch.m | .m | HouseCraft-master/asset_detector/edges/toolbox/matlab/dirSynch.m | 4,631 | utf_8 | 2223662cc84d1249ca9988061e3a09b9 | function dirSynch( root1, root2, showOnly, flag, ignDate )
% Synchronize two directory trees (or show differences between them).
%
% If a file or directory 'name' is found in both tree1 and tree2:
% 1) if 'name' is a file in both the pair is considered the same if they
% have identical size and identical datestamp... |
github | chuhang/HouseCraft-master | plotRoc.m | .m | HouseCraft-master/asset_detector/edges/toolbox/matlab/plotRoc.m | 5,273 | utf_8 | 0dcc7565dcc02ab5872f275c26a2aef2 | function [h,miss,stds] = plotRoc( D, varargin )
% Function for display of rocs (receiver operator characteristic curves).
%
% Display roc curves. Consistent usage ensures uniform look for rocs. The
% input D should have n rows, each of which is of the form:
% D = [falsePosRate truePosRate]
% D is generated, for exampl... |
github | chuhang/HouseCraft-master | simpleCache.m | .m | HouseCraft-master/asset_detector/edges/toolbox/matlab/simpleCache.m | 4,159 | utf_8 | 3ee36c5476544f93ae5bf5d39c45726e | function varargout = simpleCache( op, cache, varargin )
% A simple cache that can be used to store results of computations.
%
% Can save and retrieve arbitrary values using a vector (includnig char
% vectors) as a key. Especially useful if a function must perform heavy
% computation but is often called with the same in... |
github | chuhang/HouseCraft-master | tpsInterpolate.m | .m | HouseCraft-master/asset_detector/edges/toolbox/matlab/tpsInterpolate.m | 1,707 | utf_8 | cd63faf887af2215d7433b56deac6871 | function [xsR,ysR] = tpsInterpolate( warp, xs, ys, show )
% Apply warp (obtained by tpsGetWarp) to a set of new points.
%
% USAGE
% [xsR,ysR] = tpsInterpolate( warp, xs, ys, [show] )
%
% INPUTS
% warp - [see tpsGetWarp] bookstein warping parameters
% xs, ys - points to apply warp to
% show - [1] will disp... |
github | chuhang/HouseCraft-master | checkNumArgs.m | .m | HouseCraft-master/asset_detector/edges/toolbox/matlab/checkNumArgs.m | 3,857 | utf_8 | 9d88a888a42acf2c5373eaf21d79eae2 | function [ x, er ] = checkNumArgs( x, siz, intFlag, signFlag )
% Helper utility for checking numeric vector arguments.
%
% Runs a number of tests on the numeric array x. Tests to see if x has all
% integer values, all positive values, and so on, depending on the values
% for intFlag and signFlag. Also tests to see if ... |
github | chuhang/HouseCraft-master | fevalDistr.m | .m | HouseCraft-master/asset_detector/edges/toolbox/matlab/fevalDistr.m | 12,495 | utf_8 | 71e24d6774571bc7b25533c6ed6dcb85 | function [out,res] = fevalDistr( funNm, jobs, varargin )
% Wrapper for embarrassingly parallel function evaluation.
%
% Runs "r=feval(funNm,jobs{i}{:})" for each job in a parallel manner. jobs
% should be a cell array of length nJob and each job should be a cell array
% of parameters to pass to funNm. funNm must be a f... |
github | chuhang/HouseCraft-master | medfilt1m.m | .m | HouseCraft-master/asset_detector/edges/toolbox/filters/medfilt1m.m | 3,059 | utf_8 | 01a128348997e708cb6e1d84361525e4 | function y = medfilt1m( x, r, z )
% One-dimensional adaptive median filtering with missing values.
%
% Applies a width s=2*r+1 one-dimensional median filter to vector x, which
% may contain missing values (elements equal to z). If x contains no
% missing values, y(j) is set to the median of x(j-r:j+r). If x contains
% ... |
github | chuhang/HouseCraft-master | FbMake.m | .m | HouseCraft-master/asset_detector/edges/toolbox/filters/FbMake.m | 6,753 | utf_8 | 8dee6d0e7665b6fd4d3c6f02680d4a8f | function FB = FbMake( dim, flag, show )
% Various 1D/2D/3D filterbanks (hardcoded).
%
% USAGE
% FB = FbMake( dim, flag, [show] )
%
% INPUTS
% dim - dimension
% flag - controls type of filterbank to create
% - if d==1
% 1: gabor filter bank for spatiotemporal stuff
% - if d==2
% ... |
github | voanna/consistency-master | chi2dist.m | .m | consistency-master/third_party/mpi-chi2/chi2dist.m | 1,299 | utf_8 | e314aee5245b1264de919b395ca31a70 | %K = chi2dist(X1,X2);
%
% computes 0.5 * chi2(X1,X2)
function K = chi2dist(X1,X2);
warning off
addpath ~/software/mpi-chi2
warning on
stepsize = 500;
if ~exist('X2','var')
X2 = [];
end
if numel(X2) == 0
X1 = X1';
try
K = 0.5*chi2_mex(full(X1),full(X1));
catch
% compute the matrix in batches
... |
github | voanna/consistency-master | chi2.m | .m | consistency-master/third_party/mpi-chi2/chi2.m | 884 | utf_8 | f763975cb55675e12e49729c67ac1819 | % K = chi2(X1,X2,kerParam,K);
%
% computes exp( -(1/kerParam) * chi2(X1,X2)), cell array output if
% numel(kerParam)>1
% to speed up K=chi2dist(X1,X2) can be given as an argument
function K = chi2(X1,X2,kerParam,K);
if ~exist('kerParam','var')
kerParam = [];
end
if ~exist('K','var')
K = [];
end
warning off
... |
github | voanna/consistency-master | consistency_score_K_S.m | .m | consistency-master/src/constistency/consistency_score_K_S.m | 3,750 | utf_8 | c4b0730a313c604a66321f4989ecd9f5 | function[] = consistency_score_K_S (K, S, metric)
disp([num2str(K) ' observers in subset, ' num2str(S) ' splits, using ' metric])
cd ('~/predicting_consistency/data/MIT/DatabaseCode/')
%Run from DatabaseCode folder
folder = '~/predicting_consistency/data/MIT/ALLSTIMULI/';
P = 6; % comes from readme in database code fol... |
github | voanna/consistency-master | LMimpad.m | .m | consistency-master/src/constistency/LMimpad.m | 1,574 | utf_8 | b97cfe565519ecd7b0b3127ee628cb97 | % From LabelMe toolbox
% https://github.com/CSAILVision/LabelMeToolbox/blob/master/imagemanipulation/LMimpad.m
function [annotation, img] = LMimpad(annotation, img, PADSIZE, PADVAL)
% [annotation, img] = LMimpad(annotation, img, PADSIZE, PADVAL)
%
% [annotation, img] = LMimpad(annotation, img, [256 256], 0)
% PADSIZE =... |
github | voanna/consistency-master | gridsearch_epsSVR.m | .m | consistency-master/src/SVR/gridsearch_epsSVR.m | 1,170 | utf_8 | 7cf54042e1db6f32d63373be11f8ff4b | function [best_C, best_gamma, best_p] = gridsearch_epsSVR(training_label_vector, training_instance_matrix, C_range, ...
gamma_range, p_range, param)
% Finds the best C, gamma, and p parameters for training an epsilon SVR
% with RBF kernel
svm_options = param_string_libsvm(param);
gridsearch_record = struct('correl... |
github | urbste/MLPnP_matlab-master | MLPnP.m | .m | MLPnP_matlab-master/MLPnP.m | 7,826 | utf_8 | 15b113f908d73d9cc201205d217d0c6d | % Steffen Urban email: urbste@googlemail.com
% Copyright (C) 2016 Steffen Urban
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% ... |
github | urbste/MLPnP_matlab-master | optim_MLPnP_GN.m | .m | MLPnP_matlab-master/optim_MLPnP_GN.m | 2,228 | utf_8 | af445840b833a0de35d6d61ca9922e25 | % Steffen Urban email: urbste@googlemail.com
% Copyright (C) 2016 Steffen Urban
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% ... |
github | urbste/MLPnP_matlab-master | jacobians_Rodrigues.m | .m | MLPnP_matlab-master/jacobians_Rodrigues.m | 13,690 | utf_8 | 4493413b9b6e5b3fa9e0813528b5875d | % Steffen Urban email: urbste@googlemail.com
% Copyright (C) 2016 Steffen Urban
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% ... |
github | urbste/MLPnP_matlab-master | residualsAndJacobian.m | .m | MLPnP_matlab-master/residualsAndJacobian.m | 1,387 | utf_8 | 89d0256a7c3f30ed205d43dd9c2208c6 | % Steffen Urban email: urbste@googlemail.com
% Copyright (C) 2016 Steffen Urban
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% ... |
github | urbste/MLPnP_matlab-master | Rodrigues2.m | .m | MLPnP_matlab-master/Rodrigues2.m | 1,658 | utf_8 | 392768dcf4fc591828ec95d9e0cb3814 | % Steffen Urban email: urbste@googlemail.com
% Copyright (C) 2016 Steffen Urban
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% ... |
github | gkaguirrelab/retinaTOMEAnalysis-master | processMRIAnatMeasures.m | .m | retinaTOMEAnalysis-master/xDeprecated/processMRIAnatMeasures.m | 2,355 | utf_8 | e4416a1b0632a224bcbf16af3b01e006 | % Load the biometric and demographic table
ocularMeasuresFileName='~//Dropbox (Aguirre-Brainard Lab)/TOME_subject/TOME-AOSO_SubjectInfo.xlsx';
opts = detectImportOptions(ocularMeasuresFileName);
ocularMeasures = readtable(ocularMeasuresFileName, opts);
idOcularTOME = ocularMeasures.TOME_ID;
age = ocularMeasures.Age;
... |
github | gkaguirrelab/retinaTOMEAnalysis-master | fixelAnalysis.m | .m | retinaTOMEAnalysis-master/code/fixelAnalysis/fixelAnalysis.m | 46,970 | utf_8 | 5e97b7c3093cf3d7838250b03b23b400 | function fixelAnalysis(varargin)
%% Set the dropboxBaseDir and flywheel id
% We need this for the default loations of some the directories
% dropboxBaseDir=fullfile(getpref('retinaTOMEAnalysis','dropboxBaseDir'));
dropboxBaseDir='/home/ozzy/Dropbox (Aguirre-Brainard Lab)';
fw = flywheel.Flywheel(getpref('flywheelMRSup... |
github | gkaguirrelab/retinaTOMEAnalysis-master | fixelAnalysisPaper.m | .m | retinaTOMEAnalysis-master/code/fixelAnalysis/xOld/fixelAnalysisPaper.m | 71,446 | utf_8 | 1d3a15e5f344bdbf3a1f8b942cd2c689 | function fixelAnalysisPaper(varargin)
% What to plot
sizeBarPlots = false;
adjacentFC = true;
adjacentFD = false;
allGCFC = false;
allGCFD = false;
allOTFC = false;
allOTFD = false;
allLGN = false;
allORFC = false;
radiationControl = false;
extraCalc = false;
mainTrackCorrelations = false;
allOTFDC = false;
allORFD =... |
github | gkaguirrelab/retinaTOMEAnalysis-master | fixelAnalysisMain.m | .m | retinaTOMEAnalysis-master/code/fixelAnalysis/xOld/fixelAnalysisMain.m | 49,875 | utf_8 | b05b66f55947bba04ade63ac042658df | function fixelAnalysisMain(varargin)
% Relate variation in GCL volue to fixel measurements on the visual pathway
%
% Examples:
%{
%}
%% Set the dropboxBaseDir
% We need this for the default loations of some the directories
% dropboxBaseDir=fullfile(getpref('retinaTOMEAnalysis','dropboxBaseDir'));
dropboxBaseDir='/home... |
github | gkaguirrelab/retinaTOMEAnalysis-master | alignHandVscans.m | .m | retinaTOMEAnalysis-master/code/octSupport/alignHandVscans.m | 496 | utf_8 | 650baa654ffe72af46f2d43e3feaad2a | %Takes a transformation between a Vscan and Hscan and move them into the
%same space.
function [RegisteredVScan,BothMean,BothAbsDiff] = alignHandVscans(VScan,HScan,tform)
RegisteredVScan = imwarp(VScan,tform,'OutputView',imref2d(size(double(HScan))));
BothMean = (HScan + RegisteredVScan)/2;
Bo... |
github | gkaguirrelab/retinaTOMEAnalysis-master | xml2volmask.m | .m | retinaTOMEAnalysis-master/code/octSupport/xml2volmask.m | 3,030 | utf_8 | d6224372484777746d3660c4c80dd27c | function mask = xml2volmask(xmlfile)
% convert xml from OCT Explorer to volume mask
% syntax: mask = xml2volmask(xmlfile)
% input: xmlfile - xml filepath
% output: mask - volume mask of each slab (1 to 10) (3D matrix: nx*ny*nz)
% written by Jin Gahm, LONI, USC
fin = fopen(xmlfile,'r');
l = 0;
while ~feof(fin... |
github | gkaguirrelab/retinaTOMEAnalysis-master | make_nii.m | .m | retinaTOMEAnalysis-master/code/octSupport/make_nii.m | 6,849 | utf_8 | 3c7c8b81655c111a9ce4b82086bde4f5 | % Make NIfTI structure specified by an N-D matrix. Usually, N is 3 for
% 3D matrix [x y z], or 4 for 4D matrix with time series [x y z t].
% Optional parameters can also be included, such as: voxel_size,
% origin, datatype, and description.
%
% Once the NIfTI structure is made, it can be saved into NIfTI fil... |
github | gkaguirrelab/retinaTOMEAnalysis-master | save_nii.m | .m | retinaTOMEAnalysis-master/code/octSupport/save_nii.m | 9,404 | utf_8 | 88aa93174482539fe993ac335fb01541 | % Save NIFTI dataset. Support both *.nii and *.hdr/*.img file extension.
% If file extension is not provided, *.hdr/*.img will be used as default.
%
% Usage: save_nii(nii, filename, [old_RGB])
%
% nii.hdr - struct with NIFTI header fields (from load_nii.m or make_nii.m)
%
% nii.img - 3D (or 4D) matrix of NIFTI... |
github | gkaguirrelab/retinaTOMEAnalysis-master | subdir.m | .m | retinaTOMEAnalysis-master/code/octSupport/subdir.m | 3,733 | utf_8 | 00ecbfe501a10bbea84b9fbaaaaf5f8e | function varargout = subdir(varargin)
%SUBDIR Performs a recursive file search
%
% subdir
% subdir(name)
% files = subdir(...)
%
% This function performs a recursive file search. The input and output
% format is identical to the dir function.
%
% Input variables:
%
% name: pathname or filename for search, can be a... |
github | gkaguirrelab/retinaTOMEAnalysis-master | save_nii_hdr.m | .m | retinaTOMEAnalysis-master/code/octSupport/save_nii_hdr.m | 9,270 | utf_8 | f97c194f5bfc667eb4f96edf12be02a7 | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function save_nii_hdr(hdr, fid)
if ~exist('hdr','var') | ~exist('fid','var')
error('Usage: save_nii_hdr(hdr, fid)');
end
if ~isequal(hdr.hk.sizeof_hdr,348),
error('hdr.hk.sizeof_hdr must be 348.');
end
if hdr.h... |
github | gkaguirrelab/retinaTOMEAnalysis-master | thicknessMapPCAAnalysis.m | .m | retinaTOMEAnalysis-master/code/macularVolumeMaps/thicknessMapPCAAnalysis.m | 17,214 | utf_8 | 12bd5d9498be9b60270396ba2ad35f00 | function thicknessMapPCAAnalysis(dataDir, varargin)
% Do some analysis
%
% Description:
% Foo
%
%% Parse vargin for options passed here
p = inputParser;
% Required
p.addRequired('dataDir',@ischar);
% Optional analysis params
p.addParameter('layerSetLabels',{'RGCIPL'},@iscell);
p.addParameter('showPlots',true,@isl... |
github | gkaguirrelab/retinaTOMEAnalysis-master | fitDensitySurface.m | .m | retinaTOMEAnalysis-master/code/coneDensity/fitDensitySurface.m | 7,543 | utf_8 | a874d84199ce96aa455cbb6c050682cc | function [p, Yfit, fVal, RSquared, polarMultiplier] = fitDensitySurface(Y,w,preFitAvgEccen,simplePolarModel,useAsymptoteConstraint,p0,supportDeg,maxSupportDeg,refEccen,refDensity)
% Fit a multi-parameter surface to cone density data
%
% Syntax:
% [p, Yfit, fVal, RSquared, polarMultiplier] = fitDensitySurface(Y,w,preF... |
github | gkaguirrelab/retinaTOMEAnalysis-master | ImToPolar.m | .m | retinaTOMEAnalysis-master/code/coneDensity/ImToPolar.m | 1,746 | utf_8 | 47fafd502539bc30412f933df1738ebb | function imP = ImToPolar (imR, rMin, rMax, M, N)
% IMTOPOLAR converts rectangular image to polar form. The output image is
% an MxN image with M points along the r axis and N points along the theta
% axis. The origin of the image is assumed to be at the center of the given
% image. The image is assumed to be grayscale... |
github | gkaguirrelab/retinaTOMEAnalysis-master | PolarToIm.m | .m | retinaTOMEAnalysis-master/code/coneDensity/PolarToIm.m | 2,170 | utf_8 | 033ac7242b426caf30f30672e67e8c4c | function imR = PolarToIm (imP, rMin, rMax, Mr, Nr)
% POLARTOIM converts polar image to rectangular image.
%
% V0.1 16 Dec, 2007 (Created) Prakash Manandhar, pmanandhar@umassd.edu
%
% This is the inverse of ImToPolar. imP is the polar image with M rows and
% N columns of data (double data between 0 and 1). M is t... |
github | yinglang/ImageRetrival-master | test_examples.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/utils/test_examples.m | 1,591 | utf_8 | 16831be7382a9343beff5cc3fe301e51 | function test_examples()
%TEST_EXAMPLES Test some of the examples in the `examples/` directory
addpath examples/mnist ;
addpath examples/cifar ;
trainOpts.gpus = [] ;
trainOpts.continue = true ;
num = 1 ;
exps = {} ;
for networkType = {'dagnn', 'simplenn'}
for index = 1:4
clear ex ;
ex.trainOpts = trainOp... |
github | yinglang/ImageRetrival-master | cnn_train_dag.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/examples/cnn_train_dag.m | 13,468 | utf_8 | a9acd7cb82e9dfd3e29bb5c94bee9fe7 | function [net,stats] = cnn_train_dag(net, imdb, getBatch, varargin)
%CNN_TRAIN_DAG Demonstrates training a CNN using the DagNN wrapper
% CNN_TRAIN_DAG() is similar to CNN_TRAIN(), but works with
% the DagNN wrapper instead of the SimpleNN wrapper.
% Copyright (C) 2014-16 Andrea Vedaldi.
% All rights reserved.
%
... |
github | yinglang/ImageRetrival-master | cnn_train.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/examples/cnn_train.m | 18,017 | utf_8 | a48457fdbed83db01574fdc9373e6283 | function [net, stats] = cnn_train(net, imdb, getBatch, varargin)
%CNN_TRAIN An example implementation of SGD for training CNNs
% CNN_TRAIN() is an example learner implementing stochastic
% gradient descent with momentum to train a CNN. It can be used
% with different datasets and tasks by providing a suitable... |
github | yinglang/ImageRetrival-master | cnn_stn_cluttered_mnist.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/examples/spatial_transformer/cnn_stn_cluttered_mnist.m | 3,872 | utf_8 | 3235801f70028cc27d54d15ec2964808 | function [net, info] = cnn_stn_cluttered_mnist(varargin)
%CNN_STN_CLUTTERED_MNIST Demonstrates training a spatial transformer
% The spatial transformer network (STN) is trained on the
% cluttered MNIST dataset.
run(fullfile(fileparts(mfilename('fullpath')),...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.data... |
github | yinglang/ImageRetrival-master | cnn_cifar.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/examples/cifar/cnn_cifar.m | 5,334 | utf_8 | eb9aa887d804ee635c4295a7a397206f | function [net, info] = cnn_cifar(varargin)
% CNN_CIFAR Demonstrates MatConvNet on CIFAR-10
% The demo includes two standard model: LeNet and Network in
% Network (NIN). Use the 'modelType' option to choose one.
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.... |
github | yinglang/ImageRetrival-master | cnn_imagenet_init.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/examples/imagenet/cnn_imagenet_init.m | 14,796 | utf_8 | 77b5cb742e5492a199b796e58430dfcc | function net = cnn_imagenet_init(varargin)
% CNN_IMAGENET_INIT Initialize a standard CNN for ImageNet
opts.scale = 1 ;
opts.initBias = 0.1 ;
opts.weightDecay = 1 ;
%opts.weightInitMethod = 'xavierimproved' ;
opts.weightInitMethod = 'gaussian' ;
opts.model = 'alexnet' ;
opts.batchNormalization = false ;
opts.networkTy... |
github | yinglang/ImageRetrival-master | cnn_imagenet.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/examples/imagenet/cnn_imagenet.m | 7,094 | utf_8 | 9c6d4e185ff55b33f00f87a91ff8d397 | function [net, info] = cnn_imagenet(varargin)
%CNN_IMAGENET Demonstrates training a CNN on ImageNet
% This demo demonstrates training the AlexNet, VGG-F, VGG-S, VGG-M,
% VGG-VD-16, and VGG-VD-19 architectures on ImageNet data.
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m... |
github | yinglang/ImageRetrival-master | cnn_imagenet_deploy.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/examples/imagenet/cnn_imagenet_deploy.m | 6,583 | utf_8 | d997af6242f62f37353261224655d713 | function net = cnn_imagenet_deploy(net)
%CNN_IMAGENET_DEPLOY Deploy a CNN
isDag = isa(net, 'dagnn.DagNN') ;
if isDag
dagRemoveLayersOfType(net, 'dagnn.Loss') ;
dagRemoveLayersOfType(net, 'dagnn.DropOut') ;
else
net = simpleRemoveLayersOfType(net, 'softmaxloss') ;
net = simpleRemoveLayersOfType(net, 'dropout')... |
github | yinglang/ImageRetrival-master | cnn_imagenet_evaluate.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/examples/imagenet/cnn_imagenet_evaluate.m | 5,194 | utf_8 | ba3f0f3f96ec666b73e979324c93a300 | function info = cnn_imagenet_evaluate(varargin)
% CNN_IMAGENET_EVALUATE Evauate MatConvNet models on ImageNet
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data', 'ILSVRC2012') ;
opts.expDir = fullfile('data', 'imagenet12-eval-vgg-f') ;
opts.m... |
github | yinglang/ImageRetrival-master | cnn_mnist_init.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/examples/mnist/cnn_mnist_init.m | 3,111 | utf_8 | 367b1185af58e108aec40b61818ec6e7 | function net = cnn_mnist_init(varargin)
% CNN_MNIST_LENET Initialize a CNN similar for MNIST
opts.batchNormalization = true ;
opts.networkType = 'simplenn' ;
opts = vl_argparse(opts, varargin) ;
rng('default');
rng(0) ;
f=1/100 ;
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
... |
github | yinglang/ImageRetrival-master | cnn_mnist.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/examples/mnist/cnn_mnist.m | 4,529 | utf_8 | eb7627005308bd4d978f29b279cee26e | function [net, info] = cnn_mnist(varargin)
%CNN_MNIST Demonstrates MatConvNet on MNIST
run(fullfile(fileparts(mfilename('fullpath')),...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.batchNormalization = false ;
opts.networkType = 'simplenn' ;
[opts, varargin] = vl_argparse(opts, varargin) ;
sfx = opts.networkTyp... |
github | yinglang/ImageRetrival-master | vl_nnloss.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/matlab/vl_nnloss.m | 10,914 | utf_8 | 3cb323deb2caf15d2f112af93d2b616c | function Y = vl_nnloss(X,c,dzdy,varargin)
%VL_NNLOSS CNN categorical or attribute loss.
% Y = VL_NNLOSS(X, C) computes the loss incurred by the prediction
% scores X given the categorical labels C.
%
% The prediction scores X are organised as a field of prediction
% vectors, represented by a H x W x D x N array... |
github | yinglang/ImageRetrival-master | vl_compilenn.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/matlab/vl_compilenn.m | 28,777 | utf_8 | 9752bfab3ea0e3dc4ac81b8e8fca75e6 | function vl_compilenn(varargin)
%VL_COMPILENN Compile the MatConvNet toolbox.
% The `vl_compilenn()` function compiles the MEX files in the
% MatConvNet toolbox. See below for the requirements for compiling
% CPU and GPU code, respectively.
%
% `vl_compilenn('OPTION', ARG, ...)` accepts the following options:
%... |
github | yinglang/ImageRetrival-master | getVarReceptiveFields.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/matlab/+dagnn/@DagNN/getVarReceptiveFields.m | 3,549 | utf_8 | ca843d13890184e1451248f43f7d4011 | function rfs = getVarReceptiveFields(obj, var)
%GETVARRECEPTIVEFIELDS Get the receptive field of a variable
% RFS = GETVARRECEPTIVEFIELDS(OBJ, VAR) gets the receptivie fields RFS of
% all the variables of the DagNN OBJ into variable VAR. VAR is a variable
% name or index.
%
% RFS has one entry for each variable... |
github | yinglang/ImageRetrival-master | rebuild.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/matlab/+dagnn/@DagNN/rebuild.m | 3,103 | utf_8 | 2051dfdfff3e31e12ab7ac483c251515 | function rebuild(obj)
%REBUILD Rebuild the internal data structures of a DagNN object
% REBUILD(obj) rebuilds the internal data structures
% of the DagNN obj. It is an helper function used internally
% to update the network when layers are added or removed.
varFanIn = zeros(1, numel(obj.vars)) ;
varFanOut = zero... |
github | yinglang/ImageRetrival-master | print.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/matlab/+dagnn/@DagNN/print.m | 13,352 | utf_8 | 074f69a09b01cfea5703e435b2bfc22d | function str = print(obj, inputSizes, varargin)
%PRINT Print information about the DagNN object
% PRINT(OBJ) displays a summary of the functions and parameters in the network.
% STR = PRINT(OBJ) returns the summary as a string instead of printing it.
%
% PRINT(OBJ, INPUTSIZES) where INPUTSIZES is a cell array of ... |
github | yinglang/ImageRetrival-master | fromSimpleNN.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/matlab/+dagnn/@DagNN/fromSimpleNN.m | 7,120 | utf_8 | 38d26e77f162ec60724dc4cb765e3a99 | function obj = fromSimpleNN(net, varargin)
% FROMSIMPLENN Initialize a DagNN object from a SimpleNN network
% FROMSIMPLENN(NET) initializes the DagNN object from the
% specified CNN using the SimpleNN format.
%
% SimpleNN objects are linear chains of computational layers. These
% layers exchange information th... |
github | yinglang/ImageRetrival-master | vl_simplenn_display.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/matlab/simplenn/vl_simplenn_display.m | 12,389 | utf_8 | bd99c027519a637b853c5a096f1a79b1 | function [info, str] = vl_simplenn_display(net, varargin)
%VL_SIMPLENN_DISPLAY Display the structure of a SimpleNN network.
% VL_SIMPLENN_DISPLAY(NET) prints statistics about the network NET.
%
% INFO = VL_SIMPLENN_DISPLAY(NET) returns instead a structure INFO
% with several statistics for each layer of the netw... |
github | yinglang/ImageRetrival-master | vl_test_economic_relu.m | .m | ImageRetrival-master/third_part_lib/matconvnet-1.0-beta20/matconvnet-1.0-beta20/matlab/xtest/vl_test_economic_relu.m | 790 | utf_8 | 35a3dbe98b9a2f080ee5f911630ab6f3 | % VL_TEST_ECONOMIC_RELU
function vl_test_economic_relu()
x = randn(11,12,8,'single');
w = randn(5,6,8,9,'single');
b = randn(1,9,'single') ;
net.layers{1} = struct('type', 'conv', ...
'filters', w, ...
'biases', b, ...
'stride', 1, ...
... |
github | yinglang/ImageRetrival-master | vl_compile.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/vl_compile.m | 5,060 | utf_8 | 978f5189bb9b2a16db3368891f79aaa6 | function vl_compile(compiler)
% VL_COMPILE Compile VLFeat MEX files
% VL_COMPILE() uses MEX() to compile VLFeat MEX files. This command
% works only under Windows and is used to re-build problematic
% binaries. The preferred method of compiling VLFeat on both UNIX
% and Windows is through the provided Makefile... |
github | yinglang/ImageRetrival-master | vl_noprefix.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/vl_noprefix.m | 1,875 | utf_8 | 97d8755f0ba139ac1304bc423d3d86d3 | function vl_noprefix
% VL_NOPREFIX Create a prefix-less version of VLFeat commands
% VL_NOPREFIX() creats prefix-less stubs for VLFeat functions
% (e.g. SIFT for VL_SIFT). This function is seldom used as the stubs
% are included in the VLFeat binary distribution anyways. Moreover,
% on UNIX platforms, the stub... |
github | yinglang/ImageRetrival-master | vl_pegasos.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/misc/vl_pegasos.m | 2,837 | utf_8 | d5e0915c439ece94eb5597a07090b67d | % VL_PEGASOS [deprecated]
% VL_PEGASOS is deprecated. Please use VL_SVMTRAIN() instead.
function [w b info] = vl_pegasos(X,Y,LAMBDA, varargin)
% Verbose not supported
if (sum(strcmpi('Verbose',varargin)))
varargin(find(strcmpi('Verbose',varargin),1))=[];
fprintf('Option VERBOSE is no longer supported.\n');
en... |
github | yinglang/ImageRetrival-master | vl_svmpegasos.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/misc/vl_svmpegasos.m | 1,178 | utf_8 | 009c2a2b87a375d529ed1a4dbe3af59f | % VL_SVMPEGASOS [deprecated]
% VL_SVMPEGASOS is deprecated. Please use VL_SVMTRAIN() instead.
function [w b info] = vl_svmpegasos(DATA,LAMBDA, varargin)
% Verbose not supported
if (sum(strcmpi('Verbose',varargin)))
varargin(find(strcmpi('Verbose',varargin),1))=[];
fprintf('Option VERBOSE is no longer suppor... |
github | yinglang/ImageRetrival-master | vl_override.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/misc/vl_override.m | 4,654 | utf_8 | e233d2ecaeb68f56034a976060c594c5 | function config = vl_override(config,update,varargin)
% VL_OVERRIDE Override structure subset
% CONFIG = VL_OVERRIDE(CONFIG, UPDATE) copies recursively the fileds
% of the structure UPDATE to the corresponding fields of the
% struture CONFIG.
%
% Usually CONFIG is interpreted as a list of paramters with their
... |
github | yinglang/ImageRetrival-master | vl_quickvis.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/quickshift/vl_quickvis.m | 3,696 | utf_8 | 27f199dad4c5b9c192a5dd3abc59f9da | function [Iedge dists map gaps] = vl_quickvis(I, ratio, kernelsize, maxdist, maxcuts)
% VL_QUICKVIS Create an edge image from a Quickshift segmentation.
% IEDGE = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) creates an edge
% stability image from a Quickshift segmentation. RATIO controls the tradeoff
% bet... |
github | yinglang/ImageRetrival-master | vl_demo_aib.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/demo/vl_demo_aib.m | 2,928 | utf_8 | 590c6db09451ea608d87bfd094662cac | function vl_demo_aib
% VL_DEMO_AIB Test Agglomerative Information Bottleneck (AIB)
D = 4 ;
K = 20 ;
randn('state',0) ;
rand('state',0) ;
X1 = randn(2,300) ; X1(1,:) = X1(1,:) + 2 ;
X2 = randn(2,300) ; X2(1,:) = X2(1,:) - 2 ;
X3 = randn(2,300) ; X3(2,:) = X3(2,:) + 2 ;
figure(1) ; clf ; hold on ;
vl_plotframe(X... |
github | yinglang/ImageRetrival-master | vl_demo_alldist.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/demo/vl_demo_alldist.m | 5,460 | utf_8 | 6d008a64d93445b9d7199b55d58db7eb | function vl_demo_alldist
%
numRepetitions = 3 ;
numDimensions = 1000 ;
numSamplesRange = [300] ;
settingsRange = {{'alldist2', 'double', 'l2', }, ...
{'alldist', 'double', 'l2', 'nosimd'}, ...
{'alldist', 'double', 'l2' }, ...
{'alldist2', 's... |
github | yinglang/ImageRetrival-master | vl_demo_ikmeans.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/demo/vl_demo_ikmeans.m | 774 | utf_8 | 17ff0bb7259d390fb4f91ea937ba7de0 | function vl_demo_ikmeans()
% VL_DEMO_IKMEANS
numData = 10000 ;
dimension = 2 ;
data = uint8(255*rand(dimension,numData)) ;
numClusters = 3^3 ;
[centers, assignments] = vl_ikmeans(data, numClusters);
figure(1) ; clf ; axis off ;
plotClusters(data, centers, assignments) ;
vl_demo_print('ikmeans_2d',0.6);
[tree, assig... |
github | yinglang/ImageRetrival-master | vl_demo_svm.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/demo/vl_demo_svm.m | 1,235 | utf_8 | 7cf6b3504e4fc2cbd10ff3fec6e331a7 | % VL_DEMO_SVM Demo: SVM: 2D linear learning
function vl_demo_svm
y=[];X=[];
% Load training data X and their labels y
load('vl_demo_svm_data.mat')
Xp = X(:,y==1);
Xn = X(:,y==-1);
figure
plot(Xn(1,:),Xn(2,:),'*r')
hold on
plot(Xp(1,:),Xp(2,:),'*b')
axis equal ;
vl_demo_print('svm_training') ;
% Parameters
lambda =... |
github | yinglang/ImageRetrival-master | vl_demo_kdtree_sift.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/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 | yinglang/ImageRetrival-master | vl_impattern.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/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 | yinglang/ImageRetrival-master | vl_tpsu.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/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 | yinglang/ImageRetrival-master | vl_xyz2lab.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/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 | yinglang/ImageRetrival-master | vl_test_gmm.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/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 | yinglang/ImageRetrival-master | vl_test_twister.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/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 | yinglang/ImageRetrival-master | vl_test_kdtree.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/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 | yinglang/ImageRetrival-master | vl_test_imwbackward.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/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 | yinglang/ImageRetrival-master | vl_test_alphanum.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/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 | yinglang/ImageRetrival-master | vl_test_printsize.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/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 | yinglang/ImageRetrival-master | vl_test_cummax.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/xtest/vl_test_cummax.m | 838 | utf_8 | 5e98ee1681d4823f32ecc4feaa218611 | 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 | yinglang/ImageRetrival-master | vl_test_imintegral.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/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 | yinglang/ImageRetrival-master | vl_test_sift.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/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 | yinglang/ImageRetrival-master | vl_test_binsum.m | .m | ImageRetrival-master/third_part_lib/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/xtest/vl_test_binsum.m | 1,377 | utf_8 | f07f0f29ba6afe0111c967ab0b353a9d | 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... |
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