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github | josephdviviano/qcmon-master | read_patch.m | .m | qcmon-master/assets/matlab/freesurfer/read_patch.m | 1,255 | utf_8 | 1eb4d2e88ffbbfc82cdbfe4760572f76 | %
% read_patch.m
%
% Original Author: Bruce Fischl
% CVS Revision Info:
% $Author: nicks $
% $Date: 2013/01/22 20:59:09 $
% $Revision: 1.3.2.2 $
%
% Copyright © 2011 The General Hospital Corporation (Boston, MA) "MGH"
%
% Terms and conditions for use, reproduction, distribution and contribution
% are found in ... |
github | josephdviviano/qcmon-master | pons_cut_dir_afd.m | .m | qcmon-master/assets/matlab/freesurfer/pons_cut_dir_afd.m | 4,448 | utf_8 | e4689904ebbbc4bcb996de64d47fe0b0 | function [D, Isubj]=pons_cut_dir_adf(dirname,th_pval)
% For all the subjects in the directory "dirname":
% Computes the Dice coefficients D=2Nab/Na+Nb
% where:
% Na is the volume of the Cerebellum obtrained trough the volume-based labeling
% Nb is the volume "filled" ... |
github | josephdviviano/qcmon-master | cortical_labeling_dir_afd_txt.m | .m | qcmon-master/assets/matlab/freesurfer/cortical_labeling_dir_afd_txt.m | 8,189 | utf_8 | be59e2988dcabd90486cd46037961f7c | function [Dl, Dr,I]=cortical_label_dir_adf(dirname, p_val)
% Computes the area of the different cortical labels
% and compare them to the normal range for all the
% subjects in directory "dirname"
% Uses the p_values to detect the abnormal structures
% Uses the lh/rh.parc.txt files
%
%
% cortical_labeling_dir_afd_t... |
github | josephdviviano/qcmon-master | talairaching_stats.m | .m | qcmon-master/assets/matlab/freesurfer/talairaching_stats.m | 2,681 | utf_8 | 2befe06f104a7992177b4c7e89ab4cb2 | function [D,mu,sigma]=talairach_stats_correct(dirname, outdir)
%
% Computes the mean and covariance matrix from a training set
%
% By default, the 3 translation parameters are not considered
% -> mu is a 1x9 vector and sigma a 9x9 matrix
%
% talairaching_stats.m
%
% Original Author: Laurence Wastiaux
% CVS Revi... |
github | josephdviviano/qcmon-master | pons_cut_afd.m | .m | qcmon-master/assets/matlab/freesurfer/pons_cut_afd.m | 3,884 | utf_8 | b657b76d8eb7daf12c020e3b698a8426 | function [D]=pons_cut_adf(subject, th_pval)
% For the subject "subject": computes the Dice coefficient D=2*Nab/(Na+Nb)
% Na is the volume of the Cerebellum+Brain-stem obtrained trough the volume-based labeling
% Nb is the volume "filled" obtained from the surface-based stream
% Nab is the volume of th... |
github | josephdviviano/qcmon-master | subcortical_labeling_dir_afd.m | .m | qcmon-master/assets/matlab/freesurfer/subcortical_labeling_dir_afd.m | 6,044 | utf_8 | 4be83c3fdfb548ae429ea7e468aa7d42 | function [Dvol,I]=check_ROI_dir(Dirname, th_pval)
%
% For all the subjects in a directory:
% check if the size of 20 ROIs is within the normal range
% The 20 following ROIs are checked: Left-Lateral-Ventricle Right-Lateral-Ventricle
% Left-Hippocampus Right-Hippocampus Left-Thalamus-Prop... |
github | josephdviviano/qcmon-master | ribbon_dir_afd.m | .m | qcmon-master/assets/matlab/freesurfer/ribbon_dir_afd.m | 8,832 | utf_8 | 789f78215045139f2b61108eb6adfa33 | function [D, Isubj]=ribbon_dir_adf(dirname, th_pval)
% For each subject in the directory "dirname":
% Computes the Dice coefficients measuring the overlap of the
% Cortical Ribbon volume computed
% 1- from the subcortical labeling
% 2- as the space between the white and the pial surface
%
%... |
github | josephdviviano/qcmon-master | lme_LR.m | .m | qcmon-master/assets/matlab/freesurfer/lme/univariate/lme_LR.m | 3,704 | utf_8 | 701c7420c274854bc8abf46f543f1ed6 | function lrstats = lme_LR(lrmlfull,lrmlred,q)
% lrstats = lme_LR(lrmlfull,lrmlred,q)
%
% Likelihood ratio test for the random effects. It can be used to test if a
% model with q+1 random effects is significantly better than a model with q
% random effects.
%
% Input
% lrmlfull: Maximum value for the restricted log-like... |
github | josephdviviano/qcmon-master | geodesic_convert_surface_points.m | .m | qcmon-master/assets/matlab/freesurfer/lme/geodesic/geodesic_convert_surface_points.m | 1,236 | utf_8 | ee64cc7d0c65a7cc46b3542177ca936b | %internal geodesic function
%conversion between C++ and matlab surface point representation
% Danil Kirsanov, 09/2007
function q = geodesic_convert_surface_points(p)
if isempty(p)
q = [];
return;
end;
point_types = {'vertex', 'edge', 'face'};
if ~isa(p,'numeric') %convert from matlab to ... |
github | josephdviviano/qcmon-master | geodesic_delete.m | .m | qcmon-master/assets/matlab/freesurfer/lme/geodesic/geodesic_delete.m | 793 | utf_8 | fad9450fe8bc877a9f46b519f83ad508 | % delete mesh, algorithm, or everything at once
% Danil Kirsanov, 09/2007
function object = geodesic_delete(object)
global geodesic_library;
if ~libisloaded(geodesic_library) %everything is already cleared
object = [];
return;
end
if nargin == 0 % the simplest way to delete everyth... |
github | josephdviviano/qcmon-master | geodesic_distance_and_source.m | .m | qcmon-master/assets/matlab/freesurfer/lme/geodesic/geodesic_distance_and_source.m | 1,085 | utf_8 | 791b8b168e6f0497db041c1e46ee7f10 | %finds best source and distance to the best source
% if distance is negative, the best source cannot be found (for example, because the propagation was stopped before it reached this point)
% Danil Kirsanov, 09/2007
function [source_id, distance] = geodesic_best_source(algorithm, destination)
global geodesic_l... |
github | josephdviviano/qcmon-master | create_flat_triangular_mesh.m | .m | qcmon-master/assets/matlab/freesurfer/lme/geodesic/create_flat_triangular_mesh.m | 859 | utf_8 | 6348e2ebbdc6c1a1cf36676bfa63aede | %good mesh to catch possible bugs in geodesic algorithms
%Copyright (c) 2007 Danil Kirsanov
function [p,tri] = create_flat_triangular_mesh(step, smoothness)
x = -1:step:1;
y = x;
N_p = length(x)*length(y); %regular grid
p = zeros(N_p,3);
N_t = (length(x)-1)*(length(y)-1)*2; %two trian... |
github | josephdviviano/qcmon-master | create_subdivision_pattern.m | .m | qcmon-master/assets/matlab/freesurfer/lme/geodesic/create_subdivision_pattern.m | 999 | utf_8 | 63e73d01411a263f051cff9cba4570bb | %regular subdivision pattern of a triangle
%used in drawing approximate equidistant lines in example5.m
%Copyright (c) 2007 Danil Kirsanov
function [weights,tri] = create_subdivision_pattern(level) %"level" is the number of additional vertices per edge
step = 1/(level + 1);
N = level + 2; ... |
github | josephdviviano/qcmon-master | create_hedgehog_mesh.m | .m | qcmon-master/assets/matlab/freesurfer/lme/geodesic/create_hedgehog_mesh.m | 745 | utf_8 | 1d51edcdb91cd8c04dd02d88d04b56ab | %"smoothness" should be specified between 0(smooth, convex mesh) and 1 (a lot of sharp features)
%"waist" should be between 0 and 1; 0 means spherical mesh
function [p,tri] = create_hedgehog_mesh(N, smoothness, waist)
p = rand(N,3) - 0.5;
for i=1:N;
p(i,:) = p(i,:)/norm(p(i,:));
end;
tri = convhulln(p); ... |
github | josephdviviano/qcmon-master | lme_mass_RgGrow.m | .m | qcmon-master/assets/matlab/freesurfer/lme/mass_univariate/lme_mass_RgGrow.m | 10,664 | utf_8 | 6077ecf126b0960e60fdede6f582b7f9 | function [Regions,RgMeans] = lme_mass_RgGrow(SphSurf,Re,Theta,maskvtx,nst,prc)
% [Regions,RgMeans] = lme_mass_RgGrow(SphSurf,Re,Theta,maskvtx,nst,prc)
%
% This function implements a region growing algorithm along the spherical
% surface to find homogeneous regions comprising locations with similar
% covariance compo... |
github | josephdviviano/qcmon-master | lme_mass_fit.m | .m | qcmon-master/assets/matlab/freesurfer/lme/mass_univariate/lme_mass_fit.m | 7,798 | utf_8 | 2bf97a51a992ee28017163a199cd6623 | function [stats,st] = lme_mass_fit(X,Xcols,Xrows,Zcols,Y,ni,prs,e)
% [stats,st] = lme_mass_fit(X,Xcols,Xrows,Zcols,Y,ni,prs,e)
%
% Location-wise linear mixed-effects estimation. Allows to have different
% models across locations (this is useful when there are either missing
% values at some locations or the number o... |
github | josephdviviano/qcmon-master | AdjMtx.m | .m | qcmon-master/assets/matlab/freesurfer/lme/mass_univariate/AdjMtx.m | 2,044 | utf_8 | 4db7d56be3da0882650d62b2fbf3d6b4 | function [AdjM,cn] = AdjMtx(Surf,maskvtx)
% AdjM = AdjMtx(Surf,maskvtx)
%
% This function finds the adjacent vertices for all vertices along the
% surface.
%
% Input
% Surf: Surface. It is a structure with Surf.tri = t x 3 matrix of triangle
% indices, 1-based, t=#triangles and Surf.coord = 3 x nv matrix of
% coord... |
github | josephdviviano/qcmon-master | lme_mass_RgFSfit.m | .m | qcmon-master/assets/matlab/freesurfer/lme/mass_univariate/lme_mass_RgFSfit.m | 7,416 | utf_8 | d6d32ed06f03cdbc8e1b0390d3f1489a | function [stats,st,a,b] = lme_mass_RgFSfit(X,Zcols,Y,ni,Dist,model,prs,e)
% [stats,st,a,b] = lme_mass_RgFSfit(X,Zcols,Y,ni,Dist,model,prs,e)
%
% Linear mixed-effects estimation by the Fisher scoring algorithm for a
% whole region.
%
% Input
% X: Ordered (according to time for each subject) design matrix (nmxp, nm
% t... |
github | josephdviviano/qcmon-master | lme_RgFSfit.m | .m | qcmon-master/assets/matlab/freesurfer/lme/mass_univariate/lme_RgFSfit.m | 7,002 | utf_8 | fc157c94f0b7cfcd000463b771249ba6 | function [stats,st,a,b] = lme_RgFSfit(X,Zcols,Y,ni,Theta0,Dist,model,e)
% [stats,st,a,b] = lme_RgFSfit(X,Zcols,Y,ni,Theta0,Dist,model,e)
%
% Linear mixed-effects estimation by the Fisher scoring algorithm for a
% whole region. This function is intended to only be called from other
% functions to perform region-wise m... |
github | josephdviviano/qcmon-master | lme_mass_FDR2.m | .m | qcmon-master/assets/matlab/freesurfer/lme/mass_univariate/lme_mass_FDR2.m | 2,735 | utf_8 | 69739ae960ad35f037a6a96ee778666c | function [detvtx,sided_pval,pth,m0] = lme_mass_FDR2(pval,sgn,maskvtx,rate,tail)
% [detvtx,sided_pval,pth,m0] = lme_mass_FDR2(pval,maskvtx,rate,tail)
%
% Two-stage FDR approach to achieve tighter control of the FDR. This
% procedure is more powerful than the original FDR procedure implemented in
% lme_mass_FDR.
%
% Inpu... |
github | josephdviviano/qcmon-master | lme_mass_fit_Rgw.m | .m | qcmon-master/assets/matlab/freesurfer/lme/mass_univariate/lme_mass_fit_Rgw.m | 8,700 | utf_8 | c87292c822d96708d2d3f19e5a54e207 | function [stats,st] = lme_mass_fit_Rgw(X,Zcols,Y,ni,Th0,Rgs,Surf,fname,...
Dtype,sptm,prs,e)
% [stats,st] = lme_mass_fit_Rgw(X,Zcols,Y,ni,Th0,Rgs,Surf,fname,Dtype,sptm,prs,e)
%
% Region-wise linear mi... |
github | josephdviviano/qcmon-master | load_nii_ext.m | .m | qcmon-master/assets/matlab/nifti-tools/load_nii_ext.m | 5,544 | utf_8 | 09a2960b9d48f4b0363d5065f1780cbd | % Load NIFTI header extension after its header is loaded using load_nii_hdr.
%
% Usage: ext = load_nii_ext(filename)
%
% filename - NIFTI file name.
%
% Returned values:
%
% ext - Structure of NIFTI header extension, which includes num_ext,
% and all the extended header sections in the header extens... |
github | josephdviviano/qcmon-master | rri_orient.m | .m | qcmon-master/assets/matlab/nifti-tools/rri_orient.m | 2,357 | utf_8 | e1b7cfcaf2517b7887ac6e02d9ab504d | % Convert image of different orientations to standard Analyze orientation
%
% Usage: nii = rri_orient(nii);
% Jimmy Shen (jimmy@rotman-baycrest.on.ca), 26-APR-04
%___________________________________________________________________
function [nii, orient, pattern] = rri_orient(nii, varargin)
if nargin >... |
github | josephdviviano/qcmon-master | save_untouch0_nii_hdr.m | .m | qcmon-master/assets/matlab/nifti-tools/save_untouch0_nii_hdr.m | 8,813 | utf_8 | a0a201073cb18f09b62842e94094c451 | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function save_nii_hdr(hdr, fid)
if ~isequal(hdr.hk.sizeof_hdr,348),
error('hdr.hk.sizeof_hdr must be 348.');
end
write_header(hdr, fid);
return; % save_nii_hdr
%------------------------------------------------... |
github | josephdviviano/qcmon-master | rri_zoom_menu.m | .m | qcmon-master/assets/matlab/nifti-tools/rri_zoom_menu.m | 770 | utf_8 | f0bae2b3d88fd719c47fd467e867e19f | % Imbed a zoom menu to any figure.
%
% Usage: rri_zoom_menu(fig);
%
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
%
%--------------------------------------------------------------------
function menu_hdl = rri_zoom_menu(fig)
if isnumeric(fig)
menu_hdl = uimenu('Parent',fig, ...
'Label','... |
github | josephdviviano/qcmon-master | rri_select_file.m | .m | qcmon-master/assets/matlab/nifti-tools/rri_select_file.m | 17,235 | utf_8 | 0e0b14435a670dd8805aa514f7dbb6bb | function [selected_file, selected_path] = rri_select_file(varargin)
%
% USAGE: [selected_file, selected_path] = ...
% rri_select_file(dir_name, fig_title)
%
% Allow user to select a file from a list of Matlab competible
% file format
%
% Example:
%
% [selected_file, selected_path] = ...
% ... |
github | josephdviviano/qcmon-master | clip_nii.m | .m | qcmon-master/assets/matlab/nifti-tools/clip_nii.m | 3,421 | utf_8 | 19da887808bddae362df38b0e9f35076 | % CLIP_NII: Clip the NIfTI volume from any of the 6 sides
%
% Usage: nii = clip_nii(nii, [option])
%
% Inputs:
%
% nii - NIfTI volume.
%
% option - struct instructing how many voxel to be cut from which side.
%
% option.cut_from_L = ( number of voxel )
% option.cut_from_R = ( number of voxel )
% option... |
github | josephdviviano/qcmon-master | affine.m | .m | qcmon-master/assets/matlab/nifti-tools/affine.m | 16,664 | utf_8 | 419b609560eb98534c0e32cc4506cc7f | % Using 2D or 3D affine matrix to rotate, translate, scale, reflect and
% shear a 2D image or 3D volume. 2D image is represented by a 2D matrix,
% 3D volume is represented by a 3D matrix, and data type can be real
% integer or floating-point.
%
% You may notice that MATLAB has a function called 'imtransform.... |
github | josephdviviano/qcmon-master | load_untouch_nii_img.m | .m | qcmon-master/assets/matlab/nifti-tools/load_untouch_nii_img.m | 15,224 | utf_8 | 46fb6696904467f1848e2882cd7a72f6 | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function [img,hdr] = load_untouch_nii_img(hdr,filetype,fileprefix,machine,img_idx,dim5_idx,dim6_idx,dim7_idx,old_RGB,slice_idx)
if ~exist('hdr','var') | ~exist('filetype','var') | ~exist('fileprefix','var') | ~exist('machine','var')
e... |
github | josephdviviano/qcmon-master | load_untouch_nii.m | .m | qcmon-master/assets/matlab/nifti-tools/load_untouch_nii.m | 6,373 | utf_8 | 303eb6438d7d37e2144d554504fbdf54 | % Load NIFTI or ANALYZE dataset, but not applying any appropriate affine
% geometric transform or voxel intensity scaling.
%
% Although according to NIFTI website, all those header information are
% supposed to be applied to the loaded NIFTI image, there are some
% situations that people do want to leave the ... |
github | josephdviviano/qcmon-master | collapse_nii_scan.m | .m | qcmon-master/assets/matlab/nifti-tools/collapse_nii_scan.m | 7,038 | utf_8 | 2d30d10b884719503df2974ff39b7093 | % Collapse multiple single-scan NIFTI files into a multiple-scan NIFTI file
%
% Usage: collapse_nii_scan(scan_file_pattern, [collapsed_fileprefix], [scan_file_folder])
%
% Here, scan_file_pattern should look like: 'myscan_0*.img'
% If collapsed_fileprefix is omit, 'multi_scan' will be used
% If scan_file_fol... |
github | josephdviviano/qcmon-master | rri_orient_ui.m | .m | qcmon-master/assets/matlab/nifti-tools/rri_orient_ui.m | 5,635 | utf_8 | 3361ce417798ffe2c6b53cf194b2a146 | % Return orientation of the current image:
% orient is orientation 1x3 matrix, in that:
% Three elements represent: [x y z]
% Element value: 1 - Left to Right; 2 - Posterior to Anterior;
% 3 - Inferior to Superior; 4 - Right to Left;
% 5 - Anterior to Posterior; 6 - Superior to Inferior;
% e.g.:
% Standard... |
github | josephdviviano/qcmon-master | load_untouch0_nii_hdr.m | .m | qcmon-master/assets/matlab/nifti-tools/load_untouch0_nii_hdr.m | 8,293 | utf_8 | d823050e9ba931a2ba7f9d9a3893d2d1 | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function hdr = load_nii_hdr(fileprefix, machine)
fn = sprintf('%s.hdr',fileprefix);
fid = fopen(fn,'r',machine);
if fid < 0,
msg = sprintf('Cannot open file %s.',fn);
error(msg);
else
fseek(fid,0,'bof')... |
github | josephdviviano/qcmon-master | load_nii.m | .m | qcmon-master/assets/matlab/nifti-tools/load_nii.m | 7,006 | utf_8 | 71beffc9e2b0c7e14c2f8dc8adbadbf1 | % Load NIFTI or ANALYZE dataset. Support both *.nii and *.hdr/*.img
% file extension. If file extension is not provided, *.hdr/*.img will
% be used as default.
%
% A subset of NIFTI transform is included. For non-orthogonal rotation,
% shearing etc., please use 'reslice_nii.m' to reslice the NIFTI file.
% I... |
github | josephdviviano/qcmon-master | unxform_nii.m | .m | qcmon-master/assets/matlab/nifti-tools/unxform_nii.m | 1,221 | utf_8 | ff8be64760837046b931857d59ca304e | % Undo the flipping and rotations performed by xform_nii; spit back only
% the raw img data block. Initial cut will only deal with 3D volumes
% strongly assume we have called xform_nii to write down the steps used
% in xform_nii.
%
% Usage: a = load_nii('original_name');
% manipulate a.img to make... |
github | josephdviviano/qcmon-master | load_untouch_nii_hdr.m | .m | qcmon-master/assets/matlab/nifti-tools/load_untouch_nii_hdr.m | 8,739 | utf_8 | eb068c88e2b7bb518ea557d0734bc65d | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function hdr = load_nii_hdr(fileprefix, machine, filetype)
if filetype == 2
fn = sprintf('%s.nii',fileprefix);
if ~exist(fn)
msg = sprintf('Cannot find file "%s.nii".', fileprefix);
error(msg);
end
... |
github | josephdviviano/qcmon-master | save_nii_ext.m | .m | qcmon-master/assets/matlab/nifti-tools/save_nii_ext.m | 1,015 | utf_8 | db919f3a7a4b2f64dae641b1e97fa4a0 | % Save NIFTI header extension.
%
% Usage: save_nii_ext(ext, fid)
%
% ext - struct with NIFTI header extension fields.
%
% NIFTI data format can be found on: http://nifti.nimh.nih.gov
%
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
%
function save_nii_ext(ext, fid)
if ~exist('ext','var') | ~exist('fi... |
github | josephdviviano/qcmon-master | view_nii_menu.m | .m | qcmon-master/assets/matlab/nifti-tools/view_nii_menu.m | 14,895 | utf_8 | d81fb80884a14ae659630258fbc330bc | % Imbed Zoom, Interp, and Info menu to view_nii window.
%
% Usage: view_nii_menu(fig);
%
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
%
%--------------------------------------------------------------------
function menu_hdl = view_nii_menu(fig, varargin)
if isnumeric(fig)
menu_hdl = init(fig);
... |
github | josephdviviano/qcmon-master | save_untouch_header_only.m | .m | qcmon-master/assets/matlab/nifti-tools/save_untouch_header_only.m | 2,203 | utf_8 | 6622b1835d5ad8ce504298473ab7684f | % This function is only used to save Analyze or NIfTI header that is
% ended with .hdr and loaded by load_untouch_header_only.m. If you
% have NIfTI file that is ended with .nii and you want to change its
% header only, you can use load_untouch_nii / save_untouch_nii pair.
%
% Usage: save_untouch_header_on... |
github | josephdviviano/qcmon-master | pad_nii.m | .m | qcmon-master/assets/matlab/nifti-tools/pad_nii.m | 3,854 | utf_8 | a38d813f9f822362d873bc92725f565b | % PAD_NII: Pad the NIfTI volume from any of the 6 sides
%
% Usage: nii = pad_nii(nii, [option])
%
% Inputs:
%
% nii - NIfTI volume.
%
% option - struct instructing how many voxel to be padded from which side.
%
% option.pad_from_L = ( number of voxel )
% option.pad_from_R = ( number of voxel )
% option... |
github | josephdviviano/qcmon-master | load_nii_hdr.m | .m | qcmon-master/assets/matlab/nifti-tools/load_nii_hdr.m | 10,311 | utf_8 | ef81f82b43da4fbd79a9de1787b5ae22 | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function [hdr, filetype, fileprefix, machine] = load_nii_hdr(fileprefix)
if ~exist('fileprefix','var'),
error('Usage: [hdr, filetype, fileprefix, machine] = load_nii_hdr(filename)');
end
machine = 'ieee-le';
new_ext = 0;... |
github | josephdviviano/qcmon-master | save_untouch_slice.m | .m | qcmon-master/assets/matlab/nifti-tools/save_untouch_slice.m | 20,263 | utf_8 | 833f175c0298d11697418454a03993db | % Save back to the original image with a portion of slices that was
% loaded by "load_untouch_nii". You can process those slices matrix
% in any way, as long as their dimension is not altered.
%
% Usage: save_untouch_slice(slice, filename, ...
% slice_idx, [img_idx], [dim5_idx], [dim6_idx], [dim7_idx])
%
% ... |
github | josephdviviano/qcmon-master | load_nii_img.m | .m | qcmon-master/assets/matlab/nifti-tools/load_nii_img.m | 12,720 | utf_8 | 5670adb84a76f241bd221003bee8187d | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function [img,hdr] = load_nii_img(hdr,filetype,fileprefix,machine,img_idx,dim5_idx,dim6_idx,dim7_idx,old_RGB)
if ~exist('hdr','var') | ~exist('filetype','var') | ~exist('fileprefix','var') | ~exist('machine','var')
error('Usage: [img,... |
github | josephdviviano/qcmon-master | bresenham_line3d.m | .m | qcmon-master/assets/matlab/nifti-tools/bresenham_line3d.m | 4,682 | utf_8 | f2e52d1f3ac9779b22baf3bb4d2ac201 | % Generate X Y Z coordinates of a 3D Bresenham's line between
% two given points.
%
% A very useful application of this algorithm can be found in the
% implementation of Fischer's Bresenham interpolation method in my
% another program that can rotate three dimensional image volume
% with an affine matrix:
... |
github | josephdviviano/qcmon-master | make_nii.m | .m | qcmon-master/assets/matlab/nifti-tools/make_nii.m | 7,105 | utf_8 | 6b1565392965b164217621e71d213ddd | % 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 NIfT... |
github | josephdviviano/qcmon-master | verify_nii_ext.m | .m | qcmon-master/assets/matlab/nifti-tools/verify_nii_ext.m | 1,721 | utf_8 | 0339aeb8d7286e4f08165c9eeeb4c2cd | % Verify NIFTI header extension to make sure that each extension section
% must be an integer multiple of 16 byte long that includes the first 8
% bytes of esize and ecode. If the length of extension section is not the
% above mentioned case, edata should be padded with all 0.
%
% Usage: [ext, esize_total] = ... |
github | josephdviviano/qcmon-master | get_nii_frame.m | .m | qcmon-master/assets/matlab/nifti-tools/get_nii_frame.m | 4,497 | utf_8 | cc9b1b92f34e5ae67dc34c35a5174c75 | % Return time frame of a NIFTI dataset. Support both *.nii and
% *.hdr/*.img file extension. If file extension is not provided,
% *.hdr/*.img will be used as default.
%
% It is a lightweighted "load_nii_hdr", and is equivalent to
% hdr.dime.dim(5)
%
% Usage: [ total_scan ] = get_nii_frame(filename)
%
... |
github | josephdviviano/qcmon-master | flip_lr.m | .m | qcmon-master/assets/matlab/nifti-tools/flip_lr.m | 3,568 | utf_8 | d95b62698d44a65a3c2f02fbabc632ac | % When you load any ANALYZE or NIfTI file with 'load_nii.m', and view
% it with 'view_nii.m', you may find that the image is L-R flipped.
% This is because of the confusion of radiological and neurological
% convention in the medical image before NIfTI format is adopted. You
% can find more details from:
%
%... |
github | josephdviviano/qcmon-master | save_nii.m | .m | qcmon-master/assets/matlab/nifti-tools/save_nii.m | 9,690 | utf_8 | ed292054cab74afaf953455bfbc200aa | % 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 o... |
github | josephdviviano/qcmon-master | rri_file_menu.m | .m | qcmon-master/assets/matlab/nifti-tools/rri_file_menu.m | 4,153 | utf_8 | c9faa3905c642854eeed98ab8b02998e | % Imbed a file menu to any figure. If file menu exist, it will append
% to the existing file menu. This file menu includes: Copy to clipboard,
% print, save, close etc.
%
% Usage: rri_file_menu(fig);
%
% rri_file_menu(fig,0) means no 'Close' menu.
%
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
%
... |
github | josephdviviano/qcmon-master | reslice_nii.m | .m | qcmon-master/assets/matlab/nifti-tools/reslice_nii.m | 10,138 | utf_8 | ea18d2f994fd5d9989449feaced1e4dd | % The basic application of the 'reslice_nii.m' program is to perform
% any 3D affine transform defined by a NIfTI format image.
%
% In addition, the 'reslice_nii.m' program can also be applied to
% generate an isotropic image from either a NIfTI format image or
% an ANALYZE format image.
%
% The resliced N... |
github | josephdviviano/qcmon-master | save_untouch_nii.m | .m | qcmon-master/assets/matlab/nifti-tools/save_untouch_nii.m | 6,726 | utf_8 | cb98e2799abc112dca5b10078bde09bf | % Save NIFTI or ANALYZE dataset that is loaded by "load_untouch_nii.m".
% The output image format and file extension will be the same as the
% input one (NIFTI.nii, NIFTI.img or ANALYZE.img). Therefore, any file
% extension that you specified will be ignored.
%
% Usage: save_untouch_nii(nii, filename)
%
%... |
github | josephdviviano/qcmon-master | view_nii.m | .m | qcmon-master/assets/matlab/nifti-tools/view_nii.m | 144,481 | utf_8 | 8ea68ec34d3a6bec721497afb56cfb54 | % VIEW_NII: Create or update a 3-View (Front, Top, Side) of the
% brain data that is specified by nii structure
%
% Usage: status = view_nii([h], nii, [option]) or
% status = view_nii(h, [option])
%
% Where, h is the figure on which the 3-View will be plotted;
% nii is the brain data in NIFTI format;
% o... |
github | josephdviviano/qcmon-master | mat_into_hdr.m | .m | qcmon-master/assets/matlab/nifti-tools/mat_into_hdr.m | 2,691 | utf_8 | 847d96698f45f7c5e7decbb3a0c3187f | %MAT_INTO_HDR The old versions of SPM (any version before SPM5) store
% an affine matrix of the SPM Reoriented image into a matlab file
% (.mat extension). The file name of this SPM matlab file is the
% same as the SPM Reoriented image file (.img/.hdr extension).
%
% This program will convert the ANALYZE 7.5 SPM... |
github | josephdviviano/qcmon-master | xform_nii.m | .m | qcmon-master/assets/matlab/nifti-tools/xform_nii.m | 18,628 | utf_8 | e39c421e7f117cbc81c56e9d023774a3 | % internal function
% 'xform_nii.m' is an internal function called by "load_nii.m", so
% you do not need run this program by yourself. It does simplified
% NIfTI sform/qform affine transform, and supports some of the
% affine transforms, including translation, reflection, and
% orthogonal rotation (N*90 ... |
github | josephdviviano/qcmon-master | make_ana.m | .m | qcmon-master/assets/matlab/nifti-tools/make_ana.m | 5,665 | utf_8 | 37d574b277823f941138c9548127d720 | % Make ANALYZE 7.5 data structure specified by a 3D or 4D matrix.
% Optional parameters can also be included, such as: voxel_size,
% origin, datatype, and description.
%
% Once the ANALYZE structure is made, it can be saved into ANALYZE 7.5
% format data file using "save_untouch_nii" command (for more de... |
github | josephdviviano/qcmon-master | extra_nii_hdr.m | .m | qcmon-master/assets/matlab/nifti-tools/extra_nii_hdr.m | 8,085 | utf_8 | 4f76a8a66736025a0acf3efa15a2d2aa | % Decode extra NIFTI header information into hdr.extra
%
% Usage: hdr = extra_nii_hdr(hdr)
%
% hdr can be obtained from load_nii_hdr
%
% NIFTI data format can be found on: http://nifti.nimh.nih.gov
%
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
%
function hdr = extra_nii_hdr(hdr)
switch hdr.dime.da... |
github | josephdviviano/qcmon-master | rri_xhair.m | .m | qcmon-master/assets/matlab/nifti-tools/rri_xhair.m | 2,300 | utf_8 | 95954b8cd43e01fba5c4b2f335be1780 | % rri_xhair: create a pair of full_cross_hair at point [x y] in
% axes h_ax, and return xhair struct
%
% Usage: xhair = rri_xhair([x y], xhair, h_ax);
%
% If omit xhair, rri_xhair will create a pair of xhair; otherwise,
% rri_xhair will update the xhair. If omit h_ax, current axes will
% b... |
github | josephdviviano/qcmon-master | save_untouch_nii_hdr.m | .m | qcmon-master/assets/matlab/nifti-tools/save_untouch_nii_hdr.m | 8,721 | utf_8 | 0d396eaeebb6114f24d56ab74a8299cf | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function save_nii_hdr(hdr, fid)
if ~isequal(hdr.hk.sizeof_hdr,348),
error('hdr.hk.sizeof_hdr must be 348.');
end
write_header(hdr, fid);
return; % save_nii_hdr
%------------------------------------------------... |
github | josephdviviano/qcmon-master | expand_nii_scan.m | .m | qcmon-master/assets/matlab/nifti-tools/expand_nii_scan.m | 1,381 | utf_8 | 0715d668d046bcc608ea78cd0c2089bd | % Expand a multiple-scan NIFTI file into multiple single-scan NIFTI files
%
% Usage: expand_nii_scan(multi_scan_filename, [img_idx], [path_to_save])
%
% NIFTI data format can be found on: http://nifti.nimh.nih.gov
%
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
%
function expand_nii_scan(filename, img_idx, n... |
github | josephdviviano/qcmon-master | load_untouch_header_only.m | .m | qcmon-master/assets/matlab/nifti-tools/load_untouch_header_only.m | 7,255 | utf_8 | f1210f851ab6610e7656121194cb5c8b | % Load NIfTI / Analyze header without applying any appropriate affine
% geometric transform or voxel intensity scaling. It is equivalent to
% hdr field when using load_untouch_nii to load dataset. Support both
% *.nii and *.hdr file extension. If file extension is not provided,
% *.hdr will be used as default.... |
github | josephdviviano/qcmon-master | bipolar.m | .m | qcmon-master/assets/matlab/nifti-tools/bipolar.m | 2,239 | utf_8 | c860ec93d96b6ab636c985280d79958d | %BIPOLAR returns an M-by-3 matrix containing a blue-red colormap, in
% in which red stands for positive, blue stands for negative,
% and white stands for 0.
%
% Usage: cmap = bipolar(M, lo, hi, contrast); or cmap = bipolar;
%
% cmap: output M-by-3 matrix for BIPOLAR colormap.
% M: number of shades in th... |
github | josephdviviano/qcmon-master | save_nii_hdr.m | .m | qcmon-master/assets/matlab/nifti-tools/save_nii_hdr.m | 9,497 | utf_8 | 66a99df0cb0f3c1f44c6e36dcd13cddf | % 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
... |
github | sayantanauddy/matsuoka_joint_control-master | basic_pso.m | .m | matsuoka_joint_control-master/matlab/pso/basic_pso.m | 5,177 | utf_8 | 0693850dc9e118004be081b4e3fcd83f |
function basic_pso(position_bounds, swarm_size, max_iters, C1, C2)
%basic_pso - Function for optimization using a basic version of PSO
%
% Syntax: basic_pso(position_bounds, swarm_size, max_iters, C1, C2)
%
% Inputs:
% position_bounds - A 2D array where each row contains 2 numbers - the
% lower bound and the u... |
github | sayantanauddy/matsuoka_joint_control-master | atre_pso.m | .m | matsuoka_joint_control-master/matlab/pso/atre_pso.m | 7,711 | utf_8 | b9a4a27592dd087e2caf2b776b2680ce |
function atre_pso(position_bounds, swarm_size, max_iters, C1, C2, d_low, d_high, sobol_flag)
%atre_pso - Function for optimization using ATRE-PSO (a modification of
%atraction repulsion PSO). Refer to http://ieeexplore.ieee.org/document/4424896/
%
% Syntax: atre_pso(position_bounds, swarm_size, max_iters, C1, C2, d_... |
github | sayantanauddy/matsuoka_joint_control-master | qi1_pso.m | .m | matsuoka_joint_control-master/matlab/pso/qi1_pso.m | 7,386 | utf_8 | 68d177a4939cac21919b5688d135a528 |
function qi1_pso(position_bounds, swarm_size, max_iters, C1, C2, d_low, d_high, sobol_flag)
%qi1_pso - Function for optimization using QI-PSO-1. A slight modification
%is made in the way new individuals are added to the population. In
%QI-PSO-1, the new individual is added only when the fitness of this new
%individua... |
github | sayantanauddy/matsuoka_joint_control-master | ode_test.m | .m | matsuoka_joint_control-master/matlab/ODE_Solvers/ode_test.m | 1,505 | utf_8 | 7bf1cf1561baad71f29991c80cf482fc | function ode_test
%ODE_TEST Run non-adaptive ODE solvers of different orders.
% ODE_TEST compares the orders of accuracy of several explicit Runge-Kutta
% methods. The non-adaptive ODE solvers are tested on a problem used in
% E. Hairer, S.P. Norsett, and G. Wanner, Solving Ordinary Differential
% Equatio... |
github | sayantanauddy/matsuoka_joint_control-master | simpleTest.m | .m | matsuoka_joint_control-master/matlab/matlab-vrep/simpleTest.m | 6,018 | utf_8 | e9af6dc6bfe301bf59689953d8b42b9b | % Modified by Sayantan Auddy from the original file provided by Coppelia
% Robotics
%
% This script does the following:
% 1. Connects to V-REP
% 2. Retrieves names of all objects in the scene
% 3. Retreives absolute position of the NAO robot in the scene
% 4. Resets the NAO by first removing the model and then ... |
github | sayantanauddy/matsuoka_joint_control-master | basic_pso.m | .m | matsuoka_joint_control-master/matlab/gait_optimization/pso/basic_pso.m | 6,478 | utf_8 | b8751ddaf70bb8170e102992c319dd73 |
function basic_pso(position_bounds, swarm_size, max_iters, C1, C2)
%basic_pso - Function for optimization using a basic version of PSO
%
% Syntax: basic_pso(position_bounds, swarm_size, max_iters, C1, C2)
%
% Inputs:
% position_bounds - A 2D array where each row contains 2 numbers - the
% lower bound and the u... |
github | sayantanauddy/matsuoka_joint_control-master | atre_pso.m | .m | matsuoka_joint_control-master/matlab/gait_optimization/pso/atre_pso.m | 7,712 | utf_8 | 40d32c5ff2d52a29cb7c7cca7c3b59ed |
function atre_pso(position_bounds, swarm_size, max_iters, C1, C2, d_low, d_high, sobol_flag)
%atre_pso - Function for optimization using ATRE-PSO (a modification of
%atraction repulsion PSO). Refer to http://ieeexplore.ieee.org/document/4424896/
%
% Syntax: atre_pso(position_bounds, swarm_size, max_iters, C1, C2, d_... |
github | sayantanauddy/matsuoka_joint_control-master | qi1_pso.m | .m | matsuoka_joint_control-master/matlab/gait_optimization/pso/qi1_pso.m | 7,386 | utf_8 | 68d177a4939cac21919b5688d135a528 |
function qi1_pso(position_bounds, swarm_size, max_iters, C1, C2, d_low, d_high, sobol_flag)
%qi1_pso - Function for optimization using QI-PSO-1. A slight modification
%is made in the way new individuals are added to the population. In
%QI-PSO-1, the new individual is added only when the fitness of this new
%individua... |
github | ab39826/TrafficDetection-master | matchingCriterion.m | .m | TrafficDetection-master/matchingCriterion.m | 550 | utf_8 | 4cf13e2c954c44632fc932661cdde245 | %computes distributions for which a match exists with current pixel
%based on Mahalanobis distance
function match = matchingCriterion(pixMean,pixVar,pixel)
K = size(pixMean,3);
match = 0;
global matchThres;
bestD = 0;
for k = 1:K
pixVarMatrix = eye(3)/pixVar(k);
D = sqrt((pixel - pixMean(:,:,k))'*pixVar... |
github | ab39826/TrafficDetection-master | vehicleTrackingParticle.m | .m | TrafficDetection-master/vehicleTrackingParticle.m | 1,006 | utf_8 | f9ba5a21160316f8b84358836eb62960 | %model- struct that contains
%[centroid, labelIndex, structKalmanModel]
%centroids- center pixel locations of large connected component regions
%labelVector - associated ids (should persist and remain consistent between frames)
%strucFilterModel:
%all necessary components to perform prediction and update step of kala... |
github | ab39826/TrafficDetection-master | centroidMatching.m | .m | TrafficDetection-master/centroidMatching.m | 1,067 | utf_8 | 05aa1d02577de80f9e5b38fc9f2a5aab | %this function performs the linear assignment problem using the Hungarian
%algorithm in order to minimize cost of matching predicted centroids with
%current centroids
%cost is defined as distance between centroids.
function [nextLabelVector orderedCentroids] = centroidMatching(predictCentroids, prevLabelVector, curre... |
github | ab39826/TrafficDetection-master | matchingCriterionProb.m | .m | TrafficDetection-master/matchingCriterionProb.m | 854 | utf_8 | f9999633b1bff3b83af131cc8ee04a8c | %computes distributions for which a match exists with current pixel
%based on Mahalanobis distance
function match = matchingCriterionProb(pixMean,pixVar,pixel, pixWeight)
K = size(pixMean,3);
match = 0;
global matchThres;
bestD = 0;
beta = 1;
totalProbMeasure = 0;
probCompVec = zeros(K,1);
for k = 1:K
pixVar... |
github | ab39826/TrafficDetection-master | connectedComponentCleanup.m | .m | TrafficDetection-master/connectedComponentCleanup.m | 732 | utf_8 | 36d50bad6a512fe8b3aaffcfaaee10e3 | %function takes preliminary foreground video frame and cleans it up
%based on connected component threshold processing
function [cleanTrackFrame centroids] = connectedComponentCleanup(foreFrame)
grayFrame = rgb2gray(foreFrame);
grayFrame(grayFrame(:,:) == 1) = 0;
grayFrame(grayFrame(:,:) > 0) = 255;
global ccThresh... |
github | ab39826/TrafficDetection-master | hungarian.m | .m | TrafficDetection-master/hungarian.m | 11,248 | utf_8 | 57e28fa42676424424390415c6c63217 | function [C,T]=hungarian(A)
%HUNGARIAN Solve the Assignment problem using the Hungarian method.
%
%[C,T]=hungarian(A)
%A - a square cost matrix.
%C - the optimal assignment.
%T - the cost of the optimal assignment.
% Adapted from the FORTRAN IV code in Carpaneto and Toth, "Algorithm 548:
% Solution of the assignment p... |
github | ab39826/TrafficDetection-master | vehicleTracking.m | .m | TrafficDetection-master/vehicleTracking.m | 1,792 | utf_8 | ff78592caef3aefdba061b47ed5235b2 | %model- struct that contains
%[centroid, labelIndex, structKalmanModel]
%centroids- center pixel locations of large connected component regions
%labelVector - associated ids (should persist and remain consistent between frames)
%strucFilterModel:
%all necessary components to perform prediction and update step of kala... |
github | ab39826/TrafficDetection-master | colorBox.m | .m | TrafficDetection-master/colorBox.m | 760 | utf_8 | 823b55b66041d5481df2ea4011b6162d | %This function takes a processed foreground frame, and adds colorBoxes
%corresponding to tracked cars
%modulo 8 to correspond to color labels
%each color has a corresponding index
function trackFrame = colorBox(frame, curModel,centroids, height, width)
global colorBoxVector;
colorBoxSize = size(colorBoxVector(1).f,1)... |
github | dong100136/PracticeCode-master | submit.m | .m | PracticeCode-master/warehouse/mooc-machine-learning/homework/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | dong100136/PracticeCode-master | submitWithConfiguration.m | .m | PracticeCode-master/warehouse/mooc-machine-learning/homework/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | dong100136/PracticeCode-master | savejson.m | .m | PracticeCode-master/warehouse/mooc-machine-learning/homework/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | dong100136/PracticeCode-master | loadjson.m | .m | PracticeCode-master/warehouse/mooc-machine-learning/homework/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | dong100136/PracticeCode-master | loadubjson.m | .m | PracticeCode-master/warehouse/mooc-machine-learning/homework/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | dong100136/PracticeCode-master | saveubjson.m | .m | PracticeCode-master/warehouse/mooc-machine-learning/homework/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | dong100136/PracticeCode-master | submit.m | .m | PracticeCode-master/warehouse/mooc-machine-learning/homework/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | dong100136/PracticeCode-master | submitWithConfiguration.m | .m | PracticeCode-master/warehouse/mooc-machine-learning/homework/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | dong100136/PracticeCode-master | savejson.m | .m | PracticeCode-master/warehouse/mooc-machine-learning/homework/machine-learning-ex1/ex1/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | dong100136/PracticeCode-master | loadjson.m | .m | PracticeCode-master/warehouse/mooc-machine-learning/homework/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | dong100136/PracticeCode-master | loadubjson.m | .m | PracticeCode-master/warehouse/mooc-machine-learning/homework/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | dong100136/PracticeCode-master | saveubjson.m | .m | PracticeCode-master/warehouse/mooc-machine-learning/homework/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | PavelTrutman/FocalNet-master | F_simulate.m | .m | FocalNet-master/deepag/bin/deepag/F_simulate.m | 3,366 | utf_8 | dcf768af11a522afad993532acd1ecd0 | % Simulate scene and calculate F from simulated correspondences
% in:
% 1. Fparam.f1/Fparam.f2 - focal length.
% Fparam.per_corr - number of F-s returned, each differing only in
% noise. Don't use it, it's ignored.
% 2. noise - determines std of additive noise in pixels, which is added to
% ... |
github | PavelTrutman/FocalNet-master | monomial_features.m | .m | FocalNet-master/deepag/bin/deepag/monomial_features.m | 1,115 | utf_8 | e444fbcfc552001bdca98ce843152cc0 | % calculate feature vector with coefficients corresponding to monomials in
% some equation, see ShareLatex
%
% (Zuzana?)
function [Mvec] = monomial_features(x, xp)
M = [-xp(:,2), x(:,2), xp(:,1), x(:,1), -x(:,1).*xp(:,2) - x(:,2).*xp(:,1), 2*x(:,1).*xp(:,1) - 2*x(:,2).*xp(:,2), 2*x(:,1).*xp(:,2), -2*x(:,1), 2*x(:,2),... |
github | PavelTrutman/FocalNet-master | get_features.m | .m | FocalNet-master/deepag/bin/deepag/get_features.m | 717 | utf_8 | ef6d130fa15d49fd8e245f751f782b5d | % Calculate feature vector from correspondences
%
% used in deepag, change this function to change format of deepag.m output
% in:
% x - matrix of 2d coordinates in the first image of the form size(x):=[number_of_points 2]
% p - matrix of coordinates in the second image corresponding to x
% out:
% Mvec - f... |
github | PavelTrutman/FocalNet-master | deepag.m | .m | FocalNet-master/deepag/bin/deepag/deepag.m | 17,368 | utf_8 | 1eafc22588e5e487ef0c574ace1811b1 | % deepag.m - Deep Learning 4 Algebraic Geometry
% T. Pajdla, pajdla@gmail.cz
% 1 Jan 2016 - 31 Dec 2016
%
function deepag(pst, pdAllt, datasett)
persistent ps;
persistent DEPPAGRunning;
persistent pdAll;
persistent dataset;
if exist('pst', 'var')
ps = pst;
end
if exist('pdAllt', 'var')
... |
github | PavelTrutman/FocalNet-master | k_nn_pixels.m | .m | FocalNet-master/deepag/bin/deepag/k_nn_pixels.m | 10,193 | utf_8 | 0c8662ad73f2e4ca8a92a00301cd43a3 | function k_nn_pixels()
% k-nearest neighbours in the normalized image coordinates
%
% Pavel Trutman
% INRIA, 2016
deepagpaths;
% prepare data
load('../../data/paris/correspondences_synteticKNN.mat');
% properties
margin = 1/7;
batchSize = 10;
% filter cams that their correspondences are c... |
github | PavelTrutman/FocalNet-master | bougnoux_scatter.m | .m | FocalNet-master/deepag/bin/deepag/bougnoux_scatter.m | 2,753 | utf_8 | 1ed3d4872d6684fd8f318b6af052512c | % A script for generating scatter plots of Bougnoux formula results from correspondences file
%
% Oleh Rybkin, rybkiole@fel.cvut.cz
% INRIA, 2016
function stat_data=bougnoux_scatter(file,corr,pop_size, method)
%stat_data:=[absolute_error; a_std; relative_error; r_std]
two_focals=false;
[estion,truth]=calcFocals(file,... |
github | PavelTrutman/FocalNet-master | F_features.m | .m | FocalNet-master/deepag/bin/deepag/F_features.m | 1,079 | utf_8 | 623e578fb3d944f6af2b6b11ea9592fc | % Calculate feature vector - fundamental matrix - from correspondences
% size(u):=[2 number_of_points]
% (size(u1)==size(u2)):=true
%
% Oleh Rybkin, rybkiole@fel.cvut.cz
% INRIA, 2016
function [F,A] = F_features(u1, u2,method)
if nargin<3
method='Free';
end
%some differents formats of input
... |
github | PavelTrutman/FocalNet-master | deepag_parallel.m | .m | FocalNet-master/deepag/bin/deepag/deepag_parallel.m | 19,480 | utf_8 | e43b07f657989c4eec523d3047043307 | % deepag.m - Deep Learning 4 Algebraic Geometry
% T. Pajdla, pajdla@gmail.cz
% 1 Jan 2016 - 31 Dec 2016
%
function deepag_parallel(pst, pdAllt, datasett)
persistent ps;
persistent DEPPAGRunning;
persistent pdAll;
persistent dataset;
if exist('pst', 'var')
ps = pst;
end
if exist('pdAllt', 'var')
pdA... |
github | PavelTrutman/FocalNet-master | F_generateData.m | .m | FocalNet-master/deepag/bin/deepag/F_generateData.m | 2,844 | utf_8 | 9e2a18347997e5ad2ccef2d182b8d92c | % Script for generating synthetic Fundamental matrix feature vectors
%
% saves in corresponding files simulation of F and u. Files then could be
% used in scripts bougnoux_scatter, k_nn, nn_*
%
% Oleh Rybkin, rybkiole@fel.cvut.cz
% INRIA, 2016
function F_generateData()
per_corr=1; % samples (with different noise) per c... |
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