plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | farhi/idl2matlab-master | tv.m | .m | idl2matlab-master/lib/matlablib/window/tv.m | 3,941 | utf_8 | 28c41ec14662343f75c31a468dead54c | % tv
% --------------------------------------------
% Equivalent to :
% function TV, Image [, Position]
% ou
% TV, Image [, X, Y [, Channel]]
% /CENTIMETERS
% /CHANNEL
% /INCHES
% /ORDER ... (Position & T3D & Z not yet unavai... |
github | farhi/idl2matlab-master | tvrd.m | .m | idl2matlab-master/lib/matlablib/window/tvrd.m | 2,468 | utf_8 | d698555fb95f9b3c3b3c9a1a1c041eb6 | % tvrd
% --------------------------------------------
% Equivalent to :
% function Result = TVRD([X0 [, Y0 [, Nx [, Ny ]]]])
% [,/ORDER]
% in IDL
function [res]=tvrd(varargin)
I2Mkwn=char('I2M_a1' , 'I2M_a2' , 'I2M_a3' , 'I2M_a4' ,'I2M_a5' , 'channel','order','true','words', 'I2M_pos');
... |
github | farhi/idl2matlab-master | device.m | .m | idl2matlab-master/lib/matlablib/window/device.m | 21,521 | utf_8 | 6f8f80756f215485d370054e29e7559e | % device
% --------------------------------------------
% Equivalent to :
% function DEVICE
% [, /CLOSE{Z}]
% [, SET_RESOLUTION=[width, height]{Z}]
% [, WINDOW_STATE=variable{MAC, WIN, X}]
% [, /DECOMPOSED{MAC, WIN, X}]
% [, GET_DECOMPOSED=va... |
github | farhi/idl2matlab-master | dist.m | .m | idl2matlab-master/lib/matlablib/math/dist.m | 2,017 | utf_8 | c6d47ddc9467bb1cfe0d3a5ca88e6bf7 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% File generated by IDL2Matlab v1.1. %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% $Id: dist.pro,v 1.2 1994/04/28 18:14:15 doug Exp $
% Return a rectangular array in which each pixel = euclidian
function a = dist(n,m)
% distance from the corner.
% +
% NAME:
... |
github | farhi/idl2matlab-master | i2m_not.m | .m | idl2matlab-master/lib/matlablib/math/i2m_not.m | 670 | utf_8 | 5718ef48fda1953a03d1ff807cd5fcdf | % i2m_not
% --------------------------------------------
% Equivalent to :
% operator NOT
% in IDL
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% IDL2SCILAB Project
%
%--------------------------------------------------------
% ILL (Institut Laue Langevin)
%
% 380... |
github | farhi/idl2matlab-master | subsasgn.m | .m | idl2matlab-master/lib/matlablib/@varsysD/subsasgn.m | 1,382 | utf_8 | dbd969778e6eff7877f21c7af0111350 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% IDL2SCILAB Project
%
% --------------------------------------------------------
% ILL (Institut Laue Langevin)
%
% 38000 GRENOBLE Cedex
% --------------------------------------------------------
% Fonction : methode subsasgn (... |
github | farhi/idl2matlab-master | varsysD.m | .m | idl2matlab-master/lib/matlablib/@varsysD/varsysD.m | 1,292 | utf_8 | e4236ccbe0d9a360753da868e1c884e5 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% IDL2SCILAB Project
%
% --------------------------------------------------------
% ILL (Institut Laue Langevin)
%
% 38000 GRENOBLE Cedex
% --------------------------------------------------------
% Fonction : constructeur varsysD
% ... |
github | farhi/idl2matlab-master | subsref.m | .m | idl2matlab-master/lib/matlablib/@varsysD/subsref.m | 2,299 | utf_8 | 302c8fbfe7982c88138ef3612631a08f | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% IDL2SCILAB Project
%
% --------------------------------------------------------
% ILL (Institut Laue Langevin)
%
% 38000 GRENOBLE Cedex
% --------------------------------------------------------
% Fonction : fonction subsref (... |
github | farhi/idl2matlab-master | strtrim.m | .m | idl2matlab-master/lib/matlablib/strings/strtrim.m | 759 | utf_8 | 1fe33b90d218fa766df0554093c12808 | function str=strtrim(in,flag)
%function strtrim(str [,0|1|2])
%******** *******
%**
if isa(in,'i2mstr'), in =cellstr(in(:)); end;
if isempty(in), str=in([]); return; end;
if isnumeric(in), in =strung(in); end;
if (nargin == 1), str=deblank(in); return;
elseif (~flag) | (flag ==2); str=... |
github | farhi/idl2matlab-master | strmid.m | .m | idl2matlab-master/lib/matlablib/strings/strmid.m | 830 | utf_8 | 50af5028bd764ba718beb27f30778c53 | function str=strmid(in,pos,len)
%function strmid(str, pos [,length])
%******** ******
%**
if nargin >3, disp('!!! strmid.m has to be completed...'); end;
if isa(in,'i2mstr'); in=cellstr(in(:)); end;
if isempty(in), str=in([]); return; end;
if (nargin < 3), len=0; end;
if iscell(in) , str=cell(size(in)); ... |
github | farhi/idl2matlab-master | strtrimi.m | .m | idl2matlab-master/lib/matlablib/strings/strtrimi.m | 760 | utf_8 | d91c68fa15b27436e6b36251aa0aced1 | function str=strtrimi(in,flag)
%function strtrim(str [,0|1|2])
%******** *******
%**
if isa(in,'i2mstr'), in =cellstr(in(:)); end;
if isempty(in), str=in([]); return; end;
if isnumeric(in), in =strung(in); end;
if (nargin == 1), str=deblank(in); return;
elseif (~flag) | (flag ==2); str... |
github | farhi/idl2matlab-master | eq.m | .m | idl2matlab-master/lib/matlablib/@cell/eq.m | 592 | utf_8 | f3322d0f98670666214d6f3c955b63a2 | %== Equal.
% A == B does element by element comparisons between A and B
% and returns a matrix of the same size with elements set to one
% where the relation is true and elements set to zero where it is
% not. A and B must have the same dimensions unless one is a
% scalar. A scalar can be compared with... |
github | farhi/idl2matlab-master | subsindex.m | .m | idl2matlab-master/lib/matlablib/@cell/subsindex.m | 853 | utf_8 | 0526950da8c450fdda8c44af25746813 | %SUBSINDEX Subscript index.
% I = SUBSINDEX(A) is called for the syntax 'X(A)' when A is an
% object and X is one of the built-in types (most commonly
% 'double'). SUBSINDEX must return the value of the object as a
% zero-based integer index (I must contain integer values in the
% range 0 to prod(size(X... |
github | farhi/idl2matlab-master | ne.m | .m | idl2matlab-master/lib/matlablib/@cell/ne.m | 593 | utf_8 | 0d0ba7655d933f664d5e7667bc4f0818 | %== Equal.
% A == B does element by element comparisons between A and B
% and returns a matrix of the same size with elements set to one
% where the relation is true and elements set to zero where it is
% not. A and B must have the same dimensions unless one is a
% scalar. A scalar can be compared with... |
github | farhi/idl2matlab-master | num2str.m | .m | idl2matlab-master/lib/matlablib/@int32/@double/num2str.m | 2,416 | utf_8 | 3e99821f0fa946b404d40bdd69d14473 | function s = num2str(x, f)
%NUM2STR Convert number to string.
% T = NUM2STR(X) converts the matrix X into a string representation T
% with about 4 digits and an exponent if required. This is useful for
% labeling plots with the TITLE, XLABEL, YLABEL, and TEXT commands.
%
% T = NUM2STR(X,N) converts the matrix ... |
github | farhi/idl2matlab-master | eq.m | .m | idl2matlab-master/lib/matlablib/@int32/@char/eq.m | 576 | utf_8 | 47a25ab92fa7c7ba8a2e5a7e9b7bb934 | %== Equal.
% A == B does element by element comparisons between A and B
% and returns a matrix of the same size with elements set to one
% where the relation is true and elements set to zero where it is
% not. A and B must have the same dimensions unless one is a
% scalar. A scalar can be compared with anyt... |
github | farhi/idl2matlab-master | num2str.m | .m | idl2matlab-master/lib/matlablib/@int32/@char/num2str.m | 2,429 | utf_8 | 551cc2875e37df1e99fa48b96df82c40 | function s = num2str(x, f)
%NUM2STR Convert number to string.
% T = NUM2STR(X) converts the matrix X into a string representation T
% with about 4 digits and an exponent if required. This is useful for
% labeling plots with the TITLE, XLABEL, YLABEL, and TEXT commands.
%
% T = NUM2STR(X,N) converts the matrix ... |
github | farhi/idl2matlab-master | ne2.m | .m | idl2matlab-master/lib/matlablib/@int32/@char/ne2.m | 591 | utf_8 | d453543123ab23f6dc1dbcf265abcf14 | %~= Not equal.
% A ~= B does element by element comparisons between A and B
% and returns a matrix of the same size with elements set to one
% where the relation is true and elements set to zero where it is
% not. A and B must have the same dimensions unless one is a
% scalar. A scalar can be compared with ... |
github | farhi/idl2matlab-master | ne.m | .m | idl2matlab-master/lib/matlablib/@int32/@char/ne.m | 666 | utf_8 | b22dd8f1115888cc964f12afe601a036 | %~= Not equal.
% A ~= B does element by element comparisons between A and B
% and returns a matrix of the same size with elements set to one
% where the relation is true and elements set to zero where it is
% not. A and B must have the same dimensions unless one is a
% scalar. A scalar can be compared with ... |
github | farhi/idl2matlab-master | subsasgn.m | .m | idl2matlab-master/lib/matlablib/@varsysError/subsasgn.m | 2,009 | utf_8 | e837f00e37af904a798ea45bbca55fe4 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% IDL2SCILAB Project
%
% --------------------------------------------------------
% ILL (Institut Laue Langevin)
%
% 38000 GRENOBLE Cedex
% --------------------------------------------------------
% Fonction : methode subsasgn (varsysErr... |
github | farhi/idl2matlab-master | subsref.m | .m | idl2matlab-master/lib/matlablib/@varsysError/subsref.m | 1,995 | utf_8 | 353c0be3402e6f07ba515378dbd98a47 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% IDL2SCILAB Project
%
% --------------------------------------------------------
% ILL (Institut Laue Langevin)
%
% 38000 GRENOBLE Cedex
% --------------------------------------------------------
% Fonction : fonction subsref (varsysErr... |
github | farhi/idl2matlab-master | varsysError.m | .m | idl2matlab-master/lib/matlablib/@varsysError/varsysError.m | 1,093 | utf_8 | b808e64c9877b6fca07f36dea90dfcfb | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% IDL2SCILAB Project
%
% --------------------------------------------------------
% ILL (Institut Laue Langevin)
%
% 38000 GRENOBLE Cedex
% --------------------------------------------------------
% Fonction : constructeur varsysError
% ... |
github | farhi/idl2matlab-master | eq.m | .m | idl2matlab-master/lib/matlablib/@i2mstr/eq.m | 664 | utf_8 | 5ef1f4cc059318d4fb19ef85d681af23 | %== Equal.
% A == B does element by element comparisons between A and B
% and returns a matrix of the same size with elements set to one
% where the relation is true and elements set to zero where it is
% not. A and B must have the same dimensions unless one is a
% scalar. A scalar can be compared with... |
github | farhi/idl2matlab-master | end.m | .m | idl2matlab-master/lib/matlablib/@i2mstr/end.m | 1,105 | utf_8 | 7845ad6e4a79db71aceb343c5abcd9cc | %END Terminate scope of FOR, WHILE, SWITCH, TRY, and IF statements.
% Without END's, FOR, WHILE, SWITCH, TRY, and IF wait for further input.
% Each END is paired with the closest previous unpaired FOR, WHILE,
% SWITCH, TRY or IF and serves to terminate its scope.
%
% END can also serve as the last index in... |
github | farhi/idl2matlab-master | subsindex.m | .m | idl2matlab-master/lib/matlablib/@i2mstr/subsindex.m | 863 | utf_8 | c3dd5db61e239a1d1dd1ded9a23909ed | %SUBSINDEX Subscript index.
% I = SUBSINDEX(A) is called for the syntax 'X(A)' when A is an
% object and X is one of the built-in types (most commonly
% 'double'). SUBSINDEX must return the value of the object as a
% zero-based integer index (I must contain integer values in the
% range 0 to prod(size(X... |
github | farhi/idl2matlab-master | subsasgn.m | .m | idl2matlab-master/lib/matlablib/@i2mstr/subsasgn.m | 3,188 | utf_8 | 22cdb1379f353844e0320c7d88243915 | %SUBSASGN Subscripted assignment.
% A(I) = B assigns the values of B into the elements of A specifed by
% the subscript vector I. B must have the same number of elements as I
% or be a scalar.
%
% A(I,J) = B assigns the values of B into the elements of the
% rectangular submatrix of A specified by the... |
github | farhi/idl2matlab-master | subsref.m | .m | idl2matlab-master/lib/matlablib/@i2mstr/subsref.m | 3,192 | utf_8 | d019dbb98f28cb198afa992fb5356891 | %SUBSREF Subscripted reference.
% A(I) is an array formed from the elements of A specifed by the
% subscript vector I. The resulting array is the same size as I except
% for the special case where A and I are both vectors. In this case,
% A(I) has the same number of elements as I but has the orientation o... |
github | farhi/idl2matlab-master | ne.m | .m | idl2matlab-master/lib/matlablib/@i2mstr/ne.m | 665 | utf_8 | 76232dcbf26642a28ab48c829b23cddd | %== Equal.
% A == B does element by element comparisons between A and B
% and returns a matrix of the same size with elements set to one
% where the relation is true and elements set to zero where it is
% not. A and B must have the same dimensions unless one is a
% scalar. A scalar can be compared with... |
github | farhi/idl2matlab-master | dial_mydial3.m | .m | idl2matlab-master/pro/dial/dial_mydial3.m | 161 | utf_8 | 21a48aac3133e257a94d4846f491232b | %**********************
function d=dial_mydial3
%**********************
%**
d=struct('frequency',1.5, 'duration',0,'fig',0,'cnt',0,'peaks',peaks(20),'h',0);
|
github | farhi/idl2matlab-master | dial_mydial_macro.m | .m | idl2matlab-master/pro/dial/dial_mydial_macro.m | 788 | utf_8 | 9acbdc000950b6380184b5c5421eafbc | %***************************
function d=dial_mydial_macro(d)
%***************************
%**
if d.init == 0; d.init=1; disp([d.name 'First call']);
d.frequency=2.;
end;
if (xregistered(d.name) <= 0) | (d.fig <= 0);
bas =widget_base ('title',d.name);
d.fig=widget_draw ('I2M_a1',bas,'xsize',300,'y... |
github | farhi/idl2matlab-master | dial_mydial3_macro.m | .m | idl2matlab-master/pro/dial/dial_mydial3_macro.m | 691 | utf_8 | afe7817e59dd65c6f6d64a2c1324c6ee | %***************************
function d=dial_mydial3_macro(d)
%***************************
%**
if d.init == 0; d.init=1;
d.frequency=2.;
disp([d.name 'First call']); end;
if (xregistered(d.name) <= 0) | (d.fig <= 0);
bas =widget_base ('title',d.name);
d.fig=widget_draw ('I2M_a1',bas,'xsize',300... |
github | farhi/idl2matlab-master | dial_mydial.m | .m | idl2matlab-master/pro/dial/dial_mydial.m | 134 | utf_8 | b9f9cfa5547b6532702daabe887003bf | %*********************
function d=dial_mydial
%*********************
%**
d=struct('frequency',1.5, 'duration',0,'fig',0,'cnt',0);
|
github | farhi/idl2matlab-master | dial_mydial2_macro.m | .m | idl2matlab-master/pro/dial/dial_mydial2_macro.m | 871 | utf_8 | ff238a87d980fb7c72bcdfc836eef091 | %***************************
function d=dial_mydial2_macro(d)
%***************************
%**
if d.init == 0; d.init=1; disp([d.name 'First call']);
nam=''; nam=dialtag('I2M_a1','mydial','tag','name','get',nam,'I2M_pos',3);
if isempty(nam), dialinit ('mydial'); else, dialstart('mydial'); end;
... |
github | farhi/idl2matlab-master | dial_mydial2.m | .m | idl2matlab-master/pro/dial/dial_mydial2.m | 135 | utf_8 | 1fa0adac6a5067ee58e5132e5b274786 | %*********************
function d=dial_mydial2
%*********************
%**
d=struct('frequency',1.5, 'duration',0,'fig',0,'cnt',0);
|
github | mave5/LV-segmentation-in-cardiac-MRI-master | ac_seg.m | .m | LV-segmentation-in-cardiac-MRI-master/functions/ac_seg.m | 5,813 | utf_8 | 8cc52d39549208731b89da02131aee08 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% I : input image
% init_mask: initial mask
% max_its : maximum iterations
% lengthEweight : weight of the length energy
% shapeEweight : weight of the shape energy
% display : display
% This code was edited for LV segmentation in MRI from ... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | showCurveAndPhi.m | .m | LV-segmentation-in-cardiac-MRI-master/functions/showCurveAndPhi.m | 472 | utf_8 | 0c18dfcfd34599e42177aa01baf16660 |
% show image, contours
function showCurveAndPhi(varargin)
I=varargin{1};
max_range=min(255,max(I(:)));
imshow(I,'initialmagnification',200,'displayrange',[0 max_range]); hold on;
colsty1=['g';'r';'b';'k';'y'];
for k=1:nargin-1
B1=varargin{k+1};
if size(B1,1)==2 || size(B1,2)==2
plot(B1(:,1),B1(:,2),... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | drlse_edge.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/drlse_edge.m | 3,521 | utf_8 | 7df43ccaa0e968c13b70fb5557115c9c | function phi = drlse_edge(phi_0, g, lambda,mu, alfa, epsilon, timestep, iter, potentialFunction)
% This Matlab code implements an edge-based active contour model as an
% application of the Distance Regularized Level Set Evolution (DRLSE) formulation in Li et al's paper:
%
% C. Li, C. Xu, C. Gui, M. D. Fox, "Dist... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | mask2subImage.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/mask2subImage.m | 724 | utf_8 | d14d9c61048e37fcddd0fcd64aa43914 | % this function takes a mask, an image I, and a size M, and extracts an M*M
% sub-image I_sub centered at cnt.
function [I_sub,cnt]=mask2subImage(I,mask,M)
% inputs
% I : original image
% mask : mask should be the same size of the original image
% M : size of ROI
% output
% I_sub : sub image centered at cnt
% cnt : c... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | compare_contours.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/compare_contours.m | 37,808 | utf_8 | f46e12e4d2ec7bfba5679202c09ec290 | function compare_result = compare_contours(dicom_path,manual_contour_path,auto_contour_path,para)
%COMPARE_CONTOURS Compare manual rawn contours with auto contours
% COMPARE_CONTOURS(DICOM_PATH,MANUAL_CONTOUR_PATH,AUTO_CONTOUR_PATH)
%
% Copyright: Imaging Research, Sunnybrook Health Sciences Centre, Toronto, ON, Ca... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | gatherImages.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/gatherImages.m | 1,463 | utf_8 | f054cd7d14414bb701a757aeeab9a32f | function [threedarray, xthickness, ythickness, zthickness] = gatherImages(folder)
%GATHERIMAGES looks through a folder, gets all DICOM files and assembles
%them into a viewable 3d format.
currentfolder=pwd;
d = sortDirectory(folder); %Sort in ascending order of instance number
topimage = dicomread(cell2mat(d(1,:)));
me... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | region_segLargePhi.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/region_segLargePhi.m | 6,942 | utf_8 | e10dd58e944923aa53519e0b943688b0 | % Region Based Active Contour Segmentation
%
% seg = region_seg(I,init_mask,max_its,alpha,display)
%
% Inputs: I 2D image
% init_mask Initialization (1 = foreground, 0 = bg)
% max_its Number of iterations to run segmentation for
% alpha (optional) Weight of smoothing term
%... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | disp3d.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/disp3d.m | 543 | utf_8 | 88a5f802d04a13e295642594de4ff76a |
function disp3d(V,filter_size)
if nargin==1
filter_size=9;
end
if filter_size==0
D=V;
else
D = smooth3(V,'box',filter_size);
%D = smooth3(V,'gaussian',filter_size);
end
%patch(isocaps(V,.5),...
% 'FaceColor','interp','EdgeColor','none');
p1 = patch(isosurface(D,.5),...
'FaceColor','red','EdgeCo... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | remap_mask.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/remap_mask.m | 428 | utf_8 | a3b1ecae2772ff70408a2136b5456baf | %-- re-map small mask to big mask
function out_mask = remap_mask(in_mask,m_cnt,I)
[y_max, x_max]=size(I);
M=size(in_mask,1);
% center
m_cnt_x=m_cnt(1);
m_cnt_y=m_cnt(2);
% top left corner
x1=m_cnt_x-M/2;
y1=m_cnt_y-M/2;
% bottom right corner
x4=m_cnt_x+M... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | scaleContour.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/scaleContour.m | 224 | utf_8 | a5a7c7bb2e0a6703a1b771154ee5d478 |
% scale a given contour based on a center and widow size
function Ct=scaleContour(C,cnt,M)
% C : input contour
% cnt : center
% M : window size
Ctx=C(:,1)-cnt(1)+M/2+.5;
Cty=C(:,2)-cnt(2)+M/2+.5;
Ct=[Ctx,Cty];
end
|
github | mave5/LV-segmentation-in-cardiac-MRI-master | checkNumericalGradient.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/checkNumericalGradient.m | 1,982 | utf_8 | 689a352eb2927b0838af5dc508f6374d | function [] = checkNumericalGradient()
% This code can be used to check your numerical gradient implementation
% in computeNumericalGradient.m
% It analytically evaluates the gradient of a very simple function called
% simpleQuadraticFunction (see below) and compares the result with your numerical
% solution. Your num... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | remap_mask_cnt.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/remap_mask_cnt.m | 720 | utf_8 | 3fd2da82a0be39edb9af528f11102262 | %-- roi mask will be converted to the original image size
function out_mask = remap_mask_cnt(in_mask,I,m_cnt)
[y_max, x_max]=size(I);
M=size(in_mask,1);
M2=floor(M/2);
% center
m_cnt_x=m_cnt(1);
m_cnt_y=m_cnt(2);
% top left corner
x1=max(m_cnt_x-M2,1);
y1... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | region_edge_seg_2D.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/region_edge_seg_2D.m | 6,904 | utf_8 | a5098d6188b288b9c1e09f7aa9980064 |
function [seg,phi] = region_edge_seg_2D(I,phi,max_its,option,display)
% Initialization %-- default value for parameter alpha is .1
if(~exist('display','var'))
display = true;
end
% load parameters of Deep Learning Netowrks
load DBNparams.mat;
Mroi=100;
% Edge detector parameters.
sigma = .8;
EdgeFactor ... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | DiceSimilarity2DImage.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/DiceSimilarity2DImage.m | 1,283 | utf_8 | fb87eac6c1472723e9f07d6d6747295d | %by Dr. Rex Cheung
%cheung.r100@gmail.com
%This programs calculate and visualize the dice similarity (volume overlap) of 2D binary images.
%This program is useful for quantifying the accuracy of 2D image
%registration using DICE similarity (see wikipedia):
% Dice Coef = 2*intersect(A,B)/(absolute(A)... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | calc_dm.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/calc_dm.m | 519 | utf_8 | 567bd08e09c9b04834245beda56294bd | % calculate dice metric from contours
function dm = calc_dm(autoPoints,manualPoints,para)
%calc dice metric
auto_mask = poly2mask (autoPoints(:,1),autoPoints(:,2),double(para.width),double(para.height));
manual_mask = poly2mask (manualPoints(:,1),manualPoints(:,2),double(para.width),double(para.height));
... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | smoothn.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/smoothn.m | 27,268 | utf_8 | 79b27d7c9e55777c1917a2a248e0614e | function [z,s,exitflag] = smoothn(varargin)
%SMOOTHN Robust spline smoothing for 1-D to N-D data.
% SMOOTHN provides a fast, automatized and robust discretized spline
% smoothing for data of arbitrary dimension.
%
% Z = SMOOTHN(Y) automatically smoothes the uniformly-sampled array Y. Y
% can be any N-D noisy a... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | get_dicominfo.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/get_dicominfo.m | 778 | utf_8 | 0a33d64d3ead20bd6017c74ae6c298b0 |
function para=get_dicominfo(dicom_path)
% find dcm images
dicom_path1=[dicom_path,'/*.dcm'];
dicom_files=dir(dicom_path1);
%-dicominfo
try
dicom_filename = dicom_files(1).name; %use the first dicom file.
full_dicom_filename = [dicom_path filesep dicom_filename];
dicom_meta= dicominfo(full_dicom_filename);
... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | resize_phi.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/resize_phi.m | 548 | utf_8 | 6d6156dae2b90ac6e27e73d16010911e | %-- converts a mask to a Signed Distance Function (SDF)
function [phi_r,mask_r] = resize_phi(phi,m_cnt,I)
[x_max, y_max]=size(I);
mask=phi<=0;
M=size(mask,1);
% center
m_cnt_x=m_cnt(1);
m_cnt_y=m_cnt(2);
% top left corner
x1=m_cnt_x-M/2;
y1=m_cnt_y-M/2;
... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | otsu.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/otsu.m | 5,841 | utf_8 | 03de3d4d2d403bd2577af0a3e4e1c855 | function [IDX,sep] = otsu(I,n)
%OTSU Global image thresholding/segmentation using Otsu's method.
% IDX = OTSU(I,N) segments the image I into N classes by means of Otsu's
% N-thresholding method. OTSU returns an array IDX containing the cluster
% indices (from 1 to N) of each point. Zero values are assigned to
% ... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | region_seg_subPhi.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/region_seg_subPhi.m | 7,024 | utf_8 | 2063a4aadf8711df468c5cb8b37ca92b | % Region Based Active Contour Segmentation
%
% seg = region_seg(I,init_mask,max_its,alpha,display)
%
% Inputs: I 2D image
% init_mask Initialization (1 = foreground, 0 = bg)
% max_its Number of iterations to run segmentation for
% alpha (optional) Weight of smoothing term
%... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | sparseAutoencoderCost.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/sparseAutoencoderCost.m | 4,250 | utf_8 | 3c8e2ca653d09eb0ba9c2b4b9edf6a39 | function [cost,grad] = sparseAutoencoderCost(theta, visibleSize, hiddenSize, ...
lambda, sparsityParam, beta, data)
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% lambda: weight decay parameter
% sparsityPara... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | eval_metrics.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/eval_metrics.m | 949 | utf_8 | 2025c9155f5c6e27a8884fd4216a4436 | % compute metrics
function [dm1,dm2,PD1,PD2,HD1,HD2]=eval_metrics(I,m_cnt,auto_seg,manualPoints,para)
% resize mask in the original image size
auto_seg_r=remap_mask(auto_seg,m_cnt,I);
% convex hull of resized mask
c_auto_seg_r=bwconvhull(auto_seg_r);
% convert mask to contour
auto... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | edge_region_seg_subPhi.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/edge_region_seg_subPhi.m | 7,576 | utf_8 | 8b6f68f3b080018cb8784a597e4fe248 | % Region Based Active Contour Segmentation
%
% seg = region_seg(I,init_mask,max_its,alpha,display)
%
% Inputs: I 2D image
% init_mask Initialization (1 = foreground, 0 = bg)
% max_its Number of iterations to run segmentation for
% alpha (optional) Weight of smoothing term
%... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | get_curvature_edge.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/get_curvature_edge.m | 4,425 | utf_8 | 17e1c19d24a6a7f6f40a45bfa8416fee | % compute gradient of internal energy function and edge factor
% note that gradient of internal energy is equal to contour curvature
function [curvature EdgeTerm] = get_curvature_edge(phi,idx,g,gx,gy,gz)
[dimy, dimx, dimz] = size(phi);
[y x z] = ind2sub([dimy,dimx,dimz],idx); % get subscripts
%-... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | sampleIMAGES.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/sampleIMAGES.m | 1,373 | utf_8 | 8b7e89a56ca36b0fee68ae134f3d33bf | function patches = sampleIMAGES(IMAGES,patchsize,norm_ena)
% sampleIMAGES
% inputs
% numpatches: for example 1E4
% patchsize% for example 8*8
% IMAGES: images in a 3D matrix
% output
% patches % a vector of randomly chosen patches
if nargin==2
norm_ena=1;
end
visibleSize = patchsize*patchsize; % number of input u... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | stackedAEPredict.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/stackedAEPredict.m | 1,479 | utf_8 | 35c0f897cceaa9dbd880b330cd48e752 | function [pred] = stackedAEPredict(theta, inputSize, hiddenSize, numClasses, netconfig, data)
% stackedAEPredict: Takes a trained theta and a test data set,
% and returns the predicted labels for each example.
% theta: trained weights from the autoencoder
% visibleSize: the number of input units
% hiddenSize: the nu... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | disImgs.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/disImgs.m | 302 | utf_8 | efb21e1458dd06485493950dec353d91 | % display gray images
function disImgs(I,num)
% I : input images as a 2D/3D matrix
% number of images to displayed
if nargin==1
num=1;
end
[x y z]=size(I);
npx=ceil(num/5);
npy=max([ceil(num/5),5]);
figure
for k=1:num
subplot(npx,npy,k);
imagesc(I(:,:,k));
colormap(gray);
end
end |
github | mave5/LV-segmentation-in-cardiac-MRI-master | feedForwardAutoencoder.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/feedForwardAutoencoder.m | 1,298 | utf_8 | 77150d8ab6946a03feba1e661b52e2c6 | function [activation] = feedForwardAutoencoder(theta, hiddenSize, visibleSize, data)
% theta: trained weights from the autoencoder
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% data: Our matrix containing the training data as columns. So, data(:,i) i... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | mrCost.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/mrCost.m | 1,390 | utf_8 | 0aac09a88e283a19d48abe2a35067aa1 | % softmaxCost.m
function [cost, grad] = mrCost(theta, numOuts, inputSize, lambda, data, labels)
% numOuts - the number of number of outputs
% inputSize - the size N of the input vector
% lambda - weight decay parameter
% data - the N x M input matrix, where each column data(:, i) corresponds to
% a single test set
%... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | edit_prior_shape.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/edit_prior_shape.m | 890 | utf_8 | 855361ba62de1d01b352442e394972d8 |
% prior shape might be inaccurate for bottom slices
% to get better prior shape, we use intersection between otsu and prior
% shape
function CH=edit_prior_shape(I,prior,disp_ena)
prior=logical(bwconvhull(prior));
I1=I.*prior;
% R1=40:60;
% I2=I1(R1,R1);
% imshow(I2,[0 255])
% ot1=otsu(I2)>1;
% imshow(ot1)
% otsu t... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | stackedAECost.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/stackedAECost.m | 4,020 | utf_8 | 8da2731df06a54386b1a23e3c8c1e933 | function [ cost, grad ] = stackedAECost(theta, inputSize, hiddenSize, ...
outputSize, netconfig, ...
lambda, data, labels)
% stackedAECost: Takes a trained softmaxTheta and a training da... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | intersampleIMAGES.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/intersampleIMAGES.m | 2,378 | utf_8 | 1634fee0126c570641603ca878e1ceef | % sampleIMAGES.m
% sampling patches for learning
function patches = sampleIMAGES(numpatches)
% sampleIMAGES
% Returns 10000 patches for training
load IMAGES; % load images from disk
patchsize = 8; % we'll use 8x8 patches
%numpatches = 10000;
% Initialize patches with zeros. Your code will fill in this matrix--one
% c... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | mrPredict.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/mrPredict.m | 1,077 | utf_8 | 9d945bee2592d5e609a1b69472ce2660 | function [pred] = mrPredict(mrModel, data)
% mrModel - model trained using mrTrain
% data - the N x M input matrix, where each column data(:, i) corresponds to
% a single test set
%
% Your code should produce the prediction matrix
% pred, where pred(i) is argmax_c P(y(c) | x(i)).
% Unroll the parameters fro... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | save_contours.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/save_contours.m | 926 | utf_8 | 429647bd46bfd69ce78b129d2e4a584a | % save contours into txt files
function output=save_contours(masks,t_cont_names,slice_per_patient)
% get number of studies
num_studies=length(slice_per_patient);
for k=1:num_studies
% get number of slices per patient
spp=slice_per_patient(k);
% make a new directory
dirn=['matFiles/auto_contours... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | save_contours2.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/save_contours2.m | 524 | utf_8 | c86f54f93c2b991fa964ec331fd1a646 | % save contours into txt files
function save_contours2(auto_mask,cotourfilename)
% get manual contour file name
%origStr=char(cont_name);
% replace manual with auto in the name
%modifiedStr = strrep(origStr, 'manual', 'auto');
% convert mask to contours
temp=(contourc(auto_mask,[0 0... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | DLN.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/DLN.m | 720 | utf_8 | 8212ea47a9f04ae67a98266a794174c8 | % this function takes the image, and optimized parameters of the deep
% learning network and outputs a mask
function y=DLN(I,parameters,inputSize,hiddenSizeL1,hiddenSizeL2,outputSize,netconfig)
% I : image to be masked
% parameters: the learned/optimized parameters
% inputSize : the visible size
% hiddenSizeL1 and L2... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | showCurveAndPhi.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/showCurveAndPhi.m | 511 | utf_8 | f7521a43f290d99a2dba301577a491a7 |
% show image, contours
function showCurveAndPhi(varargin)
I=varargin{1};
if iscell(I)
I=cell2mat(I);
end
max_range=min(255,max(I(:)));
imshow(I,'initialmagnification',200,'displayrange',[0 max_range]); hold on;
colsty1=['g';'r';'b';'y';'k'];
for k=1:nargin-1
B1=varargin{k+1};
if size(B1,1)==2 || size... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | png2contour.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/png2contour.m | 332 | utf_8 | c5ed1a9739aa8cf5d62105c00216255d |
function [contour,I1]=png2contour(I)
A=rgb2gray(I);
[x1,y1]=size(A);
I1=A(:,501:end-502);
[x,y]=size(I1);
count=0;
for i=1:x
for j=1:y
if I1(i,j)==150
count=count+1;
new_x(count)=j;
new_y(count)=i;
end
end
end
contour(:,1)=new_x*256/x1;
contour(:,2)=new_y*... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | mask2phi.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/functions/mask2phi.m | 536 | utf_8 | 4a2b69923ef9c538671521e9257aee11 | %-- converts a mask to a Signed Distance Function (SDF)
function phi = mask2phi(mask,m_cnt,I)
if nargin==3
[x_max, y_max]=size(I);
M=size(mask,1);
mask2=zeros(x_max,y_max);
% coordinates of the top and bottom corners
m_cnt_y=m_cnt(1);
m_cnt_x=m_cnt(2);
% top left corner
x1=m... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | WolfeLineSearch.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/minFunc/WolfeLineSearch.m | 11,106 | utf_8 | f97d9ca0bf8aab87df9aa65e74f98589 | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS,maxLS,tolX,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at starting lo... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | minFunc_processInputOptions.m | .m | LV-segmentation-in-cardiac-MRI-master/combinedDeepLearningActiveContour/minFunc/minFunc_processInputOptions.m | 3,551 | utf_8 | ea7fbcf303b9cafeca4045921adad934 |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,tolFun,tolX,method,...
corrections,c1,c2,LS_init,LS,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
DerivativeCheck,Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,useNegCu... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | compare_contours.m | .m | LV-segmentation-in-cardiac-MRI-master/LV_evaluation_code/functions/compare_contours.m | 38,443 | utf_8 | 15d7769503f7c908a73da06346afcfe9 | function compare_result = compare_contours(dicom_path,manual_contour_path,auto_contour_path,para)
%COMPARE_CONTOURS Compare manual rawn contours with auto contours
% COMPARE_CONTOURS(DICOM_PATH,MANUAL_CONTOUR_PATH,AUTO_CONTOUR_PATH)
%
% Copyright: Imaging Research, Sunnybrook Health Sciences Centre, Toronto, ON, Ca... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | BlandAltman.m | .m | LV-segmentation-in-cardiac-MRI-master/LV_evaluation_code/functions/BlandAltman.m | 13,695 | utf_8 | 1c5f7bbdfbefda432800e94f39128701 | % BlandAltman - draws a Blant-Altman and correlation graph for two
% datasets.
%
% BlandAltman(data1, data2)
% BlandAltman(data1, data2,label) - Names of data sets. Formats can be
% - {'Name1'}
% - {'Name1, 'Name2'}
% - {'Name1, 'Name2', 'Units'}
% BlandAltman(data1, data2,label,tit,gnames)
% BlandAltman(data1, d... |
github | mave5/LV-segmentation-in-cardiac-MRI-master | Correlation.m | .m | LV-segmentation-in-cardiac-MRI-master/LV_evaluation_code/functions/Correlation.m | 5,053 | utf_8 | 6c08747dbfdc9e170522602bb99fd9d7 | % Correlation - draws a correlation graph for two datasets.
%
% Correlation(data1, data2)
% Correlation(data1, data2,label) -
% Correlation(data1, data2,label,tit,gnames)
% Correlation(data1, data2,label,tit,gnames,corrinfo) - specifies what
% information to display on the correlation chart as a cell of string in
% o... |
github | happier9216/face-caffe-master | prepare_batch.m | .m | face-caffe-master/matlab/caffe/prepare_batch.m | 1,298 | utf_8 | 68088231982895c248aef25b4886eab0 | % ------------------------------------------------------------------------
function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
% ------------------------------------------------------------------------
if nargin < 2
d = load('ilsvrc_2012_mean');
IMAGE_MEAN = d.image_mean;
end
num_images = length... |
github | happier9216/face-caffe-master | matcaffe_demo_vgg.m | .m | face-caffe-master/matlab/caffe/matcaffe_demo_vgg.m | 3,036 | utf_8 | f836eefad26027ac1be6e24421b59543 | function scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
%
% Demo of the matlab wrapper using the networks described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional... |
github | happier9216/face-caffe-master | matcaffe_demo.m | .m | face-caffe-master/matlab/caffe/matcaffe_demo.m | 3,344 | utf_8 | 669622769508a684210d164ac749a614 | function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
% scores = matcaffe_demo(im, use_gpu)
%
% Demo of the matlab wrapper using the ILSVRC network.
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
%
% You m... |
github | happier9216/face-caffe-master | matcaffe_demo_vgg_mean_pix.m | .m | face-caffe-master/matlab/caffe/matcaffe_demo_vgg_mean_pix.m | 3,069 | utf_8 | 04b831d0f205ef0932c4f3cfa930d6f9 | function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
%
% Demo of the matlab wrapper based on the networks used for the "VGG" entry
% in the ILSVRC-2014 competition and described in the tech. report
% "Very Deep Convo... |
github | GEMScienceTools/rmtk-master | test_damage_to_loss.m | .m | rmtk-master/tests/vulnerability/tests_TO_BE_CHANGED/common/test_damage_to_loss.m | 742 | utf_8 | ff26ad68dfdc8ed1578af1d6ae8ad494 | clc
clear all
close all
function [ LR ] = damage_to_loss( SaT, bTSa, samp, iml )
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
cons = dlmread('inputs/consequence.tcl');
for x=1:length(iml)
for i=1:length(SaT)
if bTSa{i}(samp)==0
if iml(x)<exp(SaT{i}(samp)); poe(i)=0; else poe(i) = 1; ... |
github | ethz-asl/StructuralInspectionPlanner-master | make.m | .m | StructuralInspectionPlanner-master/optec/interfaces/simulink/make.m | 7,612 | utf_8 | 52873831f575a8899b490781208d0893 | function [] = make( varargin )
%MAKE Compiles the Simulink interface of qpOASES.
%
%Type make to compile all interfaces that
% have been modified,
%type make clean to delete all compiled interfaces,
%type make clean all to first delete and then compile
% ... |
github | ethz-asl/StructuralInspectionPlanner-master | qpOASES_options.m | .m | StructuralInspectionPlanner-master/optec/interfaces/octave/qpOASES_options.m | 10,261 | utf_8 | 071f6bcc5ad81ccfcfcce84248f3c1d0 | %qpOASES -- An Implementation of the Online Active Set Strategy.
%Copyright (C) 2007-2014 by Hans Joachim Ferreau, Andreas Potschka,
%Christian Kirches et al. All rights reserved.
%
%qpOASES is distributed under the terms of the
%GNU Lesser General Public License 2.1 in the hope that it will be
%useful, but WITHOUT ANY... |
github | ethz-asl/StructuralInspectionPlanner-master | make.m | .m | StructuralInspectionPlanner-master/optec/interfaces/octave/make.m | 7,435 | utf_8 | b2f7e7783412981f58cd9217586f7213 | function [] = make( varargin )
%MAKE Compiles the octave interface of qpOASES.
%
%Type make to compile all interfaces that
% have been modified,
%type make clean to delete all compiled interfaces,
%type make clean all to first delete and then compile
% ... |
github | ethz-asl/StructuralInspectionPlanner-master | qpOASES_options.m | .m | StructuralInspectionPlanner-master/optec/interfaces/matlab/qpOASES_options.m | 10,261 | utf_8 | 071f6bcc5ad81ccfcfcce84248f3c1d0 | %qpOASES -- An Implementation of the Online Active Set Strategy.
%Copyright (C) 2007-2014 by Hans Joachim Ferreau, Andreas Potschka,
%Christian Kirches et al. All rights reserved.
%
%qpOASES is distributed under the terms of the
%GNU Lesser General Public License 2.1 in the hope that it will be
%useful, but WITHOUT ANY... |
github | ethz-asl/StructuralInspectionPlanner-master | make.m | .m | StructuralInspectionPlanner-master/optec/interfaces/matlab/make.m | 7,384 | utf_8 | 55ca559bdc1cf6230d7245bc91bfa5db | function [] = make( varargin )
%MAKE Compiles the Matlab interface of qpOASES.
%
%Type make to compile all interfaces that
% have been modified,
%type make clean to delete all compiled interfaces,
%type make clean all to first delete and then compile
% ... |
github | UCSD-E4E/aerial_lidar-master | arrow.m | .m | aerial_lidar-master/displayQuatCurve/test_files/arrow.m | 55,316 | utf_8 | a9fba6cb870e440f70d88c6a4849cb63 | function [h,yy,zz] = arrow(varargin)
% ARROW Draw a line with an arrowhead.
%
% ARROW(Start,Stop) draws a line with an arrow from Start to Stop (points
% should be vectors of length 2 or 3, or matrices with 2 or 3
% columns), and returns the graphics handle of the arrow(s).
%
% ARROW uses the mouse (cl... |
github | UCSD-E4E/aerial_lidar-master | displayQuatCurve.m | .m | aerial_lidar-master/displayQuatCurve/test_files/displayQuatCurve.m | 5,721 | ibm852 | 90095ef68f76b01260d1c9df023534a3 | function env = displayQuatCurve( r, q, axis_order )
%DISPLAYROD Displays a curve given by r with the corresponding
% director triads for each point in r.
%INPUT r 3xN matix, where N is the total count of points.
% r(1,:) x-axis, r(2,:) x-axis, r(3,:) x-axis.
% q 4xN matrix, N ... |
github | nickabattista/IB2d-master | BeamCurve.m | .m | IB2d-master/pyIB2d/Examples/Wobbly_Beam/BeamCurve.m | 5,907 | utf_8 | e71df69130493dcbb8385dcb6c783ddf | %-------------------------------------------------------------------------------------------------------------------%
%
% IB2d is an Immersed Boundary Code (IB) for solving fully coupled non-linear
% fluid-structure interaction models. This version of the code is based off of
% Peskin's Immersed Boundary Method Paper... |
github | nickabattista/IB2d-master | Make_Tub_Geometry_and_Initial_Concentration.m | .m | IB2d-master/pyIB2d/Examples/Rayleigh_Taylor_Instability/Make_Tub_Geometry_and_Initial_Concentration.m | 11,774 | utf_8 | e59d229d3538e4ba2f58fb2c0988a7a3 | %-------------------------------------------------------------------------------------------------------------------%
%
% IB2d is an Immersed Boundary Code (IB) for solving fully coupled non-linear
% fluid-structure interaction models. This version of the code is based off of
% Peskin's Immersed Boundary Method Paper... |
github | nickabattista/IB2d-master | please_Compute_External_Forcing.m | .m | IB2d-master/pyIB2d/Examples/Rayleigh_Taylor_Instability/please_Compute_External_Forcing.m | 6,262 | utf_8 | 80af5c95699cb52c18bc5297b798ca6d | %-------------------------------------------------------------------------------------------------------------------%
%
% IB2d is an Immersed Boundary Code (IB) for solving fully coupled non-linear
% fluid-structure interaction models. This version of the code is based off of
% Peskin's Immersed Boundary Method Paper... |
github | nickabattista/IB2d-master | main2d.m | .m | IB2d-master/pyIB2d/Examples/Thrips/main2d.m | 5,909 | utf_8 | aa95163214ee32132c54ae84bf9c4c98 | %-------------------------------------------------------------------------------------------------------------------%
%
% IB2d is an Immersed Boundary Code (IB) for solving fully coupled non-linear
% fluid-structure interaction models. This version of the code is based off of
% Peskin's Immersed Boundary Method Paper... |
github | nickabattista/IB2d-master | Channel_Channel.m | .m | IB2d-master/pyIB2d/Examples/Turek_Hron/Channel_Channel.m | 8,516 | utf_8 | b95b24e9dc6c20778223a3e5653bf985 | %-------------------------------------------------------------------------------------------------------------------%
%
% IB2d is an Immersed Boundary Code (IB) for solving fully coupled non-linear
% fluid-structure interaction models. This version of the code is based off of
% Peskin's Immersed Boundary Method Paper... |
github | nickabattista/IB2d-master | BeamCurve.m | .m | IB2d-master/pyIB2d/Examples/Wobbly_NonInv_Beam/BeamCurve.m | 7,067 | utf_8 | ec07faf3d11f059de58666b550d6e63b | %-------------------------------------------------------------------------------------------------------------------%
%
% IB2d is an Immersed Boundary Code (IB) for solving fully coupled non-linear
% fluid-structure interaction models. This version of the code is based off of
% Peskin's Immersed Boundary Method Paper... |
github | nickabattista/IB2d-master | main2d.m | .m | IB2d-master/pyIB2d/Examples/MyFailedExperiment_Matlab/main2d.m | 6,183 | utf_8 | e1bf34aecbe2e564880dc7b71905641a | %-------------------------------------------------------------------------------------------------------------------%
%
% IB2d is an Immersed Boundary Code (IB) for solving fully coupled non-linear
% fluid-structure interaction models. This version of the code is based off of
% Peskin's Immersed Boundary Method P... |
github | nickabattista/IB2d-master | import_Eulerian_Data.m | .m | IB2d-master/data_analysis/analysis_in_matlab/DA_Blackbox/import_Eulerian_Data.m | 3,666 | utf_8 | e8b9ece7a69a3bab3dbd60ca2f9273b6 | %-------------------------------------------------------------------------------------------------------------------%
%
% IB2d is an Immersed Boundary Code (IB) for solving fully coupled
% fluid-structure interaction models. This version of the code is based off of
% Peskin's Immersed Boundary Method Paper in Acta N... |
github | nickabattista/IB2d-master | import_Lagrangian_Force_Data_Insect.m | .m | IB2d-master/data_analysis/analysis_in_matlab/DA_Blackbox/import_Lagrangian_Force_Data_Insect.m | 2,014 | utf_8 | a10ab2e93e56d0df8bfe6cee8ba959d3 | %-------------------------------------------------------------------------------------------------------------------%
%
% IB2d is an Immersed Boundary Code (IB) for solving fully coupled
% fluid-structure interaction models. This version of the code is based off of
% Peskin's Immersed Boundary Method Paper in Acta N... |
github | nickabattista/IB2d-master | import_Lagrangian_Force_Data_Pendulum.m | .m | IB2d-master/data_analysis/analysis_in_matlab/DA_Blackbox/import_Lagrangian_Force_Data_Pendulum.m | 2,106 | utf_8 | e512b4c47c908134e5070a9ef8defe11 | %-------------------------------------------------------------------------------------------------------------------%
%
% IB2d is an Immersed Boundary Code (IB) for solving fully coupled
% fluid-structure interaction models. This version of the code is based off of
% Peskin's Immersed Boundary Method Paper in Acta N... |
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