Fix MATLAB conversion issues and clean up.

models/matlab/CVS/smiler.json

@@ -7,19 +7,19 @@
  "model_files": [],
  "parameters": {
  "size": {
- "default": 512,
- "description": "The dimensions that the image gets resized to when calculating the saliency map. Note that this is always a square.",
- "valid_values": "Any integer greater than 0."
- },
+ "default": 512,
+ "description": "The dimensions that the image gets resized to when calculating the saliency map. Note that this is always a square.",
+ "valid_values": "Any integer greater than 0."
+ },
  "quantile": {
- "default": 0.1,
- "description": "A parameter which specifies the proportion of regions (sorted by degree of similarity) from the neighbourhood which are included in the calculation.",
- "valid_values": "A number greater than 0 and less than or equal to 1"
- },
+ "default": 0.1,
+ "description": "A parameter which specifies the proportion of regions (sorted by degree of similarity) from the neighbourhood which are included in the calculation.",
+ "valid_values": "A number greater than 0 and less than or equal to 1"
+ },
  "modeltype": {
- "default": "CovariancesOnly",
- "description": "CovariancesOnly calculates saliency based on the covariances of the image patches, whereas SigmaPoints includes both covariances and means.",
- "valid_values": ["CovariancesOnly", "SigmaPoints"]
- }
+ "default": "CovariancesOnly",
+ "description": "CovariancesOnly calculates saliency based on the covariances of the image patches, whereas SigmaPoints includes both covariances and means.",
+ "valid_values": ["CovariancesOnly", "SigmaPoints"]
+ }
  }
 }

models/matlab/FES/FES_wrap.m

@@ -2,8 +2,8 @@
 % saliency model in the common SMILER format. If you use results produced
 % by this model, please cite
 % the following paper:
-% Tavakoli, Hamed Rezazadegan, Esa Rahtu, and Janne Heikkilä. "Fast and 
-% efficient saliency detection using sparse sampling and kernel 
+% Tavakoli, Hamed Rezazadegan, Esa Rahtu, and Janne Heikkilä. "Fast and
+% efficient saliency detection using sparse sampling and kernel
 % density estimation." In Scandinavian Conference on Image Analysis,
 % pp. 666-675. Springer, Berlin, Heidelberg, 2011.
 %
@@ -34,7 +34,7 @@
 params = checkCommonParams(in_params);
 
 % get the path to smiler.json
-pathroot = mfilename('fullpath'); 
+pathroot = mfilename('fullpath');
 [pathroot, ~, ~] = fileparts(pathroot);
 
 % check the model-specific parameter fields

models/matlab/FES/computeFinalSaliency.m

@@ -2,7 +2,7 @@
 function saliency = computeFinalSaliency(img, pScale, sScale, alpha, sigma0, sigma1, p1)
 % function saliency = computeFinalSaliency(image, pScale, sScale, alpha, sigma0, sigma1, p1)
 %
-% compute multi scale saliency over an image 
+% compute multi scale saliency over an image
 %
 % @input
 % img - a given image to process
@@ -14,15 +14,15 @@
 % p1 - P(1|x) [128x171 matrix]
 % @output
 % saliency - saliency of inputed image
-% 
+%
 % please refer to the following paper for details
-% Rezazadegan Tavakoli H, Rahtu E & Heikkil? J, 
+% Rezazadegan Tavakoli H, Rahtu E & Heikkil? J,
 % "Fast and efficient saliency detection using sparse sampling and kernel density estimation."
 % Proc. Scandinavian Conference on Image Analysis (SCIA 2011), 2011, Ystad, Sweden.
 %
-% The code has been tested on Matlab 2010a (32-bit) running windows. 
+% The code has been tested on Matlab 2010a (32-bit) running windows.
 % This code is publicly available for demonstration and educational
-% purposes, any commercial use without permission is strictly prohibited. 
+% purposes, any commercial use without permission is strictly prohibited.
 %
 % Please contact the author in case of any questions, comments, or Bug
 % reports
@@ -57,5 +57,3 @@
 
 saliency = mean(saliency, 3);
 saliency = (saliency - min(saliency(:))) / (max(saliency(:)) - min(saliency(:)));
-
-

models/matlab/GBVS/.gitignore

@@ -0,0 +1 @@
+util/mypath.mat

models/matlab/GBVS/GBVS_wrap.m

@@ -1,5 +1,5 @@
 % GBVS_wrap executes the Graph-Based Visual Saliency (GBVS) model in the
-% SMILER format. If you use results produced by this model, please cite 
+% SMILER format. If you use results produced by this model, please cite
 % the following paper:
 % J. Harel, C. Koch, and P. Perona (2006). Graph-Based Visual Saliency.
 % Proc. Neural Information Processing Systems (NIPS)
@@ -39,7 +39,7 @@
 params = checkCommonParams(in_params);
 
 % get the path to smiler.json
-pathroot = mfilename('fullpath'); 
+pathroot = mfilename('fullpath');
 [pathroot, ~, ~] = fileparts(pathroot);
 
 % check the model-specific parameter fields
@@ -77,7 +77,7 @@
  params.channels = strrep(params.channels, 'C', 'D');
 end
 params.dklcolorWeight = params.colorWeight;
- 
+
 %% Reading the image
 img = checkImgInput(input_image, params.color_space);
 
@@ -87,4 +87,4 @@
 salmap = out.master_map_resized;
 
 % do any final post-processing as specified by the parameters
-salmap = fmtOutput(salmap, params);
+salmap = fmtOutput(salmap, params);

models/matlab/GBVS/gbvs.m

@@ -1,7 +1,7 @@
 function [out,motionInfo] = gbvs(img,param,prevMotionInfo)
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% % 
+% %
 % This computes the GBVS map for an image and puts it in master_map. %
 % %
 % If this image is part of a video sequence, motionInfo needs to be recycled in a %
@@ -24,7 +24,7 @@
 % Jonathan Harel, Last Revised Aug 2008. [email protected] %
 %
 % ** Code modified from the original source code as part of SMILER
-% All effort has been made to preserve original functionality while 
+% All effort has been made to preserve original functionality while
 % bringing code execution into a common format. **
 %
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -36,7 +36,7 @@
 end
 
 if ( (nargin < 2) || (~exist('param')) || isempty(param) )
- param = makeGBVSParams(); 
+ param = makeGBVSParams();
 end
 [grframe,param] = initGBVS(param,size(img));
 
@@ -49,15 +49,15 @@
  mymessage(param,'NOTE: Computing STANDARD Itti/Koch instead of Graph-Based Visual Saliency (GBVS)\n\n');
 end
 
-%%%% 
+%%%%
 %%%% STEP 1 : compute raw feature maps from image
 %%%%
 
 mymessage(param,'computing feature maps...\n');
 if ( size(img,3) == 3 ) imgcolortype = 1; else, imgcolortype = 2; end
 [rawfeatmaps motionInfo] = getFeatureMaps( img , param , prevMotionInfo );
 
-%%%% 
+%%%%
 %%%% STEP 2 : compute activation maps from feature maps
 %%%%
 
@@ -74,7 +74,7 @@
  map_types{fmapi} = mapsobj.description;
  for typei = 1 : numtypes
  if ( param.activationType == 1 )
- for lev = param.levels 
+ for lev = param.levels
  mymessage(param,'making a graph-based activation (%s) feature map.\n',mapnames{fmapi});
  i = i + 1;
  [allmaps{i}.map,tmp] = graphsalapply( mapsobj.maps.val{typei}{lev} , ...
@@ -85,10 +85,10 @@
  for centerLevel = param.ittiCenterLevels
  for deltaLevel = param.ittiDeltaLevels
  mymessage(param,'making a itti-style activation (%s) feature map using center-surround subtraction.\n',mapnames{fmapi});
- i = i + 1; 
+ i = i + 1;
  center_ = mapsobj.maps.origval{typei}{centerLevel};
  sz_ = size(center_);
- surround_ = imresize( mapsobj.maps.origval{typei}{centerLevel+deltaLevel}, sz_ , 'bicubic' ); 
+ surround_ = imresize( mapsobj.maps.origval{typei}{centerLevel+deltaLevel}, sz_ , 'bicubic' );
  allmaps{i}.map = (center_ - surround_).^2;
  allmaps{i}.maptype = [ fmapi centerLevel deltaLevel ];
  end
@@ -97,8 +97,8 @@
  end
 end
 
- 
-%%%% 
+
+%%%%
 %%%% STEP 3 : normalize activation maps
 %%%%
 
@@ -109,19 +109,19 @@
  if ( param.normalizationType == 1 )
  mymessage(param,' using fast raise to power scheme\n ', i);
  algtype = 4;
- [norm_maps{i}.map,tmp] = graphsalapply( allmaps{i}.map , grframe, param.sigma_frac_norm, param.num_norm_iters, algtype , param.tol ); 
+ [norm_maps{i}.map,tmp] = graphsalapply( allmaps{i}.map , grframe, param.sigma_frac_norm, param.num_norm_iters, algtype , param.tol );
  elseif ( param.normalizationType == 2 )
  mymessage(param,' using graph-based scheme\n');
  algtype = 1;
- [norm_maps{i}.map,tmp] = graphsalapply( allmaps{i}.map , grframe, param.sigma_frac_norm, param.num_norm_iters, algtype , param.tol ); 
+ [norm_maps{i}.map,tmp] = graphsalapply( allmaps{i}.map , grframe, param.sigma_frac_norm, param.num_norm_iters, algtype , param.tol );
  else
  mymessage(param,' using global - mean local maxima scheme.\n');
  norm_maps{i}.map = maxNormalizeStdGBVS( mat2gray(imresize(allmaps{i}.map,param.salmapsize, 'bicubic')) );
  end
  norm_maps{i}.maptype = allmaps{i}.maptype;
 end
 
-%%%% 
+%%%%
 %%%% STEP 4 : average across maps within each feature channel
 %%%%
 
@@ -139,7 +139,7 @@
 end
 %%% divide each feature channel by number of maps in that channel
 for fmapi = 1 : length(mapnames)
- if ( param.normalizeTopChannelMaps) 
+ if ( param.normalizeTopChannelMaps)
  mymessage(param,'Performing additional top-level feature map normalization.\n');
  if ( param.normalizationType == 1 )
  algtype = 4;
@@ -154,7 +154,7 @@
  comb_norm_maps{fmapi} = cmaps{fmapi};
 end
 
-%%%% 
+%%%%
 %%%% STEP 5 : sum across feature channels
 %%%%
 
@@ -185,7 +185,7 @@
  master_map = mat2gray(master_map);
 end
 
-if ( param.unCenterBias ) 
+if ( param.unCenterBias )
  invCB = load('invCenterBias');
  invCB = invCB.invCenterBias;
  centerNewWeight = 0.5;
@@ -195,7 +195,7 @@
  master_map = mat2gray(master_map);
 end
 
-%%%% 
+%%%%
 %%%% save descriptive, rescaled (0-255) output for user
 %%%%
 

models/matlab/GBVS/saltoolbox/makeGaborFilterGBVS.m

@@ -20,9 +20,9 @@
 
 % This file is part of the Saliency Toolbox - Copyright (C) 2006
 % by Dirk Walther and the California Institute of Technology.
-% The Saliency Toolbox is released under the GNU General Public 
-% License. See the enclosed COPYRIGHT document for details. 
-% For more information about this project see: 
+% The Saliency Toolbox is released under the GNU General Public
+% License. See the enclosed COPYRIGHT document for details.
+% For more information about this project see:
 % http:https://www.saliencytoolbox.net
 
 if isempty(varargin)
@@ -40,7 +40,7 @@
 if (sz == -1)
  sz = ceil(max_stddev*sqrt(10));
 else
- sz = floor(sz/2); 
+ sz = floor(sz/2);
 end
 
 psi = pi / 180 * phase;