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S_object.m

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+function Q = S_object(prediction,GT)
+% S_object Computes the object similarity between foreground maps and ground
+% truth(as proposed in "Structure-measure:A new way to evaluate foreground 
+% maps" [Deng-Ping Fan et. al - ICCV 2017])
+% Usage:
+%   Q = S_object(prediction,GT)
+% Input:
+%   prediction - Binary/Non binary foreground map with values in the range
+%                [0 1]. Type: double.
+%   GT - Binary ground truth. Type: logical.
+% Output:
+%   Q - The object similarity score
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+% compute the similarity of the foreground in the object level
+prediction_fg = prediction;
+prediction_fg(~GT)=0;
+O_FG = Object(prediction_fg,GT);
+
+% compute the similarity of the background
+prediction_bg = 1.0 - prediction;
+prediction_bg(GT) = 0;
+O_BG = Object(prediction_bg,~GT);
+
+% combine the foreground measure and background measure together
+u = mean2(GT);
+Q = u * O_FG + (1 - u) * O_BG;
+
+end
+
+function score = Object(prediction,GT)
+
+% check the input 
+if isempty(prediction)
+    score = 0;
+    return;
+end
+if isinteger(prediction)
+    prediction = double(prediction);
+end
+if (~isa( prediction, 'double' ))
+    error('prediction should be of type: double');
+end
+if ((max(prediction(:))>1) || min(prediction(:))<0)
+    error('prediction should be in the range of [0 1]');
+end
+if(~islogical(GT))
+    error('GT should be of type: logical');
+end
+
+% compute the mean of the foreground or background in prediction
+x = mean2(prediction(GT));
+
+% compute the standard deviations of the foreground or background in prediction
+sigma_x = std(prediction(GT));
+
+score = 2.0 * x./(x^2 + 1.0 + sigma_x + eps);
+end

S_region.m

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+function Q = S_region(prediction,GT)
+% S_region computes the region similarity between the foreground map and
+% ground truth(as proposed in "Structure-measure:A new way to evaluate
+% foreground maps" [Deng-Ping Fan et. al - ICCV 2017])
+% Usage:
+%   Q = S_region(prediction,GT)
+% Input:
+%   prediction - Binary/Non binary foreground map with values in the range
+%                [0 1]. Type: double.
+%   GT - Binary ground truth. Type: logical.
+% Output:
+%   Q - The region similarity score
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+% find the centroid of the GT
+[X,Y] = centroid(GT);
+
+% divide GT into 4 regions
+[GT_1,GT_2,GT_3,GT_4,w1,w2,w3,w4] = divideGT(GT,X,Y);
+
+%Divede prediction into 4 regions
+[prediction_1,prediction_2,prediction_3,prediction_4] = Divideprediction(prediction,X,Y);
+
+%Compute the ssim score for each regions
+Q1 = ssim(prediction_1,GT_1);
+Q2 = ssim(prediction_2,GT_2);
+Q3 = ssim(prediction_3,GT_3);
+Q4 = ssim(prediction_4,GT_4);
+
+%Sum the 4 scores
+Q = w1 * Q1 + w2 * Q2 + w3 * Q3 + w4 * Q4;
+
+end
+
+function [X,Y] = centroid(GT)
+% Centroid Compute the centroid of the GT
+% Usage:
+%   [X,Y] = Centroid(GT)
+% Input:
+%   GT - Binary ground truth. Type: logical.
+% Output:
+%   [X,Y] - The coordinates of centroid.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+[rows,cols] = size(GT);
+
+if(sum(GT(:))==0)
+    X = round(cols/2);
+    Y = round(rows/2);
+else
+    dGT = double(GT);
+    x = ones(rows,1)*(1:cols);
+    y = (1:rows)'*ones(1,cols);
+    area = sum(dGT(:));
+    X = round(sum(sum(dGT.*x))/area);
+    Y = round(sum(sum(dGT.*y))/area);
+end
+
+end
+
+% divide the GT into 4 regions according to the centroid of the GT and return the weights
+function [LT,RT,LB,RB,w1,w2,w3,w4] = divideGT(GT,X,Y)
+% LT - left top;
+% RT - right top;
+% LB - left bottom;
+% RB - right bottom;
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%width and height of the GT
+[hei,wid] = size(GT);
+area = wid * hei;
+
+%copy the 4 regions 
+LT = GT(1:Y,1:X);
+RT = GT(1:Y,X+1:wid);
+LB = GT(Y+1:hei,1:X);
+RB = GT(Y+1:hei,X+1:wid);
+
+%The different weight (each block proportional to the GT foreground region).
+w1 = (X*Y)./area;
+w2 = ((wid-X)*Y)./area;
+w3 = (X*(hei-Y))./area;
+w4 = 1.0 - w1 - w2 - w3;
+end
+
+%Divide the prediction into 4 regions according to the centroid of the GT 
+function [LT,RT,LB,RB] = Divideprediction(prediction,X,Y)
+
+%width and height of the prediction
+[hei,wid] = size(prediction);
+
+%copy the 4 regions 
+LT = prediction(1:Y,1:X);
+RT = prediction(1:Y,X+1:wid);
+LB = prediction(Y+1:hei,1:X);
+RB = prediction(Y+1:hei,X+1:wid);
+
+end
+
+function Q = ssim(prediction,GT)
+% ssim computes the region similarity between foreground maps and ground
+% truth(as proposed in "Structure-measure: A new way to evaluate foreground 
+% maps" [Deng-Ping Fan et. al - ICCV 2017])
+% Usage:
+%   Q = ssim(prediction,GT)
+% Input:
+%   prediction - Binary/Non binary foreground map with values in the range
+%                [0 1]. Type: double.
+%   GT - Binary ground truth. Type: logical.
+% Output:
+%   Q - The region similarity score
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+dGT = double(GT);
+
+[hei,wid] = size(prediction);
+N = wid*hei;
+
+%Compute the mean of SM,GT
+x = mean2(prediction);
+y = mean2(dGT);
+
+%Compute the variance of SM,GT
+sigma_x2 = sum(sum((prediction - x).^2))./(N - 1 + eps);
+sigma_y2 = sum(sum((dGT - y).^2))./(N - 1 + eps);
+
+%Compute the covariance between SM and GT
+sigma_xy = sum(sum((prediction - x).*(dGT - y)))./(N - 1 + eps);
+
+alpha = 4 * x * y * sigma_xy;
+beta = (x.^2 + y.^2).*(sigma_x2 + sigma_y2);
+
+if(alpha ~= 0)
+    Q = alpha./(beta + eps);
+elseif(alpha == 0 && beta == 0)
+    Q = 1.0;
+else
+    Q = 0;
+end
+
+end

StructureMeasure.m

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+function Q = StructureMeasure(prediction,GT)
+% StructureMeasure computes the similarity between the foreground map and
+% ground truth(as proposed in "Structure-measure: A new way to evaluate
+% foreground maps" [Deng-Ping Fan et. al - ICCV 2017])
+% Usage:
+%   Q = StructureMeasure(prediction,GT)
+% Input:
+%   prediction - Binary/Non binary foreground map with values in the range
+%                [0 1]. Type: double.
+%   GT - Binary ground truth. Type: logical. 
+% Output:
+%   Q - The computed similarity score
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+% Check input
+if (~isa(prediction,'double'))
+    error('The prediction should be double type...');
+end
+if ((max(prediction(:))>1) || min(prediction(:))<0)
+    error('The prediction should be in the range of [0 1]...');
+end
+if (~islogical(GT))
+    error('GT should be logical type...');
+end
+
+y = mean2(GT);
+
+if (y==0)% if the GT is completely black
+    x = mean2(prediction);
+    Q = 1.0 - x; %only calculate the area of intersection
+elseif(y==1)%if the GT is completely white
+    x = mean2(prediction);
+    Q = x; %only calcualte the area of intersection
+else
+    alpha = 0.5;
+    Q = alpha*S_object(prediction,GT)+(1-alpha)*S_region(prediction,GT);
+end
+
+end

demo.m

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+close all; clear; clc;
+
+% set the RoodDir according to your own environment
+RootDir = pwd;
+
+% set the ground truth path and the foreground map path
+gtPath = fullfile(RootDir,'demo','GT');
+fgPath = fullfile(RootDir,'demo','FG');
+
+% set the result path
+resPath = fullfile(RootDir,'demo','Result');
+if ~exist(resPath,'dir')
+    mkdir(resPath);
+end
+
+% set the foreground map methods
+MethodNames = {'MDF','mc','DISC','rfcn','DCL','dhsnet'};
+
+% load the gtFiles
+gtFiles = dir(fullfile(gtPath,'*.png'));
+
+% for each gtFiles
+for i = 1:length(gtFiles)
+    fprintf('Processing %d/%d...\n',i,length(gtFiles));
+
+    % load the gt file
+    [GT,map] = imread(fullfile(gtPath,gtFiles(i).name));
+    if numel(size(GT))>2
+        GT = rgb2gray(GT);
+    end
+    GT = logical(GT);
+
+    % in some dataset(ECSSD) some ground truth is reverse when map is not none
+    if ~isempty(map) && (map(1)>map(2))
+        GT = ~GT;
+    end
+
+    % for each saliency method
+    for j = 1 : length(MethodNames)
+        % load the saliency map file
+        predname = [gtFiles(i).name(1:end-4) '_' MethodNames{j} '.png'];
+        prediction = imread(fullfile(fgPath,predname));
+        if numel(size(prediction))>2
+            prediction = rgb2gray(prediction);
+        end
+
+        d_prediction = double(prediction);
+        d_prediction = reshape(mapminmax(d_prediction(:)',0,1),size(d_prediction));
+
+        % evaluate the predicted map against the GT
+        score = StructureMeasure(d_prediction,GT);
+        score = roundn(score,-4);
+
+        % save the result
+        resName = sprintf([gtFiles(i).name(1:end-4) '_%.4f_' MethodNames{j} '.png'],score);
+        imwrite(prediction,fullfile(resPath,resName));
+    end
+end
+
+fprintf('The results are saved in %s\n',resPath);
+
+