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Author: Tejas1415

Diff for: Ada_boost1_mdl2.mat

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+
+function [kalman_data,blob_data,blob_state] = Kalman_state_reorder...
+    (nw_order,kalman_data,blob_data,blob_state)
+%variable initialization
+tmp_kalman_data = kalman_data;
+tmp_blob_data = blob_data;
+max_cnt = length(blob_state.Area_new);
+pst_cnt = length(blob_state.Area_pst);
+A_new = blob_state.Area_new;
+C_new = blob_state.Centroid_new;
+BB_new = blob_state.BB_Box_new;
+%Rearranging the filter orders and initializing new states
+for i = 1:max_cnt
+    kalman_data(i) = tmp_kalman_data(nw_order(i));
+    blob_data(i) = tmp_blob_data(nw_order(i));
+    if(blob_data(i).detect_cnt == 1)%Initializing the states
+        kalman_data(i).X = blob_state.BB_Box_new(i,:)';
+    end   
+end
+
+%Deleting the lost filters
+m = 0;
+for i = 1:max_cnt
+    if(blob_data(i).lost_cnt > blob_state.lost_th)%Deleting the states      
+        nw_cnt = length(A_new);
+        pos = nw_cnt - (max_cnt-i);
+        if(pos < nw_cnt)
+            A_new = [A_new(1:pos-1),A_new(pos+1:nw_cnt)];
+            C_new = [C_new(1:pos-1,:);C_new(pos+1:nw_cnt,:)];
+            BB_new = [BB_new(1:pos-1,:);BB_new(pos+1:nw_cnt,:)];
+        else
+            A_new = A_new(1:pos-1);
+            C_new = C_new(1:pos-1,:);
+            BB_new = BB_new(1:pos-1,:);
+        end
+        skip=1;
+    else
+        m = m+1;
+        skip = 0;
+    end
+    if(~skip)
+        kalman_data(m) = kalman_data(i);
+        blob_data(m) = blob_data(i);
+    end
+end
+% Resetting the deleted states to zeros
+if (m < max_cnt)
+    resest_cnt = m+1;
+    for i = resest_cnt:max_cnt
+        kalman_data(i).X = zeros(4,1);
+        kalman_data(i).P = eye(4);
+        blob_data(i).lost_cnt = 0;
+        blob_data(i).detect_cnt = 0;
+    end
+end
+% assigning them back to the state
+blob_state.Area_new = A_new;
+blob_state.Centroid_new = C_new;
+blob_state.BB_Box_new = BB_new;

Diff for: Kalman_state_reorder.m

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+%% training a adaboost classifier
+close all; clear; clc;
+% Reading annotations file
+
+fileID = fopen('MVI_20011.xml','r');
+A = fscanf(fileID,'%c');
+idcs = strfind(A,['num=']);
+blue = [255,20,147];
+n_indx = 1;
+
+% for loop over each video frame
+for l_indx = 1:length(idcs)
+    % searching for annotations in each frame
+    if(l_indx < 10)
+        str1 =  '0000';
+    elseif(l_indx < 100)
+        str1 =  '000';
+    else
+        str1 = '00';
+    end
+    if(l_indx ~= 664)
+    b_str = A(idcs(l_indx):idcs(l_indx+1));
+    else
+        b_str = A(idcs(l_indx):end);
+    end
+    idx_targets = strfind(b_str,'<target id=');
+    num_obj = length(idx_targets);
+
+    BB_box_num = zeros(num_obj,4);
+    for box_i = 1:num_obj
+        if(box_i ~= num_obj)
+        tmp_str = b_str(idx_targets(box_i):idx_targets(box_i+1));
+        else
+            tmp_str = b_str(idx_targets(box_i):end);
+        end
+        b_indx1 = strfind(tmp_str,'<box left="');
+        b_indx2 = strfind(tmp_str,'top="');
+        b_indx3 = strfind(tmp_str,' width=');
+        b_indx4 = strfind(tmp_str,' height=');
+        b_indx5 = strfind(tmp_str,'<attribute');
+        BB_box_num(box_i,1) = str2num(tmp_str(b_indx1+length('<box left="'):b_indx2-3));
+        BB_box_num(box_i,2) = str2num(tmp_str(b_indx2+length('top="'):b_indx3-2));
+        BB_box_num(box_i,3) = str2num(tmp_str(b_indx3+length(' width="'):b_indx4-2));
+        BB_box_num(box_i,4) = str2num(tmp_str(b_indx4+length(' height="'):b_indx5-18));
+    end
+    % reading image
+    img_file_name = ['img',str1,num2str(l_indx),'.jpg'];
+    folder = 'MVI_20011';
+    fullname = fullfile(folder,img_file_name);
+    Currentframe = imread(fullname);
+    
+    for box_i = 1:1:num_obj
+        crop_img = imcrop(Currentframe,BB_box_num(box_i,:));
+%         imshow(crop_img);
+        
+        cell_num1 = (length(crop_img(:,1,1)));
+        cell_num2 = (length(crop_img(1,:,1)));
+        cell_num1 = floor(cell_num1/2);
+        cell_num2 = floor(cell_num2/2);
+        tmp = [extractHOGFeatures(crop_img,'numbins',8,'cellsize',[cell_num1,cell_num2])];
+        HOG_feature(box_i, :) = tmp;
+        HOG_feature(box_i, :) = (HOG_feature(box_i, :) );
+        colrhist_feature(box_i,:) = [imhist(crop_img(:,:,1),8);...
+            imhist(crop_img(:,:,2), 8);imhist(crop_img(:,:,2), 8)]';
+        hus_feature(box_i,:) = hus_invariance(crop_img);
+    end
+    
+     %Displaying the output    
+%         shapeInserter = vision.ShapeInserter('BorderColor','Custom',...
+%             'CustomBorderColor',blue);
+%         out1    = step(shapeInserter, Currentframe, int32(BB_box_num));
+%         imshow(out1)
+    if(l_indx > 1)
+        NUM_OF_PAST_BLOBS = length(past_colrhist_feature(:,1)) ;
+
+         for indx1 = 1:num_obj
+             for indx2 = 1:NUM_OF_PAST_BLOBS
+            sim_values(indx1,indx2,1) = pdist2(past_HOG_features(indx2,:),HOG_feature(indx1,:));
+            sim_values(indx1,indx2,2) = pdist2(past_colrhist_feature(indx2,:),colrhist_feature(indx1,:));
+            sim_values(indx1,indx2,3) = pdist2(past_hus_feature(indx2,:),hus_feature(indx1,:));
+             end
+         end
+        sim_values(:,:,1) = (sim_values(:,:,1))./max(sim_values(:,:,1));
+        sim_values(:,:,2) = (sim_values(:,:,2))./max(sim_values(:,:,2));
+         sim_values(:,:,3) = (sim_values(:,:,3))./max(sim_values(:,:,3));
+%         sim_values(:,:,4) = zscore(sim_values(:,:,4));
+         if(mod(l_indx,6) == 0)
+         X(n_indx,:) = sim_values(6,6,:);
+         Y(n_indx) = 1;
+         elseif(mod(l_indx,6) == 1)
+         X(n_indx,:) = sim_values(3,3,:);
+         Y(n_indx) = 1;
+         elseif(mod(l_indx,6) == 2)
+         X(n_indx,:) = sim_values(1,1,:);
+         Y(n_indx) = 1;
+         elseif(mod(l_indx,6) == 4)
+         X(n_indx,:) = sim_values(1,6,:);
+         Y(n_indx) = 0;
+         elseif(mod(l_indx,6) == 5)
+         X(n_indx,:) = sim_values(3,4,:);
+         Y(n_indx) = 0;
+         elseif(mod(l_indx,6) == 6)
+         X(n_indx,:) = sim_values(6,1,:);
+         Y(n_indx) = 0;
+         end
+
+         clear past_HOG_features;
+         clear past_hus_feature
+         clear past_colrhist_feature;
+         clear sim_values;
+    end
+    
+    clear BB_box_num
+    
+    if(l_indx > 1)
+    past_HOG_features = HOG_feature;
+    past_colrhist_feature = colrhist_feature;
+    past_hus_feature = hus_feature;
+%     past_centroid_feat = centroid_feat;
+    clear HOG_feature;
+    clear colrhist_feature;
+    clear hus_feature
+    
+    else
+        past_HOG_features = zeros(1,32);
+        past_colrhist_feature = zeros(1,24);
+        past_centroid_feat = zeros(1,2);
+        past_hus_feature = zeros(1,8);
+    end
+    n_indx = n_indx + 1;
+end
+
+
+%% Training part
+%          Mdl_ada = fitcensemble(X,Y,'OptimizeHyperparameters','auto',...
+%     'HyperparameterOptimizationOptions',struct('AcquisitionFunctionName',...
+%     'expected-improvement-plus'));
+       Mdl_ada = fitcensemble(X,Y,'Method','AdaBoostM1');
+         
+%% Testing part
+fileID = fopen('MVI_20012.xml','r');
+A = fscanf(fileID,'%c');
+idcs = strfind(A,['num=']);
+n_indx = 1;
+for  l_indx = 1:500
+    % searching for annotations in each frame
+    if(l_indx < 10)
+        str1 =  '0000';
+    elseif(l_indx < 100)
+        str1 =  '000';
+    else
+        str1 = '00';
+    end
+    if(l_indx ~= length(idcs))
+    b_str = A(idcs(l_indx):idcs(l_indx+1));
+    else
+        b_str = A(idcs(l_indx):end);
+    end
+    idx_targets = strfind(b_str,'<target id=');
+    num_obj = length(idx_targets);
+
+    BB_box_num = zeros(num_obj,4);
+    for box_i = 1:num_obj
+        if(box_i ~= num_obj)
+        tmp_str = b_str(idx_targets(box_i):idx_targets(box_i+1));
+        else
+            tmp_str = b_str(idx_targets(box_i):end);
+        end
+        b_indx1 = strfind(tmp_str,'<box left="');
+        b_indx2 = strfind(tmp_str,'top="');
+        b_indx3 = strfind(tmp_str,' width=');
+        b_indx4 = strfind(tmp_str,' height=');
+        b_indx5 = strfind(tmp_str,'<attribute');
+        BB_box_num(box_i,1) = str2num(tmp_str(b_indx1+length('<box left="'):b_indx2-3));
+        BB_box_num(box_i,2) = str2num(tmp_str(b_indx2+length('top="'):b_indx3-2));
+        BB_box_num(box_i,3) = str2num(tmp_str(b_indx3+length(' width="'):b_indx4-2));
+        BB_box_num(box_i,4) = str2num(tmp_str(b_indx4+length(' height="'):b_indx5-18));
+    end
+    % reading image
+    img_file_name = ['img',str1,num2str(l_indx),'.jpg'];
+    folder = 'MVI_20012';
+    fullname = fullfile(folder,img_file_name);
+    Currentframe = imread(fullname);
+    for box_i = 1:1:num_obj
+        crop_img = imcrop(Currentframe,BB_box_num(box_i,:));
+%         imshow(crop_img);
+        
+        cell_num1 = (length(crop_img(:,1,1)));
+        cell_num2 = (length(crop_img(1,:,1)));
+        cell_num1 = floor(cell_num1/2);
+        cell_num2 = floor(cell_num2/2);
+        tmp = [extractHOGFeatures(crop_img,'numbins',8,'cellsize',[cell_num1,cell_num2])];
+        HOG_feature(box_i, :) = tmp;
+        HOG_feature(box_i, :) = (HOG_feature(box_i, :) );
+        colrhist_feature(box_i,:) = [imhist(crop_img(:,:,1),8);...
+            imhist(crop_img(:,:,2), 8);imhist(crop_img(:,:,2), 8)]';
+        colrhist_feature(box_i,:) = (colrhist_feature(box_i, :) );
+        hus_feature(box_i,:) = hus_invariance(crop_img);
+    end
+    
+    
+     %Displaying the output    
+        shapeInserter = vision.ShapeInserter('BorderColor','Custom',...
+            'CustomBorderColor',blue);
+        out1    = step(shapeInserter, Currentframe, int32(BB_box_num));
+        
+       
+    if(l_indx > 1)
+        NUM_OF_PAST_BLOBS = length(past_colrhist_feature(:,1)) ;
+
+         for indx1 = 1:num_obj
+             for indx2 = 1:NUM_OF_PAST_BLOBS
+            sim_values(indx1,indx2,1) = pdist2(past_HOG_features(indx2,:),HOG_feature(indx1,:));
+            sim_values(indx1,indx2,2) = pdist2(past_colrhist_feature(indx2,:),colrhist_feature(indx1,:));
+            sim_values(indx1,indx2,3) = pdist2(past_hus_feature(indx2,:),hus_feature(indx1,:));
+             end
+         end
+         sim_values(:,:,1) = (sim_values(:,:,1))./max((sim_values(:,:,1)));
+         sim_values(:,:,2) = (sim_values(:,:,2))./max((sim_values(:,:,2)));
+         sim_values(:,:,3) = (sim_values(:,:,3))./max((sim_values(:,:,3)));
+
+         if (l_indx < 10)
+        X_match(l_indx,:)=  [sim_values(1,1,:)];
+         else
+             X_match(l_indx,:)=  [sim_values(2,2,:)];
+         end
+        X_nomatch(l_indx,:)=  [sim_values(1,end,:)];
+%         for indx = 1:length(sim_values(:,1,1))
+%             X_ip = [sim_values(indx,:,1);sim_values(indx,:,2);sim_values(indx,:,3)];
+        
+%         tracks_i = find(labels);
+%         new_track = min_indx2(tracks_i);
+      
+          
+%         end
+%         track_i = find(labels);
+         clear past_HOG_features;
+         clear past_colrhist_feature;
+         clear sim_values;
+         clear score;
+         clear labels;
+    end
+     clear BB_box_num
+    
+    if(l_indx > 1)
+    past_HOG_features = HOG_feature;
+    past_colrhist_feature = colrhist_feature;
+%     past_centroid_feat = centroid_feat;
+    clear HOG_feature;
+    clear colrhist_feature;
+    past_hus_feature = hus_feature;
+    else
+        past_HOG_features = zeros(1,32);
+        past_colrhist_feature = zeros(1,24);
+        past_centroid_feat = zeros(1,2);
+        past_hus_feature = zeros(1,8);
+    end
+    n_indx = n_indx + 1;
+end
+[labels,score] = predict(Mdl_ada,[X_match',X_nomatch']');
+
+Y = [ones(1,500),zeros(1,500)];
+% Loss_1 =loss(Mdl_ada,X',Y);
+% Loss_2 =loss(Mdl_ada,X',Y);
+save(['Ada_boost1_mdl2'],'Mdl_ada');
+Confusion_matrix = confusionmat(Y,labels)./500