source: proiecte/pmake3d/make3d_original/Make3dSingleImageStanford_version0.1/image3dstiching/Occlu/SearchCorrMatchEpipoarLine.m @ 37

% *  This code was used in the following articles:
% *  [1] Learning 3-D Scene Structure from a Single Still Image, 
% *      Ashutosh Saxena, Min Sun, Andrew Y. Ng, 
% *      In ICCV workshop on 3D Representation for Recognition (3dRR-07), 2007.
% *      (best paper)
% *  [2] 3-D Reconstruction from Sparse Views using Monocular Vision, 
% *      Ashutosh Saxena, Min Sun, Andrew Y. Ng, 
% *      In ICCV workshop on Virtual Representations and Modeling 
% *      of Large-scale environments (VRML), 2007. 
% *  [3] 3-D Depth Reconstruction from a Single Still Image, 
% *      Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng. 
% *      International Journal of Computer Vision (IJCV), Aug 2007. 
% *  [6] Learning Depth from Single Monocular Images, 
% *      Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng. 
% *      In Neural Information Processing Systems (NIPS) 18, 2005.
% *
% *  These articles are available at:
% *  http://make3d.stanford.edu/publications
% * 
% *  We request that you cite the papers [1], [3] and [6] in any of
% *  your reports that uses this code. 
% *  Further, if you use the code in image3dstiching/ (multiple image version),
% *  then please cite [2].
% *  
% *  If you use the code in third_party/, then PLEASE CITE and follow the
% *  LICENSE OF THE CORRESPONDING THIRD PARTY CODE.
% *
% *  Finally, this code is for non-commercial use only.  For further 
% *  information and to obtain a copy of the license, see 
% *
% *  http://make3d.stanford.edu/publications/code
% *
% *  Also, the software distributed under the License is distributed on an 
% * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either 
% *  express or implied.   See the License for the specific language governing 
% *  permissions and limitations under the License.
% *
% */
function [X, Y, StepLen, Coeff, Coeffs ]=SearchCorrMatchEpipoarLine...
                (defaultPara, ITarget, IField, POriReproj, Pocclu, TargetFullyInField)

% This function serch along the epipolar line from 
% POriReproj to Pocclu with fix step size

% ParameterSetting
Field2TargetScale = 2;
TargetSize = size(ITarget);%round([Iy/55*5 Ix/305*10]);
FieldSize = round(TargetSize*Field2TargetScale); % 1.5 gives bad result
StepSize = min(FieldSize)/10; % make sure after each close by step the Field overlap % /2 since too big step size
EpiolarLen = norm(POriReproj - Pocclu);
P2 = POriReproj - Pocclu;
UnitEpipoVector = Pocclu - POriReproj; %( X_Hori, Y_Vert)
MargimSerachValue = 0.2*EpiolarLen; % percentage of the marginal search area
DeviateFromEpipoleThre = 10; % pixels
[IyField IxField] = size( IField);
% equalized the images
%ITarget = histeq(ITarget);
% IField = histeq(IField);
UnitEpipoVector = UnitEpipoVector./norm(UnitEpipoVector);
best_max     = 0.0;
best_id = [];
Coeffs = [];
% Loop over Scale
%NumScale = length(Scale)

%for i = 1:NumScale

        % loop for step size along the epipolar line
        adj_row         = TargetSize(1) ;%- 1;
        adj_col         = TargetSize(2) ;%- 1;
        StepLen = -MargimSerachValue;%0;
        Shift_id = 1;
    
        while StepLen <= EpiolarLen+MargimSerachValue
                
        % Scale the ITarget
        %
        
                % create the new field for normxcorr2 to run
                FieldCenter = round(POriReproj + StepLen * UnitEpipoVector);%( X_Hori, Y_Vert)
        FieldCenter(1) = min(max( FieldCenter(1),1) , IxField);%( X_Hori, Y_Vert)
        FieldCenter(2) = min(max( FieldCenter(2),1) , IyField);%( X_Hori, Y_Vert)
        %figure(1); hold on; scatter(FieldCenter(1,1), FieldCenter(2,1),'y');
                IField_tmp = IField( FieldCenter(2):min( FieldCenter(2)+FieldSize(1), IyField) , ...
                                                FieldCenter(1):min( FieldCenter(1)+FieldSize(2), IxField) );
                if ( (size(ITarget, 1 ) <= size(IField_tmp,1)) && (size(ITarget, 2 ) <= size(IField_tmp,2)) ) % make sure 
                        res             = normxcorr2( ITarget, IField_tmp);           
                        if (TargetFullyInField)
                                % If we know that the target is compeltely within the field,
                                %   exclude portions of the correlation that are generated by
                                %   only a partial overlap of the target and  field.
                                top     = size(ITarget,1) + 1;
                                left    = size(ITarget,2) + 1;
                                height  = size(IField_tmp,1) - size(ITarget,1) ;%+ 1;
                                width   = size(IField_tmp,2) - size(ITarget,2) ;%+ 1;
                                % use only those results where the Target is compeltely
                                %   inside the Field.
                                res     = res( top:(top+height), left:(left+width) );
                                adj_row  = 0;
                        
                                adj_col  = 0;
            end

            if Shift_id == 2
               disp('check corrolation');
            end
                        % Max(matrix) returns the largest value in each col, in a row vector
                        [ max_vals, max_row_inds ] = max( abs( res ) );
                        % Find the Col with the bigest Max
                        [ max_val , max_col_ind  ] = max( max_vals );
                        % Get the row & col with the largest correlation
                        max_row_ind = max_row_inds( max_col_ind );
                        % Adjust to get offset of scaled Target, in field, in units of
                        %   FIELD pixels.       
                        tmp_offsetRow = max_row_ind - adj_row + FieldCenter(2);
                        tmp_offsetCol = max_col_ind - adj_col + FieldCenter(1);
                        
%                       if distances <= DeviateFromEpipoleThre
                                Coeffs( Shift_id ).offsetRow    = tmp_offsetRow;
                                Coeffs( Shift_id ).offsetCol    = tmp_offsetCol;
                                Coeffs( Shift_id ).index        = Shift_id;
                                Coeffs( Shift_id ).StepLen  = StepLen;
                                Coeffs( Shift_id ).correlation  = max_val;
%                               StepLen, max_val, best_max, max_row_ind, max_col_ind
                                % imview close all;
                                % imview( res );
                                % imview( target_tmp);
                                if (( Shift_id==1) || (max_val > best_max) )
                                        best_max = max_val;
                                        best_id = Shift_id;
                                end % check for new best location
%                       end
                end

                Shift_id = Shift_id + 1;
                StepLen = StepLen+StepSize;     
    end
    if ~isempty(best_id)
        P3 = [Coeffs( best_id ).offsetCol; Coeffs( best_id ).offsetRow] - Pocclu;
                u = P2' * P3 / (P2' * P2);
                distances = sqrt( sum( ( repmat(u,[2,1]) .* repmat(P2,[1,size(P3,2)]) - P3).^2 ,1));
        if distances <= DeviateFromEpipoleThre
            X       = Coeffs( best_id ).offsetCol;
            Y       = Coeffs( best_id ).offsetRow;
            StepLen   = Coeffs( best_id ).StepLen;
            Coeff   = Coeffs( best_id ).correlation;
        else
            X       = NaN;
            Y       = NaN;
            StepLen   = 0;
            Coeff   = 0;
        end
    else
        X       = NaN;
        Y       = NaN;
        StepLen   = 0;
        Coeff   = 0;
    end

%end