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- Committer: Suren A. Chilingaryan
- Date: 2009-12-09 13:00:50 UTC
- Revision ID: csa@dside.dyndns.org-20091209130050-z27djqs8ed68fqnk
Complete elimination of cpcorr
- files added:
- dic_basecorr3.m
- files modified:
- automate_image.m
added
removed
automate_image.m
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end
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end
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% Initialize variables
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base_points_x=grid_x;
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base_points_y=grid_y;
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if resume==1
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if OPTIMIZE>2
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input_points_x=single(validx(:,Imagenum));
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input_points_y=single(validy(:,Imagenum));
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else
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input_points_x=validx(:,Imagenum);
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input_points_y=validy(:,Imagenum);
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end
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inputpoints=1;
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else
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if OPTIMIZE>2
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input_points_x=single(grid_x);
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input_points_y=single(grid_y);
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else
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input_points_x=grid_x;
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input_points_y=grid_y;
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end
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end
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[row,col]=size(base_points_x); % this will determine the number of rasterpoints we have to run through
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[r,c]=size(filenamelist); % this will determine the number of images we have to loop through
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% Open new figure so previous ones (if open) are not overwritten
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if (~SILENT)
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h=figure;
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%Initializing hardware code
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base = uint8(mean(double(imread([imagedir, filenamelist(1,:)])),3)); % read in the base image ( which is always image number one. You might want to change that to improve correlation results in case the light conditions are changing during the experiment
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base_points_for(:,1)=reshape(base_points_x,[],1);
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base_points_for(:,2)=reshape(base_points_y,[],1);
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%some checks (moved here from cpcorr)
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if any(base_points_for(:)<0.5) || any(base_points_for(:,1)>size(base,2)+0.5) || any(base_points_for(:,2)>size(base,1)+0.5)
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msg = sprintf('In function %s, Control Points must be in pixel coordinates.',mfilename);
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eid = sprintf('Images:%s:cpPointsMustBeInPixCoord',mfilename);
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error(eid, msg);
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end
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%moved from normxcorr2
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% We can precalculate it here, since near-edge images are dropped and, therefore, A,T always have the same size
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% n is width and height of T, but following computations are done for A
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% outsize is sum if widths and heights of T and A - 1 (outsize = size(A) + size(T) - 1)
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outsize = 6 * CORRSIZE + 1;
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n = 2*CORRSIZE + 1;
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mn = n*n;
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rects_base = dic_calc_rects(base_points_for,2*CORRSIZE,base);
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data_base = struct;
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ncp = size(base_points_for, 1);
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ncp = prod(size(grid_x));
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if OPTIMIZE > 2
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hwid = normxcorr_hw();
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if hwid > 0
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err = normxcorr_hw(hwid, 1, ncp, CORRSIZE);
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err = normxcorr_hw(hwid, 1, ncp, CORRSIZE, PRECISION);
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if (err ~= 0)
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normxcorr_hw(1);
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OPTIMIZE = 2;
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hwid = 0;
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end
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if OPTIMIZE > 0
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% if OPTIMIZE > 2
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% Initialize variables
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if OPTIMIZE>2
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base_points_x=single(grid_x);
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base_points_y=single(grid_y);
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else
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base_points_x=grid_x;
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base_points_y=grid_y;
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end
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if resume==1
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if OPTIMIZE>2
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input_points_x=single(validx(:,Imagenum));
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input_points_y=single(validy(:,Imagenum));
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else
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input_points_x=validx(:,Imagenum);
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input_points_y=validy(:,Imagenum);
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end
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inputpoints=1;
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else
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if OPTIMIZE>2
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input_points_x=single(grid_x);
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input_points_y=single(grid_y);
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else
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input_points_x=grid_x;
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input_points_y=grid_y;
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end
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end
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base = uint8(mean(double(imread([imagedir, filenamelist(1,:)])),3)); % read in the base image ( which is always image number one. You might want to change that to improve correlation results in case the light conditions are changing during the experiment
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if OPTIMIZE > 2
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% crop_dim = [min(base_points_x(:)) - 2*CORRSIZE, min(base_points_y(:)) - 2 * CORRSIZE, max(base_points_x(:)) + 2*CORRSIZE, max(base_points_y(:)) + 2 * CORRSIZE]
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% crop_size = (crop_dim(3) - crop_dim(1)) * (crop_dim(4) - crop_dim(2))
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% mesh_size = ncp * 4 * CORRSIZE * CORRSIZE
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% end
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for icp = 1:ncp
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if isequal(rects_base(icp,3:4),[0 0])
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%near edge
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data_base(icp).skip = 1;
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continue
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% Verification of GPU local sum and denom computations
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% dic_basecorr3(CORRSIZE, PRECISION, OPTIMIZE, hwid, base_points_x, base_points_y, base);
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data_base = struct;
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base_points_for(:,1)=reshape(base_points_x,[],1);
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base_points_for(:,2)=reshape(base_points_y,[],1);
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for icp = 1:ncp
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data_base(icp).base_fractional_offset = base_points_for(icp,:) - round(base_points_for(icp,:)*PRECISION)/PRECISION;
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end
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sub_base = imcrop(base,rects_base(icp,:));
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normxcorr_hw(hwid, 3, base_points_x, base_points_y);
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normxcorr_hw(hwid, 4, base);
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normxcorr_hw(hwid, 5);
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else
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base_points_for(:,1)=reshape(base_points_x,[],1);
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base_points_for(:,2)=reshape(base_points_y,[],1);
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%some checks (moved here from cpcorr)
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if any(base_points_for(:)<0.5) || any(base_points_for(:,1)>size(base,2)+0.5) || any(base_points_for(:,2)>size(base,1)+0.5)
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msg = sprintf('In function %s, Control Points must be in pixel coordinates.',mfilename);
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eid = sprintf('Images:%s:cpPointsMustBeInPixCoord',mfilename);
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error(eid, msg);
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end
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if OPTIMIZE > 0
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data_base = struct;
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if any(~isfinite(sub_base(:)))
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%NaN or Inf
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data_base(icp).skip = 1;
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continue
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end
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data_base(icp).skip = 0;
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if (OPTIMIZE > 2)
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if sum(sub_base(:)) == sub_base(1)*numel(sub_base)
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eid = sprintf('Images:%s:sameElementsInTemplate',mfilename);
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msg = 'The values of TEMPLATE cannot all be the same.';
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error(eid,'%s',msg);
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%moved from normxcorr2
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% We can precalculate it here, since near-edge images are dropped and, therefore, A,T always have the same size
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% n is width and height of T, but following computations are done for A
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% outsize is sum if widths and heights of T and A - 1 (outsize = size(A) + size(T) - 1)
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outsize = 6 * CORRSIZE + 1;
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n = 2*CORRSIZE + 1;
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mn = n*n;
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rects_base = dic_calc_rects(base_points_for,2*CORRSIZE,base);
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for icp = 1:ncp
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if isequal(rects_base(icp,3:4),[0 0])
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%near edge
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data_base(icp).skip = 1;
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continue
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end
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normxcorr_hw(hwid, 10, icp, sub_base);
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%{
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double_sub_base = double(sub_base);
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local_sum_A = dic_local_sum(double_sub_base,n,n);
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local_sum_A2 = dic_local_sum(double_sub_base.*double_sub_base,n,n);
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denom_A = sqrt( max(( local_sum_A2 - (local_sum_A.^2)/mn ) / (mn-1), 0) );
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i_nonzero = find(denom_A~=0);
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normxcorr_hw(hwid, 10, icp, sub_base, single(local_sum_A), single(denom_A), uint16(i_nonzero));
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%}
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else
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sub_base = imcrop(base,rects_base(icp,:));
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data_base(icp).skip = 0;
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double_sub_base = double(sub_base);
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if (numel(sub_base) < 2) || (std(double_sub_base(:)) == 0)
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else
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data_base(icp).sub_base_fft = fft2(double_sub_base, outsize, outsize);
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end
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data_base(icp).base_fractional_offset = base_points_for(icp,:) - round(base_points_for(icp,:)*PRECISION)/PRECISION;
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end
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data_base(icp).base_fractional_offset = base_points_for(icp,:) - round(base_points_for(icp,:)*PRECISION)/PRECISION;
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end
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if (OPTIMIZE > 2)
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normxcorr_hw(hwid, 2);
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end
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end
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%normxcorr_hw(hwid);
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%return;
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%if OPTIMIZE > 2
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normxcorr_hw(hwid, 3, input_points_x, input_points_y);
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%else
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if OPTIMIZE > 2
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normxcorr_hw(hwid, 12, input_points_x, input_points_y);
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else
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input_correl(:,1)=reshape(input_points_x,[],1); % we reshape the input points to one row of values since this is the shape cpcorr will accept
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input_correl(:,2)=reshape(input_points_y,[],1);
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%end
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end
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for i=firstimage:(r-1) % run through all images
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tic % start the timer
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input = uint8(mean(double(imread([imagedir, filenamelist((i+1),:)])),3)); % read in the image which has to be correlated
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if OPTIMIZE > 2
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input_correl(:,:)=dic_cpcorr3(CORRSIZE, PRECISION, OPTIMIZE, hwid, data_base, input_correl, input);
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%Validate findpeak
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%dic_cpcorr3(CORRSIZE, PRECISION, OPTIMIZE, hwid, ncp, input);
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normxcorr_hw(hwid, 13, input)
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input_correl = normxcorr_hw(hwid, 14);
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elseif OPTIMIZE > 0
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input_correl(:,:)=dic_cpcorr(CORRSIZE, PRECISION, OPTIMIZE, hwid, data_base, input_correl, input);
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else
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input_correl(:,:)=cpcorr(input_correl, base_points_for, input, base);
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end
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%We need to copy
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validx(:,i)=double(input_correl(:,1)); % the results we get from cpcorr for the x-direction
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validy(:,i)=double(input_correl(:,2)); % the results we get from cpcorr for the y-direction
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