基于harris和sift特征提取的图像配准算法matlab仿真+仿真录像

function [numGoodMatches,numBadMatches,precision,accuracy100] = evaluate_correspondence(imgA, imgB, ground_truth_correspondence_file, scale_factor, x1_est, y1_est, x2_est, y2_est, vis) x1_est = x1_est ./ scale_factor; y1_est = y1_est ./ scale_factor; x2_est = x2_est ./ scale_factor; y2_est = y2_est ./ scale_factor; good_matches = zeros(length(x1_est),1); %indicator vector % Loads `ground truth' positions x1, y1, x2, y2 load(ground_truth_correspondence_file) %%%%%%%%%%%%%%%%%%%%%% % Uniqueness test % x1_est_tmp = x1_est; y1_est_tmp = y1_est; x2_est_tmp = x2_est; y2_est_tmp = y2_est; uniquenessDist = 5; % For each ground truth point numPreMerge = length(x1_est); for i=1:length(x1) % Compute distance of each estimated point to % the ground truth point x_dists = x1(i) - x1_est_tmp; y_dists = y1(i) - y1_est_tmp; dists = sqrt( x_dists.^2 + y_dists.^2 ); toMerge = dists < uniquenessDist; if sum(toMerge) > 1 avgX1 = mean( x1_est_tmp( toMerge ) ); avgY1 = mean( y1_est_tmp( toMerge ) ); avgX2 = mean( x2_est_tmp( toMerge ) ); avgY2 = mean( y2_est_tmp( toMerge ) ); x1_est_tmp( toMerge ) = []; y1_est_tmp( toMerge ) = []; x2_est_tmp( toMerge ) = []; y2_est_tmp( toMerge ) = []; % Add back point x1_est_tmp(end+1) = avgX1; y1_est_tmp(end+1) = avgY1; x2_est_tmp(end+1) = avgX2; y2_est_tmp(end+1) = avgY2; end end x1_est = x1_est_tmp; y1_est = y1_est_tmp; x2_est = x2_est_tmp; y2_est = y2_est_tmp; numPostMerge = length(x1_est); % % Uniqueness test end %%%%%%%%%%%%%%%%%%%%%% if vis figure; Height = max(size(imgA,1),size(imgB,1)); Width = size(imgA,2)+size(imgB,2); numColors = size(imgA, 3); newImg = zeros(Height, Width,numColors); newImg(1:size(imgA,1),1:size(imgA,2),:) = imgA; newImg(1:size(imgB,1),1+size(imgA,2):end,:) = imgB; imshow(newImg, 'Border', 'tight') shiftX = size(imgA,2); hold on; end % Distance test for i = 1:length(x1_est) fprintf('( %4.0f, %4.0f) to ( %4.0f, %4.0f)', x1_est(i), y1_est(i), x2_est(i), y2_est(i)); % For each x1_est, find nearest ground truth point in x1 x_dists = x1_est(i) - x1; y_dists = y1_est(i) - y1; dists = sqrt( x_dists.^2 + y_dists.^2 ); [dists, best_matches] = sort(dists); current_offset = [x1_est(i) - x2_est(i), y1_est(i) - y2_est(i)]; most_similar_offset = [x1(best_matches(1)) - x2(best_matches(1)), y1(best_matches(1)) - y2(best_matches(1))]; match_dist = sqrt( sum((current_offset - most_similar_offset).^2)); fprintf(' g.t. point %4.0f px. Match error %4.0f px.', dists(1), match_dist); if(dists(1) > 150 || match_dist > 40) good_matches(i) = 0; edgeColor = [1 0 0]; fprintf(' incorrect\n'); else good_matches(i) = 1; edgeColor = [0 1 0]; fprintf(' correct\n'); end if vis cur_color = rand(1,3); plot(x1_est(i)*scale_factor,y1_est(i)*scale_factor, 'o', 'LineWidth',2, 'MarkerEdgeColor',edgeColor,... 'MarkerFaceColor', cur_color, 'MarkerSize',10) plot(x2_est(i)*scale_factor+shiftX,y2_est(i)*scale_factor, 'o', 'LineWidth',2, 'MarkerEdgeColor',edgeColor,... 'MarkerFaceColor', cur_color, 'MarkerSize',10) end end if vis hold off; end title("x"); numGoodMatches = sum(good_matches); numBadMatches = length(x1_est) - sum(good_matches); precision = (sum(good_matches) / length(x1_est)) * 100; accuracy100 = min(sum(good_matches),100); % / 100) * 100; % If we were testing more than the top 100, then this would be important. fprintf('Uniqueness: Pre-merge: %d Post-merge: %d\n', numPreMerge, numPostMerge ); fprintf('%d total good matches, %d total bad matches.\n', numGoodMatches, numBadMatches) fprintf(' %.2f%% precision\n', precision); fprintf(' %.2f%% accuracy (top 100)\n', accuracy100); % Visualize and save the result if vis fprintf('Saving visualization to eval.png\n') saveas( gcf, 'eval.png' ); end