140 lines
3.7 KiB
Matlab
140 lines
3.7 KiB
Matlab
% load('ocv.mat')
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% Move into Matlab space
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% bboxes = bboxes + 1;
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use_afw = false;
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use_lfpw = true;
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use_helen = true;
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use_ibug = false;
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root_test_data = 'F:\Dropbox\Dropbox\AAM\test data/';
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% this loads the ground truths
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% [gt_images, gt_detections, gt_labels] = Collect_wild_imgs(root_test_data, use_afw, use_lfpw, use_helen, use_ibug);
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%% Find the width and height mappings
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tlx_gt = min(gt_labels(:,:,1)')' - 1;
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tly_gt = min(gt_labels(:,:,2)')' - 1;
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blx_gt = max(gt_labels(:,:,1)')' - 1;
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bly_gt = max(gt_labels(:,:,2)')' - 1;
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bboxes_gt = [tlx_gt, tly_gt, blx_gt, bly_gt];
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%% Extract the detector bounding boxes as well
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f = fopen('F:\dlib_baseline\helen.txt');
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det_file = textscan(f, '%s %f %f %f %f %f');
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fclose(f);
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f = fopen('F:\dlib_baseline\lfpw.txt');
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det_file_2 = textscan(f, '%s %f %f %f %f %f');
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fclose(f);
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det_images = cat(1, det_file{1}, det_file_2{1});
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scores_det = cat(1, det_file{2}, det_file_2{2});
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bboxes_det = [det_file{3}, det_file{4}, det_file{5}, det_file{6}];
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bboxes_det_2 = [det_file_2{3}, det_file_2{4}, det_file_2{5}, det_file_2{6}];
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bboxes_det = cat(1, bboxes_det, bboxes_det_2);
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%% Go through ground truth picking out the best detections
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bbox_gt_with_tp = [];
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bbox_det_tp = [];
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scores_tp = [];
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overlaps_tp = [];
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tps_all = zeros(size(bboxes_det, 1), 1);
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for gt_ind = 1:size(bboxes_gt, 1)
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[~, fname, ~] = fileparts(gt_images(gt_ind).img);
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det_inds = find(strcmp(det_images, fname))';
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bbox_gt = bboxes_gt(gt_ind,:);
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bbox_det = [];
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max_overlap = 0;
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max_ind = 0;
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for det_ind=det_inds
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curr_ov = overlap(bbox_gt, bboxes_det(det_ind,:));
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if(curr_ov > max_overlap)
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max_overlap = curr_ov;
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bbox_det = bboxes_det(det_ind,:);
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score = scores_det(det_ind);
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max_ind = det_ind;
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end
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end
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if(max_overlap > 0.4)
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tps_all(max_ind) = 1;
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bbox_gt_with_tp = cat(1, bbox_gt_with_tp, bbox_gt);
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bbox_det_tp = cat(1, bbox_det_tp, bbox_det);
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scores_tp = cat(1, scores_tp, score);
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overlaps_tp = cat(1, overlaps_tp, max_overlap);
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end
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end
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%% Work out the mapping and a suitable threshold, with EER?
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%% some visualisations
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% Want to find out what scaling and translation would lead to the smallest
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% RMSE error between initialised landmarks and gt landmarks TODO
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% Find the width and height mappings
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widths_gt = bbox_gt_with_tp(:,3) - bbox_gt_with_tp(:,1);
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heights_gt = bbox_gt_with_tp(:,4) - bbox_gt_with_tp(:,2);
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widths_det = bbox_det_tp(:,3) - bbox_det_tp(:,1);
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heights_det = bbox_det_tp(:,4) - bbox_det_tp(:,2);
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s_width = widths_det \ widths_gt;
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s_height = heights_det \ heights_gt;
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tx_gt = bbox_gt_with_tp(:,1);
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ty_gt = bbox_gt_with_tp(:,2);
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tx_det = bbox_det_tp(:,1);
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ty_det = bbox_det_tp(:,2);
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s_tx = mean((tx_gt - tx_det) ./ widths_det);
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s_ty = mean((ty_gt - ty_det) ./ heights_det);
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% newbbox
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new_widths = widths_det * s_width;
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new_heights = heights_det * s_height;
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new_tx = widths_det * s_tx + tx_det;
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new_ty = heights_det * s_ty + ty_det;
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bbox_det_tp_new = [new_tx, new_ty, new_tx + new_widths, new_ty + new_heights];
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new_overlaps = zeros(size(bbox_det_tp_new, 1), 1);
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for gt_ind = 1:size(bbox_det_tp_new, 1)
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new_overlaps(gt_ind) = overlap(bbox_gt_with_tp(gt_ind,:), bbox_det_tp_new(gt_ind,:));
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end
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%% Draw an ROC curve
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score_bands = min(scores_det):(max(scores_det)-min(scores_det))/50:max(scores_det);
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recalls = zeros(numel(score_bands), 1);
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precisions = zeros(numel(score_bands), 1);
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for i=1:numel(score_bands)
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precisions(i) = sum(tps_all(scores_det >= score_bands(i))) / sum(scores_det >= score_bands(i));
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recalls(i) = sum(tps_all(scores_det >= score_bands(i))) / size(gt_labels,1);
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end
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% tp
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% bbox_det_new = bbox |