61 lines
1.6 KiB
Mathematica
61 lines
1.6 KiB
Mathematica
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% load('ocv.mat')
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% Move into Matlab space
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% bboxes = bboxes + 1;
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load('matlab.mat')
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% Find the actual bboxes
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bboxes_detector = bboxes(:,3:6);
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% add x offset
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bboxes_detector(:,1) = bboxes(:,1) + bboxes_detector(:,1);
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% add y offset
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bboxes_detector(:,2) = bboxes(:,2) + bboxes_detector(:,2);
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non_detected = bboxes(:,3) == 1;
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% Find the width and height mappings
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widths_gt = (max(gt_labels(:,:,1)') - min(gt_labels(:,:,1)'))';
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widths_det = bboxes_detector(:,3);
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bad_det = abs(1 - widths_gt ./ widths_det) > 0.5;
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non_detected = non_detected | bad_det;
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% if the width is quite different from detection then it failed
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bboxes_detector = bboxes_detector(~non_detected,:);
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gt_labels = gt_labels(~non_detected,:,:);
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dets = dets(~non_detected,:);
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%% some visualisations
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% a = 1;
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% plot(gt_labels(a,:,1), gt_labels(a,:,2), '.r');
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% hold on;
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% bbox = bboxes_detector(a,:);
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% % bbox(2) = -bbox(2);
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% rectangle('Position', bbox);
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% hold off;
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% axis equal;
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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 = (max(gt_labels(:,:,1)') - min(gt_labels(:,:,1)'))';
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heights_gt = (max(gt_labels(:,:,2)') - min(gt_labels(:,:,2)'))';
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widths_det = bboxes_detector(:,3);
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heights_det = bboxes_detector(:,4);
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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 = min(gt_labels(:,:,1)')';
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ty_gt = min(gt_labels(:,:,2)')';
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tx_det = bboxes_detector(:,1);
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ty_det = bboxes_detector(:,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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