Add optimized IoU function #226
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facundo-lezama
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dekked
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Dec 12, 2022
norfair/distances.py
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| to be in `[x_min, y_min, x_max, y_max]` format. | ||
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| Normal IoU is 1 when the boxes are the same and 0 when they don't overlap, | ||
| to transform that into a distance that makes sense we return `1 - iou`. | ||
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| Performs faster but errors might be cryptic if the bounding boxes are not valid. |
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| The distance. | ||
| """ | ||
| return _iou(detection.points, tracked_object.estimate) | ||
| iou_opt = iou # deprecated |
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Is it still necessary with the change in line 415?
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I did this so that users can still import iou_opt as a function, trying not to make that many breaking changes.
DiegoFernandezC
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Dec 20, 2022
javiber
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Dec 20, 2022
tests/test_distances.py
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| np.testing.assert_almost_equal(iou.distance_function(det, obj), 0) | ||
| np.testing.assert_almost_equal(iou_opt.distance_function(det, obj), 0) | ||
| det.points = det.points.reshape(1, 4) | ||
| obj.estimate = obj.estimate.reshape(1, 4) |
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why is the reshaping necessary?
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As we discussed, I simplified these.
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Also enables kwargs to be passed to scipy's cdist
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This PR adds a new implementation for the IoU distance calculation by leveraging
numpyfor vectorized calculations.Changes include:
iou_optPending: