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Changed to 5 point layout since it works better.

This commit is contained in:
Davis King 2018-02-28 22:07:28 -05:00
parent f8adfbfd96
commit 1cf6dbf423
2 changed files with 22 additions and 13 deletions
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10
dlib/image_processing/shape_predictor_trainer.h
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@ -827,10 +827,12 @@ namespace dlib
{
// Remove any existing parts and replace them with the truth_box corners.
truth_box.parts.clear();
truth_box.parts["top_left"] = truth_box.rect.tl_corner();
truth_box.parts["top_right"] = truth_box.rect.tr_corner();
truth_box.parts["bottom_left"] = truth_box.rect.bl_corner();
truth_box.parts["bottom_right"] = truth_box.rect.br_corner();
auto b = truth_box.rect;
truth_box.parts["left"] = (b.tl_corner()+b.bl_corner())/2;
truth_box.parts["right"] = (b.tr_corner()+b.br_corner())/2;
truth_box.parts["top"] = (b.tl_corner()+b.tr_corner())/2;
truth_box.parts["bottom"] = (b.bl_corner()+b.br_corner())/2;
truth_box.parts["middle"] = center(b);
// Now replace the bounding truth_box with the detector's bounding truth_box.
truth_box.rect = det.first;

25
dlib/image_processing/shape_predictor_trainer_abstract.h
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@ -388,17 +388,24 @@ namespace dlib
positional accuracy is going to be, at best, +/-8 pixels.
If you want to get better positional accuracy one easy thing to do is train a
shape_predictor to give you the corners of the object. The
shape_predictor to give you the location of the object's box. The
make_bounding_box_regression_training_data() routine helps you do this by
creating an appropriate training dataset. It does this by taking the dataset
you used to train your detector (the truth object), and combining that with
the output of your detector on each image in the training dataset (the
detections object). In particular, it will create a new annotated dataset
where each object box is one of the rectangles from detections and that
object has 4 part annotations, the corners of the truth rectangle
corresponding to that detection rectangle. You can then take the returned
dataset and train a shape_predictor on it. The resulting shape_predictor can
then be used to do bounding box regression.
you used to train your detector (given by the truth object), and combining
that with the output of your detector on each image in the training dataset
(given by the detections object). In particular, it will create a new
annotated dataset where each object box is one of the rectangles from
detections and that object has 5 part annotations. These annotations
identify the sides and middle of the truth rectangle corresponding to the
detection rectangle. You can then take the returned dataset and train a
shape_predictor on it. The resulting shape_predictor can then be used to do
bounding box regression.
As an aside, the reason we create 5 part annotations in this way is because
it gives the best shape_predictor when trained. If instead you used the 4
corners it wouldn't work as well, due to tedious vagaries of the shape_predictor
training process.
- We assume that detections[i] contains object detections corresponding to
the image truth.images[i].
!*/