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Commit 687799f3 authored by Davis King's avatar Davis King
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fleshed out the spec

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dlib/image_processing/shape_predictor.h
View file @ 687799f3
......@@ -397,7 +397,7 @@ namespace dlib
)
{
_cascade_depth = 10;
_tree_depth = 2;
_tree_depth = 4;
_num_trees_per_cascade_level = 500;
_nu = 0.1;
_oversampling_amount = 20;
......
dlib/image_processing/shape_predictor_abstract.h
View file @ 687799f3
......@@ -18,9 +18,9 @@ namespace dlib
/*!
WHAT THIS OBJECT REPRESENTS
This object is a tool that takes in an image region containing some object
and outputs a "shape" or set of point locations that define the pose of the
object. The classic example of this is human face pose prediction, where
you take an image of a human face as input and are expected to identify the
and outputs a set of point locations that define the pose of the object.
The classic example of this is human face pose prediction, where you take
an image of a human face as input and are expected to identify the
locations of important facial landmarks such as the corners of the mouth
and eyes, tip of the nose, and so forth.
......@@ -88,28 +88,35 @@ namespace dlib
images. Its implementation uses the algorithm described in:
One Millisecond Face Alignment with an Ensemble of Regression Trees
by Vahid Kazemi and Josephine Sullivan, CVPR 2014
!*/
public:
shape_predictor_trainer (
)
{
_cascade_depth = 10;
_tree_depth = 2;
_num_trees_per_cascade_level = 500;
_nu = 0.1;
_oversampling_amount = 20;
_feature_pool_size = 400;
_lambda = 0.1;
_num_test_splits = 20;
_feature_pool_region_padding = 0;
_verbose = false;
}
);
/*!
ensures
- #get_cascade_depth() == 10
- #get_tree_depth() == 4
- #get_num_trees_per_cascade_level() == 500
- #get_nu() == 0.1
- #get_oversampling_amount() == 20
- #get_feature_pool_size() == 400
- #get_lambda() == 0.1
- #get_num_test_splits() == 20
- #get_feature_pool_region_padding() == 0
- #get_random_seed() == ""
- This object will not be verbose
!*/
unsigned long get_cascade_depth (
) const;
/*!
ensures
- returns the number of cascades created when you train a model. This
means that the total number of trees in the learned model is equal to
get_cascade_depth()*get_num_trees_per_cascade_level().
!*/
void set_cascade_depth (
......@@ -125,6 +132,9 @@ namespace dlib
unsigned long get_tree_depth (
) const;
/*!
ensures
- returns the depth of the trees used in the cascade. In particular, there
are pow(2,get_tree_depth()) leaves in each tree.
!*/
void set_tree_depth (
......@@ -140,6 +150,10 @@ namespace dlib
unsigned long get_num_trees_per_cascade_level (
) const;
/*!
ensures
- returns the number of trees created for each cascade. This means that
the total number of trees in the learned model is equal to
get_cascade_depth()*get_num_trees_per_cascade_level().
!*/
void set_num_trees_per_cascade_level (
......@@ -155,6 +169,10 @@ namespace dlib
double get_nu (
) const;
/*!
ensures
- returns the regularization parameter. Larger values of this parameter
will cause the algorithm to fit the training data better but may also
cause overfitting.
!*/
void set_nu (
......@@ -170,6 +188,11 @@ namespace dlib
std::string get_random_seed (
) const;
/*!
ensures
- returns the random seed used by the internal random number generator.
Since this algorithm is a random forest style algorithm it relies on a
random number generator for generating the trees. So each setting of the
random seed will produce slightly different outputs.
!*/
void set_random_seed (
......@@ -183,6 +206,17 @@ namespace dlib
unsigned long get_oversampling_amount (
) const;
/*!
ensures
- You give annotated images to this object as training examples. You
can effectively increase the amount of training data by adding in each
training example multiple times but with a randomly selected deformation
applied to it. That is what this parameter controls. That is, if you
supply N training samples to train() then the algorithm runs internally
with N*get_oversampling_amount() training samples. So the bigger this
parameter the better (excepting that larger values make training take
longer). In terms of the Kazemi paper, this parameter is the number of
randomly selected initial starting points sampled for each training
example.
!*/
void set_oversampling_amount (
......@@ -198,6 +232,11 @@ namespace dlib
unsigned long get_feature_pool_size (
) const;
/*!
ensures
- At each level of the cascade we randomly sample get_feature_pool_size()
pixels from the image. These pixels are used to generate features for
the random trees. So in general larger settings of this parameter give
better accuracy but make the algorithm run slower.
!*/
void set_feature_pool_size (
......@@ -210,9 +249,51 @@ namespace dlib
- #get_feature_pool_size() == size
!*/
double get_feature_pool_region_padding (
) const;
/*!
ensures
- When we randomly sample the pixels for the feature pool we do so in a box
fit around the provided training landmarks. By default, this box is the
tightest box that contains the landmarks (i.e. this is what happens when
get_feature_pool_region_padding()==0). However, you can expand or shrink
the size of the pixel sampling region by setting a different value of
get_feature_pool_region_padding().
To explain this precisely, for a padding of 0 we say that the pixels are
sampled from a box of size 1x1. The padding value is added to each side
of the box. So a padding of 0.5 would cause the algorithm to sample
pixels from a box that was 2x2, effectively multiplying the area pixels
are sampled from by 4. Similarly, setting the padding to -0.2 would
cause it to sample from a box 0.8x0.8 in size.
!*/
void set_feature_pool_region_padding (
double padding
);
/*!
ensures
- #get_feature_pool_region_padding() == padding
!*/
double get_lambda (
) const;
/*!
ensures
- To decide how to split nodes in the regression trees the algorithm looks
at pairs of pixels in the image. These pixel pairs are sampled randomly
but with a preference for selecting pixels that are near each other.
get_lambda() controls this "nearness" preference. In particular, smaller
values of get_lambda() will make the algorithm prefer to select pixels
close together and larger values of get_lambda() will make it care less
about picking nearby pixel pairs.
Note that this is the inverse of how it is defined in the Kazemi paper.
For this object, you should think of lambda as "the fraction of the
bounding box will we traverse to find a neighboring pixel". Nominally,
this is normalized between 0 and 1. So reasonable settings of lambda are
values in the range 0 < lambda < 1.
!*/
void set_lambda (
......@@ -228,6 +309,11 @@ namespace dlib
unsigned long get_num_test_splits (
) const;
/*!
ensures
- When generating the random trees we randomly sample get_num_test_splits()
possible split features at each node and pick the one that gives the best
split. Larger values of this parameter will usually give more accurate
outputs but take longer to train.
!*/
void set_num_test_splits (
......@@ -240,19 +326,6 @@ namespace dlib
- #get_num_test_splits() == num
!*/
double get_feature_pool_region_padding (
) const;
/*!
!*/
void set_feature_pool_region_padding (
double padding
);
/*!
ensures
- #get_feature_pool_region_padding() == padding
!*/
void be_verbose (
);
/*!
......@@ -265,7 +338,7 @@ namespace dlib
);
/*!
ensures
- this object will not print anything to standard out
- This object will not print anything to standard out
!*/
template <typename image_array>
......@@ -339,6 +412,14 @@ namespace dlib
const std::vector<std::vector<full_object_detection> >& objects
);
/*!
requires
- images.size() == objects.size()
- for all valid i and j:
- objects[i][j].num_parts() == sp.num_parts()
ensures
- returns test_shape_predictor(sp, images, objects, no_scales) where no_scales
is an empty vector. So this is just a convenience function for calling the
above test_shape_predictor() routine without a scales argument.
!*/
// ----------------------------------------------------------------------------------------
......
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