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Commit 465e9542 authored by emptyVoid's avatar emptyVoid Committed by Davis E. King
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Move shape_predictor_trainer to a separate header (#599) 

Moved shape_predictor_trainer to its own header in order to support the
use of shape_predictor with DLIB_ISO_CPP_ONLY defined (i.e. without
threading API wrappers).
parent 10d3f933
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Showing with 1097 additions and 1062 deletions
dlib/image_processing.h
View file @ 465e9542
......@@ -20,6 +20,7 @@
#include "image_processing/remove_unobtainable_rectangles.h"
#include "image_processing/scan_fhog_pyramid.h"
#include "image_processing/shape_predictor.h"
#include "image_processing/shape_predictor_trainer.h"
#include "image_processing/correlation_tracker.h"
#endif // DLIB_IMAGE_PROCESSInG_H_h_
......
dlib/image_processing/shape_predictor.h
View file @ 465e9542
......@@ -9,9 +9,7 @@
#include "../matrix.h"
#include "../geometry.h"
#include "../pixel.h"
#include "../console_progress_indicator.h"
#include "../statistics.h"
#include "../threads.h"
#include <utility>
namespace dlib
......@@ -431,745 +429,6 @@ namespace dlib
dlib::deserialize(item.anchor_idx, in);
dlib::deserialize(item.deltas, in);
}
// ----------------------------------------------------------------------------------------
class shape_predictor_trainer
{
/*!
This thing really only works with unsigned char or rgb_pixel images (since we assume the threshold
should be in the range [-128,128]).
!*/
public:
shape_predictor_trainer (
)
{
_cascade_depth = 10;
_tree_depth = 4;
_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;
_num_threads = 0;
}
unsigned long get_cascade_depth (
) const { return _cascade_depth; }
void set_cascade_depth (
unsigned long depth
)
{
DLIB_CASSERT(depth > 0,
"\t void shape_predictor_trainer::set_cascade_depth()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t depth: " << depth
);
_cascade_depth = depth;
}
unsigned long get_tree_depth (
) const { return _tree_depth; }
void set_tree_depth (
unsigned long depth
)
{
DLIB_CASSERT(depth > 0,
"\t void shape_predictor_trainer::set_tree_depth()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t depth: " << depth
);
_tree_depth = depth;
}
unsigned long get_num_trees_per_cascade_level (
) const { return _num_trees_per_cascade_level; }
void set_num_trees_per_cascade_level (
unsigned long num
)
{
DLIB_CASSERT( num > 0,
"\t void shape_predictor_trainer::set_num_trees_per_cascade_level()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t num: " << num
);
_num_trees_per_cascade_level = num;
}
double get_nu (
) const { return _nu; }
void set_nu (
double nu
)
{
DLIB_CASSERT(0 < nu && nu <= 1,
"\t void shape_predictor_trainer::set_nu()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t nu: " << nu
);
_nu = nu;
}
std::string get_random_seed (
) const { return rnd.get_seed(); }
void set_random_seed (
const std::string& seed
) { rnd.set_seed(seed); }
unsigned long get_oversampling_amount (
) const { return _oversampling_amount; }
void set_oversampling_amount (
unsigned long amount
)
{
DLIB_CASSERT(amount > 0,
"\t void shape_predictor_trainer::set_oversampling_amount()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t amount: " << amount
);
_oversampling_amount = amount;
}
unsigned long get_feature_pool_size (
) const { return _feature_pool_size; }
void set_feature_pool_size (
unsigned long size
)
{
DLIB_CASSERT(size > 1,
"\t void shape_predictor_trainer::set_feature_pool_size()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t size: " << size
);
_feature_pool_size = size;
}
double get_lambda (
) const { return _lambda; }
void set_lambda (
double lambda
)
{
DLIB_CASSERT(lambda > 0,
"\t void shape_predictor_trainer::set_lambda()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t lambda: " << lambda
);
_lambda = lambda;
}
unsigned long get_num_test_splits (
) const { return _num_test_splits; }
void set_num_test_splits (
unsigned long num
)
{
DLIB_CASSERT(num > 0,
"\t void shape_predictor_trainer::set_num_test_splits()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t num: " << num
);
_num_test_splits = num;
}
double get_feature_pool_region_padding (
) const { return _feature_pool_region_padding; }
void set_feature_pool_region_padding (
double padding
)
{
_feature_pool_region_padding = padding;
}
void be_verbose (
)
{
_verbose = true;
}
void be_quiet (
)
{
_verbose = false;
}
unsigned long get_num_threads (
) const { return _num_threads; }
void set_num_threads (
unsigned long num
)
{
_num_threads = num;
}
template <typename image_array>
shape_predictor train (
const image_array& images,
const std::vector<std::vector<full_object_detection> >& objects
) const
{
using namespace impl;
DLIB_CASSERT(images.size() == objects.size() && images.size() > 0,
"\t shape_predictor shape_predictor_trainer::train()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t images.size(): " << images.size()
<< "\n\t objects.size(): " << objects.size()
);
// make sure the objects agree on the number of parts and that there is at
// least one full_object_detection.
unsigned long num_parts = 0;
std::vector<int> part_present;
for (unsigned long i = 0; i < objects.size(); ++i)
{
for (unsigned long j = 0; j < objects[i].size(); ++j)
{
if (num_parts == 0)
{
num_parts = objects[i][j].num_parts();
DLIB_CASSERT(objects[i][j].num_parts() != 0,
"\t shape_predictor shape_predictor_trainer::train()"
<< "\n\t You can't give objects that don't have any parts to the trainer."
);
part_present.resize(num_parts);
}
else
{
DLIB_CASSERT(objects[i][j].num_parts() == num_parts,
"\t shape_predictor shape_predictor_trainer::train()"
<< "\n\t All the objects must agree on the number of parts. "
<< "\n\t objects["<<i<<"]["<<j<<"].num_parts(): " << objects[i][j].num_parts()
<< "\n\t num_parts: " << num_parts
);
}
for (unsigned long p = 0; p < objects[i][j].num_parts(); ++p)
{
if (objects[i][j].part(p) != OBJECT_PART_NOT_PRESENT)
part_present[p] = 1;
}
}
}
DLIB_CASSERT(num_parts != 0,
"\t shape_predictor shape_predictor_trainer::train()"
<< "\n\t You must give at least one full_object_detection if you want to train a shape model and it must have parts."
);
DLIB_CASSERT(sum(mat(part_present)) == (long)num_parts,
"\t shape_predictor shape_predictor_trainer::train()"
<< "\n\t Each part must appear at least once in this training data. That is, "
<< "\n\t you can't have a part that is always set to OBJECT_PART_NOT_PRESENT."
);
// creating thread pool. if num_threads <= 1, trainer should work in caller thread
thread_pool tp(_num_threads > 1 ? _num_threads : 0);
// determining the type of features used for this type of images
typedef typename std::remove_const<typename std::remove_reference<decltype(images[0])>::type>::type image_type;
typedef typename image_traits<image_type>::pixel_type pixel_type;
typedef typename pixel_traits<pixel_type>::basic_pixel_type feature_type;
rnd.set_seed(get_random_seed());
std::vector<training_sample<feature_type>> samples;
const matrix<float,0,1> initial_shape = populate_training_sample_shapes(objects, samples);
const std::vector<std::vector<dlib::vector<float,2> > > pixel_coordinates = randomly_sample_pixel_coordinates(initial_shape);
unsigned long trees_fit_so_far = 0;
console_progress_indicator pbar(get_cascade_depth()*get_num_trees_per_cascade_level());
if (_verbose)
std::cout << "Fitting trees..." << std::endl;
std::vector<std::vector<impl::regression_tree> > forests(get_cascade_depth());
// Now start doing the actual training by filling in the forests
for (unsigned long cascade = 0; cascade < get_cascade_depth(); ++cascade)
{
// Each cascade uses a different set of pixels for its features. We compute
// their representations relative to the initial shape first.
std::vector<unsigned long> anchor_idx;
std::vector<dlib::vector<float,2> > deltas;
create_shape_relative_encoding(initial_shape, pixel_coordinates[cascade], anchor_idx, deltas);
// First compute the feature_pixel_values for each training sample at this
// level of the cascade.
parallel_for(tp, 0, samples.size(), [&](unsigned long i)
{
impl::extract_feature_pixel_values(images[samples[i].image_idx], samples[i].rect,
samples[i].current_shape, initial_shape, anchor_idx,
deltas, samples[i].feature_pixel_values);
}, 1);
// Now start building the trees at this cascade level.
for (unsigned long i = 0; i < get_num_trees_per_cascade_level(); ++i)
{
forests[cascade].push_back(make_regression_tree(tp, samples, pixel_coordinates[cascade]));
if (_verbose)
{
++trees_fit_so_far;
pbar.print_status(trees_fit_so_far);
}
}
}
if (_verbose)
std::cout << "Training complete " << std::endl;
return shape_predictor(initial_shape, forests, pixel_coordinates);
}
private:
static void object_to_shape (
const full_object_detection& obj,
matrix<float,0,1>& shape,
matrix<float,0,1>& present // a mask telling which elements of #shape are present.
)
{
shape.set_size(obj.num_parts()*2);
present.set_size(obj.num_parts()*2);
const point_transform_affine tform_from_img = impl::normalizing_tform(obj.get_rect());
for (unsigned long i = 0; i < obj.num_parts(); ++i)
{
if (obj.part(i) != OBJECT_PART_NOT_PRESENT)
{
vector<float,2> p = tform_from_img(obj.part(i));
shape(2*i) = p.x();
shape(2*i+1) = p.y();
present(2*i) = 1;
present(2*i+1) = 1;
}
else
{
shape(2*i) = 0;
shape(2*i+1) = 0;
present(2*i) = 0;
present(2*i+1) = 0;
}
}
}
template<typename feature_type>
struct training_sample
{
/*!
CONVENTION
- feature_pixel_values.size() == get_feature_pool_size()
- feature_pixel_values[j] == the value of the j-th feature pool
pixel when you look it up relative to the shape in current_shape.
- target_shape == The truth shape. Stays constant during the whole
training process (except for the parts that are not present, those are
always equal to the current_shape values).
- present == 0/1 mask saying which parts of target_shape are present.
- rect == the position of the object in the image_idx-th image. All shape
coordinates are coded relative to this rectangle.
- diff_shape == temporary value for holding difference between current
shape and target shape
!*/
unsigned long image_idx;
rectangle rect;
matrix<float,0,1> target_shape;
matrix<float,0,1> present;
matrix<float,0,1> current_shape;
matrix<float,0,1> diff_shape;
std::vector<feature_type> feature_pixel_values;
void swap(training_sample& item)
{
std::swap(image_idx, item.image_idx);
std::swap(rect, item.rect);
target_shape.swap(item.target_shape);
present.swap(item.present);
current_shape.swap(item.current_shape);
diff_shape.swap(item.diff_shape);
feature_pixel_values.swap(item.feature_pixel_values);
}
};
template<typename feature_type>
impl::regression_tree make_regression_tree (
thread_pool& tp,
std::vector<training_sample<feature_type>>& samples,
const std::vector<dlib::vector<float,2> >& pixel_coordinates
) const
{
using namespace impl;
std::deque<std::pair<unsigned long, unsigned long> > parts;
parts.push_back(std::make_pair(0, (unsigned long)samples.size()));
impl::regression_tree tree;
// walk the tree in breadth first order
const unsigned long num_split_nodes = static_cast<unsigned long>(std::pow(2.0, (double)get_tree_depth())-1);
std::vector<matrix<float,0,1> > sums(num_split_nodes*2+1);
if (tp.num_threads_in_pool() > 1)
{
// Here we need to calculate shape differences and store sum of differences into sums[0]
// to make it I am splitting of samples into blocks, each block will be processed by
// separate thread, and the sum of differences of each block is stored into separate
// place in block_sums
const unsigned long num_workers = std::max(1UL, tp.num_threads_in_pool());
const unsigned long num = samples.size();
const unsigned long block_size = std::max(1UL, (num + num_workers - 1) / num_workers);
std::vector<matrix<float,0,1> > block_sums(num_workers);
parallel_for(tp, 0, num_workers, [&](unsigned long block)
{
const unsigned long block_begin = block * block_size;
const unsigned long block_end = std::min(num, block_begin + block_size);
for (unsigned long i = block_begin; i < block_end; ++i)
{
samples[i].diff_shape = samples[i].target_shape - samples[i].current_shape;
block_sums[block] += samples[i].diff_shape;
}
}, 1);
// now calculate the total result from separate blocks
for (unsigned long i = 0; i < block_sums.size(); ++i)
sums[0] += block_sums[i];
}
else
{
// synchronous implementation
for (unsigned long i = 0; i < samples.size(); ++i)
{
samples[i].diff_shape = samples[i].target_shape - samples[i].current_shape;
sums[0] += samples[i].diff_shape;
}
}
for (unsigned long i = 0; i < num_split_nodes; ++i)
{
std::pair<unsigned long,unsigned long> range = parts.front();
parts.pop_front();
const impl::split_feature split = generate_split(tp, samples, range.first,
range.second, pixel_coordinates, sums[i], sums[left_child(i)],
sums[right_child(i)]);
tree.splits.push_back(split);
const unsigned long mid = partition_samples(split, samples, range.first, range.second);
parts.push_back(std::make_pair(range.first, mid));
parts.push_back(std::make_pair(mid, range.second));
}
// Now all the parts contain the ranges for the leaves so we can use them to
// compute the average leaf values.
matrix<float,0,1> present_counts(samples[0].target_shape.size());
tree.leaf_values.resize(parts.size());
for (unsigned long i = 0; i < parts.size(); ++i)
{
// Get the present counts for each dimension so we can divide each
// dimension by the number of observations we have on it to find the mean
// displacement in each leaf.
present_counts = 0;
for (unsigned long j = parts[i].first; j < parts[i].second; ++j)
present_counts += samples[j].present;
present_counts = dlib::reciprocal(present_counts);
if (parts[i].second != parts[i].first)
tree.leaf_values[i] = pointwise_multiply(present_counts,sums[num_split_nodes+i]*get_nu());
else
tree.leaf_values[i] = zeros_matrix(samples[0].target_shape);
// now adjust the current shape based on these predictions
parallel_for(tp, parts[i].first, parts[i].second, [&](unsigned long j)
{
samples[j].current_shape += tree.leaf_values[i];
// For parts that aren't present in the training data, we just make
// sure that the target shape always matches and therefore gives zero
// error. So this makes the algorithm simply ignore non-present
// landmarks.
for (long k = 0; k < samples[j].present.size(); ++k)
{
// if this part is not present
if (samples[j].present(k) == 0)
samples[j].target_shape(k) = samples[j].current_shape(k);
}
}, 1);
}
return tree;
}
impl::split_feature randomly_generate_split_feature (
const std::vector<dlib::vector<float,2> >& pixel_coordinates
) const
{
const double lambda = get_lambda();
impl::split_feature feat;
double accept_prob;
do
{
feat.idx1 = rnd.get_random_32bit_number()%get_feature_pool_size();
feat.idx2 = rnd.get_random_32bit_number()%get_feature_pool_size();
const double dist = length(pixel_coordinates[feat.idx1]-pixel_coordinates[feat.idx2]);
accept_prob = std::exp(-dist/lambda);
}
while(feat.idx1 == feat.idx2 || !(accept_prob > rnd.get_random_double()));
feat.thresh = (rnd.get_random_double()*256 - 128)/2.0;
return feat;
}
template<typename feature_type>
impl::split_feature generate_split (
thread_pool& tp,
const std::vector<training_sample<feature_type>>& samples,
unsigned long begin,
unsigned long end,
const std::vector<dlib::vector<float,2> >& pixel_coordinates,
const matrix<float,0,1>& sum,
matrix<float,0,1>& left_sum,
matrix<float,0,1>& right_sum
) const
{
// generate a bunch of random splits and test them and return the best one.
const unsigned long num_test_splits = get_num_test_splits();
// sample the random features we test in this function
std::vector<impl::split_feature> feats;
feats.reserve(num_test_splits);
for (unsigned long i = 0; i < num_test_splits; ++i)
feats.push_back(randomly_generate_split_feature(pixel_coordinates));
std::vector<matrix<float,0,1> > left_sums(num_test_splits);
std::vector<unsigned long> left_cnt(num_test_splits);
const unsigned long num_workers = std::max(1UL, tp.num_threads_in_pool());
const unsigned long block_size = std::max(1UL, (num_test_splits + num_workers - 1) / num_workers);
// now compute the sums of vectors that go left for each feature
parallel_for(tp, 0, num_workers, [&](unsigned long block)
{
const unsigned long block_begin = block * block_size;
const unsigned long block_end = std::min(block_begin + block_size, num_test_splits);
for (unsigned long j = begin; j < end; ++j)
{
for (unsigned long i = block_begin; i < block_end; ++i)
{
if ((float)samples[j].feature_pixel_values[feats[i].idx1] - (float)samples[j].feature_pixel_values[feats[i].idx2] > feats[i].thresh)
{
left_sums[i] += samples[j].diff_shape;
++left_cnt[i];
}
}
}
}, 1);
// now figure out which feature is the best
double best_score = -1;
unsigned long best_feat = 0;
matrix<float,0,1> temp;
for (unsigned long i = 0; i < num_test_splits; ++i)
{
// check how well the feature splits the space.
double score = 0;
unsigned long right_cnt = end-begin-left_cnt[i];
if (left_cnt[i] != 0 && right_cnt != 0)
{
temp = sum - left_sums[i];
score = dot(left_sums[i],left_sums[i])/left_cnt[i] + dot(temp,temp)/right_cnt;
if (score > best_score)
{
best_score = score;
best_feat = i;
}
}
}
left_sums[best_feat].swap(left_sum);
if (left_sum.size() != 0)
{
right_sum = sum - left_sum;
}
else
{
right_sum = sum;
left_sum = zeros_matrix(sum);
}
return feats[best_feat];
}
template<typename feature_type>
unsigned long partition_samples (
const impl::split_feature& split,
std::vector<training_sample<feature_type>>& samples,
unsigned long begin,
unsigned long end
) const
{
// splits samples based on split (sorta like in quick sort) and returns the mid
// point. make sure you return the mid in a way compatible with how we walk
// through the tree.
unsigned long i = begin;
for (unsigned long j = begin; j < end; ++j)
{
if ((float)samples[j].feature_pixel_values[split.idx1] - (float)samples[j].feature_pixel_values[split.idx2] > split.thresh)
{
samples[i].swap(samples[j]);
++i;
}
}
return i;
}
template<typename feature_type>
matrix<float,0,1> populate_training_sample_shapes(
const std::vector<std::vector<full_object_detection> >& objects,
std::vector<training_sample<feature_type>>& samples
) const
{
samples.clear();
matrix<float,0,1> mean_shape;
matrix<float,0,1> count;
// first fill out the target shapes
for (unsigned long i = 0; i < objects.size(); ++i)
{
for (unsigned long j = 0; j < objects[i].size(); ++j)
{
training_sample<feature_type> sample;
sample.image_idx = i;
sample.rect = objects[i][j].get_rect();
object_to_shape(objects[i][j], sample.target_shape, sample.present);
for (unsigned long itr = 0; itr < get_oversampling_amount(); ++itr)
samples.push_back(sample);
mean_shape += sample.target_shape;
count += sample.present;
}
}
mean_shape = pointwise_multiply(mean_shape,reciprocal(count));
// now go pick random initial shapes
for (unsigned long i = 0; i < samples.size(); ++i)
{
if ((i%get_oversampling_amount()) == 0)
{
// The mean shape is what we really use as an initial shape so always
// include it in the training set as an example starting shape.
samples[i].current_shape = mean_shape;
}
else
{
samples[i].current_shape.set_size(0);
matrix<float,0,1> hits(mean_shape.size());
hits = 0;
int iter = 0;
// Pick a few samples at random and randomly average them together to
// make the initial shape. Note that we make sure we get at least one
// observation (i.e. non-OBJECT_PART_NOT_PRESENT) on each part
// location.
while(min(hits) == 0 || iter < 2)
{
++iter;
const unsigned long rand_idx = rnd.get_random_32bit_number()%samples.size();
const double alpha = rnd.get_random_double()+0.1;
samples[i].current_shape += alpha*samples[rand_idx].target_shape;
hits += alpha*samples[rand_idx].present;
}
samples[i].current_shape = pointwise_multiply(samples[i].current_shape, reciprocal(hits));
}
}
for (unsigned long i = 0; i < samples.size(); ++i)
{
for (long k = 0; k < samples[i].present.size(); ++k)
{
// if this part is not present
if (samples[i].present(k) == 0)
samples[i].target_shape(k) = samples[i].current_shape(k);
}
}
return mean_shape;
}
void randomly_sample_pixel_coordinates (
std::vector<dlib::vector<float,2> >& pixel_coordinates,
const double min_x,
const double min_y,
const double max_x,
const double max_y
) const
/*!
ensures
- #pixel_coordinates.size() == get_feature_pool_size()
- for all valid i:
- pixel_coordinates[i] == a point in the box defined by the min/max x/y arguments.
!*/
{
pixel_coordinates.resize(get_feature_pool_size());
for (unsigned long i = 0; i < get_feature_pool_size(); ++i)
{
pixel_coordinates[i].x() = rnd.get_random_double()*(max_x-min_x) + min_x;
pixel_coordinates[i].y() = rnd.get_random_double()*(max_y-min_y) + min_y;
}
}
std::vector<std::vector<dlib::vector<float,2> > > randomly_sample_pixel_coordinates (
const matrix<float,0,1>& initial_shape
) const
{
const double padding = get_feature_pool_region_padding();
// Figure figure out the bounds on the object shapes. We will sample uniformly
// from this box.
matrix<float> temp = reshape(initial_shape, initial_shape.size()/2, 2);
const double min_x = min(colm(temp,0))-padding;
const double min_y = min(colm(temp,1))-padding;
const double max_x = max(colm(temp,0))+padding;
const double max_y = max(colm(temp,1))+padding;
std::vector<std::vector<dlib::vector<float,2> > > pixel_coordinates;
pixel_coordinates.resize(get_cascade_depth());
for (unsigned long i = 0; i < get_cascade_depth(); ++i)
randomly_sample_pixel_coordinates(pixel_coordinates[i], min_x, min_y, max_x, max_y);
return pixel_coordinates;
}
mutable dlib::rand rnd;
unsigned long _cascade_depth;
unsigned long _tree_depth;
unsigned long _num_trees_per_cascade_level;
double _nu;
unsigned long _oversampling_amount;
unsigned long _feature_pool_size;
double _lambda;
unsigned long _num_test_splits;
double _feature_pool_region_padding;
bool _verbose;
unsigned long _num_threads;
};
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
......
dlib/image_processing/shape_predictor_abstract.h
View file @ 465e9542
......@@ -25,9 +25,9 @@ namespace dlib
and eyes, tip of the nose, and so forth.
To create useful instantiations of this object you need to use the
shape_predictor_trainer object defined below to train a shape_predictor
using a set of training images, each annotated with shapes you want to
predict.
shape_predictor_trainer object defined in the
shape_predictor_trainer_abstract.h file to train a shape_predictor using a
set of training images, each annotated with shapes you want to predict.
THREAD SAFETY
No synchronization is required when using this object. In particular, a
......@@ -119,324 +119,6 @@ namespace dlib
provides serialization support
!*/
// ----------------------------------------------------------------------------------------
class shape_predictor_trainer
{
/*!
WHAT THIS OBJECT REPRESENTS
This object is a tool for training shape_predictors based on annotated training
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 (
);
/*!
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() == ""
- #get_num_threads() == 0
- 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 (
unsigned long depth
);
/*!
requires
- depth > 0
ensures
- #get_cascade_depth() == depth
!*/
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 (
unsigned long depth
);
/*!
requires
- depth > 0
ensures
- #get_tree_depth() == depth
!*/
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 (
unsigned long num
);
/*!
requires
- num > 0
ensures
- #get_num_trees_per_cascade_level() == num
!*/
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 (
double nu
);
/*!
requires
- 0 < nu <= 1
ensures
- #get_nu() == nu
!*/
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 (
const std::string& seed
);
/*!
ensures
- #get_random_seed() == seed
!*/
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 (
unsigned long amount
);
/*!
requires
- amount > 0
ensures
- #get_oversampling_amount() == amount
!*/
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 (
unsigned long size
);
/*!
requires
- size > 1
ensures
- #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 0.6x0.6 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 (
double lambda
);
/*!
requires
- lambda > 0
ensures
- #get_lambda() == lambda
!*/
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 (
unsigned long num
);
/*!
requires
- num > 0
ensures
- #get_num_test_splits() == num
!*/
unsigned long get_num_threads (
) const;
/*!
ensures
- When running training process, it is possible to make some parts of it parallel
using CPU threads with #parallel_for() extension and creating #thread_pool internally
When get_num_threads() == 0, trainer will not create threads and all processing will
be done in the calling thread
!*/
void set_num_threads (
unsigned long num
);
/*!
requires
- num >= 0
ensures
- #get_num_threads() == num
!*/
void be_verbose (
);
/*!
ensures
- This object will print status messages to standard out so that a
user can observe the progress of the algorithm.
!*/
void be_quiet (
);
/*!
ensures
- This object will not print anything to standard out
!*/
template <typename image_array>
shape_predictor train (
const image_array& images,
const std::vector<std::vector<full_object_detection> >& objects
) const;
/*!
requires
- image_array is a dlib::array of image objects where each image object
implements the interface defined in dlib/image_processing/generic_image.h
- images.size() == objects.size()
- images.size() > 0
- for some i: objects[i].size() != 0
(i.e. there has to be at least one full_object_detection in the training set)
- for all valid p, there must exist i and j such that:
objects[i][j].part(p) != OBJECT_PART_NOT_PRESENT.
(i.e. You can't define a part that is always set to OBJECT_PART_NOT_PRESENT.)
- for all valid i,j,k,l:
- objects[i][j].num_parts() == objects[k][l].num_parts()
(i.e. all objects must agree on the number of parts)
- objects[i][j].num_parts() > 0
ensures
- This object will try to learn to predict the locations of an object's parts
based on the object bounding box (i.e. full_object_detection::get_rect())
and the image pixels in that box. That is, we will try to learn a
shape_predictor, SP, such that:
SP(images[i], objects[i][j].get_rect()) == objects[i][j]
This learned SP object is then returned.
- Not all parts are required to be observed for all objects. So if you
have training instances with missing parts then set the part positions
equal to OBJECT_PART_NOT_PRESENT and this algorithm will basically ignore
those missing parts.
!*/
};
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
......
dlib/image_processing/shape_predictor_trainer.h 0 → 100644
View file @ 465e9542
// Copyright (C) 2014 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_SHAPE_PREDICToR_TRAINER_H_
#define DLIB_SHAPE_PREDICToR_TRAINER_H_
#include "shape_predictor_trainer_abstract.h"
#include "shape_predictor.h"
#include "../console_progress_indicator.h"
#include "../threads.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
class shape_predictor_trainer
{
/*!
This thing really only works with unsigned char or rgb_pixel images (since we assume the threshold
should be in the range [-128,128]).
!*/
public:
shape_predictor_trainer (
)
{
_cascade_depth = 10;
_tree_depth = 4;
_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;
_num_threads = 0;
}
unsigned long get_cascade_depth (
) const { return _cascade_depth; }
void set_cascade_depth (
unsigned long depth
)
{
DLIB_CASSERT(depth > 0,
"\t void shape_predictor_trainer::set_cascade_depth()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t depth: " << depth
);
_cascade_depth = depth;
}
unsigned long get_tree_depth (
) const { return _tree_depth; }
void set_tree_depth (
unsigned long depth
)
{
DLIB_CASSERT(depth > 0,
"\t void shape_predictor_trainer::set_tree_depth()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t depth: " << depth
);
_tree_depth = depth;
}
unsigned long get_num_trees_per_cascade_level (
) const { return _num_trees_per_cascade_level; }
void set_num_trees_per_cascade_level (
unsigned long num
)
{
DLIB_CASSERT( num > 0,
"\t void shape_predictor_trainer::set_num_trees_per_cascade_level()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t num: " << num
);
_num_trees_per_cascade_level = num;
}
double get_nu (
) const { return _nu; }
void set_nu (
double nu
)
{
DLIB_CASSERT(0 < nu && nu <= 1,
"\t void shape_predictor_trainer::set_nu()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t nu: " << nu
);
_nu = nu;
}
std::string get_random_seed (
) const { return rnd.get_seed(); }
void set_random_seed (
const std::string& seed
) { rnd.set_seed(seed); }
unsigned long get_oversampling_amount (
) const { return _oversampling_amount; }
void set_oversampling_amount (
unsigned long amount
)
{
DLIB_CASSERT(amount > 0,
"\t void shape_predictor_trainer::set_oversampling_amount()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t amount: " << amount
);
_oversampling_amount = amount;
}
unsigned long get_feature_pool_size (
) const { return _feature_pool_size; }
void set_feature_pool_size (
unsigned long size
)
{
DLIB_CASSERT(size > 1,
"\t void shape_predictor_trainer::set_feature_pool_size()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t size: " << size
);
_feature_pool_size = size;
}
double get_lambda (
) const { return _lambda; }
void set_lambda (
double lambda
)
{
DLIB_CASSERT(lambda > 0,
"\t void shape_predictor_trainer::set_lambda()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t lambda: " << lambda
);
_lambda = lambda;
}
unsigned long get_num_test_splits (
) const { return _num_test_splits; }
void set_num_test_splits (
unsigned long num
)
{
DLIB_CASSERT(num > 0,
"\t void shape_predictor_trainer::set_num_test_splits()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t num: " << num
);
_num_test_splits = num;
}
double get_feature_pool_region_padding (
) const { return _feature_pool_region_padding; }
void set_feature_pool_region_padding (
double padding
)
{
_feature_pool_region_padding = padding;
}
void be_verbose (
)
{
_verbose = true;
}
void be_quiet (
)
{
_verbose = false;
}
unsigned long get_num_threads (
) const { return _num_threads; }
void set_num_threads (
unsigned long num
)
{
_num_threads = num;
}
template <typename image_array>
shape_predictor train (
const image_array& images,
const std::vector<std::vector<full_object_detection> >& objects
) const
{
using namespace impl;
DLIB_CASSERT(images.size() == objects.size() && images.size() > 0,
"\t shape_predictor shape_predictor_trainer::train()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t images.size(): " << images.size()
<< "\n\t objects.size(): " << objects.size()
);
// make sure the objects agree on the number of parts and that there is at
// least one full_object_detection.
unsigned long num_parts = 0;
std::vector<int> part_present;
for (unsigned long i = 0; i < objects.size(); ++i)
{
for (unsigned long j = 0; j < objects[i].size(); ++j)
{
if (num_parts == 0)
{
num_parts = objects[i][j].num_parts();
DLIB_CASSERT(objects[i][j].num_parts() != 0,
"\t shape_predictor shape_predictor_trainer::train()"
<< "\n\t You can't give objects that don't have any parts to the trainer."
);
part_present.resize(num_parts);
}
else
{
DLIB_CASSERT(objects[i][j].num_parts() == num_parts,
"\t shape_predictor shape_predictor_trainer::train()"
<< "\n\t All the objects must agree on the number of parts. "
<< "\n\t objects["<<i<<"]["<<j<<"].num_parts(): " << objects[i][j].num_parts()
<< "\n\t num_parts: " << num_parts
);
}
for (unsigned long p = 0; p < objects[i][j].num_parts(); ++p)
{
if (objects[i][j].part(p) != OBJECT_PART_NOT_PRESENT)
part_present[p] = 1;
}
}
}
DLIB_CASSERT(num_parts != 0,
"\t shape_predictor shape_predictor_trainer::train()"
<< "\n\t You must give at least one full_object_detection if you want to train a shape model and it must have parts."
);
DLIB_CASSERT(sum(mat(part_present)) == (long)num_parts,
"\t shape_predictor shape_predictor_trainer::train()"
<< "\n\t Each part must appear at least once in this training data. That is, "
<< "\n\t you can't have a part that is always set to OBJECT_PART_NOT_PRESENT."
);
// creating thread pool. if num_threads <= 1, trainer should work in caller thread
thread_pool tp(_num_threads > 1 ? _num_threads : 0);
// determining the type of features used for this type of images
typedef typename std::remove_const<typename std::remove_reference<decltype(images[0])>::type>::type image_type;
typedef typename image_traits<image_type>::pixel_type pixel_type;
typedef typename pixel_traits<pixel_type>::basic_pixel_type feature_type;
rnd.set_seed(get_random_seed());
std::vector<training_sample<feature_type>> samples;
const matrix<float,0,1> initial_shape = populate_training_sample_shapes(objects, samples);
const std::vector<std::vector<dlib::vector<float,2> > > pixel_coordinates = randomly_sample_pixel_coordinates(initial_shape);
unsigned long trees_fit_so_far = 0;
console_progress_indicator pbar(get_cascade_depth()*get_num_trees_per_cascade_level());
if (_verbose)
std::cout << "Fitting trees..." << std::endl;
std::vector<std::vector<impl::regression_tree> > forests(get_cascade_depth());
// Now start doing the actual training by filling in the forests
for (unsigned long cascade = 0; cascade < get_cascade_depth(); ++cascade)
{
// Each cascade uses a different set of pixels for its features. We compute
// their representations relative to the initial shape first.
std::vector<unsigned long> anchor_idx;
std::vector<dlib::vector<float,2> > deltas;
create_shape_relative_encoding(initial_shape, pixel_coordinates[cascade], anchor_idx, deltas);
// First compute the feature_pixel_values for each training sample at this
// level of the cascade.
parallel_for(tp, 0, samples.size(), [&](unsigned long i)
{
impl::extract_feature_pixel_values(images[samples[i].image_idx], samples[i].rect,
samples[i].current_shape, initial_shape, anchor_idx,
deltas, samples[i].feature_pixel_values);
}, 1);
// Now start building the trees at this cascade level.
for (unsigned long i = 0; i < get_num_trees_per_cascade_level(); ++i)
{
forests[cascade].push_back(make_regression_tree(tp, samples, pixel_coordinates[cascade]));
if (_verbose)
{
++trees_fit_so_far;
pbar.print_status(trees_fit_so_far);
}
}
}
if (_verbose)
std::cout << "Training complete " << std::endl;
return shape_predictor(initial_shape, forests, pixel_coordinates);
}
private:
static void object_to_shape (
const full_object_detection& obj,
matrix<float,0,1>& shape,
matrix<float,0,1>& present // a mask telling which elements of #shape are present.
)
{
shape.set_size(obj.num_parts()*2);
present.set_size(obj.num_parts()*2);
const point_transform_affine tform_from_img = impl::normalizing_tform(obj.get_rect());
for (unsigned long i = 0; i < obj.num_parts(); ++i)
{
if (obj.part(i) != OBJECT_PART_NOT_PRESENT)
{
vector<float,2> p = tform_from_img(obj.part(i));
shape(2*i) = p.x();
shape(2*i+1) = p.y();
present(2*i) = 1;
present(2*i+1) = 1;
}
else
{
shape(2*i) = 0;
shape(2*i+1) = 0;
present(2*i) = 0;
present(2*i+1) = 0;
}
}
}
template<typename feature_type>
struct training_sample
{
/*!
CONVENTION
- feature_pixel_values.size() == get_feature_pool_size()
- feature_pixel_values[j] == the value of the j-th feature pool
pixel when you look it up relative to the shape in current_shape.
- target_shape == The truth shape. Stays constant during the whole
training process (except for the parts that are not present, those are
always equal to the current_shape values).
- present == 0/1 mask saying which parts of target_shape are present.
- rect == the position of the object in the image_idx-th image. All shape
coordinates are coded relative to this rectangle.
- diff_shape == temporary value for holding difference between current
shape and target shape
!*/
unsigned long image_idx;
rectangle rect;
matrix<float,0,1> target_shape;
matrix<float,0,1> present;
matrix<float,0,1> current_shape;
matrix<float,0,1> diff_shape;
std::vector<feature_type> feature_pixel_values;
void swap(training_sample& item)
{
std::swap(image_idx, item.image_idx);
std::swap(rect, item.rect);
target_shape.swap(item.target_shape);
present.swap(item.present);
current_shape.swap(item.current_shape);
diff_shape.swap(item.diff_shape);
feature_pixel_values.swap(item.feature_pixel_values);
}
};
template<typename feature_type>
impl::regression_tree make_regression_tree (
thread_pool& tp,
std::vector<training_sample<feature_type>>& samples,
const std::vector<dlib::vector<float,2> >& pixel_coordinates
) const
{
using namespace impl;
std::deque<std::pair<unsigned long, unsigned long> > parts;
parts.push_back(std::make_pair(0, (unsigned long)samples.size()));
impl::regression_tree tree;
// walk the tree in breadth first order
const unsigned long num_split_nodes = static_cast<unsigned long>(std::pow(2.0, (double)get_tree_depth())-1);
std::vector<matrix<float,0,1> > sums(num_split_nodes*2+1);
if (tp.num_threads_in_pool() > 1)
{
// Here we need to calculate shape differences and store sum of differences into sums[0]
// to make it I am splitting of samples into blocks, each block will be processed by
// separate thread, and the sum of differences of each block is stored into separate
// place in block_sums
const unsigned long num_workers = std::max(1UL, tp.num_threads_in_pool());
const unsigned long num = samples.size();
const unsigned long block_size = std::max(1UL, (num + num_workers - 1) / num_workers);
std::vector<matrix<float,0,1> > block_sums(num_workers);
parallel_for(tp, 0, num_workers, [&](unsigned long block)
{
const unsigned long block_begin = block * block_size;
const unsigned long block_end = std::min(num, block_begin + block_size);
for (unsigned long i = block_begin; i < block_end; ++i)
{
samples[i].diff_shape = samples[i].target_shape - samples[i].current_shape;
block_sums[block] += samples[i].diff_shape;
}
}, 1);
// now calculate the total result from separate blocks
for (unsigned long i = 0; i < block_sums.size(); ++i)
sums[0] += block_sums[i];
}
else
{
// synchronous implementation
for (unsigned long i = 0; i < samples.size(); ++i)
{
samples[i].diff_shape = samples[i].target_shape - samples[i].current_shape;
sums[0] += samples[i].diff_shape;
}
}
for (unsigned long i = 0; i < num_split_nodes; ++i)
{
std::pair<unsigned long,unsigned long> range = parts.front();
parts.pop_front();
const impl::split_feature split = generate_split(tp, samples, range.first,
range.second, pixel_coordinates, sums[i], sums[left_child(i)],
sums[right_child(i)]);
tree.splits.push_back(split);
const unsigned long mid = partition_samples(split, samples, range.first, range.second);
parts.push_back(std::make_pair(range.first, mid));
parts.push_back(std::make_pair(mid, range.second));
}
// Now all the parts contain the ranges for the leaves so we can use them to
// compute the average leaf values.
matrix<float,0,1> present_counts(samples[0].target_shape.size());
tree.leaf_values.resize(parts.size());
for (unsigned long i = 0; i < parts.size(); ++i)
{
// Get the present counts for each dimension so we can divide each
// dimension by the number of observations we have on it to find the mean
// displacement in each leaf.
present_counts = 0;
for (unsigned long j = parts[i].first; j < parts[i].second; ++j)
present_counts += samples[j].present;
present_counts = dlib::reciprocal(present_counts);
if (parts[i].second != parts[i].first)
tree.leaf_values[i] = pointwise_multiply(present_counts,sums[num_split_nodes+i]*get_nu());
else
tree.leaf_values[i] = zeros_matrix(samples[0].target_shape);
// now adjust the current shape based on these predictions
parallel_for(tp, parts[i].first, parts[i].second, [&](unsigned long j)
{
samples[j].current_shape += tree.leaf_values[i];
// For parts that aren't present in the training data, we just make
// sure that the target shape always matches and therefore gives zero
// error. So this makes the algorithm simply ignore non-present
// landmarks.
for (long k = 0; k < samples[j].present.size(); ++k)
{
// if this part is not present
if (samples[j].present(k) == 0)
samples[j].target_shape(k) = samples[j].current_shape(k);
}
}, 1);
}
return tree;
}
impl::split_feature randomly_generate_split_feature (
const std::vector<dlib::vector<float,2> >& pixel_coordinates
) const
{
const double lambda = get_lambda();
impl::split_feature feat;
double accept_prob;
do
{
feat.idx1 = rnd.get_random_32bit_number()%get_feature_pool_size();
feat.idx2 = rnd.get_random_32bit_number()%get_feature_pool_size();
const double dist = length(pixel_coordinates[feat.idx1]-pixel_coordinates[feat.idx2]);
accept_prob = std::exp(-dist/lambda);
}
while(feat.idx1 == feat.idx2 || !(accept_prob > rnd.get_random_double()));
feat.thresh = (rnd.get_random_double()*256 - 128)/2.0;
return feat;
}
template<typename feature_type>
impl::split_feature generate_split (
thread_pool& tp,
const std::vector<training_sample<feature_type>>& samples,
unsigned long begin,
unsigned long end,
const std::vector<dlib::vector<float,2> >& pixel_coordinates,
const matrix<float,0,1>& sum,
matrix<float,0,1>& left_sum,
matrix<float,0,1>& right_sum
) const
{
// generate a bunch of random splits and test them and return the best one.
const unsigned long num_test_splits = get_num_test_splits();
// sample the random features we test in this function
std::vector<impl::split_feature> feats;
feats.reserve(num_test_splits);
for (unsigned long i = 0; i < num_test_splits; ++i)
feats.push_back(randomly_generate_split_feature(pixel_coordinates));
std::vector<matrix<float,0,1> > left_sums(num_test_splits);
std::vector<unsigned long> left_cnt(num_test_splits);
const unsigned long num_workers = std::max(1UL, tp.num_threads_in_pool());
const unsigned long block_size = std::max(1UL, (num_test_splits + num_workers - 1) / num_workers);
// now compute the sums of vectors that go left for each feature
parallel_for(tp, 0, num_workers, [&](unsigned long block)
{
const unsigned long block_begin = block * block_size;
const unsigned long block_end = std::min(block_begin + block_size, num_test_splits);
for (unsigned long j = begin; j < end; ++j)
{
for (unsigned long i = block_begin; i < block_end; ++i)
{
if ((float)samples[j].feature_pixel_values[feats[i].idx1] - (float)samples[j].feature_pixel_values[feats[i].idx2] > feats[i].thresh)
{
left_sums[i] += samples[j].diff_shape;
++left_cnt[i];
}
}
}
}, 1);
// now figure out which feature is the best
double best_score = -1;
unsigned long best_feat = 0;
matrix<float,0,1> temp;
for (unsigned long i = 0; i < num_test_splits; ++i)
{
// check how well the feature splits the space.
double score = 0;
unsigned long right_cnt = end-begin-left_cnt[i];
if (left_cnt[i] != 0 && right_cnt != 0)
{
temp = sum - left_sums[i];
score = dot(left_sums[i],left_sums[i])/left_cnt[i] + dot(temp,temp)/right_cnt;
if (score > best_score)
{
best_score = score;
best_feat = i;
}
}
}
left_sums[best_feat].swap(left_sum);
if (left_sum.size() != 0)
{
right_sum = sum - left_sum;
}
else
{
right_sum = sum;
left_sum = zeros_matrix(sum);
}
return feats[best_feat];
}
template<typename feature_type>
unsigned long partition_samples (
const impl::split_feature& split,
std::vector<training_sample<feature_type>>& samples,
unsigned long begin,
unsigned long end
) const
{
// splits samples based on split (sorta like in quick sort) and returns the mid
// point. make sure you return the mid in a way compatible with how we walk
// through the tree.
unsigned long i = begin;
for (unsigned long j = begin; j < end; ++j)
{
if ((float)samples[j].feature_pixel_values[split.idx1] - (float)samples[j].feature_pixel_values[split.idx2] > split.thresh)
{
samples[i].swap(samples[j]);
++i;
}
}
return i;
}
template<typename feature_type>
matrix<float,0,1> populate_training_sample_shapes(
const std::vector<std::vector<full_object_detection> >& objects,
std::vector<training_sample<feature_type>>& samples
) const
{
samples.clear();
matrix<float,0,1> mean_shape;
matrix<float,0,1> count;
// first fill out the target shapes
for (unsigned long i = 0; i < objects.size(); ++i)
{
for (unsigned long j = 0; j < objects[i].size(); ++j)
{
training_sample<feature_type> sample;
sample.image_idx = i;
sample.rect = objects[i][j].get_rect();
object_to_shape(objects[i][j], sample.target_shape, sample.present);
for (unsigned long itr = 0; itr < get_oversampling_amount(); ++itr)
samples.push_back(sample);
mean_shape += sample.target_shape;
count += sample.present;
}
}
mean_shape = pointwise_multiply(mean_shape,reciprocal(count));
// now go pick random initial shapes
for (unsigned long i = 0; i < samples.size(); ++i)
{
if ((i%get_oversampling_amount()) == 0)
{
// The mean shape is what we really use as an initial shape so always
// include it in the training set as an example starting shape.
samples[i].current_shape = mean_shape;
}
else
{
samples[i].current_shape.set_size(0);
matrix<float,0,1> hits(mean_shape.size());
hits = 0;
int iter = 0;
// Pick a few samples at random and randomly average them together to
// make the initial shape. Note that we make sure we get at least one
// observation (i.e. non-OBJECT_PART_NOT_PRESENT) on each part
// location.
while(min(hits) == 0 || iter < 2)
{
++iter;
const unsigned long rand_idx = rnd.get_random_32bit_number()%samples.size();
const double alpha = rnd.get_random_double()+0.1;
samples[i].current_shape += alpha*samples[rand_idx].target_shape;
hits += alpha*samples[rand_idx].present;
}
samples[i].current_shape = pointwise_multiply(samples[i].current_shape, reciprocal(hits));
}
}
for (unsigned long i = 0; i < samples.size(); ++i)
{
for (long k = 0; k < samples[i].present.size(); ++k)
{
// if this part is not present
if (samples[i].present(k) == 0)
samples[i].target_shape(k) = samples[i].current_shape(k);
}
}
return mean_shape;
}
void randomly_sample_pixel_coordinates (
std::vector<dlib::vector<float,2> >& pixel_coordinates,
const double min_x,
const double min_y,
const double max_x,
const double max_y
) const
/*!
ensures
- #pixel_coordinates.size() == get_feature_pool_size()
- for all valid i:
- pixel_coordinates[i] == a point in the box defined by the min/max x/y arguments.
!*/
{
pixel_coordinates.resize(get_feature_pool_size());
for (unsigned long i = 0; i < get_feature_pool_size(); ++i)
{
pixel_coordinates[i].x() = rnd.get_random_double()*(max_x-min_x) + min_x;
pixel_coordinates[i].y() = rnd.get_random_double()*(max_y-min_y) + min_y;
}
}
std::vector<std::vector<dlib::vector<float,2> > > randomly_sample_pixel_coordinates (
const matrix<float,0,1>& initial_shape
) const
{
const double padding = get_feature_pool_region_padding();
// Figure figure out the bounds on the object shapes. We will sample uniformly
// from this box.
matrix<float> temp = reshape(initial_shape, initial_shape.size()/2, 2);
const double min_x = min(colm(temp,0))-padding;
const double min_y = min(colm(temp,1))-padding;
const double max_x = max(colm(temp,0))+padding;
const double max_y = max(colm(temp,1))+padding;
std::vector<std::vector<dlib::vector<float,2> > > pixel_coordinates;
pixel_coordinates.resize(get_cascade_depth());
for (unsigned long i = 0; i < get_cascade_depth(); ++i)
randomly_sample_pixel_coordinates(pixel_coordinates[i], min_x, min_y, max_x, max_y);
return pixel_coordinates;
}
mutable dlib::rand rnd;
unsigned long _cascade_depth;
unsigned long _tree_depth;
unsigned long _num_trees_per_cascade_level;
double _nu;
unsigned long _oversampling_amount;
unsigned long _feature_pool_size;
double _lambda;
unsigned long _num_test_splits;
double _feature_pool_region_padding;
bool _verbose;
unsigned long _num_threads;
};
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SHAPE_PREDICToR_TRAINER_H_
dlib/image_processing/shape_predictor_trainer_abstract.h 0 → 100644
View file @ 465e9542
// Copyright (C) 2014 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_SHAPE_PREDICToR_TRAINER_ABSTRACT_H_
#ifdef DLIB_SHAPE_PREDICToR_TRAINER_ABSTRACT_H_
#include "shape_predictor_abstract.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
class shape_predictor_trainer
{
/*!
WHAT THIS OBJECT REPRESENTS
This object is a tool for training shape_predictors based on annotated training
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 (
);
/*!
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() == ""
- #get_num_threads() == 0
- 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 (
unsigned long depth
);
/*!
requires
- depth > 0
ensures
- #get_cascade_depth() == depth
!*/
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 (
unsigned long depth
);
/*!
requires
- depth > 0
ensures
- #get_tree_depth() == depth
!*/
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 (
unsigned long num
);
/*!
requires
- num > 0
ensures
- #get_num_trees_per_cascade_level() == num
!*/
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 (
double nu
);
/*!
requires
- 0 < nu <= 1
ensures
- #get_nu() == nu
!*/
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 (
const std::string& seed
);
/*!
ensures
- #get_random_seed() == seed
!*/
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 (
unsigned long amount
);
/*!
requires
- amount > 0
ensures
- #get_oversampling_amount() == amount
!*/
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 (
unsigned long size
);
/*!
requires
- size > 1
ensures
- #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 0.6x0.6 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 (
double lambda
);
/*!
requires
- lambda > 0
ensures
- #get_lambda() == lambda
!*/
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 (
unsigned long num
);
/*!
requires
- num > 0
ensures
- #get_num_test_splits() == num
!*/
unsigned long get_num_threads (
) const;
/*!
ensures
- When running training process, it is possible to make some parts of it parallel
using CPU threads with #parallel_for() extension and creating #thread_pool internally
When get_num_threads() == 0, trainer will not create threads and all processing will
be done in the calling thread
!*/
void set_num_threads (
unsigned long num
);
/*!
requires
- num >= 0
ensures
- #get_num_threads() == num
!*/
void be_verbose (
);
/*!
ensures
- This object will print status messages to standard out so that a
user can observe the progress of the algorithm.
!*/
void be_quiet (
);
/*!
ensures
- This object will not print anything to standard out
!*/
template <typename image_array>
shape_predictor train (
const image_array& images,
const std::vector<std::vector<full_object_detection> >& objects
) const;
/*!
requires
- image_array is a dlib::array of image objects where each image object
implements the interface defined in dlib/image_processing/generic_image.h
- images.size() == objects.size()
- images.size() > 0
- for some i: objects[i].size() != 0
(i.e. there has to be at least one full_object_detection in the training set)
- for all valid p, there must exist i and j such that:
objects[i][j].part(p) != OBJECT_PART_NOT_PRESENT.
(i.e. You can't define a part that is always set to OBJECT_PART_NOT_PRESENT.)
- for all valid i,j,k,l:
- objects[i][j].num_parts() == objects[k][l].num_parts()
(i.e. all objects must agree on the number of parts)
- objects[i][j].num_parts() > 0
ensures
- This object will try to learn to predict the locations of an object's parts
based on the object bounding box (i.e. full_object_detection::get_rect())
and the image pixels in that box. That is, we will try to learn a
shape_predictor, SP, such that:
SP(images[i], objects[i][j].get_rect()) == objects[i][j]
This learned SP object is then returned.
- Not all parts are required to be observed for all objects. So if you
have training instances with missing parts then set the part positions
equal to OBJECT_PART_NOT_PRESENT and this algorithm will basically ignore
those missing parts.
!*/
};
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SHAPE_PREDICToR_TRAINER_ABSTRACT_H_
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