dlib C++ Library - shape_predictor_trainer.h

// 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"
#include "../data_io/image_dataset_metadata.h"
#include "box_overlap_testing.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:
 enum padding_mode_t
 {
 bounding_box_relative,
 landmark_relative 
 };
 shape_predictor_trainer (
 )
 {
 _cascade_depth = 10;
 _tree_depth = 4;
 _num_trees_per_cascade_level = 500;
 _nu = 0.1;
 _oversampling_amount = 20;
 _oversampling_translation_jitter = 0;
 _feature_pool_size = 400;
 _lambda = 0.1;
 _num_test_splits = 20;
 _feature_pool_region_padding = 0;
 _verbose = false;
 _num_threads = 0;
 _padding_mode = landmark_relative;
 }
 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;
 }
 double get_oversampling_translation_jitter (
 ) const { return _oversampling_translation_jitter; }
 void set_oversampling_translation_jitter (
 double amount
 )
 {
 DLIB_CASSERT(amount >= 0, 
 "\t void shape_predictor_trainer::set_oversampling_translation_jitter()"
 << "\n\t Invalid inputs were given to this function. "
 << "\n\t amount: " << amount 
 );
 _oversampling_translation_jitter = 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;
 }
 void set_padding_mode (
 padding_mode_t mode
 )
 {
 _padding_mode = mode;
 }
 padding_mode_t get_padding_mode (
 ) const { return _padding_mode; }
 double get_feature_pool_region_padding (
 ) const { return _feature_pool_region_padding; }
 void set_feature_pool_region_padding (
 double padding 
 )
 {
 DLIB_CASSERT(padding > -0.5,
 "\t void shape_predictor_trainer::set_feature_pool_region_padding()"
 << "\n\t Invalid inputs were given to this function. "
 << "\n\t padding: " << 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 << "\nTraining 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;
 if (length(p) > 100)
 {
 std::cout << "Warning, one of your objects has parts that are way outside its bounding box! This is probably an error in your annotation." << std::endl;
 }
 }
 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 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;
 const size_t max_iters = get_feature_pool_size()*get_feature_pool_size();
 for (size_t i = 0; i < max_iters; ++i)
 {
 feat.idx1 = rnd.get_integer(get_feature_pool_size());
 feat.idx2 = rnd.get_integer(get_feature_pool_size());
 while (feat.idx1 == feat.idx2)
 feat.idx2 = rnd.get_integer(get_feature_pool_size());
 const double dist = length(pixel_coordinates[feat.idx1]-pixel_coordinates[feat.idx2]);
 const double accept_prob = std::exp(-dist/lambda);
 if (accept_prob > rnd.get_random_double())
 break;
 }
 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));
 if (_oversampling_translation_jitter != 0)
 {
 dpoint off;
 off.x() = rnd.get_double_in_range(-_oversampling_translation_jitter,_oversampling_translation_jitter);
 off.y() = rnd.get_double_in_range(-_oversampling_translation_jitter,_oversampling_translation_jitter);
 for (long j = 0; j < samples[i].current_shape.size()/2; ++j)
 {
 samples[i].current_shape(2*j) += off.x();
 samples[i].current_shape(2*j+1) += off.y();
 }
 }
 }
 }
 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 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);
 double min_x = min(colm(temp,0));
 double min_y = min(colm(temp,1));
 double max_x = max(colm(temp,0));
 double max_y = max(colm(temp,1));
 if (get_padding_mode() == bounding_box_relative)
 {
 min_x = std::min(0.0, min_x);
 min_y = std::min(0.0, min_y);
 max_x = std::max(1.0, max_x);
 max_y = std::max(1.0, max_y);
 }
 min_x -= padding;
 min_y -= padding;
 max_x += padding;
 max_y += 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;
 padding_mode_t _padding_mode;
 double _oversampling_translation_jitter;
 };
// ----------------------------------------------------------------------------------------
 template <
 typename some_type_of_rectangle
 >
 image_dataset_metadata::dataset make_bounding_box_regression_training_data (
 const image_dataset_metadata::dataset& truth,
 const std::vector<std::vector<some_type_of_rectangle>>& detections
 )
 {
 DLIB_CASSERT(truth.images.size() == detections.size(), 
 "truth.images.size(): "<< truth.images.size() <<
 "\tdetections.size(): "<< detections.size()
 );
 image_dataset_metadata::dataset result = truth;
 for (size_t i = 0; i < truth.images.size(); ++i)
 {
 result.images[i].boxes.clear();
 for (auto truth_box : truth.images[i].boxes)
 {
 if (truth_box.ignore)
 continue;
 // Find the detection that best matches the current truth_box.
 auto det = max_scoring_element(detections[i], [&truth_box](const rectangle& r) { return box_intersection_over_union(r, truth_box.rect); });
 if (det.second > 0.5)
 {
 // Remove any existing parts and replace them with the truth_box corners.
 truth_box.parts.clear();
 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;
 result.images[i].boxes.push_back(truth_box);
 }
 }
 }
 return result;
 }
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SHAPE_PREDICToR_TRAINER_H_