    // 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_