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Diffstat (limited to 'ml/dlib/tools/python/src/shape_predictor.h')
| -rw-r--r-- | ml/dlib/tools/python/src/shape_predictor.h | 259 |
1 files changed, 0 insertions, 259 deletions
diff --git a/ml/dlib/tools/python/src/shape_predictor.h b/ml/dlib/tools/python/src/shape_predictor.h deleted file mode 100644 index f7a071a75..000000000 --- a/ml/dlib/tools/python/src/shape_predictor.h +++ /dev/null @@ -1,259 +0,0 @@ -// Copyright (C) 2014 Davis E. King (davis@dlib.net) -// License: Boost Software License See LICENSE.txt for the full license. -#ifndef DLIB_SHAPE_PREDICTOR_H__ -#define DLIB_SHAPE_PREDICTOR_H__ - -#include "dlib/string.h" -#include "dlib/geometry.h" -#include "dlib/data_io/load_image_dataset.h" -#include "dlib/image_processing.h" - -using namespace std; - -namespace dlib -{ - -// ---------------------------------------------------------------------------------------- - - struct shape_predictor_training_options - { - shape_predictor_training_options() - { - be_verbose = false; - cascade_depth = 10; - tree_depth = 4; - num_trees_per_cascade_level = 500; - nu = 0.1; - oversampling_amount = 20; - feature_pool_size = 400; - lambda_param = 0.1; - num_test_splits = 20; - feature_pool_region_padding = 0; - random_seed = ""; - num_threads = 0; - } - - bool be_verbose; - 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_param; - unsigned long num_test_splits; - double feature_pool_region_padding; - std::string random_seed; - - // not serialized - unsigned long num_threads; - }; - - inline void serialize ( - const shape_predictor_training_options& item, - std::ostream& out - ) - { - try - { - serialize(item.be_verbose,out); - serialize(item.cascade_depth,out); - serialize(item.tree_depth,out); - serialize(item.num_trees_per_cascade_level,out); - serialize(item.nu,out); - serialize(item.oversampling_amount,out); - serialize(item.feature_pool_size,out); - serialize(item.lambda_param,out); - serialize(item.num_test_splits,out); - serialize(item.feature_pool_region_padding,out); - serialize(item.random_seed,out); - } - catch (serialization_error& e) - { - throw serialization_error(e.info + "\n while serializing an object of type shape_predictor_training_options"); - } - } - - inline void deserialize ( - shape_predictor_training_options& item, - std::istream& in - ) - { - try - { - deserialize(item.be_verbose,in); - deserialize(item.cascade_depth,in); - deserialize(item.tree_depth,in); - deserialize(item.num_trees_per_cascade_level,in); - deserialize(item.nu,in); - deserialize(item.oversampling_amount,in); - deserialize(item.feature_pool_size,in); - deserialize(item.lambda_param,in); - deserialize(item.num_test_splits,in); - deserialize(item.feature_pool_region_padding,in); - deserialize(item.random_seed,in); - } - catch (serialization_error& e) - { - throw serialization_error(e.info + "\n while deserializing an object of type shape_predictor_training_options"); - } - } - - string print_shape_predictor_training_options(const shape_predictor_training_options& o) - { - std::ostringstream sout; - sout << "shape_predictor_training_options(" - << "be_verbose=" << o.be_verbose << "," - << "cascade_depth=" << o.cascade_depth << "," - << "tree_depth=" << o.tree_depth << "," - << "num_trees_per_cascade_level=" << o.num_trees_per_cascade_level << "," - << "nu=" << o.nu << "," - << "oversampling_amount=" << o.oversampling_amount << "," - << "feature_pool_size=" << o.feature_pool_size << "," - << "lambda_param=" << o.lambda_param << "," - << "num_test_splits=" << o.num_test_splits << "," - << "feature_pool_region_padding=" << o.feature_pool_region_padding << "," - << "random_seed=" << o.random_seed << "," - << "num_threads=" << o.num_threads - << ")"; - return sout.str(); - } - -// ---------------------------------------------------------------------------------------- - - namespace impl - { - inline bool contains_any_detections ( - const std::vector<std::vector<full_object_detection> >& detections - ) - { - for (unsigned long i = 0; i < detections.size(); ++i) - { - if (detections[i].size() != 0) - return true; - } - return false; - } - } - -// ---------------------------------------------------------------------------------------- - - template <typename image_array> - inline shape_predictor train_shape_predictor_on_images ( - image_array& images, - std::vector<std::vector<full_object_detection> >& detections, - const shape_predictor_training_options& options - ) - { - if (options.lambda_param <= 0) - throw error("Invalid lambda_param value given to train_shape_predictor(), lambda_param must be > 0."); - if (!(0 < options.nu && options.nu <= 1)) - throw error("Invalid nu value given to train_shape_predictor(). It is required that 0 < nu <= 1."); - if (options.feature_pool_region_padding <= -0.5) - throw error("Invalid feature_pool_region_padding value given to train_shape_predictor(), feature_pool_region_padding must be > -0.5."); - - if (images.size() != detections.size()) - throw error("The list of images must have the same length as the list of detections."); - - if (!impl::contains_any_detections(detections)) - throw error("Error, the training dataset does not have any labeled object detections in it."); - - shape_predictor_trainer trainer; - - trainer.set_cascade_depth(options.cascade_depth); - trainer.set_tree_depth(options.tree_depth); - trainer.set_num_trees_per_cascade_level(options.num_trees_per_cascade_level); - trainer.set_nu(options.nu); - trainer.set_random_seed(options.random_seed); - trainer.set_oversampling_amount(options.oversampling_amount); - trainer.set_feature_pool_size(options.feature_pool_size); - trainer.set_feature_pool_region_padding(options.feature_pool_region_padding); - trainer.set_lambda(options.lambda_param); - trainer.set_num_test_splits(options.num_test_splits); - trainer.set_num_threads(options.num_threads); - - if (options.be_verbose) - { - std::cout << "Training with cascade depth: " << options.cascade_depth << std::endl; - std::cout << "Training with tree depth: " << options.tree_depth << std::endl; - std::cout << "Training with " << options.num_trees_per_cascade_level << " trees per cascade level."<< std::endl; - std::cout << "Training with nu: " << options.nu << std::endl; - std::cout << "Training with random seed: " << options.random_seed << std::endl; - std::cout << "Training with oversampling amount: " << options.oversampling_amount << std::endl; - std::cout << "Training with feature pool size: " << options.feature_pool_size << std::endl; - std::cout << "Training with feature pool region padding: " << options.feature_pool_region_padding << std::endl; - std::cout << "Training with " << options.num_threads << " threads." << std::endl; - std::cout << "Training with lambda_param: " << options.lambda_param << std::endl; - std::cout << "Training with " << options.num_test_splits << " split tests."<< std::endl; - trainer.be_verbose(); - } - - shape_predictor predictor = trainer.train(images, detections); - - return predictor; - } - - inline void train_shape_predictor ( - const std::string& dataset_filename, - const std::string& predictor_output_filename, - const shape_predictor_training_options& options - ) - { - dlib::array<array2d<unsigned char> > images; - std::vector<std::vector<full_object_detection> > objects; - load_image_dataset(images, objects, dataset_filename); - - shape_predictor predictor = train_shape_predictor_on_images(images, objects, options); - - serialize(predictor_output_filename) << predictor; - - if (options.be_verbose) - std::cout << "Training complete, saved predictor to file " << predictor_output_filename << std::endl; - } - -// ---------------------------------------------------------------------------------------- - - template <typename image_array> - inline double test_shape_predictor_with_images ( - image_array& images, - std::vector<std::vector<full_object_detection> >& detections, - std::vector<std::vector<double> >& scales, - const shape_predictor& predictor - ) - { - if (images.size() != detections.size()) - throw error("The list of images must have the same length as the list of detections."); - if (scales.size() > 0 && scales.size() != images.size()) - throw error("The list of scales must have the same length as the list of detections."); - - if (scales.size() > 0) - return test_shape_predictor(predictor, images, detections, scales); - else - return test_shape_predictor(predictor, images, detections); - } - - inline double test_shape_predictor_py ( - const std::string& dataset_filename, - const std::string& predictor_filename - ) - { - // Load the images, no scales can be provided - dlib::array<array2d<unsigned char> > images; - // This interface cannot take the scales parameter. - std::vector<std::vector<double> > scales; - std::vector<std::vector<full_object_detection> > objects; - load_image_dataset(images, objects, dataset_filename); - - // Load the shape predictor - shape_predictor predictor; - deserialize(predictor_filename) >> predictor; - - return test_shape_predictor_with_images(images, objects, scales, predictor); - } - -// ---------------------------------------------------------------------------------------- - -} - -#endif // DLIB_SHAPE_PREDICTOR_H__ - |