Merging upstream version 1.44.3.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 259 insertions, 0 deletions

diff --git a/ml/dlib/tools/python/src/shape_predictor.h b/ml/dlib/tools/python/src/shape_predictor.h
new file mode 100644
index 000000000..f7a071a75
--- /dev/null
+++ b/ml/dlib/tools/python/src/shape_predictor.h
@@ -0,0 +1,259 @@
+// 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__
+