object_detection.cpp

// Copyright (C) 2014  Davis E. King (davis@dlib.net)
// License: Boost Software License   See LICENSE.txt for the full license.

#include <dlib/python.h>
#include <dlib/matrix.h>
#include <boost/python/args.hpp>
#include <dlib/geometry.h>
#include <dlib/image_processing/frontal_face_detector.h>
#include "indexing.h"
#include "simple_object_detector.h"
#include "conversion.h"

using namespace dlib;
using namespace std;
using namespace boost::python;

// ----------------------------------------------------------------------------------------

string print_simple_test_results(const simple_test_results& r)
{
    std::ostringstream sout;
    sout << "precision: "<<r.precision << ", recall: "<< r.recall << ", average precision: " << r.average_precision;
    return sout.str();
}

// ----------------------------------------------------------------------------------------

long left(const rectangle& r) { return r.left(); }
long top(const rectangle& r) { return r.top(); }
long right(const rectangle& r) { return r.right(); }
long bottom(const rectangle& r) { return r.bottom(); }
long width(const rectangle& r) { return r.width(); }
long height(const rectangle& r) { return r.height(); }

string print_rectangle_str(const rectangle& r)
{
    std::ostringstream sout;
    sout << r;
    return sout.str();
}

string print_rectangle_repr(const rectangle& r)
{
    std::ostringstream sout;
    sout << "rectangle(" << r.left() << "," << r.top() << "," << r.right() << "," << r.bottom() << ")";
    return sout.str();
}

// ----------------------------------------------------------------------------------------

std::vector<rectangle> run_detector_with_upscale (
    simple_object_detector& detector,
    object img,
    const unsigned int upsampling_amount
)
{
    pyramid_down<2> pyr;

    if (is_gray_python_image(img))
    {
        array2d<unsigned char> temp;
        if (upsampling_amount == 0)
        {
            return detector(numpy_gray_image(img));
        }
        else
        {
            pyramid_up(numpy_gray_image(img), temp, pyr);
            unsigned int levels = upsampling_amount-1;
            while (levels > 0)
            {
                levels--;
                pyramid_up(temp);
            }

            std::vector<rectangle> res = detector(temp);
            for (unsigned long i = 0; i < res.size(); ++i)
                res[i] = pyr.rect_down(res[i], upsampling_amount);
            return res;
        }
    }
    else if (is_rgb_python_image(img))
    {
        array2d<rgb_pixel> temp;
        if (upsampling_amount == 0)
        {
            return detector(numpy_rgb_image(img));
        }
        else
        {
            pyramid_up(numpy_rgb_image(img), temp, pyr);
            unsigned int levels = upsampling_amount-1;
            while (levels > 0)
            {
                levels--;
                pyramid_up(temp);
            }

            std::vector<rectangle> res = detector(temp);
            for (unsigned long i = 0; i < res.size(); ++i)
                res[i] = pyr.rect_down(res[i], upsampling_amount);
            return res;
        }
    }
    else
    {
        throw dlib::error("Unsupported image type, must be 8bit gray or RGB image.");
    }
}

// ----------------------------------------------------------------------------------------

inline void train_simple_object_detector_on_images_py (
    const object& pyimages,
    const object& pyboxes,
    const std::string& detector_output_filename,
    const simple_object_detector_training_options& options 
)
{
    const unsigned long num_images = len(pyimages);
    if (num_images != len(pyboxes))
        throw dlib::error("The length of the boxes list must match the length of the images list.");

    // We never have any ignore boxes for this version of the API.
    std::vector<std::vector<rectangle> > ignore(num_images), boxes(num_images);
    dlib::array<array2d<rgb_pixel> > images(num_images);
    images_and_nested_params_to_dlib(pyimages, pyboxes, images, boxes);

    train_simple_object_detector_on_images("", images, boxes, ignore, detector_output_filename, options);
}

inline simple_test_results test_simple_object_detector_with_images_py (
        const object& pyimages,
        const object& pyboxes,
        const std::string& detector_filename
)
{
    const unsigned long num_images = len(pyimages);
    if (num_images != len(pyboxes))
        throw dlib::error("The length of the boxes list must match the length of the images list.");

    // We never have any ignore boxes for this version of the API.
    std::vector<std::vector<rectangle> > ignore(num_images), boxes(num_images);
    dlib::array<array2d<rgb_pixel> > images(num_images);
    images_and_nested_params_to_dlib(pyimages, pyboxes, images, boxes);

    return test_simple_object_detector_with_images(images, boxes, ignore, detector_filename);
}

// ----------------------------------------------------------------------------------------

void bind_object_detection()
{
    using boost::python::arg;

    class_<simple_object_detector_training_options>("simple_object_detector_training_options", 
        "This object is a container for the options to the train_simple_object_detector() routine.")
        .add_property("be_verbose", &simple_object_detector_training_options::be_verbose, 
                                    &simple_object_detector_training_options::be_verbose,
                                    "If true, train_simple_object_detector() will print out a lot of information to the screen while training."
                                    )
        .add_property("add_left_right_image_flips", &simple_object_detector_training_options::add_left_right_image_flips, 
                                                    &simple_object_detector_training_options::add_left_right_image_flips,
"if true, train_simple_object_detector() will assume the objects are \n\
left/right symmetric and add in left right flips of the training \n\
images.  This doubles the size of the training dataset." 
                    /*!
                      if true, train_simple_object_detector() will assume the objects are
                      left/right symmetric and add in left right flips of the training
                      images.  This doubles the size of the training dataset.
                    !*/
                                                    )
        .add_property("detection_window_size", &simple_object_detector_training_options::detection_window_size,
                                               &simple_object_detector_training_options::detection_window_size,
                                               "The sliding window used will have about this many pixels inside it.")
        .add_property("C", &simple_object_detector_training_options::C,
                           &simple_object_detector_training_options::C,
"C is the usual SVM C regularization parameter.  So it is passed to \n\
structural_object_detection_trainer::set_c().  Larger values of C \n\
will encourage the trainer to fit the data better but might lead to \n\
overfitting.  Therefore, you must determine the proper setting of \n\
this parameter experimentally." 
                    /*!
                      C is the usual SVM C regularization parameter.  So it is passed to
                      structural_object_detection_trainer::set_c().  Larger values of C
                      will encourage the trainer to fit the data better but might lead to
                      overfitting.  Therefore, you must determine the proper setting of
                      this parameter experimentally.
                    !*/
                           )
        .add_property("epsilon", &simple_object_detector_training_options::epsilon,
                                 &simple_object_detector_training_options::epsilon,
"epsilon is the stopping epsilon.  Smaller values make the trainer's \n\
solver more accurate but might take longer to train." 
                    /*!
                      epsilon is the stopping epsilon.  Smaller values make the trainer's
                      solver more accurate but might take longer to train.
                    !*/
                           )
        .add_property("num_threads", &simple_object_detector_training_options::num_threads,
                                     &simple_object_detector_training_options::num_threads,
"train_simple_object_detector() will use this many threads of \n\
execution.  Set this to the number of CPU cores on your machine to \n\
obtain the fastest training speed." 
                    /*!
                      train_simple_object_detector() will use this many threads of
                      execution.  Set this to the number of CPU cores on your machine to
                      obtain the fastest training speed.
                    !*/
                                     );




    class_<simple_test_results>("simple_test_results")
        .add_property("precision", &simple_test_results::precision)
        .add_property("recall", &simple_test_results::recall)
        .add_property("average_precision", &simple_test_results::average_precision)
        .def("__str__", &::print_simple_test_results);


    {
    typedef rectangle type;
    class_<type>("rectangle", "This object represents a rectangular area of an image.")
        .def(init<long,long,long,long>( (arg("left"),arg("top"),arg("right"),arg("bottom")) ))
        .def("left",   &::left)
        .def("top",    &::top)
        .def("right",  &::right)
        .def("bottom", &::bottom)
        .def("width",  &::width)
        .def("height", &::height)
        .def("__str__", &::print_rectangle_str)
        .def("__repr__", &::print_rectangle_repr)
        .def_pickle(serialize_pickle<type>());
    }

    def("get_frontal_face_detector", get_frontal_face_detector, 
        "Returns the default face detector");

    def("train_simple_object_detector", train_simple_object_detector,
        (arg("dataset_filename"), arg("detector_output_filename"), arg("options")),
"requires \n\
    - options.C > 0 \n\
ensures \n\
    - Uses the structural_object_detection_trainer to train a \n\
      simple_object_detector based on the labeled images in the XML file \n\
      dataset_filename.  This function assumes the file dataset_filename is in the \n\
      XML format produced by dlib's save_image_dataset_metadata() routine. \n\
    - This function will apply a reasonable set of default parameters and \n\
      preprocessing techniques to the training procedure for simple_object_detector \n\
      objects.  So the point of this function is to provide you with a very easy \n\
      way to train a basic object detector.   \n\
    - The trained object detector is serialized to the file detector_output_filename." 
    /*!
        requires
            - options.C > 0
        ensures
            - Uses the structural_object_detection_trainer to train a
              simple_object_detector based on the labeled images in the XML file
              dataset_filename.  This function assumes the file dataset_filename is in the
              XML format produced by dlib's save_image_dataset_metadata() routine.
            - This function will apply a reasonable set of default parameters and
              preprocessing techniques to the training procedure for simple_object_detector
              objects.  So the point of this function is to provide you with a very easy
              way to train a basic object detector.  
            - The trained object detector is serialized to the file detector_output_filename.
    !*/
        );

    def("train_simple_object_detector", train_simple_object_detector_on_images_py,
        (arg("images"), arg("boxes"), arg("detector_output_filename"), arg("options")),
"requires \n\
    - options.C > 0 \n\
    - len(images) == len(boxes) \n\
    - images should be a list of numpy matrices that represent images, either RGB or grayscale. \n\
    - boxes should be a list of lists of dlib.rectangle object. \n\
ensures \n\
    - Uses the structural_object_detection_trainer to train a \n\
      simple_object_detector based on the labeled images and bounding boxes.  \n\
    - This function will apply a reasonable set of default parameters and \n\
      preprocessing techniques to the training procedure for simple_object_detector \n\
      objects.  So the point of this function is to provide you with a very easy \n\
      way to train a basic object detector.   \n\
    - The trained object detector is serialized to the file detector_output_filename." 
    /*!
        requires
            - options.C > 0
            - len(images) == len(boxes)
            - images should be a list of numpy matrices that represent images, either RGB or grayscale.
            - boxes should be a dlib.rectangles object (i.e. an array of rectangles).
            - boxes should be a list of lists of dlib.rectangle object.
        ensures
            - Uses the structural_object_detection_trainer to train a
              simple_object_detector based on the labeled images and bounding boxes. 
            - This function will apply a reasonable set of default parameters and
              preprocessing techniques to the training procedure for simple_object_detector
              objects.  So the point of this function is to provide you with a very easy
              way to train a basic object detector.  
            - The trained object detector is serialized to the file detector_output_filename.
    !*/
        );

    def("test_simple_object_detector", test_simple_object_detector,
            (arg("dataset_filename"), arg("detector_filename")),
            "ensures \n\
                - Loads an image dataset from dataset_filename.  We assume dataset_filename is \n\
                  a file using the XML format written by save_image_dataset_metadata(). \n\
                - Loads a simple_object_detector from the file detector_filename.  This means \n\
                  detector_filename should be a file produced by the train_simple_object_detector()  \n\
                  routine. \n\
                - This function tests the detector against the dataset and returns the \n\
                  precision, recall, and average precision of the detector.  In fact, The \n\
                  return value of this function is identical to that of dlib's \n\
                  test_object_detection_function() routine.  Therefore, see the documentation \n\
                  for test_object_detection_function() for a detailed definition of these \n\
                  metrics. "
            /*!
                ensures
                    - Loads an image dataset from dataset_filename.  We assume dataset_filename is
                      a file using the XML format written by save_image_dataset_metadata().
                    - Loads a simple_object_detector from the file detector_filename.  This means
                      detector_filename should be a file produced by the train_simple_object_detector()
                      routine.
                    - This function tests the detector against the dataset and returns the
                      precision, recall, and average precision of the detector.  In fact, The
                      return value of this function is identical to that of dlib's
                      test_object_detection_function() routine.  Therefore, see the documentation
                      for test_object_detection_function() for a detailed definition of these
                      metrics.
            !*/
        );

    def("test_simple_object_detector", test_simple_object_detector_with_images_py,
            (arg("images"), arg("boxes"), arg("detector_filename")),
            "requires \n\
               - len(images) == len(boxes) \n\
               - images should be a list of numpy matrices that represent images, either RGB or grayscale. \n\
               - boxes should be a list of lists of dlib.rectangle object. \n\
             ensures \n\
               - Loads a simple_object_detector from the file detector_filename.  This means \n\
                 detector_filename should be a file produced by the train_simple_object_detector() \n\
                 routine. \n\
               - This function tests the detector against the dataset and returns the \n\
                 precision, recall, and average precision of the detector.  In fact, The \n\
                 return value of this function is identical to that of dlib's \n\
                 test_object_detection_function() routine.  Therefore, see the documentation \n\
                 for test_object_detection_function() for a detailed definition of these \n\
                 metrics. "
    );
    {
    typedef simple_object_detector type;
    class_<type>("simple_object_detector", 
        "This object represents a sliding window histogram-of-oriented-gradients based object detector.")
        .def("__init__", make_constructor(&load_object_from_file<type>),  
"Loads a simple_object_detector from a file that contains the output of the \n\
train_simple_object_detector() routine." 
            /*!
                Loads a simple_object_detector from a file that contains the output of the
                train_simple_object_detector() routine.
            !*/)
        .def("__call__", run_detector_with_upscale, (arg("image"), arg("upsample_num_times")=0),
"requires \n\
    - image is a numpy ndarray containing either an 8bit grayscale or RGB \n\
      image. \n\
    - upsample_num_times >= 0 \n\
ensures \n\
    - This function runs the object detector on the input image and returns \n\
      a list of detections.   \n\
    - Upsamples the image upsample_num_times before running the basic \n\
      detector.  If you don't know how many times you want to upsample then \n\
      don't provide a value for upsample_num_times and an appropriate \n\
      default will be used." 
            /*!
                requires
                    - image is a numpy ndarray containing either an 8bit grayscale or RGB
                      image.
                    - upsample_num_times >= 0
                ensures
                    - This function runs the object detector on the input image and returns
                      a list of detections.  
                    - Upsamples the image upsample_num_times before running the basic
                      detector.  If you don't know how many times you want to upsample then
                      don't provide a value for upsample_num_times and an appropriate
                      default will be used.
            !*/
            )
        .def_pickle(serialize_pickle<type>());
    }
    {
    typedef std::vector<rectangle> type;
    class_<type>("rectangles", "An array of rectangle objects.")
        .def(vector_indexing_suite<type>())
        .def("clear", &type::clear)
        .def("resize", resize<type>)
        .def_pickle(serialize_pickle<type>());
    }
}

// ----------------------------------------------------------------------------------------