Function test_shape_predictor

dlib/image_processing/shape_predictor.h:441–502

        typename image_array
        >
    double test_shape_predictor (
        const shape_predictor& sp,
        const image_array& images,
        const std::vector<std::vector<full_object_detection> >& objects,
        const std::vector<std::vector<double> >& scales
    )
    {
        // make sure requires clause is not broken
#ifdef ENABLE_ASSERTS
        DLIB_CASSERT( images.size() == objects.size() ,
            "\t double test_shape_predictor()"
            << "\n\t Invalid inputs were given to this function. "
            << "\n\t images.size():  " << images.size() 
            << "\n\t objects.size(): " << objects.size() 
        );
        for (unsigned long i = 0; i < objects.size(); ++i)
        {
            for (unsigned long j = 0; j < objects[i].size(); ++j)
            {
                DLIB_CASSERT(objects[i][j].num_parts() == sp.num_parts(), 
                    "\t double test_shape_predictor()"
                    << "\n\t Invalid inputs were given to this function. "
                    << "\n\t objects["<<i<<"]["<<j<<"].num_parts(): " << objects[i][j].num_parts()
                    << "\n\t sp.num_parts(): " << sp.num_parts()
                );
            }
            if (scales.size() != 0)
            {
                DLIB_CASSERT(objects[i].size() == scales[i].size(), 
                    "\t double test_shape_predictor()"
                    << "\n\t Invalid inputs were given to this function. "
                    << "\n\t objects["<<i<<"].size(): " << objects[i].size()
                    << "\n\t scales["<<i<<"].size(): " << scales[i].size()
                );

            }
        }
#endif

        running_stats<double> rs;
        for (unsigned long i = 0; i < objects.size(); ++i)
        {
            for (unsigned long j = 0; j < objects[i].size(); ++j)
            {
                // Just use a scale of 1 (i.e. no scale at all) if the caller didn't supply
                // any scales.
                const double scale = scales.size()==0 ? 1 : scales[i][j]; 

                full_object_detection det = sp(images[i], objects[i][j].get_rect());

                for (unsigned long k = 0; k < det.num_parts(); ++k)
                {
                    if (objects[i][j].part(k) != OBJECT_PART_NOT_PRESENT)
                    {
                        double score = length(det.part(k) - objects[i][j].part(k))/scale;
                        rs.add(score);
                    }
                }