/* -*- mia-c++  -*-
 *
 * This file is part of MIA - a toolbox for medical image analysis
 * Copyright (c) Leipzig, Madrid 1999-2017 Gert Wollny
 *
 * MIA is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with MIA; if not, see <http://www.gnu.org/licenses/>.
 *
 */

#include <fstream>
#include <boost/filesystem.hpp>

#include <mia/internal/main.hh>
#include <mia/core/msgstream.hh>
#include <mia/core/cmdlineparser.hh>
#include <mia/core/errormacro.hh>
#include <mia/core/minimizer.hh>
#include <mia/core/ica.hh>
#include <mia/core/attribute_names.hh>
#include <mia/2d/nonrigidregister.hh>
#include <mia/2d/perfusion.hh>
#include <mia/2d/imageio.hh>
#include <mia/2d/segsetwithimages.hh>
#include <mia/2d/transformfactory.hh>


using namespace std;
using namespace mia;

namespace bfs = boost::filesystem;

const char *g_program_group = "Registration of series of 2D images";
const char *g_general_help =
       "This program implements the motion compensation algorithm described in "
       "[Wollny G, Kellman P, Santos A, Ledesma-Carbayo M-J, \"Automatic Motion Compensation of "
       "Free Breathing acquired Myocardial Perfusion Data by using Independent Component Analysis\" "
       "Medical Image Analysis, 2012.](https://doi.org/10.1016/j.media.2012.02.004)";
const char *g_program_example_descr =
       "Register the perfusion series given in 'segment.set' by using automatic ICA estimation. "
       "Skip two images at the beginning and otherwiese use the default parameters. "
       "Store the result in 'registered.set'.";
const char *g_program_example_code =
       "  -i segment.set -o registered.set -k 2";

const SProgramDescription description = {
       {pdi_group, g_program_group},
       {pdi_short, "Run a registration of a series of 2D images."},
       {pdi_description, g_general_help},
       {pdi_example_descr, g_program_example_descr},
       {pdi_example_code, g_program_example_code}
};

C2DFullCostList create_costs(P2DFullCost imagecost)
{
       C2DFullCostList result;
       result.push(imagecost);
       return result;
}

P2DTransformationFactory create_transform_creator(size_t c_rate, float penalty)
{
       stringstream transf;
       transf << "spline:rate=" << c_rate
              << ",imgboundary=mirror,imgkernel=[bspline:d=3],penalty=[divcurl:weight="
              << penalty << "]";
       cvinfo() << "Transform:" <<  transf.str() << "\n";
       return C2DTransformCreatorHandler::instance().produce(transf.str());
}


void segment_and_crop_input(CSegSetWithImages&  input_set,
                            const C2DPerfusionAnalysis& ica,
                            float box_scale,
                            C2DPerfusionAnalysis::EBoxSegmentation segmethod,
                            C2DImageSeries& references,
                            const string& save_crop_feature)
{
       C2DBounds crop_start;
       auto cropper = ica.get_crop_filter(box_scale, crop_start,
                                          segmethod, save_crop_feature);

       if (!cropper) {
              cvwarn() << "Cropping was requested, but segmentation failed - continuing at full image size\n";
              return;
       }

       C2DImageSeries input_images = input_set.get_images();

       for (auto i = input_images.begin(); i != input_images.end(); ++i)
              *i = cropper->filter(**i);

       for (auto i = references.begin(); i != references.end(); ++i)
              *i = cropper->filter(**i);

       auto tr_creator = C2DTransformCreatorHandler::instance().produce("translate");
       P2DTransformation shift = tr_creator->create(C2DBounds(1, 1));
       auto p = shift->get_parameters();
       p[0] = crop_start.x;
       p[1] = crop_start.y;
       shift->set_parameters(p);
       input_set.transform(*shift);
       input_set.set_images(input_images);
}

void run_registration_pass(CSegSetWithImages&  input_set,
                           const C2DImageSeries& references,
                           int skip_images, PMinimizer minimizer, size_t mg_levels,
                           double c_rate, double divcurlweight, P2DFullCost imagecost,
                           PMinimizer refinement_minimizer)
{
       CSegSetWithImages::Frames& frames = input_set.get_frames();
       C2DImageSeries input_images = input_set.get_images();
       auto costs  = create_costs(imagecost);
       auto transform_creator = create_transform_creator(c_rate, divcurlweight);
       C2DNonrigidRegister nrr(costs, minimizer,  transform_creator, mg_levels);

       if (refinement_minimizer)
              nrr.set_refinement_minimizer(refinement_minimizer);

       // this loop could be parallized
       for (size_t i = 0; i < input_images.size() - skip_images; ++i) {
              cvmsg() << "Register frame " << i << "\n";
              P2DTransformation transform = nrr.run(input_images[i + skip_images],
                                                    references[i]);
              input_images[i + skip_images] =
                     (*transform)(*input_images[i + skip_images]);
              frames[i + skip_images].inv_transform(*transform);
       }

       input_set.set_images(input_images);
}

void save_references(const string& save_ref, int current_pass, int skip_images, const C2DImageSeries& references)
{
       for (size_t i = 0 ; i < references.size(); ++i) {
              stringstream filename;
              filename << save_ref << current_pass << "-"
                       << setw(4) << setfill('0') << skip_images + i << ".v";
              save_image(filename.str(), references[i]);
       }
}

float get_relative_min_breathing_frequency(const C2DImageSeries& images, int skip, float min_breathing_frequency)
{
       if (min_breathing_frequency <= 0)
              return -1;

       int n_heartbeats = images.size() - skip;
       auto image_begin =  images[skip];
       auto image_end = images[images.size() - 1];

       if (image_begin->has_attribute("AcquisitionTime") && image_end->has_attribute(IDAcquisitionTime)) {
              double aq_time = image_end->get_attribute_as<double>(IDAcquisitionTime) -
                               image_begin->get_attribute_as<double>(IDAcquisitionTime);

              if (aq_time < 0)
                     throw create_exception<runtime_error>("Got non-postive acquisition time range ", aq_time,
                                                           ", can't handle this");

              double heart_rate = 60 * n_heartbeats / aq_time;
              cvmsg() << "Read a heartbeat rate of " << heart_rate << " beats/min\n";
              return heart_rate / min_breathing_frequency;
       } else
              return -1;
}

int do_main( int argc, char *argv[] )
{
       // IO parameters
       string in_filename;
       string out_filename;
       string registered_filebase("reg");
       // debug options: save some intermediate steps
       string cropped_filename;
       string save_crop_feature;
       string save_ref_filename;
       string save_reg_filename;
       // this parameter is currently not exported - reading the image data is
       // therefore done from the path given with the segmentation set
       bool override_src_imagepath = true;
       // registration parameters
       PMinimizer minimizer;
       PMinimizer refinement_minimizer;
       P2DFullCost imagecost;
       double c_rate = 16;
       double c_rate_divider = 2;
       double divcurlweight = 10.0;
       double divcurlweight_divider = 2.0;
       size_t mg_levels = 3;
       // ICA parameters
       size_t components = 0;
       bool normalize = false;
       bool no_meanstrip = false;
       float box_scale = 0.0;
       size_t skip_images = 0;
       size_t max_ica_iterations = 400;
       C2DPerfusionAnalysis::EBoxSegmentation
       segmethod = C2DPerfusionAnalysis::bs_features;
       PIndepCompAnalysisFactory icatool;
       float min_breathing_frequency = -1.0f;
       size_t current_pass = 0;
       size_t pass = 5;
       CCmdOptionList options(description);
       options.set_group("File-IO");
       options.add(make_opt( in_filename, "in-file", 'i',
                             "input perfusion data set", CCmdOptionFlags::required_input));
       options.add(make_opt( out_filename, "out-file", 'o',
                             "output perfusion data set", CCmdOptionFlags::required_output));
       options.add(make_opt( registered_filebase, "registered", 'r',
                             "file name base for registered fiels"));
       options.add(make_opt( cropped_filename, "save-cropped", 0,
                             "save cropped set to this file"));
       options.add(make_opt( save_crop_feature, "save-feature", 0, "save the features images resulting from the ICA and "
                             "some intermediate images used for the RV-LV segmentation with the given file name base to PNG files. "
                             "Also save the coefficients of the initial best and the final IC mixing matrix."));
       options.add(make_opt( save_ref_filename, "save-refs", 0,
                             "save synthetic reference images"));
       options.add(make_opt( save_reg_filename, "save-regs", 0,
                             "save intermediate registered images"));
       options.set_group("Registration");
       options.add(make_opt( minimizer, "gsl:opt=gd,step=0.1", "optimizer", 'O', "Optimizer used for minimization"));
       options.add(make_opt( refinement_minimizer, "", "refiner", 'R',
                             "optimizer used for refinement after the main optimizer was called"));
       options.add(make_opt( c_rate, "start-c-rate", 'a',
                             "start coefficinet rate in spines,"
                             " gets divided by --c-rate-divider with every pass"));
       options.add(make_opt( c_rate_divider, "c-rate-divider", 0,
                             "cofficient rate divider for each pass"));
       options.add(make_opt( divcurlweight, "start-divcurl", 'd',
                             "start divcurl weight, gets divided by"
                             " --divcurl-divider with every pass"));
       options.add(make_opt( divcurlweight_divider, "divcurl-divider", 0,
                             "divcurl weight scaling with each new pass"));
       options.add(make_opt( imagecost, "image:weight=1,cost=ssd", "imagecost", 'w', "image cost"));
       options.add(make_opt( mg_levels, "mg-levels", 'l', "multi-resolution levels"));
       options.add(make_opt( pass, "passes", 'P', "registration passes"));
       options.set_group("ICA");
       options.add(make_opt( icatool, "internal", "fastica", 0, "FastICA implementationto be used"));
       options.add(make_opt( components, "components", 'C', "ICA components 0 = automatic estimation"));
       options.add(make_opt( normalize, "normalize", 0, "normalized ICs"));
       options.add(make_opt( no_meanstrip, "no-meanstrip", 0,
                             "don't strip the mean from the mixing curves"));
       options.add(make_opt( box_scale, "segscale", 's',
                             "segment and scale the crop box around the LV (0=no segmentation)"));
       options.add(make_opt( skip_images, "skip", 'k', "skip images at the beginning of the series "
                             "e.g. because as they are of other modalities"));
       options.add(make_opt( max_ica_iterations, "max-ica-iter", 'm', "maximum number of iterations in ICA"));
       options.add(make_opt(segmethod, C2DPerfusionAnalysis::segmethod_dict, "segmethod", 'E',
                            "Segmentation method"));
       options.add(make_opt(min_breathing_frequency, "min-breathing-frequency", 'b',
                            "minimal mean frequency a mixing curve can have to be considered to stem from brething. "
                            "A healthy rest breating rate is 12 per minute. A negative value disables the test."));

       if (options.parse(argc, argv) != CCmdOptionList::hr_no)
              return EXIT_SUCCESS;

       // load input data set
       CSegSetWithImages  input_set(in_filename, override_src_imagepath);
       C2DImageSeries input_images = input_set.get_images();
       float rel_min_bf = get_relative_min_breathing_frequency(input_images,  skip_images, min_breathing_frequency);
       cvmsg() << "skipping " << skip_images << " images\n";
       vector<C2DFImage> series(input_images.size() - skip_images);
       transform(input_images.begin() + skip_images, input_images.end(),
                 series.begin(), FCopy2DImageToFloatRepn());
       // run ICA
       unique_ptr<C2DPerfusionAnalysis> ica(new C2DPerfusionAnalysis(components, normalize, !no_meanstrip));

       if (max_ica_iterations)
              ica->set_max_ica_iterations(max_ica_iterations);

       if (rel_min_bf > 0)
              ica->set_min_movement_frequency(rel_min_bf);

       ica->set_approach(CIndepCompAnalysis::appr_defl);

       if (!ica->run(series, *icatool)) {
              ica.reset(new C2DPerfusionAnalysis(components, normalize, !no_meanstrip));
              ica->set_approach(CIndepCompAnalysis::appr_symm);

              if (!ica->run(series, *icatool))
                     box_scale = false;
       }

       vector<C2DFImage> references_float = ica->get_references();
       C2DImageSeries references(references_float.size());
       transform(references_float.begin(), references_float.end(), references.begin(),
                 FWrapStaticDataInSharedPointer<C2DImage>());

       // crop if requested
       if (box_scale) {
              segment_and_crop_input(input_set, *ica, box_scale, segmethod, references, save_crop_feature);
              input_images = input_set.get_images();
       } else if (!save_crop_feature.empty()) {
              stringstream cfile;
              cfile << save_crop_feature << "-coeff.txt";
              ica->save_coefs(cfile.str());
       }

       // save cropped images if requested
       if (!cropped_filename.empty()) {
              bfs::path cf(cropped_filename);
              cf.replace_extension();
              input_set.rename_base(cf.filename().string());
              input_set.save_images(cropped_filename);
              ofstream outfile(cropped_filename, ios_base::out );

              if (outfile.good())
                     outfile << input_set.write().write_to_string();
              else
                     throw create_exception<runtime_error>( "unable to save to '", cropped_filename, "'");
       }

       bool do_continue = true;
       bool lastpass = false;

       do {
              ++current_pass;
              cvmsg() << "Registration pass " << current_pass << "\n";

              if (!save_ref_filename.empty())
                     save_references(save_ref_filename, current_pass, skip_images, references);

              run_registration_pass(input_set, references,  skip_images,  minimizer,
                                    mg_levels, c_rate, divcurlweight, imagecost, refinement_minimizer);

              if (!save_reg_filename.empty())
                     save_references(save_reg_filename, current_pass, 0, input_set.get_images());

              C2DPerfusionAnalysis ica2(components, normalize, !no_meanstrip);

              if (max_ica_iterations)
                     ica2.set_max_ica_iterations(max_ica_iterations);

              transform(input_set.get_images().begin() + skip_images,
                        input_set.get_images().end(), series.begin(), FCopy2DImageToFloatRepn());

              if (!ica2.run(series, *icatool)) {
                     ica2.set_approach(CIndepCompAnalysis::appr_symm);
                     ica2.run(series, *icatool);
              }

              if (lastpass)
                     break;

              divcurlweight /= divcurlweight_divider;

              if (c_rate > 1)
                     c_rate /= c_rate_divider;

              references_float = ica2.get_references();
              transform(references_float.begin(), references_float.end(),
                        references.begin(), FWrapStaticDataInSharedPointer<C2DImage>());
              do_continue =  (!pass || current_pass < pass) && ica2.has_movement();
              // run one more pass if the limit is not reached and no movement identified
              lastpass = (!do_continue && (!pass || current_pass < pass));
       } while (do_continue || lastpass);

       // run a final ICA pass to update the RV/LV peak
       C2DPerfusionAnalysis ica_final(4, normalize, !no_meanstrip);

       if (max_ica_iterations)
              ica_final.set_max_ica_iterations(max_ica_iterations);

       transform(input_set.get_images().begin() + skip_images,
                 input_set.get_images().end(), series.begin(), FCopy2DImageToFloatRepn());

       if (!ica_final.run(series, *icatool)) {
              ica_final.set_approach(CIndepCompAnalysis::appr_symm);
              ica_final.run(series, *icatool);
       }

       if ( input_set.get_RV_peak() < 0)
              input_set.set_RV_peak(ica_final.get_RV_peak_time() + skip_images);

       if ( input_set.get_LV_peak() < 0)
              input_set.set_LV_peak(ica_final.get_LV_peak_time() + skip_images);

       if (!save_crop_feature.empty()) {
              stringstream cfile;
              cfile << save_crop_feature << "-final.txt";
              ica_final.save_coefs(cfile.str());
              stringstream new_base;
              new_base << save_crop_feature << "-p" << pass << "-final";
              ica_final.save_feature_images(new_base.str());
       }

       input_set.rename_base(registered_filebase);
       input_set.save_images(out_filename);
       ofstream outfile(out_filename.c_str(), ios_base::out );

       if (outfile.good())
              outfile << input_set.write().write_to_string();

       return outfile.good() ? EXIT_SUCCESS : EXIT_FAILURE;
}

MIA_MAIN(do_main);

