#ifndef CAFFE_ACT_DETECTION_OUTPUT_LAYER_HPP_
#define CAFFE_ACT_DETECTION_OUTPUT_LAYER_HPP_

#include <boost/property_tree/json_parser.hpp>
#include <boost/property_tree/ptree.hpp>
#include <boost/regex.hpp>

#include <map>
#include <string>
#include <utility>
#include <vector>

#include "caffe/blob.hpp"
#include "caffe/data_transformer.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/bbox_util.hpp"
#include "caffe/util/ACT_cuboid_util.hpp"

using namespace boost::property_tree;  // NOLINT(build/namespaces)

namespace caffe {

/**
 * @brief Generate the detection output based on location and confidence
 * predictions by doing non maximum suppression.
 *
 * Intended for use with ACT-detector detection method.
 *
 * NOTE: does not implement Backwards operation.
 */
template <typename Dtype>
class ACTDetectionOutputLayer : public Layer<Dtype> {
 public:
  explicit ACTDetectionOutputLayer(const LayerParameter& param)
      : Layer<Dtype>(param) {}
  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);

  virtual inline const char* type() const { return "ACTDetectionOutput"; }
  virtual inline int MinBottomBlobs() const { return 3; }
  virtual inline int MaxBottomBlobs() const { return 4; }
  virtual inline int ExactNumTopBlobs() const { return 1; }

 protected:
  /**
   * @brief Do non maximum suppression (nms) on prediction results.
   *
   * @param bottom input Blob vector (at least 2)
   *   -# @f$ (N \times C1 \times 1 \times 1) @f$
   *      the location predictions with C1 predictions.
   *   -# @f$ (N \times C2 \times 1 \times 1) @f$
   *      the confidence predictions with C2 predictions.
   *   -# @f$ (N \times 2 \times C3 \times 1) @f$
   *      the prior cuboids with C3 values.
   * @param top output Blob vector (length 1)
   *   -# @f$ (1 \times 1 \times N \times 7) @f$
   *      N is the number of detections after nms, and each row is:
   *      [image_id, label, confidence, [xmin, ymin, xmax, ymax] * K], 
   *      where K is the sequence length
   */
  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  /// @brief Not implemented
  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
    NOT_IMPLEMENTED;
  }
  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
    NOT_IMPLEMENTED;
  }

  int num_classes_;
  bool share_location_;
  int num_loc_classes_;
  int background_label_id_;
  CodeType code_type_;
  bool variance_encoded_in_target_;
  int keep_top_k_;
  float confidence_threshold_;

  int num_;
  int num_priors_;

  float nms_threshold_;
  int top_k_;
  float eta_;

  // ACT-detector: length of the cuboid
  int sequence_length_; 
  // ACT-detector: save all tubes/scores
  bool save_full_; 
  
  bool need_save_;
  string output_directory_;
  string output_name_prefix_;
  string output_format_;
  map<int, string> label_to_name_;
  map<int, string> label_to_display_name_;
  vector<string> names_;
  vector<pair<int, int> > sizes_;
  int num_test_image_;
  int name_count_;
  bool has_resize_;
  ResizeParameter resize_param_;

  ptree detections_;

  bool visualize_;
  float visualize_threshold_;
  shared_ptr<DataTransformer<Dtype> > data_transformer_;
  string save_file_;
  Blob<Dtype> bbox_preds_;
  Blob<Dtype> bbox_permute_;
  Blob<Dtype> conf_permute_;
};

}  // namespace caffe

#endif  // CAFFE_ACT_DETECTION_OUTPUT_LAYER_HPP_
