/*
All modification made by Intel Corporation: © 2016 Intel Corporation

All contributions by the University of California:
Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
All rights reserved.

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Copyright (c) 2014, 2015, the respective contributors
All rights reserved.
For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md


Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

    * Redistributions of source code must retain the above copyright notice,
      this list of conditions and the following disclaimer.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.
    * Neither the name of Intel Corporation nor the names of its contributors
      may be used to endorse or promote products derived from this software
      without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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*/

#include <algorithm>
#include <map>
#include <string>
#include <vector>

#include "caffe/layers/detection_evaluate_layer.hpp"
#include "caffe/util/bbox_util.hpp"

namespace caffe {

template <typename Dtype>
void DetectionEvaluateLayer<Dtype>::LayerSetUp(
      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  const DetectionEvaluateParameter& detection_evaluate_param =
      this->layer_param_.detection_evaluate_param();
  CHECK(detection_evaluate_param.has_num_classes())
      << "Must provide num_classes.";
  num_classes_ = detection_evaluate_param.num_classes();
  background_label_id_ = detection_evaluate_param.background_label_id();
  overlap_threshold_ = detection_evaluate_param.overlap_threshold();
  CHECK_GT(overlap_threshold_, 0.) << "overlap_threshold must be non negative.";
  evaluate_difficult_gt_ = detection_evaluate_param.evaluate_difficult_gt();
  if (detection_evaluate_param.has_name_size_file()) {
    string name_size_file = detection_evaluate_param.name_size_file();
    std::ifstream infile(name_size_file.c_str());
    CHECK(infile.good())
        << "Failed to open name size file: " << name_size_file;
    // The file is in the following format:
    //    name height width
    //    ...
    string name;
    int height, width;
    while (infile >> name >> height >> width) {
      sizes_.push_back(std::make_pair(height, width));
    }
    infile.close();
  }
  count_ = 0;
  // If there is no name_size_file provided, use normalized bbox to evaluate.
  use_normalized_bbox_ = sizes_.size() == 0;

  // Retrieve resize parameter if there is any provided.
  has_resize_ = detection_evaluate_param.has_resize_param();
  if (has_resize_) {
    resize_param_ = detection_evaluate_param.resize_param();
  }
}

template <typename Dtype>
void DetectionEvaluateLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
  CHECK_LE(count_, sizes_.size());
  CHECK_EQ(bottom[0]->num(), 1);
  CHECK_EQ(bottom[0]->channels(), 1);
  CHECK_EQ(bottom[0]->width(), 7);
  CHECK_EQ(bottom[1]->num(), 1);
  CHECK_EQ(bottom[1]->channels(), 1);
  CHECK_EQ(bottom[1]->width(), 8);

  // num() and channels() are 1.
  vector<int> top_shape(2, 1);
  int num_pos_classes = background_label_id_ == -1 ?
      num_classes_ : num_classes_ - 1;
  int num_valid_det = 0;
  const Dtype* det_data = bottom[0]->cpu_data();
  for (int i = 0; i < bottom[0]->height(); ++i) {
    if (det_data[1] != -1) {
      ++num_valid_det;
    }
    det_data += 7;
  }
  top_shape.push_back(num_pos_classes + num_valid_det);
  // Each row is a 5 dimension vector, which stores
  // [image_id, label, confidence, true_pos, false_pos]
  top_shape.push_back(5);
  top[0]->Reshape(top_shape);
}

template <typename Dtype>
void DetectionEvaluateLayer<Dtype>::Forward_cpu(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  const Dtype* det_data = bottom[0]->cpu_data();
  const Dtype* gt_data = bottom[1]->cpu_data();

  // Retrieve all detection results.
  map<int, LabelBBox> all_detections;
  GetDetectionResults(det_data, bottom[0]->height(), background_label_id_,
                      &all_detections);

  // Retrieve all ground truth (including difficult ones).
  map<int, LabelBBox> all_gt_bboxes;
  GetGroundTruth(gt_data, bottom[1]->height(), background_label_id_,
                 true, &all_gt_bboxes);

  Dtype* top_data = top[0]->mutable_cpu_data();
  caffe_set(top[0]->count(), Dtype(0.), top_data);
  int num_det = 0;

  // Insert number of ground truth for each label.
  map<int, int> num_pos;
  for (map<int, LabelBBox>::iterator it = all_gt_bboxes.begin();
       it != all_gt_bboxes.end(); ++it) {
    for (LabelBBox::iterator iit = it->second.begin(); iit != it->second.end();
         ++iit) {
      int count = 0;
      if (evaluate_difficult_gt_) {
        count = iit->second.size();
      } else {
        // Get number of non difficult ground truth.
        for (int i = 0; i < iit->second.size(); ++i) {
          if (!iit->second[i].difficult()) {
            ++count;
          }
        }
      }
      if (num_pos.find(iit->first) == num_pos.end()) {
        num_pos[iit->first] = count;
      } else {
        num_pos[iit->first] += count;
      }
    }
  }
  for (int c = 0; c < num_classes_; ++c) {
    if (c == background_label_id_) {
      continue;
    }
    top_data[num_det * 5] = -1;
    top_data[num_det * 5 + 1] = c;
    if (num_pos.find(c) == num_pos.end()) {
      top_data[num_det * 5 + 2] = 0;
    } else {
      top_data[num_det * 5 + 2] = num_pos.find(c)->second;
    }
    top_data[num_det * 5 + 3] = -1;
    top_data[num_det * 5 + 4] = -1;
    ++num_det;
  }

  // Insert detection evaluate status.
  for (map<int, LabelBBox>::iterator it = all_detections.begin();
       it != all_detections.end(); ++it) {
    int image_id = it->first;
    LabelBBox& detections = it->second;
    if (all_gt_bboxes.find(image_id) == all_gt_bboxes.end()) {
      // No ground truth for current image. All detections become false_pos.
      for (LabelBBox::iterator iit = detections.begin();
           iit != detections.end(); ++iit) {
        int label = iit->first;
        if (label == -1) {
          continue;
        }
        const vector<NormalizedBBox>& bboxes = iit->second;
        for (int i = 0; i < bboxes.size(); ++i) {
          top_data[num_det * 5] = image_id;
          top_data[num_det * 5 + 1] = label;
          top_data[num_det * 5 + 2] = bboxes[i].score();
          top_data[num_det * 5 + 3] = 0;
          top_data[num_det * 5 + 4] = 1;
          ++num_det;
        }
      }
    } else {
      LabelBBox& label_bboxes = all_gt_bboxes.find(image_id)->second;
      for (LabelBBox::iterator iit = detections.begin();
           iit != detections.end(); ++iit) {
        int label = iit->first;
        if (label == -1) {
          continue;
        }
        vector<NormalizedBBox>& bboxes = iit->second;
        if (label_bboxes.find(label) == label_bboxes.end()) {
          // No ground truth for current label. All detections become false_pos.
          for (int i = 0; i < bboxes.size(); ++i) {
            top_data[num_det * 5] = image_id;
            top_data[num_det * 5 + 1] = label;
            top_data[num_det * 5 + 2] = bboxes[i].score();
            top_data[num_det * 5 + 3] = 0;
            top_data[num_det * 5 + 4] = 1;
            ++num_det;
          }
        } else {
          vector<NormalizedBBox>& gt_bboxes = label_bboxes.find(label)->second;
          // Scale ground truth if needed.
          if (!use_normalized_bbox_) {
            CHECK_LT(count_, sizes_.size());
            for (int i = 0; i < gt_bboxes.size(); ++i) {
              OutputBBox(gt_bboxes[i], sizes_[count_], has_resize_,
                         resize_param_, &(gt_bboxes[i]));
            }
          }
          vector<bool> visited(gt_bboxes.size(), false);
          // Sort detections in descend order based on scores.
          std::sort(bboxes.begin(), bboxes.end(), SortBBoxDescend);
          for (int i = 0; i < bboxes.size(); ++i) {
            top_data[num_det * 5] = image_id;
            top_data[num_det * 5 + 1] = label;
            top_data[num_det * 5 + 2] = bboxes[i].score();
            if (!use_normalized_bbox_) {
              OutputBBox(bboxes[i], sizes_[count_], has_resize_,
                         resize_param_, &(bboxes[i]));
            }
            // Compare with each ground truth bbox.
            float overlap_max = -1;
            int jmax = -1;
            for (int j = 0; j < gt_bboxes.size(); ++j) {
              float overlap = JaccardOverlap(bboxes[i], gt_bboxes[j],
                                             use_normalized_bbox_);
              if (overlap > overlap_max) {
                overlap_max = overlap;
                jmax = j;
              }
            }
            if (overlap_max >= overlap_threshold_) {
              if (evaluate_difficult_gt_ ||
                  (!evaluate_difficult_gt_ && !gt_bboxes[jmax].difficult())) {
                if (!visited[jmax]) {
                  // true positive.
                  top_data[num_det * 5 + 3] = 1;
                  top_data[num_det * 5 + 4] = 0;
                  visited[jmax] = true;
                } else {
                  // false positive (multiple detection).
                  top_data[num_det * 5 + 3] = 0;
                  top_data[num_det * 5 + 4] = 1;
                }
              }
            } else {
              // false positive.
              top_data[num_det * 5 + 3] = 0;
              top_data[num_det * 5 + 4] = 1;
            }
            ++num_det;
          }
        }
      }
    }
    if (sizes_.size() > 0) {
      ++count_;
      if (count_ == sizes_.size()) {
        // reset count after a full iterations through the DB.
        count_ = 0;
      }
    }
  }
}

INSTANTIATE_CLASS(DetectionEvaluateLayer);
REGISTER_LAYER_CLASS(DetectionEvaluate);

}  // namespace caffe
