/*
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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*/

#include <algorithm>
#include <functional>
#include <utility>
#include <vector>

#include "caffe/layers/prior_box_layer.hpp"

namespace caffe {

template <typename Dtype>
void PriorBoxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
  const PriorBoxParameter& prior_box_param =
      this->layer_param_.prior_box_param();
  CHECK_GT(prior_box_param.min_size_size(), 0) << "must provide min_size.";
  for (int i = 0; i < prior_box_param.min_size_size(); ++i) {
    min_sizes_.push_back(prior_box_param.min_size(i));
    CHECK_GT(min_sizes_.back(), 0) << "min_size must be positive.";
  }
  aspect_ratios_.clear();
  aspect_ratios_.push_back(1.);
  flip_ = prior_box_param.flip();
  for (int i = 0; i < prior_box_param.aspect_ratio_size(); ++i) {
    float ar = prior_box_param.aspect_ratio(i);
    bool already_exist = false;
    for (int j = 0; j < aspect_ratios_.size(); ++j) {
      if (fabs(ar - aspect_ratios_[j]) < 1e-6) {
        already_exist = true;
        break;
      }
    }
    if (!already_exist) {
      aspect_ratios_.push_back(ar);
      if (flip_) {
        aspect_ratios_.push_back(1./ar);
      }
    }
  }
  num_priors_ = aspect_ratios_.size() * min_sizes_.size();
  if (prior_box_param.max_size_size() > 0) {
    CHECK_EQ(prior_box_param.min_size_size(), prior_box_param.max_size_size());
    for (int i = 0; i < prior_box_param.max_size_size(); ++i) {
      max_sizes_.push_back(prior_box_param.max_size(i));
      CHECK_GT(max_sizes_[i], min_sizes_[i])
          << "max_size must be greater than min_size.";
      num_priors_ += 1;
    }
  }
  clip_ = prior_box_param.clip();
  if (prior_box_param.variance_size() > 1) {
    // Must and only provide 4 variance.
    CHECK_EQ(prior_box_param.variance_size(), 4);
    for (int i = 0; i < prior_box_param.variance_size(); ++i) {
      CHECK_GT(prior_box_param.variance(i), 0);
      variance_.push_back(prior_box_param.variance(i));
    }
  } else if (prior_box_param.variance_size() == 1) {
    CHECK_GT(prior_box_param.variance(0), 0);
    variance_.push_back(prior_box_param.variance(0));
  } else {
    // Set default to 0.1.
    variance_.push_back(0.1);
  }

  if (prior_box_param.has_img_h() || prior_box_param.has_img_w()) {
    CHECK(!prior_box_param.has_img_size())
        << "Either img_size or img_h/img_w should be specified; not both.";
    img_h_ = prior_box_param.img_h();
    CHECK_GT(img_h_, 0) << "img_h should be larger than 0.";
    img_w_ = prior_box_param.img_w();
    CHECK_GT(img_w_, 0) << "img_w should be larger than 0.";
  } else if (prior_box_param.has_img_size()) {
    const int img_size = prior_box_param.img_size();
    CHECK_GT(img_size, 0) << "img_size should be larger than 0.";
    img_h_ = img_size;
    img_w_ = img_size;
  } else {
    img_h_ = 0;
    img_w_ = 0;
  }

  if (prior_box_param.has_step_h() || prior_box_param.has_step_w()) {
    CHECK(!prior_box_param.has_step())
        << "Either step or step_h/step_w should be specified; not both.";
    step_h_ = prior_box_param.step_h();
    CHECK_GT(step_h_, 0.) << "step_h should be larger than 0.";
    step_w_ = prior_box_param.step_w();
    CHECK_GT(step_w_, 0.) << "step_w should be larger than 0.";
  } else if (prior_box_param.has_step()) {
    const float step = prior_box_param.step();
    CHECK_GT(step, 0) << "step should be larger than 0.";
    step_h_ = step;
    step_w_ = step;
  } else {
    step_h_ = 0;
    step_w_ = 0;
  }

  offset_ = prior_box_param.offset();
}

template <typename Dtype>
void PriorBoxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
  const int layer_width = bottom[0]->width();
  const int layer_height = bottom[0]->height();
  vector<int> top_shape(3, 1);
  // Since all images in a batch has same height and width, we only need to
  // generate one set of priors which can be shared across all images.
  top_shape[0] = 1;
  // 2 channels. First channel stores the mean of each prior coordinate.
  // Second channel stores the variance of each prior coordinate.
  top_shape[1] = 2;
  top_shape[2] = layer_width * layer_height * num_priors_ * 4;
  CHECK_GT(top_shape[2], 0);
  top[0]->Reshape(top_shape);
}

template <typename Dtype>
void PriorBoxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top) {
  const int layer_width = bottom[0]->width();
  const int layer_height = bottom[0]->height();
  int img_width, img_height;
  if (img_h_ == 0 || img_w_ == 0) {
    img_width = bottom[1]->width();
    img_height = bottom[1]->height();
  } else {
    img_width = img_w_;
    img_height = img_h_;
  }
  float step_w, step_h;
  if (step_w_ == 0 || step_h_ == 0) {
    step_w = static_cast<float>(img_width) / layer_width;
    step_h = static_cast<float>(img_height) / layer_height;
  } else {
    step_w = step_w_;
    step_h = step_h_;
  }
  Dtype* top_data = top[0]->mutable_cpu_data();
  int dim = layer_height * layer_width * num_priors_ * 4;
  int idx = 0;
  for (int h = 0; h < layer_height; ++h) {
    for (int w = 0; w < layer_width; ++w) {
      float center_x = (w + offset_) * step_w;
      float center_y = (h + offset_) * step_h;
      float box_width, box_height;
      for (int s = 0; s < min_sizes_.size(); ++s) {
        int min_size_ = min_sizes_[s];
        // first prior: aspect_ratio = 1, size = min_size
        box_width = box_height = min_size_;
        // xmin
        top_data[idx++] = (center_x - box_width / 2.) / img_width;
        // ymin
        top_data[idx++] = (center_y - box_height / 2.) / img_height;
        // xmax
        top_data[idx++] = (center_x + box_width / 2.) / img_width;
        // ymax
        top_data[idx++] = (center_y + box_height / 2.) / img_height;

        if (max_sizes_.size() > 0) {
          CHECK_EQ(min_sizes_.size(), max_sizes_.size());
          int max_size_ = max_sizes_[s];
          // second prior: aspect_ratio = 1, size = sqrt(min_size * max_size)
          box_width = box_height = sqrt(min_size_ * max_size_);
          // xmin
          top_data[idx++] = (center_x - box_width / 2.) / img_width;
          // ymin
          top_data[idx++] = (center_y - box_height / 2.) / img_height;
          // xmax
          top_data[idx++] = (center_x + box_width / 2.) / img_width;
          // ymax
          top_data[idx++] = (center_y + box_height / 2.) / img_height;
        }

        // rest of priors
        for (int r = 0; r < aspect_ratios_.size(); ++r) {
          float ar = aspect_ratios_[r];
          if (fabs(ar - 1.) < 1e-6) {
            continue;
          }
          box_width = min_size_ * sqrt(ar);
          box_height = min_size_ / sqrt(ar);
          // xmin
          top_data[idx++] = (center_x - box_width / 2.) / img_width;
          // ymin
          top_data[idx++] = (center_y - box_height / 2.) / img_height;
          // xmax
          top_data[idx++] = (center_x + box_width / 2.) / img_width;
          // ymax
          top_data[idx++] = (center_y + box_height / 2.) / img_height;
        }
      }
    }
  }
  // clip the prior's coordidate such that it is within [0, 1]
  if (clip_) {
    for (int d = 0; d < dim; ++d) {
      top_data[d] = std::min<Dtype>(std::max<Dtype>(top_data[d], 0.), 1.);
    }
  }
  // set the variance.
  top_data += top[0]->offset(0, 1);
  if (variance_.size() == 1) {
    caffe_set<Dtype>(dim, Dtype(variance_[0]), top_data);
  } else {
    int count = 0;
    for (int h = 0; h < layer_height; ++h) {
      for (int w = 0; w < layer_width; ++w) {
        for (int i = 0; i < num_priors_; ++i) {
          for (int j = 0; j < 4; ++j) {
            top_data[count] = variance_[j];
            ++count;
          }
        }
      }
    }
  }
}

INSTANTIATE_CLASS(PriorBoxLayer);
REGISTER_LAYER_CLASS(PriorBox);

}  // namespace caffe
