/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
==============================================================================*/

#pragma once

#include "operators/kernel/prior_box_kernel.h"

namespace paddle_mobile {
namespace operators {

template <typename T>
struct ClipFunctor {
  inline T operator()(T in) const {
    return std::min<T>(std::max<T>(in, 0.), 1.);
  }
};

template <>
void PriorBoxKernel<CPU, float>::Compute(const PriorBoxParam &param) const {
  const auto *input_ = param.Input();
  const auto &input_dims = input_->dims();

  const auto *input_image = param.InputImage();
  const auto &input_image_dims = input_image->dims();

  const auto &min_sizes = param.MinSizes();
  const auto &max_sizes = param.MaxSizes();
  const auto &variances = param.Variances();
  const auto &input_aspect_ratio = param.AspectRatios();
  const bool &flip = param.Flip();
  const bool &clip = param.Clip();
  const float &step_w = param.StepW();
  const float &step_h = param.StepH();
  const float &offset = param.Offset();

  Tensor *output_boxes = param.OutputBoxes();
  auto output_boxes_dataptr = output_boxes->mutable_data<float>();
  Tensor *output_variances = param.OutputVariances();
  auto output_variances_dataptr = output_variances->mutable_data<float>();

  std::vector<float> aspect_ratios;
  ExpandAspectRatios(input_aspect_ratio, flip, &aspect_ratios);

  auto img_width = input_image_dims[3];
  auto img_height = input_image_dims[2];

  auto feature_width = input_dims[3];
  auto feature_height = input_dims[2];

  auto stride0 = output_boxes->dims()[1] * output_boxes->dims()[2] *
                 output_boxes->dims()[3];
  auto stride1 = output_boxes->dims()[2] * output_boxes->dims()[3];
  auto stride2 = output_boxes->dims()[3];

  float step_width, step_height;
  /// 300 / 19
  if (step_w == 0 || step_h == 0) {
    step_width = static_cast<float>(img_width) / feature_width;
    step_height = static_cast<float>(img_height) / feature_height;
  } else {
    step_width = step_w;
    step_height = step_h;
  }

  int num_priors = aspect_ratios.size() * min_sizes.size();
  if (!max_sizes.empty()) {
    num_priors += max_sizes.size();
  }

  for (int h = 0; h < feature_height; ++h) {
    for (int w = 0; w < feature_width; ++w) {
      /// map origin image
      float center_x = (w + offset) * step_width;
      float center_y = (h + offset) * step_height;
      float box_width, box_height;
      int idx = 0;
      for (size_t s = 0; s < min_sizes.size(); ++s) {
        auto min_size = min_sizes[s];
        // priors with different aspect ratios
        for (float ar : aspect_ratios) {
          box_width = min_size * sqrt(ar) / 2.;
          box_height = min_size / sqrt(ar) / 2.;
          /// box_width/2 , / img_width 为了得到feature map 相对于
          /// 原图的归一化位置的比例。
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 0] =
              (center_x - box_width) / img_width;
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 1] =
              (center_y - box_height) / img_height;
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 2] =
              (center_x + box_width) / img_width;
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 3] =
              (center_y + box_height) / img_height;
          idx++;
        }
        if (!max_sizes.empty()) {
          auto max_size = max_sizes[s];
          // square prior with size sqrt(minSize * maxSize)
          box_width = box_height = sqrt(min_size * max_size) / 2.;
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 0] =
              (center_x - box_width) / img_width;
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 1] =
              (center_y - box_height) / img_height;
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 2] =
              (center_x + box_width) / img_width;
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 3] =
              (center_y + box_height) / img_height;
          idx++;
        }
      }
    }
  }
  if (clip) {
    math::Transform trans;
    ClipFunctor<float> clip_func;
    trans(output_boxes_dataptr, output_boxes_dataptr + output_boxes->numel(),
          output_boxes_dataptr, clip_func);
  }

  if ((variances.size() != 4)) {
    LOG(kLOG_ERROR) << " variances.size() must be 4.";
  }

  int64_t box_num = feature_height * feature_width * num_priors;

  for (int i = 0; i < box_num; i++) {
    output_variances_dataptr[4 * i] = variances[0];
    output_variances_dataptr[4 * i + 1] = variances[1];
    output_variances_dataptr[4 * i + 2] = variances[2];
    output_variances_dataptr[4 * i + 3] = variances[3];
  }
}

}  // namespace operators
}  // namespace paddle_mobile