/*
 * Copyright 2019 Xilinx Inc.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
/*
 * Filename: pointpillars.hpp
 *
 * Description:
 * This network is used to detecting objects from a input points data.
 *
 * Please refer to document "xilinx_XILINX_AI_SDK_user_guide.pdf" for more
 * details of these APIs.
 */
#pragma once

#include <memory>
#include <opencv2/core.hpp>
#include <vector>
#include <vitis/ai/library/tensor.hpp>

namespace vitis {
namespace ai {

using V1F = std::vector<float>;
using V2F = std::vector<V1F>;
using V1I = std::vector<int>;
using V2I = std::vector<V1I>;

enum e_flag { E_RGB = 0x01, E_BEV = 0x02 };

/**
 * @struct DISPLAY_PARAM
 * @brief  Four data structure getting from the calibration information.
 *  It is mainly used for accuracy test or bev image drawing.
 *  See detail in the overview/samples/pointpillars/readme for more information.
 */
struct DISPLAY_PARAM {
  /// P2 information
  V2F P2;
  /// rect information
  V2F rect;
  /// Trv2c information
  V2F Trv2c;
  /// p2rect information
  V2F p2rect;
};

/**
 * @struct ANNORET
 * @brief Struct of the result returned by the pointpillars neural network in
 * the annotation mode. It is mainly used for accuracy test or bev image drawing
 */
struct ANNORET {
  /// Name of detected result in vector: such as Car Cylist Pedestrian.
  std::vector<std::string> name;
  /// Label of detected result.
  V1I label;
  /// Truncated information.
  V1F truncated;
  /// Occluded information.
  V1I occluded;
  /// Alpha information.
  V1F alpha;
  /// bbox information.
  V2I bbox;
  /// Dimensions information.
  V2F dimensions;
  /// Location information.
  V2F location;
  /// rotation_y information.
  V1F rotation_y;
  /// Score information.
  V1F score;
  /// box3d_camera information.
  V2F box3d_camera;
  /// box3d_lidar information.
  V2F box3d_lidar;
  /// Inner function to clear all fields.
  void clear() {
    name.clear();
    label.clear();
    truncated.clear();
    occluded.clear();
    alpha.clear();
    bbox.clear();
    dimensions.clear();
    location.clear();
    rotation_y.clear();
    score.clear();
    box3d_camera.clear();
    box3d_lidar.clear();
  }
};

/**
 * @struct PPResult
 * @brief Struct of the final result returned by the pointpillars neural
 * network.
 */
struct PPResult {
  /// Final box predicted.
  V2F final_box_preds;
  /// Final scores predicted.
  V1F final_scores;
  /// Final label predicted.
  V1I label_preds;
};

/**
 * @struct PointPillarsResult
 * @brief Struct of the final result returned by the pointpillars
 *  neural network encapsulated with width/height information.
 */
struct PointPillarsResult {
  /// Width of network input.
  int width = 0;
  /// Height of network input.
  int height = 0;
  /// Final result returned by the pointpillars neural network.
  PPResult ppresult;
};

/**
 * @brief Base class for pointpillars .
 *
 * Input is points data.
 *
 * Output is a struct of detection results, named PointPillarsResult.
 *
 * Sample code :
   @code
     ...
     auto net = vitis::ai::PointPillars::create("pointpillars_kitti_12000_0_pt",
 "pointpillars_kitti_12000_1_pt", true); V1F PointCloud ; int len =
 getfloatfilelen( lidar_path); PointCloud.resize( len );
     myreadfile(PointCloud.data(), len, lidar_path);
     auto res = net->run(PointCloud);
     ...
   please see the test sample for detail.
   @endcode
 *
 */
class PointPillars {
 public:
  /**
   * @brief Factory function to get an instance of derived classes of class
   * PointPillars.
   *
   * @param model_name  Model name for PointNet
   * @param model_name1 Model name for RPN
   * @return An instance of PointPillars class.
   *
   */
  static std::unique_ptr<PointPillars> create(const std::string& model_name,
                                              const std::string& model_name1);
  /**
   * @cond NOCOMMENTS
   */
 protected:
  explicit PointPillars();
  PointPillars(const PointPillars&) = delete;

 public:
  virtual ~PointPillars();
  /**
   * @endcond
   */

 public:
  /**
   * @brief Function of get result of the PointPillars neural network.
   *
   * @param v1f: point data in vector<float>
   *
   * @return PointPillarsResult.
   *
   */
  virtual vitis::ai::PointPillarsResult run(const V1F& v1f) = 0;
  /**
   * @brief Function of get result of the PointPillars neural network in batch
   * mode
   *
   * @param v2f: vector of point data in vector<float>
   *
   * @return vector of PointPillarsResult.
   *
   */
  virtual std::vector<vitis::ai::PointPillarsResult> run(const V2F& v2f) = 0;

  /**
   * @brief Function of get result of the PointPillars neural network.
   *
   * @param points: point data refered by float*
   * @len: length of the points data ( float data length, not byte data length )
   *
   * @return PointPillarsResult.
   *
   */
  virtual vitis::ai::PointPillarsResult run(const float* points, int len) = 0;
  /**
   * @brief Function of get result of the PointPillars neural network in batch
   * mode.
   *
   * @param points: vector of point data refered by float*
   * @len: vector of length of the points data ( float data length, not byte
   * data length )
   *
   * @return vector of PointPillarsResult.
   *
   */
  virtual std::vector<vitis::ai::PointPillarsResult> run(
      const std::vector<const float*>& vpoints,
      const std::vector<int>& vlen) = 0;
  /**
   * @brief Function to get input batch of the PointPillars network.
   *
   * @return Input batch of the PointPillars network.
   */
  virtual size_t get_input_batch() const = 0;

  /**
   * @brief Function to produce the visible result from PointPillarsResult after
   * calling run(). This is a helper function which can be ignored if you wants
   *        to process the PointPillarsResult using another method.
   *
   * @param res: [input] PointPillarsResult from run().
   * @param flag: [input] which visible result to produce. can be assigned to
   * below values: E_BEV        : only produce BEV picture E_RGB        : only
   * produce RGB picture E_BEV|E_RGB  : produce both pictures
   * @param dispp: [input] display parameter for the Points data.
   *               Refer to the readme in the overview for more detail.
   * @param rgb_map: [input|output] : original rgb picture for drawing detect
   * result. It can be blank (cv::Mat{}), if only BEV is required
   * @param bev_map: [input|output] original bev picture for drawing detect
   * result. It can be blank (cv::Mat{}), if only RGB required
   * @param width: [input] original rgb picture width.
   * @param height: [input] original rgb picture height.
   * @param annoret: [output] return the annoret variable for accuracy
   * calculation.
   */
  virtual void do_pointpillar_display(PointPillarsResult& res, int flag,
                                      DISPLAY_PARAM& dispp, cv::Mat& rgb_map,
                                      cv::Mat& bev_map, int width, int height,
                                      ANNORET& annoret) = 0;
};

}  // namespace ai
}  // namespace vitis
