// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// 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.

#include "fastdeploy/vision/detection/ppdet/preprocessor.h"
#include "fastdeploy/function/concat.h"
#include "fastdeploy/function/pad.h"
#include "yaml-cpp/yaml.h"

namespace fastdeploy {
namespace vision {
namespace detection {

PaddleDetPreprocessor::PaddleDetPreprocessor(const std::string& config_file) {
  FDASSERT(BuildPreprocessPipelineFromConfig(config_file), "Failed to create PaddleDetPreprocessor.");
  initialized_ = true;
}

bool PaddleDetPreprocessor::BuildPreprocessPipelineFromConfig(const std::string& config_file) {
  processors_.clear();
  YAML::Node cfg;
  try {
    cfg = YAML::LoadFile(config_file);
  } catch (YAML::BadFile& e) {
    FDERROR << "Failed to load yaml file " << config_file
            << ", maybe you should check this file." << std::endl;
    return false;
  }

  processors_.push_back(std::make_shared<BGR2RGB>());

  bool has_permute = false;
  for (const auto& op : cfg["Preprocess"]) {
    std::string op_name = op["type"].as<std::string>();
    if (op_name == "NormalizeImage") {
      auto mean = op["mean"].as<std::vector<float>>();
      auto std = op["std"].as<std::vector<float>>();
      bool is_scale = true;
      if (op["is_scale"]) {
        is_scale = op["is_scale"].as<bool>();
      }
      std::string norm_type = "mean_std";
      if (op["norm_type"]) {
        norm_type = op["norm_type"].as<std::string>();
      }
      if (norm_type != "mean_std") {
        std::fill(mean.begin(), mean.end(), 0.0);
        std::fill(std.begin(), std.end(), 1.0);
      }
      processors_.push_back(std::make_shared<Normalize>(mean, std, is_scale));
    } else if (op_name == "Resize") {
      bool keep_ratio = op["keep_ratio"].as<bool>();
      auto target_size = op["target_size"].as<std::vector<int>>();
      int interp = op["interp"].as<int>();
      FDASSERT(target_size.size() == 2,
               "Require size of target_size be 2, but now it's %lu.",
               target_size.size());
      if (!keep_ratio) {
        int width = target_size[1];
        int height = target_size[0];
        processors_.push_back(
            std::make_shared<Resize>(width, height, -1.0, -1.0, interp, false));
      } else {
        int min_target_size = std::min(target_size[0], target_size[1]);
        int max_target_size = std::max(target_size[0], target_size[1]);
        std::vector<int> max_size;
        if (max_target_size > 0) {
          max_size.push_back(max_target_size);
          max_size.push_back(max_target_size);
        }
        processors_.push_back(std::make_shared<ResizeByShort>(
            min_target_size, interp, true, max_size));
      }
    } else if (op_name == "Permute") {
      // Do nothing, do permute as the last operation
      has_permute = true;
      continue;
      // processors_.push_back(std::make_shared<HWC2CHW>());
    } else if (op_name == "Pad") {
      auto size = op["size"].as<std::vector<int>>();
      auto value = op["fill_value"].as<std::vector<float>>();
      processors_.push_back(std::make_shared<Cast>("float"));
      processors_.push_back(
          std::make_shared<PadToSize>(size[1], size[0], value));
    } else if (op_name == "PadStride") {
      auto stride = op["stride"].as<int>();
      processors_.push_back(
          std::make_shared<StridePad>(stride, std::vector<float>(3, 0)));
    } else {
      FDERROR << "Unexcepted preprocess operator: " << op_name << "."
              << std::endl;
      return false;
    }
  }
  if (has_permute) {
    // permute = cast<float> + HWC2CHW
    processors_.push_back(std::make_shared<Cast>("float"));
    processors_.push_back(std::make_shared<HWC2CHW>());
  } else {
    processors_.push_back(std::make_shared<HWC2CHW>());
  }

  // Fusion will improve performance
  FuseTransforms(&processors_);

  return true;
}

bool PaddleDetPreprocessor::Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs) {
  if (!initialized_) {
    FDERROR << "The preprocessor is not initialized." << std::endl;
    return false;
  }
  if (images->size() == 0) {
    FDERROR << "The size of input images should be greater than 0." << std::endl;
    return false;
  }

  // There are 3 outputs, image, scale_factor, im_shape
  // But im_shape is not used for all the PaddleDetection models
  // So preprocessor will output the 3 FDTensors, and how to use `im_shape`
  // is decided by the model itself
  outputs->resize(3);
  int batch = static_cast<int>(images->size());
  // Allocate memory for scale_factor
  (*outputs)[1].Resize({batch, 2}, FDDataType::FP32);
  // Allocate memory for im_shape
  (*outputs)[2].Resize({batch, 2}, FDDataType::FP32);
  // Record the max size for a batch of input image
  // All the tensor will pad to the max size to compose a batched tensor
  std::vector<int> max_hw({-1, -1});

  float* scale_factor_ptr = reinterpret_cast<float*>((*outputs)[1].MutableData());
  float* im_shape_ptr = reinterpret_cast<float*>((*outputs)[2].MutableData()); 
  for (size_t i = 0; i < images->size(); ++i) {
    int origin_w = (*images)[i].Width();
    int origin_h = (*images)[i].Height();
    scale_factor_ptr[2 * i] = 1.0;
    scale_factor_ptr[2 * i + 1] = 1.0;
    for (size_t j = 0; j < processors_.size(); ++j) {
      if (!(*(processors_[j].get()))(&((*images)[i]))) {
        FDERROR << "Failed to processs image:" << i << " in " << processors_[i]->Name() << "." << std::endl;
        return false;
      }
      if (processors_[j]->Name().find("Resize") != std::string::npos) {
        scale_factor_ptr[2 * i] = (*images)[i].Height() * 1.0 / origin_h;
        scale_factor_ptr[2 * i + 1] = (*images)[i].Width() * 1.0 / origin_w;
      }
    }
    if ((*images)[i].Height() > max_hw[0]) {
      max_hw[0] = (*images)[i].Height();
    }
    if ((*images)[i].Width() > max_hw[1]) {
      max_hw[1] = (*images)[i].Width();
    }
    im_shape_ptr[2 * i] = max_hw[0];
    im_shape_ptr[2 * i + 1] = max_hw[1];
  }
   
  // Concat all the preprocessed data to a batch tensor
  std::vector<FDTensor> im_tensors(images->size()); 
  for (size_t i = 0; i < images->size(); ++i) {
    if ((*images)[i].Height() < max_hw[0] || (*images)[i].Width() < max_hw[1]) {
      // if the size of image less than max_hw, pad to max_hw
      FDTensor tensor;
      (*images)[i].ShareWithTensor(&tensor);
      function::Pad(tensor, &(im_tensors[i]), {0, 0, max_hw[0] - (*images)[i].Height(), max_hw[1] - (*images)[i].Width()}, 0);
    } else {
      // No need pad
      (*images)[i].ShareWithTensor(&(im_tensors[i]));
    }
    // Reshape to 1xCxHxW
    im_tensors[i].ExpandDim(0);
  }

  if (im_tensors.size() == 1) {
    // If there's only 1 input, no need to concat
    // skip memory copy
    (*outputs)[0] = std::move(im_tensors[0]);
  } else {
    // Else concat the im tensor for each input image
    // compose a batched input tensor
    function::Concat(im_tensors, &((*outputs)[0]), 0);
  }

  return true;
}

}  // namespace detection
}  // namespace vision
}  // namespace fastdeploy