// 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/pybind/main.h"

namespace fastdeploy {
void BindRKYOLO(pybind11::module& m) {
  pybind11::class_<vision::detection::RKYOLOPreprocessor>(
      m, "RKYOLOPreprocessor")
      .def(pybind11::init<>())
      .def("run", [](vision::detection::RKYOLOPreprocessor& self,
                     std::vector<pybind11::array>& im_list) {
        std::vector<vision::FDMat> images;
        for (size_t i = 0; i < im_list.size(); ++i) {
          images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i])));
        }
        std::vector<FDTensor> outputs;
        if (!self.Run(&images, &outputs)) {
          throw std::runtime_error("Failed to preprocess the input data in PaddleClasPreprocessor.");
        }
        for (size_t i = 0; i < outputs.size(); ++i) {
          outputs[i].StopSharing();
        }
        return outputs;
      })
      .def_property("size", &vision::detection::RKYOLOPreprocessor::GetSize,
                    &vision::detection::RKYOLOPreprocessor::SetSize)
      .def_property("padding_value", &vision::detection::RKYOLOPreprocessor::GetPaddingValue,
                    &vision::detection::RKYOLOPreprocessor::SetPaddingValue)
      .def_property("is_scale_up", &vision::detection::RKYOLOPreprocessor::GetScaleUp,
                    &vision::detection::RKYOLOPreprocessor::SetScaleUp);

  pybind11::class_<vision::detection::RKYOLOPostprocessor>(
      m, "RKYOLOPostprocessor")
      .def(pybind11::init<>())
      .def("run", [](vision::detection::RKYOLOPostprocessor& self,
                     std::vector<FDTensor>& inputs) {
        std::vector<vision::DetectionResult> results;
        if (!self.Run(inputs, &results)) {
          throw std::runtime_error("Failed to postprocess the runtime result in RKYOLOV5Postprocessor.");
        }
        return results;
      })
      .def("run", [](vision::detection::RKYOLOPostprocessor& self,
                     std::vector<pybind11::array>& input_array) {
        std::vector<vision::DetectionResult> results;
        std::vector<FDTensor> inputs;
        PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
        if (!self.Run(inputs, &results)) {
          throw std::runtime_error("Failed to postprocess the runtime result in RKYOLOV5Postprocessor.");
        }
        return results;
      })
      .def_property("conf_threshold", &vision::detection::RKYOLOPostprocessor::GetConfThreshold,
                    &vision::detection::RKYOLOPostprocessor::SetConfThreshold)
      .def_property("nms_threshold", &vision::detection::RKYOLOPostprocessor::GetNMSThreshold,
                    &vision::detection::RKYOLOPostprocessor::SetNMSThreshold)
      .def_property("class_num", &vision::detection::RKYOLOPostprocessor::GetClassNum,
                    &vision::detection::RKYOLOPostprocessor::SetClassNum);

  pybind11::class_<vision::detection::RKYOLOV5, FastDeployModel>(m, "RKYOLOV5")
      .def(pybind11::init<std::string,
                          RuntimeOption,
                          ModelFormat>())
      .def("predict",
           [](vision::detection::RKYOLOV5& self,
              pybind11::array& data) {
             auto mat = PyArrayToCvMat(data);
             vision::DetectionResult res;
             self.Predict(mat, &res);
             return res;
           })
      .def("batch_predict", [](vision::detection::RKYOLOV5& self,
                               std::vector<pybind11::array>& data) {
        std::vector<cv::Mat> images;
        for (size_t i = 0; i < data.size(); ++i) {
          images.push_back(PyArrayToCvMat(data[i]));
        }
        std::vector<vision::DetectionResult> results;
        self.BatchPredict(images, &results);
        return results;
      })
      .def_property_readonly("preprocessor", &vision::detection::RKYOLOV5::GetPreprocessor)
      .def_property_readonly("postprocessor", &vision::detection::RKYOLOV5::GetPostprocessor);
}
}  // namespace fastdeploy