// 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 <pybind11/stl.h>
#include "fastdeploy/pybind/main.h"

namespace fastdeploy {
void BindPPOCRv3(pybind11::module& m) {
  // PPOCRv3
  pybind11::class_<pipeline::PPOCRv3, FastDeployModel>(
      m, "PPOCRv3")

      .def(pybind11::init<fastdeploy::vision::ocr::DBDetector*,
                          fastdeploy::vision::ocr::Classifier*,
                          fastdeploy::vision::ocr::Recognizer*>())
      .def(pybind11::init<fastdeploy::vision::ocr::DBDetector*,
                          fastdeploy::vision::ocr::Recognizer*>())
      .def_property("cls_batch_size", &pipeline::PPOCRv3::GetClsBatchSize, &pipeline::PPOCRv3::SetClsBatchSize)
      .def_property("rec_batch_size", &pipeline::PPOCRv3::GetRecBatchSize, &pipeline::PPOCRv3::SetRecBatchSize)
      .def("clone", [](pipeline::PPOCRv3& self) {
        return self.Clone();
      })
      .def("predict", [](pipeline::PPOCRv3& self,
                         pybind11::array& data) {
        auto mat = PyArrayToCvMat(data);
        vision::OCRResult res;
        self.Predict(&mat, &res);
        return res;
      })
      .def("batch_predict", [](pipeline::PPOCRv3& 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::OCRResult> results;
        self.BatchPredict(images, &results);
        return results;
      });
}

void BindPPOCRv2(pybind11::module& m) {
  // PPOCRv2
  pybind11::class_<pipeline::PPOCRv2, FastDeployModel>(
      m, "PPOCRv2")
      .def(pybind11::init<fastdeploy::vision::ocr::DBDetector*,
                          fastdeploy::vision::ocr::Classifier*,
                          fastdeploy::vision::ocr::Recognizer*>())
      .def(pybind11::init<fastdeploy::vision::ocr::DBDetector*,
                          fastdeploy::vision::ocr::Recognizer*>())
      .def_property("cls_batch_size", &pipeline::PPOCRv2::GetClsBatchSize, &pipeline::PPOCRv2::SetClsBatchSize)
      .def_property("rec_batch_size", &pipeline::PPOCRv2::GetRecBatchSize, &pipeline::PPOCRv2::SetRecBatchSize)
      .def("clone", [](pipeline::PPOCRv2& self) {
        return self.Clone();
      })
      .def("predict", [](pipeline::PPOCRv2& self,
                         pybind11::array& data) {
        auto mat = PyArrayToCvMat(data);
        vision::OCRResult res;
        self.Predict(&mat, &res);
        return res;
      })
      .def("batch_predict", [](pipeline::PPOCRv2& 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::OCRResult> results;
        self.BatchPredict(images, &results);
        return results;
      });
}

}  // namespace fastdeploy