FastDeploy/fastdeploy/vision/detection/contrib/yolov5seg/yolov5seg_pybind.cc

// 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 BindYOLOv5Seg(pybind11::module& m) {
  pybind11::class_<vision::detection::YOLOv5SegPreprocessor>(
      m, "YOLOv5SegPreprocessor")
      .def(pybind11::init<>())
      .def("run", [](vision::detection::YOLOv5SegPreprocessor& 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;
        std::vector<std::map<std::string, std::array<float, 2>>> ims_info;
        if (!self.Run(&images, &outputs, &ims_info)) {
          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 make_pair(outputs, ims_info);
      })
      .def_property("size", &vision::detection::YOLOv5SegPreprocessor::GetSize, &vision::detection::YOLOv5SegPreprocessor::SetSize)
      .def_property("padding_value", &vision::detection::YOLOv5SegPreprocessor::GetPaddingValue, &vision::detection::YOLOv5SegPreprocessor::SetPaddingValue)
      .def_property("is_scale_up", &vision::detection::YOLOv5SegPreprocessor::GetScaleUp, &vision::detection::YOLOv5SegPreprocessor::SetScaleUp)
      .def_property("is_mini_pad", &vision::detection::YOLOv5SegPreprocessor::GetMiniPad, &vision::detection::YOLOv5SegPreprocessor::SetMiniPad)
      .def_property("stride", &vision::detection::YOLOv5SegPreprocessor::GetStride, &vision::detection::YOLOv5SegPreprocessor::SetStride);

  pybind11::class_<vision::detection::YOLOv5SegPostprocessor>(
      m, "YOLOv5SegPostprocessor")
      .def(pybind11::init<>())
      .def("run", [](vision::detection::YOLOv5SegPostprocessor& self, std::vector<FDTensor>& inputs,
                     const std::vector<std::map<std::string, std::array<float, 2>>>& ims_info) {
        std::vector<vision::DetectionResult> results;
        if (!self.Run(inputs, &results, ims_info)) {
          throw std::runtime_error("Failed to postprocess the runtime result in YOLOv5SegPostprocessor.");
        }
        return results;
      })
      .def("run", [](vision::detection::YOLOv5SegPostprocessor& self, std::vector<pybind11::array>& input_array,
                     const std::vector<std::map<std::string, std::array<float, 2>>>& ims_info) {
        std::vector<vision::DetectionResult> results;
        std::vector<FDTensor> inputs;
        PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
        if (!self.Run(inputs, &results, ims_info)) {
          throw std::runtime_error("Failed to postprocess the runtime result in YOLOv5SegPostprocessor.");
        }
        return results;
      })
      .def_property("conf_threshold", &vision::detection::YOLOv5SegPostprocessor::GetConfThreshold, &vision::detection::YOLOv5SegPostprocessor::SetConfThreshold)
      .def_property("nms_threshold", &vision::detection::YOLOv5SegPostprocessor::GetNMSThreshold, &vision::detection::YOLOv5SegPostprocessor::SetNMSThreshold)
      .def_property("multi_label", &vision::detection::YOLOv5SegPostprocessor::GetMultiLabel, &vision::detection::YOLOv5SegPostprocessor::SetMultiLabel);

  pybind11::class_<vision::detection::YOLOv5Seg, FastDeployModel>(m, "YOLOv5Seg")
      .def(pybind11::init<std::string, std::string, RuntimeOption,
                          ModelFormat>())
      .def("predict",
           [](vision::detection::YOLOv5Seg& self, pybind11::array& data) {
             auto mat = PyArrayToCvMat(data);
             vision::DetectionResult res;
             self.Predict(mat, &res);
             return res;
           })
      .def("batch_predict", [](vision::detection::YOLOv5Seg& 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::YOLOv5Seg::GetPreprocessor)
      .def_property_readonly("postprocessor", &vision::detection::YOLOv5Seg::GetPostprocessor);
}
}  // namespace fastdeploy