// 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 BindPPDet(pybind11::module& m) { pybind11::class_( m, "PaddleDetPreprocessor") .def(pybind11::init()) .def("run", [](vision::detection::PaddleDetPreprocessor& self, std::vector& im_list) { std::vector images; for (size_t i = 0; i < im_list.size(); ++i) { images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i]))); } std::vector outputs; if (!self.Run(&images, &outputs)) { pybind11::eval("raise Exception('Failed to preprocess the input data in PaddleDetPreprocessor.')"); } for (size_t i = 0; i < outputs.size(); ++i) { outputs[i].StopSharing(); } return outputs; }); pybind11::class_( m, "PaddleDetPostprocessor") .def(pybind11::init<>()) .def("run", [](vision::detection::PaddleDetPostprocessor& self, std::vector& inputs) { std::vector results; if (!self.Run(inputs, &results)) { pybind11::eval("raise Exception('Failed to postprocess the runtime result in PaddleDetPostprocessor.')"); } return results; }) .def("apply_decode_and_nms", [](vision::detection::PaddleDetPostprocessor& self){ self.ApplyDecodeAndNMS(); }) .def("run", [](vision::detection::PaddleDetPostprocessor& self, std::vector& input_array) { std::vector results; std::vector inputs; PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true); if (!self.Run(inputs, &results)) { pybind11::eval("raise Exception('Failed to postprocess the runtime result in PaddleDetPostprocessor.')"); } return results; }); pybind11::class_(m, "PPDetBase") .def(pybind11::init()) .def("predict", [](vision::detection::PPDetBase& self, pybind11::array& data) { auto mat = PyArrayToCvMat(data); vision::DetectionResult res; self.Predict(&mat, &res); return res; }) .def("batch_predict", [](vision::detection::PPDetBase& self, std::vector& data) { std::vector images; for (size_t i = 0; i < data.size(); ++i) { images.push_back(PyArrayToCvMat(data[i])); } std::vector results; self.BatchPredict(images, &results); return results; }) .def_property_readonly("preprocessor", &vision::detection::PPDetBase::GetPreprocessor) .def_property_readonly("postprocessor", &vision::detection::PPDetBase::GetPostprocessor); pybind11::class_(m, "PPYOLO") .def(pybind11::init()); pybind11::class_(m, "PPYOLOE") .def(pybind11::init()); pybind11::class_(m, "PicoDet") .def(pybind11::init()); pybind11::class_(m, "PaddleYOLOX") .def(pybind11::init()); pybind11::class_(m, "FasterRCNN") .def(pybind11::init()); pybind11::class_(m, "YOLOv3") .def(pybind11::init()); pybind11::class_(m, "MaskRCNN") .def(pybind11::init()); } } // namespace fastdeploy