Files
FastDeploy/fastdeploy/vision/ocr/ppocr/ppocr_pybind.cc
thunder95 2c5fd91a7f [Hackthon_4th 242] Support en_ppstructure_mobile_v2.0_SLANet (#1816)
* first draft

* update api name

* fix bug

* fix bug and

* fix bug in c api

* fix bug in c_api

---------

Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
2023-04-27 10:45:14 +08:00

116 lines
4.9 KiB
C++

// 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;
});
}
void BindPPStructureV2Table(pybind11::module& m) {
// PPStructureV2Table
pybind11::class_<pipeline::PPStructureV2Table, FastDeployModel>(
m, "PPStructureV2Table")
.def(pybind11::init<fastdeploy::vision::ocr::DBDetector*,
fastdeploy::vision::ocr::Recognizer*,
fastdeploy::vision::ocr::StructureV2Table*>())
.def_property("rec_batch_size",
&pipeline::PPStructureV2Table::GetRecBatchSize,
&pipeline::PPStructureV2Table::SetRecBatchSize)
.def("clone",
[](pipeline::PPStructureV2Table& self) { return self.Clone(); })
.def("predict",
[](pipeline::PPStructureV2Table& self, pybind11::array& data) {
auto mat = PyArrayToCvMat(data);
vision::OCRResult res;
self.Predict(&mat, &res);
return res;
})
.def("batch_predict", [](pipeline::PPStructureV2Table& 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