mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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6d0261e9e4
* [Model] init pp-structurev2-layout code * [Model] init pp-structurev2-layout code * [Model] init pp-structurev2-layout code * [Model] add structurev2_layout_preprocessor * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [pybind] add pp-structurev2-layout model pybind * [pybind] add pp-structurev2-layout model pybind * [Bug Fix] fixed code style * [examples] add pp-structurev2-layout c++ examples * [PP-StructureV2] add python example and docs * [benchmark] add pp-structurev2-layout benchmark support
527 lines
23 KiB
C++
527 lines
23 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <pybind11/stl.h>
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#include "fastdeploy/pybind/main.h"
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namespace fastdeploy {
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void BindPPOCRModel(pybind11::module& m) {
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m.def("sort_boxes", [](std::vector<std::array<int, 8>>& boxes) {
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vision::ocr::SortBoxes(&boxes);
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return boxes;
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});
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// DBDetector
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pybind11::class_<vision::ocr::DBDetectorPreprocessor,
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vision::ProcessorManager>(m, "DBDetectorPreprocessor")
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.def(pybind11::init<>())
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.def_property("static_shape_infer",
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&vision::ocr::DBDetectorPreprocessor::GetStaticShapeInfer,
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&vision::ocr::DBDetectorPreprocessor::SetStaticShapeInfer)
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.def_property("max_side_len",
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&vision::ocr::DBDetectorPreprocessor::GetMaxSideLen,
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&vision::ocr::DBDetectorPreprocessor::SetMaxSideLen)
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.def("set_normalize",
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[](vision::ocr::DBDetectorPreprocessor& self,
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const std::vector<float>& mean, const std::vector<float>& std,
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bool is_scale) { self.SetNormalize(mean, std, is_scale); })
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.def("run",
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[](vision::ocr::DBDetectorPreprocessor& self,
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std::vector<pybind11::array>& im_list) {
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std::vector<vision::FDMat> images;
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for (size_t i = 0; i < im_list.size(); ++i) {
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images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i])));
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}
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std::vector<FDTensor> outputs;
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self.Run(&images, &outputs);
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auto batch_det_img_info = self.GetBatchImgInfo();
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for (size_t i = 0; i < outputs.size(); ++i) {
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outputs[i].StopSharing();
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}
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return std::make_pair(outputs, *batch_det_img_info);
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})
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.def("disable_normalize",
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[](vision::ocr::DBDetectorPreprocessor& self) {
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self.DisableNormalize();
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})
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.def("disable_permute", [](vision::ocr::DBDetectorPreprocessor& self) {
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self.DisablePermute();
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});
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pybind11::class_<vision::ocr::DBDetectorPostprocessor>(
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m, "DBDetectorPostprocessor")
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.def(pybind11::init<>())
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.def_property("det_db_thresh",
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&vision::ocr::DBDetectorPostprocessor::GetDetDBThresh,
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&vision::ocr::DBDetectorPostprocessor::SetDetDBThresh)
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.def_property("det_db_box_thresh",
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&vision::ocr::DBDetectorPostprocessor::GetDetDBBoxThresh,
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&vision::ocr::DBDetectorPostprocessor::SetDetDBBoxThresh)
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.def_property("det_db_unclip_ratio",
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&vision::ocr::DBDetectorPostprocessor::GetDetDBUnclipRatio,
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&vision::ocr::DBDetectorPostprocessor::SetDetDBUnclipRatio)
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.def_property("det_db_score_mode",
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&vision::ocr::DBDetectorPostprocessor::GetDetDBScoreMode,
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&vision::ocr::DBDetectorPostprocessor::SetDetDBScoreMode)
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.def_property("use_dilation",
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&vision::ocr::DBDetectorPostprocessor::GetUseDilation,
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&vision::ocr::DBDetectorPostprocessor::SetUseDilation)
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.def("run",
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[](vision::ocr::DBDetectorPostprocessor& self,
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std::vector<FDTensor>& inputs,
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const std::vector<std::array<int, 4>>& batch_det_img_info) {
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std::vector<std::vector<std::array<int, 8>>> results;
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if (!self.Run(inputs, &results, batch_det_img_info)) {
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throw std::runtime_error(
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"Failed to preprocess the input data in "
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"DBDetectorPostprocessor.");
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}
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return results;
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})
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.def("run",
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[](vision::ocr::DBDetectorPostprocessor& self,
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std::vector<pybind11::array>& input_array,
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const std::vector<std::array<int, 4>>& batch_det_img_info) {
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std::vector<std::vector<std::array<int, 8>>> results;
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std::vector<FDTensor> inputs;
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PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
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if (!self.Run(inputs, &results, batch_det_img_info)) {
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throw std::runtime_error(
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"Failed to preprocess the input data in "
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"DBDetectorPostprocessor.");
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}
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return results;
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});
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pybind11::class_<vision::ocr::DBDetector, FastDeployModel>(m, "DBDetector")
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.def(pybind11::init<std::string, std::string, RuntimeOption,
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ModelFormat>())
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.def(pybind11::init<>())
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.def_property_readonly("preprocessor",
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&vision::ocr::DBDetector::GetPreprocessor)
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.def_property_readonly("postprocessor",
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&vision::ocr::DBDetector::GetPostprocessor)
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.def("predict",
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[](vision::ocr::DBDetector& self, pybind11::array& data) {
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auto mat = PyArrayToCvMat(data);
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vision::OCRResult ocr_result;
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self.Predict(mat, &ocr_result);
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return ocr_result;
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})
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.def("batch_predict", [](vision::ocr::DBDetector& self,
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std::vector<pybind11::array>& data) {
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std::vector<cv::Mat> images;
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for (size_t i = 0; i < data.size(); ++i) {
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images.push_back(PyArrayToCvMat(data[i]));
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}
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std::vector<vision::OCRResult> ocr_results;
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self.BatchPredict(images, &ocr_results);
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return ocr_results;
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});
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// Classifier
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pybind11::class_<vision::ocr::ClassifierPreprocessor,
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vision::ProcessorManager>(m, "ClassifierPreprocessor")
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.def(pybind11::init<>())
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.def_property("cls_image_shape",
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&vision::ocr::ClassifierPreprocessor::GetClsImageShape,
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&vision::ocr::ClassifierPreprocessor::SetClsImageShape)
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.def("set_normalize",
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[](vision::ocr::ClassifierPreprocessor& self,
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const std::vector<float>& mean, const std::vector<float>& std,
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bool is_scale) { self.SetNormalize(mean, std, is_scale); })
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.def("run",
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[](vision::ocr::ClassifierPreprocessor& self,
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std::vector<pybind11::array>& im_list) {
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std::vector<vision::FDMat> images;
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for (size_t i = 0; i < im_list.size(); ++i) {
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images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i])));
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}
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std::vector<FDTensor> outputs;
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if (!self.Run(&images, &outputs)) {
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throw std::runtime_error(
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"Failed to preprocess the input data in "
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"ClassifierPreprocessor.");
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}
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for (size_t i = 0; i < outputs.size(); ++i) {
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outputs[i].StopSharing();
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}
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return outputs;
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})
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.def("disable_normalize",
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[](vision::ocr::ClassifierPreprocessor& self) {
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self.DisableNormalize();
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})
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.def("disable_permute", [](vision::ocr::ClassifierPreprocessor& self) {
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self.DisablePermute();
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});
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pybind11::class_<vision::ocr::ClassifierPostprocessor>(
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m, "ClassifierPostprocessor")
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.def(pybind11::init<>())
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.def_property("cls_thresh",
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&vision::ocr::ClassifierPostprocessor::GetClsThresh,
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&vision::ocr::ClassifierPostprocessor::SetClsThresh)
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.def("run",
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[](vision::ocr::ClassifierPostprocessor& self,
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std::vector<FDTensor>& inputs) {
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std::vector<int> cls_labels;
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std::vector<float> cls_scores;
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if (!self.Run(inputs, &cls_labels, &cls_scores)) {
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throw std::runtime_error(
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"Failed to preprocess the input data in "
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"ClassifierPostprocessor.");
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}
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return std::make_pair(cls_labels, cls_scores);
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})
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.def("run", [](vision::ocr::ClassifierPostprocessor& self,
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std::vector<pybind11::array>& input_array) {
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std::vector<FDTensor> inputs;
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PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
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std::vector<int> cls_labels;
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std::vector<float> cls_scores;
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if (!self.Run(inputs, &cls_labels, &cls_scores)) {
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throw std::runtime_error(
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"Failed to preprocess the input data in "
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"ClassifierPostprocessor.");
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}
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return std::make_pair(cls_labels, cls_scores);
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});
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pybind11::class_<vision::ocr::Classifier, FastDeployModel>(m, "Classifier")
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.def(pybind11::init<std::string, std::string, RuntimeOption,
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ModelFormat>())
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.def(pybind11::init<>())
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.def_property_readonly("preprocessor",
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&vision::ocr::Classifier::GetPreprocessor)
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.def_property_readonly("postprocessor",
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&vision::ocr::Classifier::GetPostprocessor)
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.def("predict",
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[](vision::ocr::Classifier& self, pybind11::array& data) {
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auto mat = PyArrayToCvMat(data);
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vision::OCRResult ocr_result;
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self.Predict(mat, &ocr_result);
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return ocr_result;
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})
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.def("batch_predict", [](vision::ocr::Classifier& self,
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std::vector<pybind11::array>& data) {
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std::vector<cv::Mat> images;
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for (size_t i = 0; i < data.size(); ++i) {
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images.push_back(PyArrayToCvMat(data[i]));
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}
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vision::OCRResult ocr_result;
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self.BatchPredict(images, &ocr_result);
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return ocr_result;
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});
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// Recognizer
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pybind11::class_<vision::ocr::RecognizerPreprocessor,
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vision::ProcessorManager>(m, "RecognizerPreprocessor")
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.def(pybind11::init<>())
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.def_property("static_shape_infer",
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&vision::ocr::RecognizerPreprocessor::GetStaticShapeInfer,
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&vision::ocr::RecognizerPreprocessor::SetStaticShapeInfer)
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.def_property("rec_image_shape",
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&vision::ocr::RecognizerPreprocessor::GetRecImageShape,
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&vision::ocr::RecognizerPreprocessor::SetRecImageShape)
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.def("set_normalize",
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[](vision::ocr::RecognizerPreprocessor& self,
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const std::vector<float>& mean, const std::vector<float>& std,
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bool is_scale) { self.SetNormalize(mean, std, is_scale); })
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.def("run",
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[](vision::ocr::RecognizerPreprocessor& self,
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std::vector<pybind11::array>& im_list) {
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std::vector<vision::FDMat> images;
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for (size_t i = 0; i < im_list.size(); ++i) {
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images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i])));
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}
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std::vector<FDTensor> outputs;
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if (!self.Run(&images, &outputs)) {
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throw std::runtime_error(
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"Failed to preprocess the input data in "
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"RecognizerPreprocessor.");
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}
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for (size_t i = 0; i < outputs.size(); ++i) {
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outputs[i].StopSharing();
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}
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return outputs;
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})
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.def("disable_normalize",
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[](vision::ocr::RecognizerPreprocessor& self) {
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self.DisableNormalize();
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})
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.def("disable_permute", [](vision::ocr::RecognizerPreprocessor& self) {
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self.DisablePermute();
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});
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pybind11::class_<vision::ocr::RecognizerPostprocessor>(
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m, "RecognizerPostprocessor")
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.def(pybind11::init<std::string>())
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.def("run",
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[](vision::ocr::RecognizerPostprocessor& self,
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std::vector<FDTensor>& inputs) {
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std::vector<std::string> texts;
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std::vector<float> rec_scores;
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if (!self.Run(inputs, &texts, &rec_scores)) {
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throw std::runtime_error(
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"Failed to preprocess the input data in "
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"RecognizerPostprocessor.");
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}
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return std::make_pair(texts, rec_scores);
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})
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.def("run", [](vision::ocr::RecognizerPostprocessor& self,
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std::vector<pybind11::array>& input_array) {
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std::vector<FDTensor> inputs;
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PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
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std::vector<std::string> texts;
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std::vector<float> rec_scores;
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if (!self.Run(inputs, &texts, &rec_scores)) {
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throw std::runtime_error(
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"Failed to preprocess the input data in "
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"RecognizerPostprocessor.");
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}
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return std::make_pair(texts, rec_scores);
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});
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pybind11::class_<vision::ocr::Recognizer, FastDeployModel>(m, "Recognizer")
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.def(pybind11::init<std::string, std::string, std::string, RuntimeOption,
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ModelFormat>())
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.def(pybind11::init<>())
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.def_property_readonly("preprocessor",
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&vision::ocr::Recognizer::GetPreprocessor)
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.def_property_readonly("postprocessor",
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&vision::ocr::Recognizer::GetPostprocessor)
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.def("clone", [](vision::ocr::Recognizer& self) { return self.Clone(); })
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.def("predict",
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[](vision::ocr::Recognizer& self, pybind11::array& data) {
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auto mat = PyArrayToCvMat(data);
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vision::OCRResult ocr_result;
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self.Predict(mat, &ocr_result);
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return ocr_result;
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})
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.def("batch_predict", [](vision::ocr::Recognizer& self,
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std::vector<pybind11::array>& data) {
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std::vector<cv::Mat> images;
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for (size_t i = 0; i < data.size(); ++i) {
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images.push_back(PyArrayToCvMat(data[i]));
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}
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vision::OCRResult ocr_result;
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self.BatchPredict(images, &ocr_result);
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return ocr_result;
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});
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// Table
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pybind11::class_<vision::ocr::StructureV2TablePreprocessor,
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vision::ProcessorManager>(m, "StructureV2TablePreprocessor")
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.def(pybind11::init<>())
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.def("run", [](vision::ocr::StructureV2TablePreprocessor& self,
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std::vector<pybind11::array>& im_list) {
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std::vector<vision::FDMat> images;
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for (size_t i = 0; i < im_list.size(); ++i) {
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images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i])));
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}
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std::vector<FDTensor> outputs;
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if (!self.Run(&images, &outputs)) {
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throw std::runtime_error(
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"Failed to preprocess the input data in "
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"StructureV2TablePreprocessor.");
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}
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auto batch_det_img_info = self.GetBatchImgInfo();
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for (size_t i = 0; i < outputs.size(); ++i) {
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outputs[i].StopSharing();
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}
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return std::make_pair(outputs, *batch_det_img_info);
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});
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pybind11::class_<vision::ocr::StructureV2TablePostprocessor>(
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m, "StructureV2TablePostprocessor")
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.def(pybind11::init<std::string>())
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.def("run",
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[](vision::ocr::StructureV2TablePostprocessor& self,
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std::vector<FDTensor>& inputs,
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const std::vector<std::array<int, 4>>& batch_det_img_info) {
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std::vector<std::vector<std::array<int, 8>>> boxes;
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std::vector<std::vector<std::string>> structure_list;
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if (!self.Run(inputs, &boxes, &structure_list,
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batch_det_img_info)) {
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throw std::runtime_error(
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"Failed to postprocess the input data in "
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"StructureV2TablePostprocessor.");
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}
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return std::make_pair(boxes, structure_list);
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})
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.def("run",
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[](vision::ocr::StructureV2TablePostprocessor& self,
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std::vector<pybind11::array>& input_array,
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const std::vector<std::array<int, 4>>& batch_det_img_info) {
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std::vector<FDTensor> inputs;
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PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
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std::vector<std::vector<std::array<int, 8>>> boxes;
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std::vector<std::vector<std::string>> structure_list;
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if (!self.Run(inputs, &boxes, &structure_list,
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batch_det_img_info)) {
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throw std::runtime_error(
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"Failed to postprocess the input data in "
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"StructureV2TablePostprocessor.");
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}
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return std::make_pair(boxes, structure_list);
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});
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pybind11::class_<vision::ocr::StructureV2Table, FastDeployModel>(
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m, "StructureV2Table")
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.def(pybind11::init<std::string, std::string, std::string, RuntimeOption,
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ModelFormat>())
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.def(pybind11::init<>())
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.def_property_readonly("preprocessor",
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&vision::ocr::StructureV2Table::GetPreprocessor)
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.def_property_readonly("postprocessor",
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&vision::ocr::StructureV2Table::GetPostprocessor)
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.def("clone",
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[](vision::ocr::StructureV2Table& self) { return self.Clone(); })
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.def("predict",
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[](vision::ocr::StructureV2Table& self, pybind11::array& data) {
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auto mat = PyArrayToCvMat(data);
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vision::OCRResult ocr_result;
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self.Predict(mat, &ocr_result);
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return ocr_result;
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})
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.def("batch_predict", [](vision::ocr::StructureV2Table& self,
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std::vector<pybind11::array>& data) {
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std::vector<cv::Mat> images;
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for (size_t i = 0; i < data.size(); ++i) {
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images.push_back(PyArrayToCvMat(data[i]));
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}
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std::vector<vision::OCRResult> ocr_results;
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self.BatchPredict(images, &ocr_results);
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return ocr_results;
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});
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// Layout
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pybind11::class_<vision::ocr::StructureV2LayoutPreprocessor,
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vision::ProcessorManager>(m, "StructureV2LayoutPreprocessor")
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.def(pybind11::init<>())
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.def_property(
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"static_shape_infer",
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&vision::ocr::StructureV2LayoutPreprocessor::GetStaticShapeInfer,
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&vision::ocr::StructureV2LayoutPreprocessor::SetStaticShapeInfer)
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.def_property(
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"layout_image_shape",
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&vision::ocr::StructureV2LayoutPreprocessor::GetLayoutImageShape,
|
|
&vision::ocr::StructureV2LayoutPreprocessor::SetLayoutImageShape)
|
|
.def("set_normalize",
|
|
[](vision::ocr::StructureV2LayoutPreprocessor& self,
|
|
const std::vector<float>& mean, const std::vector<float>& std,
|
|
bool is_scale) { self.SetNormalize(mean, std, is_scale); })
|
|
.def("run",
|
|
[](vision::ocr::StructureV2LayoutPreprocessor& 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 "
|
|
"StructureV2LayoutPreprocessor.");
|
|
}
|
|
|
|
auto batch_layout_img_info = self.GetBatchLayoutImgInfo();
|
|
for (size_t i = 0; i < outputs.size(); ++i) {
|
|
outputs[i].StopSharing();
|
|
}
|
|
|
|
return std::make_pair(outputs, *batch_layout_img_info);
|
|
})
|
|
.def("disable_normalize",
|
|
[](vision::ocr::StructureV2LayoutPreprocessor& self) {
|
|
self.DisableNormalize();
|
|
})
|
|
.def("disable_permute",
|
|
[](vision::ocr::StructureV2LayoutPreprocessor& self) {
|
|
self.DisablePermute();
|
|
});
|
|
|
|
pybind11::class_<vision::ocr::StructureV2LayoutPostprocessor>(
|
|
m, "StructureV2LayoutPostprocessor")
|
|
.def(pybind11::init<>())
|
|
.def_property(
|
|
"score_threshold",
|
|
&vision::ocr::StructureV2LayoutPostprocessor::GetScoreThreshold,
|
|
&vision::ocr::StructureV2LayoutPostprocessor::SetScoreThreshold)
|
|
.def_property(
|
|
"nms_threshold",
|
|
&vision::ocr::StructureV2LayoutPostprocessor::GetNMSThreshold,
|
|
&vision::ocr::StructureV2LayoutPostprocessor::SetNMSThreshold)
|
|
.def_property("num_class",
|
|
&vision::ocr::StructureV2LayoutPostprocessor::GetNumClass,
|
|
&vision::ocr::StructureV2LayoutPostprocessor::SetNumClass)
|
|
.def_property("fpn_stride",
|
|
&vision::ocr::StructureV2LayoutPostprocessor::GetFPNStride,
|
|
&vision::ocr::StructureV2LayoutPostprocessor::SetFPNStride)
|
|
.def_property("reg_max",
|
|
&vision::ocr::StructureV2LayoutPostprocessor::GetRegMax,
|
|
&vision::ocr::StructureV2LayoutPostprocessor::SetRegMax)
|
|
.def("run",
|
|
[](vision::ocr::StructureV2LayoutPostprocessor& self,
|
|
std::vector<FDTensor>& inputs,
|
|
const std::vector<std::array<int, 4>>& batch_layout_img_info) {
|
|
std::vector<vision::DetectionResult> results;
|
|
|
|
if (!self.Run(inputs, &results, batch_layout_img_info)) {
|
|
throw std::runtime_error(
|
|
"Failed to postprocess the input data in "
|
|
"StructureV2LayoutPostprocessor.");
|
|
}
|
|
return results;
|
|
});
|
|
|
|
pybind11::class_<vision::ocr::StructureV2Layout, FastDeployModel>(
|
|
m, "StructureV2Layout")
|
|
.def(pybind11::init<std::string, std::string, RuntimeOption,
|
|
ModelFormat>())
|
|
.def(pybind11::init<>())
|
|
.def_property_readonly("preprocessor",
|
|
&vision::ocr::StructureV2Layout::GetPreprocessor)
|
|
.def_property_readonly("postprocessor",
|
|
&vision::ocr::StructureV2Layout::GetPostprocessor)
|
|
.def("clone",
|
|
[](vision::ocr::StructureV2Layout& self) { return self.Clone(); })
|
|
.def("predict",
|
|
[](vision::ocr::StructureV2Layout& self, pybind11::array& data) {
|
|
auto mat = PyArrayToCvMat(data);
|
|
vision::DetectionResult result;
|
|
self.Predict(mat, &result);
|
|
return result;
|
|
})
|
|
.def("batch_predict", [](vision::ocr::StructureV2Layout& 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;
|
|
});
|
|
}
|
|
} // namespace fastdeploy
|