mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-07 01:22:59 +08:00
91a1c72f98
* move manager initialized_ flag to ppcls * update dbdetector preprocess api * declare processor op * ppocr detector preprocessor support cvcuda * move cvcuda op to class member * ppcls use manager register api * refactor det preprocessor init api * add set preprocessor api * add create processor macro * new processor call api * ppcls preprocessor init resize on cpu * ppocr detector preprocessor set normalize api * revert ppcls pybind * remove dbdetector set preprocessor * refine dbdetector preprocessor includes * remove mean std in py constructor * add comments * update comment * Update __init__.py
307 lines
14 KiB
C++
307 lines
14 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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m, "DBDetectorPreprocessor")
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.def(pybind11::init<>())
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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", [](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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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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std::vector<std::array<int, 8>> boxes_result;
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self.Predict(mat, &boxes_result);
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return boxes_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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std::vector<std::vector<std::array<int, 8>>> det_results;
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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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self.BatchPredict(images, &det_results);
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return det_results;
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});
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// Classifier
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pybind11::class_<vision::ocr::ClassifierPreprocessor>(
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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_property("mean", &vision::ocr::ClassifierPreprocessor::GetMean,
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&vision::ocr::ClassifierPreprocessor::SetMean)
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.def_property("scale", &vision::ocr::ClassifierPreprocessor::GetScale,
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&vision::ocr::ClassifierPreprocessor::SetScale)
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.def_property("is_scale",
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&vision::ocr::ClassifierPreprocessor::GetIsScale,
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&vision::ocr::ClassifierPreprocessor::SetIsScale)
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.def("run", [](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 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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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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int32_t cls_label;
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float cls_score;
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self.Predict(mat, &cls_label, &cls_score);
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return std::make_pair(cls_label, cls_score);
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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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std::vector<int32_t> cls_labels;
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std::vector<float> cls_scores;
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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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self.BatchPredict(images, &cls_labels, &cls_scores);
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return std::make_pair(cls_labels, cls_scores);
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});
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// Recognizer
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pybind11::class_<vision::ocr::RecognizerPreprocessor>(
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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_property("mean", &vision::ocr::RecognizerPreprocessor::GetMean,
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&vision::ocr::RecognizerPreprocessor::SetMean)
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.def_property("scale", &vision::ocr::RecognizerPreprocessor::GetScale,
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&vision::ocr::RecognizerPreprocessor::SetScale)
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.def_property("is_scale",
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&vision::ocr::RecognizerPreprocessor::GetIsScale,
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&vision::ocr::RecognizerPreprocessor::SetIsScale)
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.def("run", [](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 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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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("predict",
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[](vision::ocr::Recognizer& self, pybind11::array& data) {
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auto mat = PyArrayToCvMat(data);
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std::string text;
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float rec_score;
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self.Predict(mat, &text, &rec_score);
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return std::make_pair(text, rec_score);
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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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std::vector<std::string> texts;
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std::vector<float> rec_scores;
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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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self.BatchPredict(images, &texts, &rec_scores);
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return std::make_pair(texts, rec_scores);
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});
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}
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} // namespace fastdeploy
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