mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 08:37:06 +08:00
58d63f3e90
* Add Huawei Ascend NPU deploy through PaddleLite CANN * Add NNAdapter interface for paddlelite * Modify Huawei Ascend Cmake * Update way for compiling Huawei Ascend NPU deployment * remove UseLiteBackend in UseCANN * Support compile python whlee * Change names of nnadapter API * Add nnadapter pybind and remove useless API * Support Python deployment on Huawei Ascend NPU * Add models suppor for ascend * Add PPOCR rec reszie for ascend * fix conflict for ascend * Rename CANN to Ascend * Rename CANN to Ascend * Improve ascend * fix ascend bug * improve ascend docs * improve ascend docs * improve ascend docs * Improve Ascend * Improve Ascend * Move ascend python demo * Imporve ascend * Improve ascend * Improve ascend * Improve ascend * Improve ascend * Imporve ascend * Imporve ascend * Improve ascend * acc eval script * acc eval * remove acc_eval from branch huawei * Add detection and segmentation examples for Ascend deployment * Add detection and segmentation examples for Ascend deployment * Add PPOCR example for ascend deploy * Imporve paddle lite compiliation * Add FlyCV doc * Add FlyCV doc * Add FlyCV doc * Imporve Ascend docs * Imporve Ascend docs
241 lines
12 KiB
C++
Executable File
241 lines
12 KiB
C++
Executable File
// 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 BindPPOCRModel(pybind11::module& m) {
|
|
m.def("sort_boxes", [](std::vector<std::array<int, 8>>& boxes) {
|
|
vision::ocr::SortBoxes(&boxes);
|
|
return boxes;
|
|
});
|
|
|
|
// DBDetector
|
|
pybind11::class_<vision::ocr::DBDetectorPreprocessor>(m, "DBDetectorPreprocessor")
|
|
.def(pybind11::init<>())
|
|
.def_property("max_side_len", &vision::ocr::DBDetectorPreprocessor::GetMaxSideLen, &vision::ocr::DBDetectorPreprocessor::SetMaxSideLen)
|
|
.def_property("mean", &vision::ocr::DBDetectorPreprocessor::GetMean, &vision::ocr::DBDetectorPreprocessor::SetMean)
|
|
.def_property("scale", &vision::ocr::DBDetectorPreprocessor::GetScale, &vision::ocr::DBDetectorPreprocessor::SetScale)
|
|
.def_property("is_scale", &vision::ocr::DBDetectorPreprocessor::GetIsScale, &vision::ocr::DBDetectorPreprocessor::SetIsScale)
|
|
.def("run", [](vision::ocr::DBDetectorPreprocessor& 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::array<int, 4>> batch_det_img_info;
|
|
self.Run(&images, &outputs, &batch_det_img_info);
|
|
for(size_t i = 0; i< outputs.size(); ++i){
|
|
outputs[i].StopSharing();
|
|
}
|
|
return std::make_pair(outputs, batch_det_img_info);
|
|
});
|
|
|
|
pybind11::class_<vision::ocr::DBDetectorPostprocessor>(m, "DBDetectorPostprocessor")
|
|
.def(pybind11::init<>())
|
|
.def_property("det_db_thresh", &vision::ocr::DBDetectorPostprocessor::GetDetDBThresh, &vision::ocr::DBDetectorPostprocessor::SetDetDBThresh)
|
|
.def_property("det_db_box_thresh", &vision::ocr::DBDetectorPostprocessor::GetDetDBBoxThresh, &vision::ocr::DBDetectorPostprocessor::SetDetDBBoxThresh)
|
|
.def_property("det_db_unclip_ratio", &vision::ocr::DBDetectorPostprocessor::GetDetDBUnclipRatio, &vision::ocr::DBDetectorPostprocessor::SetDetDBUnclipRatio)
|
|
.def_property("det_db_score_mode", &vision::ocr::DBDetectorPostprocessor::GetDetDBScoreMode, &vision::ocr::DBDetectorPostprocessor::SetDetDBScoreMode)
|
|
.def_property("use_dilation", &vision::ocr::DBDetectorPostprocessor::GetUseDilation, &vision::ocr::DBDetectorPostprocessor::SetUseDilation)
|
|
|
|
.def("run", [](vision::ocr::DBDetectorPostprocessor& self,
|
|
std::vector<FDTensor>& inputs,
|
|
const std::vector<std::array<int, 4>>& batch_det_img_info) {
|
|
std::vector<std::vector<std::array<int, 8>>> results;
|
|
|
|
if (!self.Run(inputs, &results, batch_det_img_info)) {
|
|
throw std::runtime_error("Failed to preprocess the input data in DBDetectorPostprocessor.");
|
|
}
|
|
return results;
|
|
})
|
|
.def("run", [](vision::ocr::DBDetectorPostprocessor& self,
|
|
std::vector<pybind11::array>& input_array,
|
|
const std::vector<std::array<int, 4>>& batch_det_img_info) {
|
|
std::vector<std::vector<std::array<int, 8>>> results;
|
|
std::vector<FDTensor> inputs;
|
|
PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
|
|
if (!self.Run(inputs, &results, batch_det_img_info)) {
|
|
throw std::runtime_error("Failed to preprocess the input data in DBDetectorPostprocessor.");
|
|
}
|
|
return results;
|
|
});
|
|
|
|
pybind11::class_<vision::ocr::DBDetector, FastDeployModel>(m, "DBDetector")
|
|
.def(pybind11::init<std::string, std::string, RuntimeOption,
|
|
ModelFormat>())
|
|
.def(pybind11::init<>())
|
|
.def_property_readonly("preprocessor", &vision::ocr::DBDetector::GetPreprocessor)
|
|
.def_property_readonly("postprocessor", &vision::ocr::DBDetector::GetPostprocessor)
|
|
.def("predict", [](vision::ocr::DBDetector& self,
|
|
pybind11::array& data) {
|
|
auto mat = PyArrayToCvMat(data);
|
|
std::vector<std::array<int, 8>> boxes_result;
|
|
self.Predict(mat, &boxes_result);
|
|
return boxes_result;
|
|
})
|
|
.def("batch_predict", [](vision::ocr::DBDetector& self, std::vector<pybind11::array>& data) {
|
|
std::vector<cv::Mat> images;
|
|
std::vector<std::vector<std::array<int, 8>>> det_results;
|
|
for (size_t i = 0; i < data.size(); ++i) {
|
|
images.push_back(PyArrayToCvMat(data[i]));
|
|
}
|
|
self.BatchPredict(images, &det_results);
|
|
return det_results;
|
|
});
|
|
|
|
// Classifier
|
|
pybind11::class_<vision::ocr::ClassifierPreprocessor>(m, "ClassifierPreprocessor")
|
|
.def(pybind11::init<>())
|
|
.def_property("cls_image_shape", &vision::ocr::ClassifierPreprocessor::GetClsImageShape, &vision::ocr::ClassifierPreprocessor::SetClsImageShape)
|
|
.def_property("mean", &vision::ocr::ClassifierPreprocessor::GetMean, &vision::ocr::ClassifierPreprocessor::SetMean)
|
|
.def_property("scale", &vision::ocr::ClassifierPreprocessor::GetScale, &vision::ocr::ClassifierPreprocessor::SetScale)
|
|
.def_property("is_scale", &vision::ocr::ClassifierPreprocessor::GetIsScale, &vision::ocr::ClassifierPreprocessor::SetIsScale)
|
|
.def("run", [](vision::ocr::ClassifierPreprocessor& 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 ClassifierPreprocessor.");
|
|
}
|
|
for(size_t i = 0; i< outputs.size(); ++i){
|
|
outputs[i].StopSharing();
|
|
}
|
|
return outputs;
|
|
});
|
|
|
|
pybind11::class_<vision::ocr::ClassifierPostprocessor>(m, "ClassifierPostprocessor")
|
|
.def(pybind11::init<>())
|
|
.def_property("cls_thresh", &vision::ocr::ClassifierPostprocessor::GetClsThresh, &vision::ocr::ClassifierPostprocessor::SetClsThresh)
|
|
.def("run", [](vision::ocr::ClassifierPostprocessor& self,
|
|
std::vector<FDTensor>& inputs) {
|
|
std::vector<int> cls_labels;
|
|
std::vector<float> cls_scores;
|
|
if (!self.Run(inputs, &cls_labels, &cls_scores)) {
|
|
throw std::runtime_error("Failed to preprocess the input data in ClassifierPostprocessor.");
|
|
}
|
|
return std::make_pair(cls_labels,cls_scores);
|
|
})
|
|
.def("run", [](vision::ocr::ClassifierPostprocessor& self,
|
|
std::vector<pybind11::array>& input_array) {
|
|
std::vector<FDTensor> inputs;
|
|
PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
|
|
std::vector<int> cls_labels;
|
|
std::vector<float> cls_scores;
|
|
if (!self.Run(inputs, &cls_labels, &cls_scores)) {
|
|
throw std::runtime_error("Failed to preprocess the input data in ClassifierPostprocessor.");
|
|
}
|
|
return std::make_pair(cls_labels,cls_scores);
|
|
});
|
|
|
|
pybind11::class_<vision::ocr::Classifier, FastDeployModel>(m, "Classifier")
|
|
.def(pybind11::init<std::string, std::string, RuntimeOption,
|
|
ModelFormat>())
|
|
.def(pybind11::init<>())
|
|
.def_property_readonly("preprocessor", &vision::ocr::Classifier::GetPreprocessor)
|
|
.def_property_readonly("postprocessor", &vision::ocr::Classifier::GetPostprocessor)
|
|
.def("predict", [](vision::ocr::Classifier& self,
|
|
pybind11::array& data) {
|
|
auto mat = PyArrayToCvMat(data);
|
|
int32_t cls_label;
|
|
float cls_score;
|
|
self.Predict(mat, &cls_label, &cls_score);
|
|
return std::make_pair(cls_label, cls_score);
|
|
})
|
|
.def("batch_predict", [](vision::ocr::Classifier& self, std::vector<pybind11::array>& data) {
|
|
std::vector<cv::Mat> images;
|
|
std::vector<int32_t> cls_labels;
|
|
std::vector<float> cls_scores;
|
|
for (size_t i = 0; i < data.size(); ++i) {
|
|
images.push_back(PyArrayToCvMat(data[i]));
|
|
}
|
|
self.BatchPredict(images, &cls_labels, &cls_scores);
|
|
return std::make_pair(cls_labels, cls_scores);
|
|
});
|
|
|
|
// Recognizer
|
|
pybind11::class_<vision::ocr::RecognizerPreprocessor>(m, "RecognizerPreprocessor")
|
|
.def(pybind11::init<>())
|
|
.def_property("static_shape_infer", &vision::ocr::RecognizerPreprocessor::GetStaticShapeInfer, &vision::ocr::RecognizerPreprocessor::SetStaticShapeInfer)
|
|
.def_property("rec_image_shape", &vision::ocr::RecognizerPreprocessor::GetRecImageShape, &vision::ocr::RecognizerPreprocessor::SetRecImageShape)
|
|
.def_property("mean", &vision::ocr::RecognizerPreprocessor::GetMean, &vision::ocr::RecognizerPreprocessor::SetMean)
|
|
.def_property("scale", &vision::ocr::RecognizerPreprocessor::GetScale, &vision::ocr::RecognizerPreprocessor::SetScale)
|
|
.def_property("is_scale", &vision::ocr::RecognizerPreprocessor::GetIsScale, &vision::ocr::RecognizerPreprocessor::SetIsScale)
|
|
.def("run", [](vision::ocr::RecognizerPreprocessor& 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 RecognizerPreprocessor.");
|
|
}
|
|
for(size_t i = 0; i< outputs.size(); ++i){
|
|
outputs[i].StopSharing();
|
|
}
|
|
return outputs;
|
|
});
|
|
|
|
pybind11::class_<vision::ocr::RecognizerPostprocessor>(m, "RecognizerPostprocessor")
|
|
.def(pybind11::init<std::string>())
|
|
.def("run", [](vision::ocr::RecognizerPostprocessor& self,
|
|
std::vector<FDTensor>& inputs) {
|
|
std::vector<std::string> texts;
|
|
std::vector<float> rec_scores;
|
|
if (!self.Run(inputs, &texts, &rec_scores)) {
|
|
throw std::runtime_error("Failed to preprocess the input data in RecognizerPostprocessor.");
|
|
}
|
|
return std::make_pair(texts, rec_scores);
|
|
})
|
|
.def("run", [](vision::ocr::RecognizerPostprocessor& self,
|
|
std::vector<pybind11::array>& input_array) {
|
|
std::vector<FDTensor> inputs;
|
|
PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
|
|
std::vector<std::string> texts;
|
|
std::vector<float> rec_scores;
|
|
if (!self.Run(inputs, &texts, &rec_scores)) {
|
|
throw std::runtime_error("Failed to preprocess the input data in RecognizerPostprocessor.");
|
|
}
|
|
return std::make_pair(texts, rec_scores);
|
|
});
|
|
|
|
pybind11::class_<vision::ocr::Recognizer, FastDeployModel>(m, "Recognizer")
|
|
.def(pybind11::init<std::string, std::string, std::string, RuntimeOption,
|
|
ModelFormat>())
|
|
.def(pybind11::init<>())
|
|
.def_property_readonly("preprocessor", &vision::ocr::Recognizer::GetPreprocessor)
|
|
.def_property_readonly("postprocessor", &vision::ocr::Recognizer::GetPostprocessor)
|
|
.def("predict", [](vision::ocr::Recognizer& self,
|
|
pybind11::array& data) {
|
|
auto mat = PyArrayToCvMat(data);
|
|
std::string text;
|
|
float rec_score;
|
|
self.Predict(mat, &text, &rec_score);
|
|
return std::make_pair(text, rec_score);
|
|
})
|
|
.def("batch_predict", [](vision::ocr::Recognizer& self, std::vector<pybind11::array>& data) {
|
|
std::vector<cv::Mat> images;
|
|
std::vector<std::string> texts;
|
|
std::vector<float> rec_scores;
|
|
for (size_t i = 0; i < data.size(); ++i) {
|
|
images.push_back(PyArrayToCvMat(data[i]));
|
|
}
|
|
self.BatchPredict(images, &texts, &rec_scores);
|
|
return std::make_pair(texts, rec_scores);
|
|
});
|
|
}
|
|
} // namespace fastdeploy
|