mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-30 11:26:39 +08:00
[Bug Fix] add ocr new feature and fix codestyle (#764)
* fix ocr bug and add new feature * fix bug * fix bug * fix bug * fix bug * fix bug * fix bug * add property * add test * fix code style * fix bug * fix bug * fix bug * fix port * fix ocr * fix_ocr * fix ocr * fix ocr * fix ocr * fix ocr * Update paddle2onnx.cmake * Update paddle2onnx.cmake * Update paddle2onnx.cmake Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: Jason <928090362@qq.com>
This commit is contained in:
Thomas Young
@@ -20,14 +20,8 @@ void BindPPOCRModel(pybind11::module& m) {
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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::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_readwrite("preprocessor", &vision::ocr::DBDetector::preprocessor_)
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.def_readwrite("postprocessor", &vision::ocr::DBDetector::postprocessor_);
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pybind11::class_<vision::ocr::DBDetectorPreprocessor>(m, "DBDetectorPreprocessor")
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.def(pybind11::init<>())
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.def_readwrite("max_side_len", &vision::ocr::DBDetectorPreprocessor::max_side_len_)
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@@ -45,7 +39,7 @@ void BindPPOCRModel(pybind11::module& m) {
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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 make_pair(outputs, batch_det_img_info);
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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>(m, "DBDetectorPostprocessor")
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@@ -77,15 +71,31 @@ void BindPPOCRModel(pybind11::module& m) {
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return results;
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});
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// Classifier
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pybind11::class_<vision::ocr::Classifier, FastDeployModel>(m, "Classifier")
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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_readwrite("preprocessor", &vision::ocr::Classifier::preprocessor_)
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.def_readwrite("postprocessor", &vision::ocr::Classifier::postprocessor_);
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.def_readwrite("preprocessor", &vision::ocr::DBDetector::preprocessor_)
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.def_readwrite("postprocessor", &vision::ocr::DBDetector::postprocessor_)
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.def("predict", [](vision::ocr::DBDetector& self,
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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, 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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pybind11::class_<vision::ocr::ClassifierPreprocessor>(m, "ClassifierPreprocessor")
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// Classifier
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pybind11::class_<vision::ocr::ClassifierPreprocessor>(m, "ClassifierPreprocessor")
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.def(pybind11::init<>())
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.def_readwrite("cls_image_shape", &vision::ocr::ClassifierPreprocessor::cls_image_shape_)
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.def_readwrite("mean", &vision::ocr::ClassifierPreprocessor::mean_)
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@@ -116,7 +126,7 @@ void BindPPOCRModel(pybind11::module& m) {
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if (!self.Run(inputs, &cls_labels, &cls_scores)) {
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throw std::runtime_error("Failed to preprocess the input data in ClassifierPostprocessor.");
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}
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return make_pair(cls_labels,cls_scores);
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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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@@ -127,39 +137,56 @@ void BindPPOCRModel(pybind11::module& m) {
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if (!self.Run(inputs, &cls_labels, &cls_scores)) {
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throw std::runtime_error("Failed to preprocess the input data in ClassifierPostprocessor.");
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}
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return make_pair(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::Recognizer, FastDeployModel>(m, "Recognizer")
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.def(pybind11::init<std::string, std::string, std::string, RuntimeOption,
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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_readwrite("preprocessor", &vision::ocr::Recognizer::preprocessor_)
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.def_readwrite("postprocessor", &vision::ocr::Recognizer::postprocessor_);
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pybind11::class_<vision::ocr::RecognizerPreprocessor>(m, "RecognizerPreprocessor")
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.def(pybind11::init<>())
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.def_readwrite("rec_image_shape", &vision::ocr::RecognizerPreprocessor::rec_image_shape_)
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.def_readwrite("mean", &vision::ocr::RecognizerPreprocessor::mean_)
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.def_readwrite("scale", &vision::ocr::RecognizerPreprocessor::scale_)
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.def_readwrite("is_scale", &vision::ocr::RecognizerPreprocessor::is_scale_)
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.def("run", [](vision::ocr::RecognizerPreprocessor& self, 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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.def_readwrite("preprocessor", &vision::ocr::Classifier::preprocessor_)
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.def_readwrite("postprocessor", &vision::ocr::Classifier::postprocessor_)
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.def("predict", [](vision::ocr::Classifier& self,
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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, 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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std::vector<FDTensor> outputs;
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if (!self.Run(&images, &outputs)) {
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throw std::runtime_error("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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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>(m, "RecognizerPreprocessor")
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.def(pybind11::init<>())
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.def_readwrite("rec_image_shape", &vision::ocr::RecognizerPreprocessor::rec_image_shape_)
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.def_readwrite("mean", &vision::ocr::RecognizerPreprocessor::mean_)
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.def_readwrite("scale", &vision::ocr::RecognizerPreprocessor::scale_)
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.def_readwrite("is_scale", &vision::ocr::RecognizerPreprocessor::is_scale_)
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.def("run", [](vision::ocr::RecognizerPreprocessor& self, 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("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>(m, "RecognizerPostprocessor")
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.def(pybind11::init<std::string>())
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.def("run", [](vision::ocr::RecognizerPostprocessor& self,
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@@ -169,7 +196,7 @@ void BindPPOCRModel(pybind11::module& m) {
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if (!self.Run(inputs, &texts, &rec_scores)) {
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throw std::runtime_error("Failed to preprocess the input data in RecognizerPostprocessor.");
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}
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return make_pair(texts, rec_scores);
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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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@@ -180,7 +207,32 @@ void BindPPOCRModel(pybind11::module& m) {
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if (!self.Run(inputs, &texts, &rec_scores)) {
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throw std::runtime_error("Failed to preprocess the input data in RecognizerPostprocessor.");
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}
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return make_pair(texts, rec_scores);
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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_readwrite("preprocessor", &vision::ocr::Recognizer::preprocessor_)
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.def_readwrite("postprocessor", &vision::ocr::Recognizer::postprocessor_)
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.def("predict", [](vision::ocr::Recognizer& self,
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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, 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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