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https://github.com/PaddlePaddle/FastDeploy.git
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[CVCUDA] PP-OCR detector preprocessor integrate CV-CUDA (#1382)
* 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
This commit is contained in:
Wang Xinyu
Executable → Regular
+192
-126
@@ -12,80 +12,106 @@
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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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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>(m, "DBDetectorPreprocessor")
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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", &vision::ocr::DBDetectorPreprocessor::GetMaxSideLen, &vision::ocr::DBDetectorPreprocessor::SetMaxSideLen)
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.def_property("mean", &vision::ocr::DBDetectorPreprocessor::GetMean, &vision::ocr::DBDetectorPreprocessor::SetMean)
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.def_property("scale", &vision::ocr::DBDetectorPreprocessor::GetScale, &vision::ocr::DBDetectorPreprocessor::SetScale)
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.def_property("is_scale", &vision::ocr::DBDetectorPreprocessor::GetIsScale, &vision::ocr::DBDetectorPreprocessor::SetIsScale)
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.def("run", [](vision::ocr::DBDetectorPreprocessor& self, std::vector<pybind11::array>& im_list) {
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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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std::vector<std::array<int, 4>> batch_det_img_info;
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self.Run(&images, &outputs, &batch_det_img_info);
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for(size_t i = 0; i< outputs.size(); ++i){
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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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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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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", &vision::ocr::DBDetectorPostprocessor::GetDetDBThresh, &vision::ocr::DBDetectorPostprocessor::SetDetDBThresh)
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.def_property("det_db_box_thresh", &vision::ocr::DBDetectorPostprocessor::GetDetDBBoxThresh, &vision::ocr::DBDetectorPostprocessor::SetDetDBBoxThresh)
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.def_property("det_db_unclip_ratio", &vision::ocr::DBDetectorPostprocessor::GetDetDBUnclipRatio, &vision::ocr::DBDetectorPostprocessor::SetDetDBUnclipRatio)
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.def_property("det_db_score_mode", &vision::ocr::DBDetectorPostprocessor::GetDetDBScoreMode, &vision::ocr::DBDetectorPostprocessor::SetDetDBScoreMode)
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.def_property("use_dilation", &vision::ocr::DBDetectorPostprocessor::GetUseDilation, &vision::ocr::DBDetectorPostprocessor::SetUseDilation)
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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", [](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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.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("Failed to preprocess the input data in DBDetectorPostprocessor.");
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}
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return results;
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})
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.def("run", [](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("Failed to preprocess the input data in DBDetectorPostprocessor.");
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}
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return results;
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});
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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", &vision::ocr::DBDetector::GetPreprocessor)
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.def_property_readonly("postprocessor", &vision::ocr::DBDetector::GetPostprocessor)
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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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.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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@@ -96,39 +122,54 @@ void BindPPOCRModel(pybind11::module& m) {
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});
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// Classifier
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pybind11::class_<vision::ocr::ClassifierPreprocessor>(m, "ClassifierPreprocessor")
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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", &vision::ocr::ClassifierPreprocessor::GetClsImageShape, &vision::ocr::ClassifierPreprocessor::SetClsImageShape)
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.def_property("mean", &vision::ocr::ClassifierPreprocessor::GetMean, &vision::ocr::ClassifierPreprocessor::SetMean)
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.def_property("scale", &vision::ocr::ClassifierPreprocessor::GetScale, &vision::ocr::ClassifierPreprocessor::SetScale)
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.def_property("is_scale", &vision::ocr::ClassifierPreprocessor::GetIsScale, &vision::ocr::ClassifierPreprocessor::SetIsScale)
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.def("run", [](vision::ocr::ClassifierPreprocessor& self, std::vector<pybind11::array>& im_list) {
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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("Failed to preprocess the input data in ClassifierPreprocessor.");
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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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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>(m, "ClassifierPostprocessor")
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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", &vision::ocr::ClassifierPostprocessor::GetClsThresh, &vision::ocr::ClassifierPostprocessor::SetClsThresh)
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.def("run", [](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("Failed to preprocess the input data in 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_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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@@ -136,26 +177,31 @@ void BindPPOCRModel(pybind11::module& m) {
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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("Failed to preprocess the input data in ClassifierPostprocessor.");
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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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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", &vision::ocr::Classifier::GetPreprocessor)
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.def_property_readonly("postprocessor", &vision::ocr::Classifier::GetPostprocessor)
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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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.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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@@ -167,39 +213,54 @@ void BindPPOCRModel(pybind11::module& m) {
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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_property("static_shape_infer", &vision::ocr::RecognizerPreprocessor::GetStaticShapeInfer, &vision::ocr::RecognizerPreprocessor::SetStaticShapeInfer)
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.def_property("rec_image_shape", &vision::ocr::RecognizerPreprocessor::GetRecImageShape, &vision::ocr::RecognizerPreprocessor::SetRecImageShape)
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.def_property("mean", &vision::ocr::RecognizerPreprocessor::GetMean, &vision::ocr::RecognizerPreprocessor::SetMean)
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.def_property("scale", &vision::ocr::RecognizerPreprocessor::GetScale, &vision::ocr::RecognizerPreprocessor::SetScale)
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.def_property("is_scale", &vision::ocr::RecognizerPreprocessor::GetIsScale, &vision::ocr::RecognizerPreprocessor::SetIsScale)
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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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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("Failed to preprocess the input data in RecognizerPostprocessor.");
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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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return std::make_pair(texts, rec_scores);
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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>())
|
||||
.def("run",
|
||||
[](vision::ocr::RecognizerPostprocessor& self,
|
||||
std::vector<FDTensor>& inputs) {
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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;
|
||||
@@ -207,7 +268,9 @@ void BindPPOCRModel(pybind11::module& m) {
|
||||
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.");
|
||||
throw std::runtime_error(
|
||||
"Failed to preprocess the input data in "
|
||||
"RecognizerPostprocessor.");
|
||||
}
|
||||
return std::make_pair(texts, rec_scores);
|
||||
});
|
||||
@@ -216,17 +279,20 @@ void BindPPOCRModel(pybind11::module& m) {
|
||||
.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) {
|
||||
.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;
|
||||
|
||||
Reference in New Issue
Block a user