mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 00:33:03 +08:00
Optimize ocr system code (#209)
* Support PPYOLOE plus model * Optimize ocr system code * modify example code * fix patchelf of openvino * optimize demo code of ocr * remove debug code * update demo code of ocr Co-authored-by: Jack Zhou <zhoushunjie@baidu.com>
This commit is contained in:
Jason
@@ -199,13 +199,8 @@ if(ENABLE_TRT_BACKEND)
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list(APPEND ALL_DEPLOY_SRCS ${DEPLOY_TRT_SRCS})
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find_library(TRT_INFER_LIB nvinfer ${TRT_DIRECTORY}/lib)
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find_library(TRT_ONNX_LIB nvonnxparser ${TRT_DIRECTORY}/lib)
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if(NOT WIN32)
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find_library(TRT_CAFFE_LIB nvcaffe_parser ${TRT_DIRECTORY}/lib)
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else()
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set(TRT_CAFFE_LIB "")
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endif()
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find_library(TRT_PLUGIN_LIB nvinfer_plugin ${TRT_DIRECTORY}/lib)
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list(APPEND DEPEND_LIBS ${TRT_INFER_LIB} ${TRT_ONNX_LIB} ${TRT_CAFFE_LIB} ${TRT_PLUGIN_LIB})
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list(APPEND DEPEND_LIBS ${TRT_INFER_LIB} ${TRT_ONNX_LIB} ${TRT_PLUGIN_LIB})
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if(NOT EXISTS "${CMAKE_CURRENT_BINARY_DIR}/third_libs/install/tensorrt")
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file(MAKE_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/third_libs/install/tensorrt")
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@@ -70,12 +70,7 @@ if(WITH_GPU)
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find_library(TRT_INFER_LIB nvinfer ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/tensorrt/lib)
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find_library(TRT_ONNX_LIB nvonnxparser ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/tensorrt/lib)
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find_library(TRT_PLUGIN_LIB nvinfer_plugin ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/tensorrt/lib)
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if (NOT WIN32)
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find_library(TRT_CAFFE_LIB nvcaffe_parser ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/tensorrt/lib)
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list(APPEND FASTDEPLOY_LIBS ${TRT_INFER_LIB} ${TRT_ONNX_LIB} ${TRT_CAFFE_LIB} ${TRT_PLUGIN_LIB})
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else()
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list(APPEND FASTDEPLOY_LIBS ${TRT_INFER_LIB} ${TRT_ONNX_LIB} ${TRT_PLUGIN_LIB})
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endif()
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list(APPEND FASTDEPLOY_LIBS ${TRT_INFER_LIB} ${TRT_ONNX_LIB} ${TRT_PLUGIN_LIB})
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endif()
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endif()
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15
build_scripts/patch_lib.sh
Executable file
15
build_scripts/patch_lib.sh
Executable file
@@ -0,0 +1,15 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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patchelf --set-rpath '${ORIGIN}' $1/*.so*
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@@ -144,6 +144,9 @@ def process_libraries(current_dir):
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for flt in filters:
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if f.count(flt) > 0:
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remain = False
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filename = os.path.split(f)[-1]
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if filename in ["libnvinfer_plugin.so", "libnvinfer_plugin.so.8.4.1", "libnvinfer.so", "libnvinfer.so.8.4.1", "libnvonnxparser.so", "libnvonnxparser.so.8.4.1", "libnvparsers.so", "libnvparsers.so.8.4.1"]:
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continue
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if remain:
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package_data.append(
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os.path.relpath(f, os.path.join(current_dir, "fastdeploy")))
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@@ -261,12 +261,12 @@ bool Runtime::Init(const RuntimeOption& _option) {
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FDASSERT(option.device == Device::CPU || option.device == Device::GPU,
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"Backend::ORT only supports Device::CPU/Device::GPU.");
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CreateOrtBackend();
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FDINFO << "Runtime initialized with Backend::ORT." << std::endl;
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FDINFO << "Runtime initialized with Backend::ORT in device " << Str(option.device) << "." << std::endl;
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} else if (option.backend == Backend::TRT) {
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FDASSERT(option.device == Device::GPU,
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"Backend::TRT only supports Device::GPU.");
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CreateTrtBackend();
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FDINFO << "Runtime initialized with Backend::TRT." << std::endl;
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FDINFO << "Runtime initialized with Backend::TRT in device " << Str(option.device) << "." << std::endl;
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} else if (option.backend == Backend::PDINFER) {
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FDASSERT(option.device == Device::CPU || option.device == Device::GPU,
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"Backend::TRT only supports Device::CPU/Device::GPU.");
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@@ -274,12 +274,12 @@ bool Runtime::Init(const RuntimeOption& _option) {
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option.model_format == Frontend::PADDLE,
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"Backend::PDINFER only supports model format of Frontend::PADDLE.");
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CreatePaddleBackend();
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FDINFO << "Runtime initialized with Backend::PDINFER." << std::endl;
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FDINFO << "Runtime initialized with Backend::PDINFER in device " << Str(option.device) << "." << std::endl;
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} else if (option.backend == Backend::OPENVINO) {
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FDASSERT(option.device == Device::CPU,
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"Backend::OPENVINO only supports Device::CPU");
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CreateOpenVINOBackend();
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FDINFO << "Runtime initialized with Backend::OPENVINO." << std::endl;
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FDINFO << "Runtime initialized with Backend::OPENVINO in device " << Str(option.device) << "." << std::endl;
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} else {
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FDERROR << "Runtime only support "
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"Backend::ORT/Backend::TRT/Backend::PDINFER as backend now."
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@@ -72,6 +72,14 @@ std::string DetectionResult::Str() {
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return out;
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}
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void OCRResult::Clear() {
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boxes.clear();
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text.clear();
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rec_scores.clear();
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cls_scores.clear();
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cls_labels.clear();
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}
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FaceDetectionResult::FaceDetectionResult(const FaceDetectionResult& res) {
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boxes.assign(res.boxes.begin(), res.boxes.end());
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landmarks.assign(res.landmarks.begin(), res.landmarks.end());
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@@ -71,6 +71,8 @@ struct FASTDEPLOY_DECL OCRResult : public BaseResult {
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ResultType type = ResultType::OCR;
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void Clear();
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std::string Str();
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};
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@@ -26,10 +26,10 @@ Classifier::Classifier(const std::string& model_file,
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const RuntimeOption& custom_option,
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const Frontend& model_format) {
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if (model_format == Frontend::ONNX) {
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valid_cpu_backends = {Backend::ORT}; // 指定可用的CPU后端
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valid_cpu_backends = {Backend::ORT, Backend::OPENVINO}; // 指定可用的CPU后端
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valid_gpu_backends = {Backend::ORT, Backend::TRT}; // 指定可用的GPU后端
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} else {
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valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
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valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::OPENVINO};
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valid_gpu_backends = {Backend::PDINFER, Backend::TRT, Backend::ORT};
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}
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runtime_option = custom_option;
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@@ -145,4 +145,4 @@ bool Classifier::Predict(cv::Mat* img, std::tuple<int, float>* cls_result) {
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} // namesapce ocr
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} // namespace vision
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} // namespace fastdeploy
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} // namespace fastdeploy
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@@ -26,10 +26,10 @@ DBDetector::DBDetector(const std::string& model_file,
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const RuntimeOption& custom_option,
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const Frontend& model_format) {
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if (model_format == Frontend::ONNX) {
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valid_cpu_backends = {Backend::ORT}; // 指定可用的CPU后端
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valid_cpu_backends = {Backend::ORT, Backend::OPENVINO}; // 指定可用的CPU后端
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valid_gpu_backends = {Backend::ORT, Backend::TRT}; // 指定可用的GPU后端
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} else {
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valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
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valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::OPENVINO};
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valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
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}
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@@ -202,4 +202,4 @@ bool DBDetector::Predict(cv::Mat* img,
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} // namesapce ocr
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} // namespace vision
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} // namespace fastdeploy
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} // namespace fastdeploy
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@@ -23,10 +23,10 @@ void BindPPOCRSystemv3(pybind11::module& m) {
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.def(pybind11::init<fastdeploy::vision::ocr::DBDetector*,
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fastdeploy::vision::ocr::Classifier*,
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fastdeploy::vision::ocr::Recognizer*>())
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.def(pybind11::init<fastdeploy::vision::ocr::DBDetector*,
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fastdeploy::vision::ocr::Recognizer*>())
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.def("predict", [](application::ocrsystem::PPOCRSystemv3& self,
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pybind11::array& data) {
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auto mat = PyArrayToCvMat(data);
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vision::OCRResult res;
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self.Predict(&mat, &res);
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@@ -38,14 +38,13 @@ void BindPPOCRSystemv2(pybind11::module& m) {
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// OCRSys
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pybind11::class_<application::ocrsystem::PPOCRSystemv2, FastDeployModel>(
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m, "PPOCRSystemv2")
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.def(pybind11::init<fastdeploy::vision::ocr::DBDetector*,
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fastdeploy::vision::ocr::Classifier*,
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fastdeploy::vision::ocr::Recognizer*>())
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.def(pybind11::init<fastdeploy::vision::ocr::DBDetector*,
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fastdeploy::vision::ocr::Recognizer*>())
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.def("predict", [](application::ocrsystem::PPOCRSystemv2& self,
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pybind11::array& data) {
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auto mat = PyArrayToCvMat(data);
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vision::OCRResult res;
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self.Predict(&mat, &res);
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@@ -19,92 +19,88 @@
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namespace fastdeploy {
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namespace application {
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namespace ocrsystem {
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PPOCRSystemv2::PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* ocr_det,
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fastdeploy::vision::ocr::Classifier* ocr_cls,
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fastdeploy::vision::ocr::Recognizer* ocr_rec)
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: detector(ocr_det), classifier(ocr_cls), recognizer(ocr_rec) {}
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void PPOCRSystemv2::Detect(cv::Mat* img,
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fastdeploy::vision::OCRResult* result) {
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std::vector<std::array<int, 8>> boxes;
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this->detector->Predict(img, &boxes);
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result->boxes = boxes;
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PPOCRSystemv2::PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* det_model,
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fastdeploy::vision::ocr::Classifier* cls_model,
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fastdeploy::vision::ocr::Recognizer* rec_model)
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: detector_(det_model), classifier_(cls_model), recognizer_(rec_model) {
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recognizer_->rec_image_shape[1] = 32;
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}
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void PPOCRSystemv2::Recognize(cv::Mat* img,
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PPOCRSystemv2::PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* det_model,
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fastdeploy::vision::ocr::Recognizer* rec_model)
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: detector_(det_model), recognizer_(rec_model) {
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recognizer_->rec_image_shape[1] = 32;
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}
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bool PPOCRSystemv2::Detect(cv::Mat* img,
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fastdeploy::vision::OCRResult* result) {
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if (!detector_->Predict(img, &(result->boxes))) {
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FDERROR << "There's error while detecting image in PPOCRSystem." << std::endl;
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return false;
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}
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vision::ocr::SortBoxes(result);
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return true;
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}
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bool PPOCRSystemv2::Recognize(cv::Mat* img,
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fastdeploy::vision::OCRResult* result) {
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std::tuple<std::string, float> rec_result;
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this->recognizer->rec_image_shape[1] = 32;
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this->recognizer->Predict(img, &rec_result);
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if (!recognizer_->Predict(img, &rec_result)) {
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FDERROR << "There's error while recognizing image in PPOCRSystem." << std::endl;
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return false;
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}
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result->text.push_back(std::get<0>(rec_result));
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result->rec_scores.push_back(std::get<1>(rec_result));
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return true;
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}
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void PPOCRSystemv2::Classify(cv::Mat* img,
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bool PPOCRSystemv2::Classify(cv::Mat* img,
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fastdeploy::vision::OCRResult* result) {
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std::tuple<int, float> cls_result;
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this->classifier->Predict(img, &cls_result);
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if (!classifier_->Predict(img, &cls_result)) {
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FDERROR << "There's error while classifying image in PPOCRSystem." << std::endl;
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return false;
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}
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result->cls_labels.push_back(std::get<0>(cls_result));
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result->cls_scores.push_back(std::get<1>(cls_result));
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return true;
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}
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bool PPOCRSystemv2::Predict(cv::Mat* img,
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fastdeploy::vision::OCRResult* result) {
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if (this->detector->initialized == 0) { //没det
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//输入单张“小图片”给分类器
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if (this->classifier->initialized != 0) {
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this->Classify(img, result);
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//摆正单张图像
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if ((result->cls_labels)[0] % 2 == 1 &&
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(result->cls_scores)[0] > this->classifier->cls_thresh) {
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cv::rotate(*img, *img, 1);
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}
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}
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//输入单张“小图片”给识别器
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if (this->recognizer->initialized != 0) {
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this->Recognize(img, result);
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}
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} else {
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//从DET模型开始
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//一张图,会输出多个“小图片”，送给后续模型
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this->Detect(img, result);
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// crop image
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std::vector<cv::Mat> img_list;
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for (int j = 0; j < (result->boxes).size(); j++) {
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cv::Mat crop_img;
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crop_img =
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fastdeploy::vision::ocr::GetRotateCropImage(*img, (result->boxes)[j]);
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img_list.push_back(crop_img);
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}
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// cls
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if (this->classifier->initialized != 0) {
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for (int i = 0; i < img_list.size(); i++) {
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this->Classify(&img_list[0], result);
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}
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for (int i = 0; i < img_list.size(); i++) {
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if ((result->cls_labels)[i] % 2 == 1 &&
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(result->cls_scores)[i] > this->classifier->cls_thresh) {
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std::cout << "Rotate this image " << std::endl;
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cv::rotate(img_list[i], img_list[i], 1);
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}
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}
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}
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// rec
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if (this->recognizer->initialized != 0) {
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for (int i = 0; i < img_list.size(); i++) {
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this->Recognize(&img_list[i], result);
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}
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}
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result->Clear();
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if (nullptr != detector_ && !Detect(img, result)) {
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FDERROR << "Failed to detect image." << std::endl;
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return false;
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}
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// Get croped images by detection result
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std::vector<cv::Mat> image_list;
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for (size_t i = 0; i < result->boxes.size(); ++i) {
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auto crop_im = vision::ocr::GetRotateCropImage(*img, (result->boxes)[i]);
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image_list.push_back(crop_im);
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}
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if (result->boxes.size() == 0) {
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image_list.push_back(*img);
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}
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for (size_t i = 0; i < image_list.size(); ++i) {
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if (nullptr != classifier_ && !Classify(&(image_list[i]), result)) {
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FDERROR << "Failed to classify croped image of index " << i << "." << std::endl;
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return false;
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}
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if (nullptr != classifier_ && result->cls_labels[i] % 2 == 1 && result->cls_scores[i] > classifier_->cls_thresh) {
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cv::rotate(image_list[i], image_list[i], 1);
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}
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if (nullptr != recognizer_ && !Recognize(&(image_list[i]), result)) {
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FDERROR << "Failed to recgnize croped image of index " << i << "." << std::endl;
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return false;
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}
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||||
}
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return true;
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};
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@@ -31,20 +31,23 @@ namespace ocrsystem {
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class FASTDEPLOY_DECL PPOCRSystemv2 : public FastDeployModel {
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public:
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PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* ocr_det = nullptr,
|
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fastdeploy::vision::ocr::Classifier* ocr_cls = nullptr,
|
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fastdeploy::vision::ocr::Recognizer* ocr_rec = nullptr);
|
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PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* det_model,
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fastdeploy::vision::ocr::Classifier* cls_model,
|
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fastdeploy::vision::ocr::Recognizer* rec_model);
|
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|
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fastdeploy::vision::ocr::DBDetector* detector = nullptr;
|
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fastdeploy::vision::ocr::Classifier* classifier = nullptr;
|
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fastdeploy::vision::ocr::Recognizer* recognizer = nullptr;
|
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PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* det_model,
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fastdeploy::vision::ocr::Recognizer* rec_model);
|
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|
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bool Predict(cv::Mat* img, fastdeploy::vision::OCRResult* result);
|
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virtual bool Predict(cv::Mat* img, fastdeploy::vision::OCRResult* result);
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private:
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void Detect(cv::Mat* img, fastdeploy::vision::OCRResult* result);
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void Recognize(cv::Mat* img, fastdeploy::vision::OCRResult* result);
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void Classify(cv::Mat* img, fastdeploy::vision::OCRResult* result);
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protected:
|
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fastdeploy::vision::ocr::DBDetector* detector_ = nullptr;
|
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fastdeploy::vision::ocr::Classifier* classifier_ = nullptr;
|
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fastdeploy::vision::ocr::Recognizer* recognizer_ = nullptr;
|
||||
|
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virtual bool Detect(cv::Mat* img, fastdeploy::vision::OCRResult* result);
|
||||
virtual bool Recognize(cv::Mat* img, fastdeploy::vision::OCRResult* result);
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virtual bool Classify(cv::Mat* img, fastdeploy::vision::OCRResult* result);
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};
|
||||
|
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} // namespace ocrsystem
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@@ -1,114 +0,0 @@
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// 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 "fastdeploy/vision/ocr/ppocr/ppocr_system_v3.h"
|
||||
#include "fastdeploy/utils/perf.h"
|
||||
#include "fastdeploy/vision/ocr/ppocr/utils/ocr_utils.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace application {
|
||||
namespace ocrsystem {
|
||||
PPOCRSystemv3::PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* ocr_det,
|
||||
fastdeploy::vision::ocr::Classifier* ocr_cls,
|
||||
fastdeploy::vision::ocr::Recognizer* ocr_rec)
|
||||
: detector(ocr_det), classifier(ocr_cls), recognizer(ocr_rec) {}
|
||||
|
||||
void PPOCRSystemv3::Detect(cv::Mat* img,
|
||||
fastdeploy::vision::OCRResult* result) {
|
||||
std::vector<std::array<int, 8>> boxes_result;
|
||||
|
||||
this->detector->Predict(img, &boxes_result);
|
||||
|
||||
result->boxes = boxes_result;
|
||||
|
||||
fastdeploy::vision::ocr::SortBoxes(result);
|
||||
}
|
||||
|
||||
void PPOCRSystemv3::Recognize(cv::Mat* img,
|
||||
fastdeploy::vision::OCRResult* result) {
|
||||
std::tuple<std::string, float> rec_result;
|
||||
|
||||
this->recognizer->Predict(img, &rec_result);
|
||||
|
||||
result->text.push_back(std::get<0>(rec_result));
|
||||
result->rec_scores.push_back(std::get<1>(rec_result));
|
||||
}
|
||||
|
||||
void PPOCRSystemv3::Classify(cv::Mat* img,
|
||||
fastdeploy::vision::OCRResult* result) {
|
||||
std::tuple<int, float> cls_result;
|
||||
|
||||
this->classifier->Predict(img, &cls_result);
|
||||
|
||||
result->cls_labels.push_back(std::get<0>(cls_result));
|
||||
result->cls_scores.push_back(std::get<1>(cls_result));
|
||||
}
|
||||
|
||||
bool PPOCRSystemv3::Predict(cv::Mat* img,
|
||||
fastdeploy::vision::OCRResult* result) {
|
||||
if (this->detector->initialized == 0) { //没det
|
||||
//输入单张“小图片”给分类器
|
||||
if (this->classifier->initialized != 0) {
|
||||
this->Classify(img, result);
|
||||
//摆正单张图像
|
||||
if ((result->cls_labels)[0] % 2 == 1 &&
|
||||
(result->cls_scores)[0] > this->classifier->cls_thresh) {
|
||||
cv::rotate(*img, *img, 1);
|
||||
}
|
||||
}
|
||||
//输入单张“小图片”给识别器
|
||||
if (this->recognizer->initialized != 0) {
|
||||
this->Recognize(img, result);
|
||||
}
|
||||
|
||||
} else {
|
||||
//从DET模型开始
|
||||
//一张图,会输出多个“小图片”，送给后续模型
|
||||
this->Detect(img, result);
|
||||
// crop image
|
||||
std::vector<cv::Mat> img_list;
|
||||
|
||||
for (int j = 0; j < (result->boxes).size(); j++) {
|
||||
cv::Mat crop_img;
|
||||
crop_img =
|
||||
fastdeploy::vision::ocr::GetRotateCropImage(*img, (result->boxes)[j]);
|
||||
img_list.push_back(crop_img);
|
||||
}
|
||||
// cls
|
||||
if (this->classifier->initialized != 0) {
|
||||
for (int i = 0; i < img_list.size(); i++) {
|
||||
this->Classify(&img_list[i], result);
|
||||
}
|
||||
|
||||
for (int i = 0; i < img_list.size(); i++) {
|
||||
if ((result->cls_labels)[i] % 2 == 1 &&
|
||||
(result->cls_scores)[i] > this->classifier->cls_thresh) {
|
||||
cv::rotate(img_list[i], img_list[i], 1);
|
||||
}
|
||||
}
|
||||
}
|
||||
// rec
|
||||
if (this->recognizer->initialized != 0) {
|
||||
for (int i = 0; i < img_list.size(); i++) {
|
||||
this->Recognize(&img_list[i], result);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
};
|
||||
|
||||
} // namesapce ocrsystem
|
||||
} // namespace application
|
||||
} // namespace fastdeploy
|
||||
@@ -14,37 +14,25 @@
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <vector>
|
||||
|
||||
#include "fastdeploy/fastdeploy_model.h"
|
||||
#include "fastdeploy/vision/common/processors/transform.h"
|
||||
#include "fastdeploy/vision/common/result.h"
|
||||
|
||||
#include "fastdeploy/vision/ocr/ppocr/classifier.h"
|
||||
#include "fastdeploy/vision/ocr/ppocr/dbdetector.h"
|
||||
#include "fastdeploy/vision/ocr/ppocr/recognizer.h"
|
||||
#include "fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.h"
|
||||
#include "fastdeploy/vision/ocr/ppocr/ppocr_system_v2.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace application {
|
||||
namespace ocrsystem {
|
||||
|
||||
class FASTDEPLOY_DECL PPOCRSystemv3 : public FastDeployModel {
|
||||
class FASTDEPLOY_DECL PPOCRSystemv3 : public PPOCRSystemv2 {
|
||||
public:
|
||||
PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* ocr_det = nullptr,
|
||||
fastdeploy::vision::ocr::Classifier* ocr_cls = nullptr,
|
||||
fastdeploy::vision::ocr::Recognizer* ocr_rec = nullptr);
|
||||
PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* det_model,
|
||||
fastdeploy::vision::ocr::Classifier* cls_model,
|
||||
fastdeploy::vision::ocr::Recognizer* rec_model) : PPOCRSystemv2(det_model, cls_model, rec_model) {
|
||||
// The only difference between v2 and v3
|
||||
recognizer_->rec_image_shape[1] = 48;
|
||||
}
|
||||
|
||||
fastdeploy::vision::ocr::DBDetector* detector = nullptr;
|
||||
fastdeploy::vision::ocr::Classifier* classifier = nullptr;
|
||||
fastdeploy::vision::ocr::Recognizer* recognizer = nullptr;
|
||||
|
||||
bool Predict(cv::Mat* img, fastdeploy::vision::OCRResult* result);
|
||||
|
||||
private:
|
||||
void Detect(cv::Mat* img, fastdeploy::vision::OCRResult* result);
|
||||
void Recognize(cv::Mat* img, fastdeploy::vision::OCRResult* result);
|
||||
void Classify(cv::Mat* img, fastdeploy::vision::OCRResult* result);
|
||||
PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* det_model,
|
||||
fastdeploy::vision::ocr::Recognizer* rec_model) : PPOCRSystemv2(det_model, rec_model) {
|
||||
recognizer_->rec_image_shape[1] = 48;
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ocrsystem
|
||||
|
||||
@@ -44,11 +44,11 @@ Recognizer::Recognizer(const std::string& model_file,
|
||||
const RuntimeOption& custom_option,
|
||||
const Frontend& model_format) {
|
||||
if (model_format == Frontend::ONNX) {
|
||||
valid_cpu_backends = {Backend::ORT}; // 指定可用的CPU后端
|
||||
valid_cpu_backends = {Backend::ORT, Backend::OPENVINO}; // 指定可用的CPU后端
|
||||
valid_gpu_backends = {Backend::ORT, Backend::TRT}; // 指定可用的GPU后端
|
||||
} else {
|
||||
// NOTE:此模型暂不支持paddle-inference-Gpu推理
|
||||
valid_cpu_backends = {Backend::ORT, Backend::PDINFER};
|
||||
valid_cpu_backends = {Backend::ORT, Backend::PDINFER, Backend::OPENVINO};
|
||||
valid_gpu_backends = {Backend::ORT, Backend::TRT};
|
||||
}
|
||||
|
||||
@@ -204,4 +204,4 @@ bool Recognizer::Predict(cv::Mat* img,
|
||||
|
||||
} // namesapce ocr
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
} // namespace fastdeploy
|
||||
|
||||
@@ -32,6 +32,10 @@ bool CompareBox(const std::array<int, 8>& result1,
|
||||
void SortBoxes(OCRResult* result) {
|
||||
std::sort(result->boxes.begin(), result->boxes.end(), CompareBox);
|
||||
|
||||
if (result->boxes.size() == 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
for (int i = 0; i < result->boxes.size() - 1; i++) {
|
||||
if (abs(result->boxes[i + 1][1] - result->boxes[i][1]) < 10 &&
|
||||
(result->boxes[i + 1][0] < result->boxes[i][0])) {
|
||||
@@ -42,4 +46,4 @@ void SortBoxes(OCRResult* result) {
|
||||
|
||||
} // namesoace ocr
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
} // namespace fastdeploy
|
||||
|
||||
84
docs/api/runtime/runtime.md
Normal file
84
docs/api/runtime/runtime.md
Normal file
@@ -0,0 +1,84 @@
|
||||
# Runtime
|
||||
|
||||
在配置`RuntimeOption`后，即可基于不同后端在不同硬件上创建Runtime用于模型推理。
|
||||
|
||||
## Python 类
|
||||
|
||||
```
|
||||
class Runtime(runtime_option)
|
||||
```
|
||||
**参数**
|
||||
> * **runtime_option**(fastdeploy.RuntimeOption): 配置好的RuntimeOption类实例
|
||||
|
||||
### 成员函数
|
||||
|
||||
```
|
||||
infer(data)
|
||||
```
|
||||
根据输入数据进行模型推理
|
||||
|
||||
**参数**
|
||||
|
||||
> * **data**(dict({str: np.ndarray}): 输入数据，字典dict类型，key为输入名，value为np.ndarray数据类型
|
||||
|
||||
**返回值**
|
||||
|
||||
返回list, list的长度与原始模型输出个数一致；list中元素为np.ndarray类型
|
||||
|
||||
|
||||
```
|
||||
num_inputs()
|
||||
```
|
||||
返回模型的输入个数
|
||||
|
||||
```
|
||||
num_outputs()
|
||||
```
|
||||
返回模型的输出个数
|
||||
|
||||
|
||||
## C++ 类
|
||||
|
||||
```
|
||||
class Runtime
|
||||
```
|
||||
|
||||
### 成员函数
|
||||
|
||||
```
|
||||
bool Init(const RuntimeOption& runtime_option)
|
||||
```
|
||||
模型加载初始化
|
||||
|
||||
**参数**
|
||||
|
||||
> * **runtime_option**: 配置好的RuntimeOption实例
|
||||
|
||||
**返回值**
|
||||
|
||||
初始化成功返回true，否则返回false
|
||||
|
||||
|
||||
```
|
||||
bool Infer(vector<FDTensor>& inputs, vector<FDTensor>* outputs)
|
||||
```
|
||||
根据输入进行推理，并将结果写回到outputs
|
||||
|
||||
**参数**
|
||||
|
||||
> * **inputs**: 输入数据
|
||||
> * **outputs**: 输出数据
|
||||
|
||||
**返回值**
|
||||
|
||||
推理成功返回true，否则返回false
|
||||
|
||||
```
|
||||
int NumInputs()
|
||||
```
|
||||
返回模型输入个数
|
||||
|
||||
```
|
||||
input NumOutputs()
|
||||
```
|
||||
返回模型输出个数
|
||||
233
docs/api/runtime/runtime_option.md
Normal file
233
docs/api/runtime/runtime_option.md
Normal file
@@ -0,0 +1,233 @@
|
||||
# RuntimeOption
|
||||
|
||||
`RuntimeOption`用于配置模型在不同后端、硬件上的推理参数。
|
||||
|
||||
## Python 类
|
||||
|
||||
```
|
||||
class RuntimeOption()
|
||||
```
|
||||
|
||||
### 成员函数
|
||||
|
||||
```
|
||||
set_model_path(model_file, params_file="", model_format="paddle")
|
||||
```
|
||||
设定加载的模型路径
|
||||
|
||||
**参数**
|
||||
|
||||
> * **model_file**(str): 模型文件路径
|
||||
> * **params_file**(str): 参数文件路径，当为onnx模型格式时，无需指定
|
||||
> * **model_format**(str): 模型格式，支持paddle, onnx, 默认paddle
|
||||
|
||||
```
|
||||
use_gpu(device_id=0)
|
||||
```
|
||||
设定使用GPU推理
|
||||
|
||||
**参数**
|
||||
|
||||
> * **device_id**(int): 环境中存在多个GPU卡时，此参数指定推理的卡，默认为0
|
||||
|
||||
```
|
||||
use_cpu()
|
||||
```
|
||||
设定使用CPU推理
|
||||
|
||||
|
||||
```
|
||||
set_cpu_thread_num(thread_num=-1)
|
||||
```
|
||||
设置CPU上推理时线程数量
|
||||
|
||||
**参数**
|
||||
|
||||
> * **thread_num**(int): 线程数量，当小于或等于0时为后端自动分配，默认-1
|
||||
|
||||
```
|
||||
use_paddle_backend()
|
||||
```
|
||||
使用Paddle Inference后端进行推理，支持CPU/GPU，支持Paddle模型格式
|
||||
|
||||
```
|
||||
use_ort_backend()
|
||||
```
|
||||
使用ONNX Runtime后端进行推理，支持CPU/GPU，支持Paddle/ONNX模型格式
|
||||
|
||||
```
|
||||
use_trt_backend()
|
||||
```
|
||||
使用TensorRT后端进行推理，支持GPU，支持Paddle/ONNX模型格式
|
||||
|
||||
```
|
||||
use_openvino_backend()
|
||||
```
|
||||
使用OpenVINO后端进行推理，支持CPU, 支持Paddle/ONNX模型格式
|
||||
|
||||
```
|
||||
enable_paddle_mkldnn()
|
||||
disable_paddle_mkldnn()
|
||||
```
|
||||
当使用Paddle Inference后端时，通过此开关开启或关闭CPU上MKLDNN推理加速，后端默认为开启
|
||||
|
||||
```
|
||||
enable_paddle_log_info()
|
||||
disable_paddle_log_info()
|
||||
```
|
||||
当使用Paddle Inference后端时，通过此开关开启或关闭模型加载时的优化日志，后端默认为关闭
|
||||
|
||||
```
|
||||
set_paddle_mkldnn_cache_size(cache_size)
|
||||
```
|
||||
当使用Paddle Inference后端时，通过此接口控制MKLDNN加速时的Shape缓存大小
|
||||
|
||||
**参数**
|
||||
> * **cache_size**(int): 缓存大小
|
||||
|
||||
```
|
||||
set_trt_input_shape(tensor_name, min_shape, opt_shape=None, max_shape=None)
|
||||
```
|
||||
当使用TensorRT后端时，通过此接口设置模型各个输入的Shape范围，当只设置min_shape时，会自动将opt_shape和max_shape设定为与min_shape一致。
|
||||
|
||||
此接口用户也可以无需自行调用，FastDeploy在推理过程中，会根据推理真实数据自动更新Shape范围，但每次遇到新的shape更新范围后，会重新构造后端引擎，带来一定的耗时。可能过此接口提前配置，来避免推理过程中的引擎重新构建。
|
||||
|
||||
**参数**
|
||||
> * **tensor_name**(str): 需要设定输入范围的tensor名
|
||||
> * **min_shape(list of int): 对应tensor的最小shape，例如[1, 3, 224, 224]
|
||||
> * **opt_shape(list of int): 对应tensor的最常用shape，例如[2, 3, 224, 224], 当为None时，即保持与min_shape一致，默认为None
|
||||
> * **max_shape(list of int): 对应tensor的最大shape，例如[8, 3, 224, 224], 当为None时，即保持与min_shape一致，默认为None
|
||||
|
||||
```
|
||||
set_trt_cache_file(cache_file_path)
|
||||
```
|
||||
当使用TensorRT后端时，通过此接口将构建好的TensorRT模型引擎缓存到指定路径，或跳过构造引擎步骤，直接加载本地缓存的TensorRT模型
|
||||
- 当调用此接口，且`cache_file_path`不存在时，FastDeploy将构建TensorRT模型，并将构建好的模型保持至`cache_file_path`
|
||||
- 当调用此接口，且`cache_file_path`存在时，FastDeploy将直接加载`cache_file_path`存储的已构建好的TensorRT模型，从而大大减少模型加载初始化的耗时
|
||||
|
||||
通过此接口，可以在第二次运行代码时，加速模型加载初始化的时间，但因此也需注意，如需您修改了模型加载配置，例如TensorRT的max_workspace_size，或重新设置了`set_trt_input_shape`，以及更换了原始的paddle或onnx模型，需先删除已缓存在本地的`cache_file_path`文件，避免重新加载旧的缓存，影响程序正确性。
|
||||
|
||||
**参数**
|
||||
> * **cache_file_path**(str): 缓存文件路径，例如`/Downloads/resnet50.trt`
|
||||
|
||||
```
|
||||
enable_trt_fp16()
|
||||
disable_trt_fp16()
|
||||
```
|
||||
当使用TensorRT后端时，通过此接口开启或关闭半精度推理加速，会带来明显的性能提升，但并非所有GPU都支持半精度推理。 在不支持半精度推理的GPU上，将会回退到FP32推理，并给出提示`Detected FP16 is not supported in the current GPU, will use FP32 instead.`
|
||||
|
||||
## C++ 结构体
|
||||
|
||||
```
|
||||
struct RuntimeOption
|
||||
```
|
||||
|
||||
### 成员函数
|
||||
|
||||
```
|
||||
void SetModelPath(const string& model_file, const string& params_file = "", const string& model_format = "paddle")
|
||||
```
|
||||
设定加载的模型路径
|
||||
|
||||
**参数**
|
||||
|
||||
> * **model_file**: 模型文件路径
|
||||
> * **params_file**: 参数文件路径，当为onnx模型格式时，指定为""即可
|
||||
> * **model_format**: 模型格式，支持"paddle", "onnx", 默认"paddle"
|
||||
|
||||
```
|
||||
void UseGpu(int device_id = 0)
|
||||
```
|
||||
设定使用GPU推理
|
||||
|
||||
**参数**
|
||||
|
||||
> * **device_id**: 环境中存在多个GPU卡时，此参数指定推理的卡，默认为0
|
||||
|
||||
```
|
||||
void UseCpu()
|
||||
```
|
||||
设定使用CPU推理
|
||||
|
||||
|
||||
```
|
||||
void SetCpuThreadNum(int thread_num=-1)
|
||||
```
|
||||
设置CPU上推理时线程数量
|
||||
|
||||
**参数**
|
||||
|
||||
> * **thread_num**: 线程数量，当小于或等于0时为后端自动分配，默认-1
|
||||
|
||||
```
|
||||
void UsePaddleBackend()
|
||||
```
|
||||
使用Paddle Inference后端进行推理，支持CPU/GPU，支持Paddle模型格式
|
||||
|
||||
```
|
||||
void UseOrtBackend()
|
||||
```
|
||||
使用ONNX Runtime后端进行推理，支持CPU/GPU，支持Paddle/ONNX模型格式
|
||||
|
||||
```
|
||||
void UseTrtBackend()
|
||||
```
|
||||
使用TensorRT后端进行推理，支持GPU，支持Paddle/ONNX模型格式
|
||||
|
||||
```
|
||||
void UseOpenVINOBackend()
|
||||
```
|
||||
使用OpenVINO后端进行推理，支持CPU, 支持Paddle/ONNX模型格式
|
||||
|
||||
```
|
||||
void EnablePaddleMKLDNN()
|
||||
void DisablePaddleMKLDNN()
|
||||
```
|
||||
当使用Paddle Inference后端时，通过此开关开启或关闭CPU上MKLDNN推理加速，后端默认为开启
|
||||
|
||||
```
|
||||
void EnablePaddleLogInfo()
|
||||
void DisablePaddleLogInfo()
|
||||
```
|
||||
当使用Paddle Inference后端时，通过此开关开启或关闭模型加载时的优化日志，后端默认为关闭
|
||||
|
||||
```
|
||||
void SetPaddleMKLDNNCacheSize(int cache_size)
|
||||
```
|
||||
当使用Paddle Inference后端时，通过此接口控制MKLDNN加速时的Shape缓存大小
|
||||
|
||||
**参数**
|
||||
> * **cache_size**: 缓存大小
|
||||
|
||||
```
|
||||
void SetTrtInputShape(const string& tensor_name, const vector<int32_t>& min_shape,
|
||||
const vector<int32_t>& opt_shape = vector<int32_t>(),
|
||||
const vector<int32_t>& opt_shape = vector<int32_t>())
|
||||
```
|
||||
当使用TensorRT后端时，通过此接口设置模型各个输入的Shape范围，当只设置min_shape时，会自动将opt_shape和max_shape设定为与min_shape一致。
|
||||
|
||||
此接口用户也可以无需自行调用，FastDeploy在推理过程中，会根据推理真实数据自动更新Shape范围，但每次遇到新的shape更新范围后，会重新构造后端引擎，带来一定的耗时。可能过此接口提前配置，来避免推理过程中的引擎重新构建。
|
||||
|
||||
**参数**
|
||||
> * **tensor_name**: 需要设定输入范围的tensor名
|
||||
> * **min_shape: 对应tensor的最小shape，例如[1, 3, 224, 224]
|
||||
> * **opt_shape: 对应tensor的最常用shape，例如[2, 3, 224, 224], 当为默认参数即空vector时，则视为保持与min_shape一致，默认为空vector
|
||||
> * **max_shape: 对应tensor的最大shape，例如[8, 3, 224, 224], 当为默认参数即空vector时，则视为保持与min_shape一致，默认为空vector
|
||||
|
||||
```
|
||||
void SetTrtCacheFile(const string& cache_file_path)
|
||||
```
|
||||
当使用TensorRT后端时，通过此接口将构建好的TensorRT模型引擎缓存到指定路径，或跳过构造引擎步骤，直接加载本地缓存的TensorRT模型
|
||||
- 当调用此接口，且`cache_file_path`不存在时，FastDeploy将构建TensorRT模型，并将构建好的模型保持至`cache_file_path`
|
||||
- 当调用此接口，且`cache_file_path`存在时，FastDeploy将直接加载`cache_file_path`存储的已构建好的TensorRT模型，从而大大减少模型加载初始化的耗时
|
||||
|
||||
通过此接口，可以在第二次运行代码时，加速模型加载初始化的时间，但因此也需注意，如需您修改了模型加载配置，例如TensorRT的max_workspace_size，或重新设置了`SetTrtInputShape`，以及更换了原始的paddle或onnx模型，需先删除已缓存在本地的`cache_file_path`文件，避免重新加载旧的缓存，影响程序正确性。
|
||||
|
||||
**参数**
|
||||
> * **cache_file_path**: 缓存文件路径，例如`/Downloads/resnet50.trt`
|
||||
|
||||
```
|
||||
void EnableTrtFp16()
|
||||
void DisableTrtFp16()
|
||||
```
|
||||
当使用TensorRT后端时，通过此接口开启或关闭半精度推理加速，会带来明显的性能提升，但并非所有GPU都支持半精度推理。 在不支持半精度推理的GPU上，将会回退到FP32推理，并给出提示`Detected FP16 is not supported in the current GPU, will use FP32 instead.`
|
||||
@@ -11,11 +11,13 @@
|
||||
|:---- | :--- | :--- |
|
||||
| ENABLE_ORT_BACKEND | 启用ONNXRuntime推理后端，默认ON | 默认支持CPU，开启WITH_GPU后，同时支持GPU |
|
||||
| ENABLE_PADDLE_BACKEND | 启用Paddle Inference推理后端，默认OFF | 默认支持CPU，开启WITH_GPU后，同时支持GPU |
|
||||
| ENABLE_OPENVINO_BACKEND | 启用OpenVINO推理后端，默认OFF | 仅支持 CPU |
|
||||
| ENABLE_TRT_BACKEND | 启用TensorRT推理后端，默认OFF | 仅支持GPU |
|
||||
| WITH_GPU | 是否开启GPU使用，默认OFF | 当设为TRUE，编译后将支持Nvidia GPU部署 |
|
||||
| CUDA_DIRECTORY | 指定编译时的CUDA路径，默认为/usr/local/cuda | CUDA 11.2及以上 |
|
||||
| TRT_DIRECTORY | 当启用TensorRT推理后端时，需通过此参数指定TensorRT路径 | TensorRT 8.4及以上 |
|
||||
| ENABLE_VISION | 启用视觉模型模块，默认为ON | |
|
||||
| ENABLE_TEXT | 启用文本模型模块，默认为ON | |
|
||||
|
||||
|
||||
FastDeploy支持在编译时，用户选择自己的后端进行编译, 目前已经支持Paddle Inference、ONNXRuntime、TensorRT(加载ONNX格式)。FastDeploy已支持的模型已完成在不同后端上的验证工作，会自动根据编译时支持的后端进行选择，如若无可用后端则会给出相应提示(如YOLOv7目前仅支持ONNXRuntime/TensorRT后端，如若编译时未开启这两个后端，则推理时会提示无可用后端)。
|
||||
|
||||
18
docs/runtime/README.md
Normal file
18
docs/runtime/README.md
Normal file
@@ -0,0 +1,18 @@
|
||||
# FastDeploy推理后端
|
||||
|
||||
FastDeploy当前已集成多种推理后端，如下表格列出FastDeploy集成的各后端，与在FastDeploy中其支持的平台、硬件等信息
|
||||
|
||||
| 推理后端 | 支持平台 | 支持硬件 | 支持模型格式 |
|
||||
| :------- | :------- | :------- | :---- | :----- |
|
||||
| Paddle Inference | Windows(x64)/Linux(x64) | GPU/CPU | Paddle |
|
||||
| ONNX Runtime | Windows(x64)/Linux(x64/aarch64) | GPU/CPU | Paddle/ONNX |
|
||||
| TensorRT | Windows(x64)/Linux(x64/jetson) | GPU | Paddle/ONNX |
|
||||
| OpenVINO | Windows(x64)/Linux(x64) | CPU | Paddle/ONNX |
|
||||
| Poros[进行中] | Linux(x64) | CPU/GPU | TorchScript |
|
||||
|
||||
FastDeploy中各后端独立，用户在自行编译时可以选择开启其中一种或多种后端，FastDeploy中的`Runtime`模块为所有后端提供了统一的使用API，Runtime使用方式参阅文档[FastDeploy Runtime使用文档](usage.md)
|
||||
|
||||
|
||||
## 其它文档
|
||||
|
||||
- [FastDeploy编译](../compile)
|
||||
44
docs/runtime/usage.md
Normal file
44
docs/runtime/usage.md
Normal file
@@ -0,0 +1,44 @@
|
||||
# FastDeploy Runtime使用文档
|
||||
|
||||
`Runtime`作为FastDeploy中模型推理的模块，目前集成了多种后端，用户通过统一的后端即可快速完成不同格式的模型，在各硬件、平台、后端上的推理。本文档通过如下示例展示各硬件、后端上的推理
|
||||
|
||||
## CPU推理
|
||||
|
||||
Python示例
|
||||
|
||||
```
|
||||
import fastdeploy as fd
|
||||
import numpy as np
|
||||
option = fd.RuntimeOption()
|
||||
# 设定模型路径
|
||||
option.set_model_path("resnet50/inference.pdmodel", "resnet50/inference.pdiparams")
|
||||
# 使用OpenVINO后端
|
||||
option.use_openvino_backend()
|
||||
# 初始化runtime
|
||||
runtime = fd.Runtime(option)
|
||||
# 获取输入名
|
||||
input_name = runtime.get_input_info(0).name
|
||||
# 构造数据进行推理
|
||||
results = runtime.infer({input_name: np.random.rand(1, 3, 224, 224).astype("float32")})
|
||||
```
|
||||
|
||||
## GPU推理
|
||||
```
|
||||
import fastdeploy as fd
|
||||
import numpy as np
|
||||
option = fd.RuntimeOption()
|
||||
# 设定模型路径
|
||||
option.set_model_path("resnet50/inference.pdmodel", "resnet50/inference.pdiparams")
|
||||
# 使用GPU，并且使用第0张GPU卡
|
||||
option.use_gpu(0)
|
||||
# 使用Paddle Inference后端
|
||||
option.use_openvino_backend()
|
||||
# 初始化runtime
|
||||
runtime = fd.Runtime(option)
|
||||
# 获取输入名
|
||||
input_name = runtime.get_input_info(0).name
|
||||
# 构造数据进行推理
|
||||
results = runtime.infer({input_name: np.random.rand(1, 3, 224, 224).astype("float32")})
|
||||
```
|
||||
|
||||
更多Python/C++推理示例请直接参考[FastDeploy/examples/runtime](../../examples/runtime)
|
||||
16
examples/runtime/README.md
Normal file
16
examples/runtime/README.md
Normal file
@@ -0,0 +1,16 @@
|
||||
# FastDeploy Runtime推理示例
|
||||
|
||||
| 示例代码 | 编程语言 | 说明 |
|
||||
| :------- | :------- | :---- |
|
||||
| python/infer_paddle_paddle_inference.py | Python | paddle模型通过paddle inference在cpu/gpu上的推理 |
|
||||
| python/infer_paddle_tensorrt.py | Python | paddle模型通过tensorrt在gpu上的推理 |
|
||||
| python/infer_paddle_openvino.py | Python | paddle模型通过openvino在cpu上的推理 |
|
||||
| python/infer_paddle_onnxruntime.py | Python | paddle模型通过onnx runtime在cpu/gpu上的推理 |
|
||||
| python/infer_onnx_openvino.py | Python | onnx模型通过openvino在cpu上的推理 |
|
||||
| python/infer_onnx_tensorrt.py | Python | onnx模型通过tensorrt在gpu上的推理 |
|
||||
| cpp/infer_paddle_paddle_inference.cc | C++ | paddle模型通过paddle inference在cpu/gpu上的推理 |
|
||||
| cpp/infer_paddle_tensorrt.cc | C++ | paddle模型通过tensorrt在gpu上的推理 |
|
||||
| cpp/infer_paddle_openvino.cc | C++ | paddle模型通过openvino在cpu上的推理 |
|
||||
| cpp/infer_paddle_onnxruntime.cc | C++ | paddle模型通过onnx runtime在cpu/gpu上的推理 |
|
||||
| cpp/infer_onnx_openvino.cc | C++ | onnx模型通过openvino在cpu上的推理 |
|
||||
| cpp/infer_onnx_tensorrt.cc | C++ | onnx模型通过tensorrt在gpu上的推理 |
|
||||
39
examples/runtime/python/infer_onnx_openvino.py
Normal file
39
examples/runtime/python/infer_onnx_openvino.py
Normal file
@@ -0,0 +1,39 @@
|
||||
# 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.
|
||||
|
||||
import fastdeploy as fd
|
||||
import numpy as np
|
||||
|
||||
# 下载模型并解压
|
||||
model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.onnx"
|
||||
fd.download(model_url, path=".")
|
||||
|
||||
option = fd.RuntimeOption()
|
||||
|
||||
option.set_model_path("mobilenetv2.onnx", model_format="onnx")
|
||||
|
||||
option.use_openvino_backend()
|
||||
|
||||
# 初始化构造runtime
|
||||
runtime = fd.Runtime(option)
|
||||
|
||||
# 获取模型输入名
|
||||
input_name = runtime.get_input_info(0).name
|
||||
|
||||
# 构造随机数据进行推理
|
||||
results = runtime.infer({
|
||||
input_name: np.random.rand(1, 3, 224, 224).astype("float32")
|
||||
})
|
||||
|
||||
print(results[0].shape)
|
||||
41
examples/runtime/python/infer_onnx_tensorrt.py
Normal file
41
examples/runtime/python/infer_onnx_tensorrt.py
Normal file
@@ -0,0 +1,41 @@
|
||||
# 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.
|
||||
|
||||
import fastdeploy as fd
|
||||
import numpy as np
|
||||
|
||||
# 下载模型并解压
|
||||
model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.onnx"
|
||||
fd.download(model_url, path=".")
|
||||
|
||||
option = fd.RuntimeOption()
|
||||
|
||||
option.set_model_path("mobilenetv2.onnx", model_format="onnx")
|
||||
|
||||
# **** GPU 配置 ***
|
||||
option.use_gpu(0)
|
||||
option.use_trt_backend()
|
||||
|
||||
# 初始化构造runtime
|
||||
runtime = fd.Runtime(option)
|
||||
|
||||
# 获取模型输入名
|
||||
input_name = runtime.get_input_info(0).name
|
||||
|
||||
# 构造随机数据进行推理
|
||||
results = runtime.infer({
|
||||
input_name: np.random.rand(1, 3, 224, 224).astype("float32")
|
||||
})
|
||||
|
||||
print(results[0].shape)
|
||||
47
examples/runtime/python/infer_paddle_onnxruntime.py
Normal file
47
examples/runtime/python/infer_paddle_onnxruntime.py
Normal file
@@ -0,0 +1,47 @@
|
||||
# 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.
|
||||
|
||||
import fastdeploy as fd
|
||||
import numpy as np
|
||||
|
||||
# 下载模型并解压
|
||||
model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz"
|
||||
fd.download_and_decompress(model_url)
|
||||
|
||||
option = fd.RuntimeOption()
|
||||
|
||||
option.set_model_path("mobilenetv2/inference.pdmodel",
|
||||
"mobilenetv2/inference.pdiparams")
|
||||
|
||||
# **** CPU 配置 ****
|
||||
option.use_cpu()
|
||||
option.use_ort_backend()
|
||||
option.set_cpu_thread_num(12)
|
||||
|
||||
# **** GPU 配置 ***
|
||||
# 如需使用GPU，使用如下注释代码
|
||||
# option.use_gpu(0)
|
||||
|
||||
# 初始化构造runtime
|
||||
runtime = fd.Runtime(option)
|
||||
|
||||
# 获取模型输入名
|
||||
input_name = runtime.get_input_info(0).name
|
||||
|
||||
# 构造随机数据进行推理
|
||||
results = runtime.infer({
|
||||
input_name: np.random.rand(1, 3, 224, 224).astype("float32")
|
||||
})
|
||||
|
||||
print(results[0].shape)
|
||||
42
examples/runtime/python/infer_paddle_openvino.py
Normal file
42
examples/runtime/python/infer_paddle_openvino.py
Normal file
@@ -0,0 +1,42 @@
|
||||
# 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.
|
||||
|
||||
import fastdeploy as fd
|
||||
import numpy as np
|
||||
|
||||
# 下载模型并解压
|
||||
model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz"
|
||||
fd.download_and_decompress(model_url)
|
||||
|
||||
option = fd.RuntimeOption()
|
||||
|
||||
option.set_model_path("mobilenetv2/inference.pdmodel",
|
||||
"mobilenetv2/inference.pdiparams")
|
||||
|
||||
option.use_cpu()
|
||||
option.use_openvino_backend()
|
||||
option.set_cpu_thread_num(12)
|
||||
|
||||
# 初始化构造runtime
|
||||
runtime = fd.Runtime(option)
|
||||
|
||||
# 获取模型输入名
|
||||
input_name = runtime.get_input_info(0).name
|
||||
|
||||
# 构造随机数据进行推理
|
||||
results = runtime.infer({
|
||||
input_name: np.random.rand(1, 3, 224, 224).astype("float32")
|
||||
})
|
||||
|
||||
print(results[0].shape)
|
||||
47
examples/runtime/python/infer_paddle_paddle_inference.py
Normal file
47
examples/runtime/python/infer_paddle_paddle_inference.py
Normal file
@@ -0,0 +1,47 @@
|
||||
# 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.
|
||||
|
||||
import fastdeploy as fd
|
||||
import numpy as np
|
||||
|
||||
# 下载模型并解压
|
||||
model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz"
|
||||
fd.download_and_decompress(model_url)
|
||||
|
||||
option = fd.RuntimeOption()
|
||||
|
||||
option.set_model_path("mobilenetv2/inference.pdmodel",
|
||||
"mobilenetv2/inference.pdiparams")
|
||||
|
||||
# **** CPU 配置 ****
|
||||
option.use_cpu()
|
||||
option.use_paddle_backend()
|
||||
option.set_cpu_thread_num(12)
|
||||
|
||||
# **** GPU 配置 ***
|
||||
# 如需使用GPU，使用如下注释代码
|
||||
# option.use_gpu(0)
|
||||
|
||||
# 初始化构造runtime
|
||||
runtime = fd.Runtime(option)
|
||||
|
||||
# 获取模型输入名
|
||||
input_name = runtime.get_input_info(0).name
|
||||
|
||||
# 构造随机数据进行推理
|
||||
results = runtime.infer({
|
||||
input_name: np.random.rand(1, 3, 224, 224).astype("float32")
|
||||
})
|
||||
|
||||
print(results[0].shape)
|
||||
42
examples/runtime/python/infer_paddle_tensorrt.py
Normal file
42
examples/runtime/python/infer_paddle_tensorrt.py
Normal file
@@ -0,0 +1,42 @@
|
||||
# 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.
|
||||
|
||||
import fastdeploy as fd
|
||||
import numpy as np
|
||||
|
||||
# 下载模型并解压
|
||||
model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz"
|
||||
fd.download_and_decompress(model_url)
|
||||
|
||||
option = fd.RuntimeOption()
|
||||
|
||||
option.set_model_path("mobilenetv2/inference.pdmodel",
|
||||
"mobilenetv2/inference.pdiparams")
|
||||
|
||||
# **** GPU 配置 ***
|
||||
option.use_gpu(0)
|
||||
option.use_trt_backend()
|
||||
|
||||
# 初始化构造runtime
|
||||
runtime = fd.Runtime(option)
|
||||
|
||||
# 获取模型输入名
|
||||
input_name = runtime.get_input_info(0).name
|
||||
|
||||
# 构造随机数据进行推理
|
||||
results = runtime.infer({
|
||||
input_name: np.random.rand(1, 3, 224, 224).astype("float32")
|
||||
})
|
||||
|
||||
print(results[0].shape)
|
||||
@@ -28,10 +28,9 @@ tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_rec_infer.tar.gz
|
||||
tar -xvf ch_PP-OCRv2_rec_infer.tar.gz
|
||||
|
||||
wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/doc/imgs/12.jpg
|
||||
|
||||
wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/ppocr/utils/ppocr_keys_v1.txt
|
||||
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
|
||||
|
||||
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
|
||||
|
||||
# CPU推理
|
||||
./infer_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 0
|
||||
@@ -39,8 +38,6 @@ wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/ppocr/
|
||||
./infer_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 1
|
||||
# GPU上TensorRT推理
|
||||
./infer_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 2
|
||||
# OCR还支持det/cls/rec三个模型的组合使用，例如当我们不想使用cls模型的时候，只需要给cls模型路径的位置，传入一个空的字符串, 例子如下
|
||||
./infer_demo ./ch_PP-OCRv2_det_infer "" ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 0
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
@@ -53,9 +50,9 @@ wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/ppocr/
|
||||
### PPOCRSystemv2类
|
||||
|
||||
```
|
||||
fastdeploy::application::ocrsystem::PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* ocr_det = nullptr,
|
||||
fastdeploy::vision::ocr::Classifier* ocr_cls = nullptr,
|
||||
fastdeploy::vision::ocr::Recognizer* ocr_rec = nullptr);
|
||||
fastdeploy::application::ocrsystem::PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* det_model,
|
||||
fastdeploy::vision::ocr::Classifier* cls_model,
|
||||
fastdeploy::vision::ocr::Recognizer* rec_model);
|
||||
```
|
||||
|
||||
PPOCRSystemv2 的初始化，由检测，分类和识别模型串联构成
|
||||
@@ -66,6 +63,18 @@ PPOCRSystemv2 的初始化，由检测，分类和识别模型串联构成
|
||||
> * **Classifier**(model): OCR中的分类模型
|
||||
> * **Recognizer**(model): OCR中的识别模型
|
||||
|
||||
```
|
||||
fastdeploy::application::ocrsystem::PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* det_model,
|
||||
fastdeploy::vision::ocr::Recognizer* rec_model);
|
||||
```
|
||||
PPOCRSystemv2 的初始化，由检测，识别模型串联构成(无分类器)
|
||||
|
||||
**参数**
|
||||
|
||||
> * **DBDetector**(model): OCR中的检测模型
|
||||
> * **Recognizer**(model): OCR中的识别模型
|
||||
|
||||
|
||||
#### Predict函数
|
||||
|
||||
> ```
|
||||
|
||||
@@ -19,11 +19,7 @@ const char sep = '\\';
|
||||
const char sep = '/';
|
||||
#endif
|
||||
|
||||
void CpuInfer(const std::string& det_model_dir,
|
||||
const std::string& cls_model_dir,
|
||||
const std::string& rec_model_dir,
|
||||
const std::string& rec_label_file,
|
||||
const std::string& image_file) {
|
||||
void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model_dir, const std::string& rec_model_dir, const std::string& rec_label_file, const std::string& image_file, const fastdeploy::RuntimeOption& option) {
|
||||
auto det_model_file = det_model_dir + sep + "inference.pdmodel";
|
||||
auto det_params_file = det_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
@@ -32,238 +28,32 @@ void CpuInfer(const std::string& det_model_dir,
|
||||
|
||||
auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
|
||||
auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
|
||||
auto rec_label = rec_label_file;
|
||||
|
||||
fastdeploy::vision::ocr::DBDetector det_model;
|
||||
fastdeploy::vision::ocr::Classifier cls_model;
|
||||
fastdeploy::vision::ocr::Recognizer rec_model;
|
||||
auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, option);
|
||||
auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, option);
|
||||
auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, option);
|
||||
|
||||
if (!det_model_dir.empty()) {
|
||||
auto det_option = fastdeploy::RuntimeOption();
|
||||
det_option.UseCpu();
|
||||
det_model = fastdeploy::vision::ocr::DBDetector(
|
||||
det_model_file, det_params_file, det_option);
|
||||
assert(det_model.Initialized());
|
||||
assert(cls_model.Initialized());
|
||||
assert(rec_model.Initialized());
|
||||
|
||||
if (!det_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize det_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
if (!cls_model_dir.empty()) {
|
||||
auto cls_option = fastdeploy::RuntimeOption();
|
||||
cls_option.UseCpu();
|
||||
cls_model = fastdeploy::vision::ocr::Classifier(
|
||||
cls_model_file, cls_params_file, cls_option);
|
||||
|
||||
if (!cls_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize cls_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
if (!rec_model_dir.empty()) {
|
||||
auto rec_option = fastdeploy::RuntimeOption();
|
||||
rec_option.UseCpu();
|
||||
rec_model = fastdeploy::vision::ocr::Recognizer(
|
||||
rec_model_file, rec_params_file, rec_label, rec_option);
|
||||
|
||||
if (!rec_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize rec_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
auto ocrv2_app = fastdeploy::application::ocrsystem::PPOCRSystemv2(
|
||||
&det_model, &cls_model, &rec_model);
|
||||
// 其中分类模型可选，因此也可使用如下方式串联OCR系统
|
||||
// auto ocr_system_v2 = fastdeploy::application::ocrsystem::PPOCRSystemv2(&det_model, &rec_model);
|
||||
auto ocr_system_v2 = fastdeploy::application::ocrsystem::PPOCRSystemv2(&det_model, &cls_model, &rec_model);
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
auto im_bak = im.clone();
|
||||
|
||||
fastdeploy::vision::OCRResult res;
|
||||
//开始预测
|
||||
if (!ocrv2_app.Predict(&im, &res)) {
|
||||
|
||||
fastdeploy::vision::OCRResult result;
|
||||
if (!ocr_system_v2.Predict(&im, &result)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
//输出预测信息
|
||||
std::cout << res.Str() << std::endl;
|
||||
std::cout << result.Str() << std::endl;
|
||||
|
||||
//可视化
|
||||
auto vis_img = fastdeploy::vision::Visualize::VisOcr(im_bak, res);
|
||||
|
||||
cv::imwrite("vis_result.jpg", vis_img);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
void GpuInfer(const std::string& det_model_dir,
|
||||
const std::string& cls_model_dir,
|
||||
const std::string& rec_model_dir,
|
||||
const std::string& rec_label_file,
|
||||
const std::string& image_file) {
|
||||
auto det_model_file = det_model_dir + sep + "inference.pdmodel";
|
||||
auto det_params_file = det_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
auto cls_model_file = cls_model_dir + sep + "inference.pdmodel";
|
||||
auto cls_params_file = cls_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
|
||||
auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
|
||||
auto rec_label = rec_label_file;
|
||||
|
||||
fastdeploy::vision::ocr::DBDetector det_model;
|
||||
fastdeploy::vision::ocr::Classifier cls_model;
|
||||
fastdeploy::vision::ocr::Recognizer rec_model;
|
||||
|
||||
//准备模型
|
||||
if (!det_model_dir.empty()) {
|
||||
auto det_option = fastdeploy::RuntimeOption();
|
||||
det_option.UseGpu();
|
||||
det_model = fastdeploy::vision::ocr::DBDetector(
|
||||
det_model_file, det_params_file, det_option);
|
||||
|
||||
if (!det_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize det_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
if (!cls_model_dir.empty()) {
|
||||
auto cls_option = fastdeploy::RuntimeOption();
|
||||
cls_option.UseGpu();
|
||||
cls_model = fastdeploy::vision::ocr::Classifier(
|
||||
cls_model_file, cls_params_file, cls_option);
|
||||
|
||||
if (!cls_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize cls_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
if (!rec_model_dir.empty()) {
|
||||
auto rec_option = fastdeploy::RuntimeOption();
|
||||
rec_option.UseGpu();
|
||||
rec_option
|
||||
.UsePaddleBackend(); // OCRv2的rec模型暂不支持ORT后端与PaddleInference
|
||||
// v2.3.2
|
||||
rec_model = fastdeploy::vision::ocr::Recognizer(
|
||||
rec_model_file, rec_params_file, rec_label, rec_option);
|
||||
|
||||
if (!rec_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize rec_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
auto ocrv2_app = fastdeploy::application::ocrsystem::PPOCRSystemv2(
|
||||
&det_model, &cls_model, &rec_model);
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
auto im_bak = im.clone();
|
||||
|
||||
fastdeploy::vision::OCRResult res;
|
||||
//开始预测
|
||||
if (!ocrv2_app.Predict(&im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
//输出预测信息
|
||||
std::cout << res.Str() << std::endl;
|
||||
|
||||
//可视化
|
||||
auto vis_img = fastdeploy::vision::Visualize::VisOcr(im_bak, res);
|
||||
|
||||
cv::imwrite("vis_result.jpg", vis_img);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
void TrtInfer(const std::string& det_model_dir,
|
||||
const std::string& cls_model_dir,
|
||||
const std::string& rec_model_dir,
|
||||
const std::string& rec_label_file,
|
||||
const std::string& image_file) {
|
||||
auto det_model_file = det_model_dir + sep + "inference.pdmodel";
|
||||
auto det_params_file = det_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
auto cls_model_file = cls_model_dir + sep + "inference.pdmodel";
|
||||
auto cls_params_file = cls_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
|
||||
auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
|
||||
auto rec_label = rec_label_file;
|
||||
|
||||
fastdeploy::vision::ocr::DBDetector det_model;
|
||||
fastdeploy::vision::ocr::Classifier cls_model;
|
||||
fastdeploy::vision::ocr::Recognizer rec_model;
|
||||
|
||||
//准备模型
|
||||
if (!det_model_dir.empty()) {
|
||||
auto det_option = fastdeploy::RuntimeOption();
|
||||
det_option.UseGpu();
|
||||
det_option.UseTrtBackend();
|
||||
det_option.SetTrtInputShape("x", {1, 3, 50, 50}, {1, 3, 640, 640},
|
||||
{1, 3, 960, 960});
|
||||
|
||||
det_model = fastdeploy::vision::ocr::DBDetector(
|
||||
det_model_file, det_params_file, det_option);
|
||||
|
||||
if (!det_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize det_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
if (!cls_model_dir.empty()) {
|
||||
auto cls_option = fastdeploy::RuntimeOption();
|
||||
cls_option.UseGpu();
|
||||
cls_option.UseTrtBackend();
|
||||
cls_option.SetTrtInputShape("x", {1, 3, 48, 192});
|
||||
|
||||
cls_model = fastdeploy::vision::ocr::Classifier(
|
||||
cls_model_file, cls_params_file, cls_option);
|
||||
|
||||
if (!cls_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize cls_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
if (!rec_model_dir.empty()) {
|
||||
auto rec_option = fastdeploy::RuntimeOption();
|
||||
rec_option.UseGpu();
|
||||
rec_option.UseTrtBackend();
|
||||
rec_option.SetTrtInputShape("x", {1, 3, 48, 10}, {1, 3, 48, 320},
|
||||
{1, 3, 48, 2000});
|
||||
|
||||
rec_model = fastdeploy::vision::ocr::Recognizer(
|
||||
rec_model_file, rec_params_file, rec_label, rec_option);
|
||||
|
||||
if (!rec_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize rec_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
auto ocrv2_app = fastdeploy::application::ocrsystem::PPOCRSystemv2(
|
||||
&det_model, &cls_model, &rec_model);
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
auto im_bak = im.clone();
|
||||
|
||||
fastdeploy::vision::OCRResult res;
|
||||
//开始预测
|
||||
if (!ocrv2_app.Predict(&im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
//输出预测信息
|
||||
std::cout << res.Str() << std::endl;
|
||||
|
||||
//可视化
|
||||
auto vis_img = fastdeploy::vision::Visualize::VisOcr(im_bak, res);
|
||||
|
||||
cv::imwrite("vis_result.jpg", vis_img);
|
||||
auto vis_im = fastdeploy::vision::Visualize::VisOcr(im_bak, result);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
@@ -282,12 +72,23 @@ int main(int argc, char* argv[]) {
|
||||
return -1;
|
||||
}
|
||||
|
||||
if (std::atoi(argv[6]) == 0) {
|
||||
CpuInfer(argv[1], argv[2], argv[3], argv[4], argv[5]);
|
||||
} else if (std::atoi(argv[6]) == 1) {
|
||||
GpuInfer(argv[1], argv[2], argv[3], argv[4], argv[5]);
|
||||
} else if (std::atoi(argv[6]) == 2) {
|
||||
TrtInfer(argv[1], argv[2], argv[3], argv[4], argv[5]);
|
||||
fastdeploy::RuntimeOption option;
|
||||
int flag = std::atoi(argv[6]);
|
||||
|
||||
if (flag == 0) {
|
||||
option.UseCpu();
|
||||
} else if (flag == 1) {
|
||||
option.UseGpu();
|
||||
} else if (flag == 2) {
|
||||
option.UseGpu();
|
||||
option.UseTrtBackend();
|
||||
}
|
||||
|
||||
std::string det_model_dir = argv[1];
|
||||
std::string cls_model_dir = argv[2];
|
||||
std::string rec_model_dir = argv[3];
|
||||
std::string rec_label_file = argv[4];
|
||||
std::string test_image = argv[5];
|
||||
InitAndInfer(det_model_dir, cls_model_dir, rec_model_dir, rec_label_file, test_image, option);
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -19,9 +19,9 @@ tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_rec_infer.tar.gz
|
||||
tar -xvf ch_PP-OCRv2_rec_infer.tar.gz
|
||||
|
||||
wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/doc/imgs/12.jpg
|
||||
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
|
||||
|
||||
wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/ppocr/utils/ppocr_keys_v1.txt
|
||||
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
|
||||
|
||||
|
||||
#下载部署示例代码
|
||||
@@ -33,9 +33,7 @@ python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2
|
||||
# GPU推理
|
||||
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu
|
||||
# GPU上使用TensorRT推理
|
||||
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --det_use_trt True --cls_use_trt True --rec_use_trt True
|
||||
# OCR还支持det/cls/rec三个模型的组合使用，例如当我们不想使用cls模型的时候，只需要给--cls_model传入一个空的字符串, 例子如下：
|
||||
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model "" --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu
|
||||
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
|
||||
@@ -1,3 +1,17 @@
|
||||
# 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.
|
||||
|
||||
import fastdeploy as fd
|
||||
import cv2
|
||||
import os
|
||||
@@ -21,7 +35,6 @@ def parse_arguments():
|
||||
"--rec_label_file",
|
||||
required=True,
|
||||
help="Path of Recognization model of PPOCR.")
|
||||
|
||||
parser.add_argument(
|
||||
"--image", type=str, required=True, help="Path of test image file.")
|
||||
parser.add_argument(
|
||||
@@ -30,114 +43,83 @@ def parse_arguments():
|
||||
default='cpu',
|
||||
help="Type of inference device, support 'cpu' or 'gpu'.")
|
||||
parser.add_argument(
|
||||
"--det_use_trt",
|
||||
type=ast.literal_eval,
|
||||
default=False,
|
||||
help="Wether to use tensorrt.")
|
||||
"--backend",
|
||||
type=str,
|
||||
default="default",
|
||||
help="Type of inference backend, support ort/trt/paddle/openvino, default 'openvino' for cpu, 'tensorrt' for gpu"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--cls_use_trt",
|
||||
type=ast.literal_eval,
|
||||
default=False,
|
||||
help="Wether to use tensorrt.")
|
||||
"--device_id",
|
||||
type=int,
|
||||
default=0,
|
||||
help="Define which GPU card used to run model.")
|
||||
parser.add_argument(
|
||||
"--rec_use_trt",
|
||||
type=ast.literal_eval,
|
||||
default=False,
|
||||
help="Wether to use tensorrt.")
|
||||
"--cpu_thread_num",
|
||||
type=int,
|
||||
default=9,
|
||||
help="Number of threads while inference on CPU.")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_det_option(args):
|
||||
def build_option(args):
|
||||
option = fd.RuntimeOption()
|
||||
|
||||
if args.device.lower() == "gpu":
|
||||
option.use_gpu()
|
||||
option.use_gpu(0)
|
||||
|
||||
if args.det_use_trt:
|
||||
option.set_cpu_thread_num(args.cpu_thread_num)
|
||||
|
||||
if args.backend.lower() == "trt":
|
||||
assert args.device.lower(
|
||||
) == "gpu", "TensorRT backend require inference on device GPU."
|
||||
option.use_trt_backend()
|
||||
#det_max_side_len 默认为960,当用户更改DET模型的max_side_len参数时，请将此参数同时更改
|
||||
det_max_side_len = 960
|
||||
option.set_trt_input_shape("x", [1, 3, 50, 50], [1, 3, 640, 640],
|
||||
[1, 3, det_max_side_len, det_max_side_len])
|
||||
|
||||
return option
|
||||
|
||||
|
||||
def build_cls_option(args):
|
||||
option = fd.RuntimeOption()
|
||||
option.use_paddle_backend()
|
||||
|
||||
if args.device.lower() == "gpu":
|
||||
option.use_gpu()
|
||||
|
||||
if args.cls_use_trt:
|
||||
option.use_trt_backend()
|
||||
option.set_trt_input_shape("x", [1, 3, 32, 100])
|
||||
|
||||
return option
|
||||
|
||||
|
||||
def build_rec_option(args):
|
||||
option = fd.RuntimeOption()
|
||||
option.use_paddle_backend()
|
||||
|
||||
if args.device.lower() == "gpu":
|
||||
option.use_gpu()
|
||||
|
||||
if args.rec_use_trt:
|
||||
option.use_trt_backend()
|
||||
option.set_trt_input_shape("x", [1, 3, 48, 10], [1, 3, 48, 320],
|
||||
[1, 3, 48, 2000])
|
||||
elif args.backend.lower() == "ort":
|
||||
option.use_ort_backend()
|
||||
elif args.backend.lower() == "paddle":
|
||||
option.use_paddle_backend()
|
||||
elif args.backend.lower() == "openvino":
|
||||
assert args.device.lower(
|
||||
) == "cpu", "OpenVINO backend require inference on device CPU."
|
||||
option.use_openvino_backend()
|
||||
return option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
#Det模型
|
||||
# Detection模型, 检测文字框
|
||||
det_model_file = os.path.join(args.det_model, "inference.pdmodel")
|
||||
det_params_file = os.path.join(args.det_model, "inference.pdiparams")
|
||||
#Cls模型
|
||||
# Classification模型，方向分类，可选
|
||||
cls_model_file = os.path.join(args.cls_model, "inference.pdmodel")
|
||||
cls_params_file = os.path.join(args.cls_model, "inference.pdiparams")
|
||||
#Rec模型
|
||||
# Recognition模型，文字识别模型
|
||||
rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
|
||||
rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
|
||||
rec_label_file = args.rec_label_file
|
||||
|
||||
#默认
|
||||
det_model = fd.vision.ocr.DBDetector()
|
||||
cls_model = fd.vision.ocr.Classifier()
|
||||
rec_model = fd.vision.ocr.Recognizer()
|
||||
# 对于三个模型，均采用同样的部署配置
|
||||
# 用户也可根据自行需求分别配置
|
||||
runtime_option = build_option(args)
|
||||
|
||||
#模型初始化
|
||||
if (len(args.det_model) != 0):
|
||||
det_runtime_option = build_det_option(args)
|
||||
det_model = fd.vision.ocr.DBDetector(
|
||||
det_model_file, det_params_file, runtime_option=det_runtime_option)
|
||||
det_model = fd.vision.ocr.DBDetector(
|
||||
det_model_file, det_params_file, runtime_option=runtime_option)
|
||||
cls_model = fd.vision.ocr.Classifier(
|
||||
cls_model_file, cls_params_file, runtime_option=runtime_option)
|
||||
rec_model = fd.vision.ocr.Recognizer(
|
||||
rec_model_file,
|
||||
rec_params_file,
|
||||
rec_label_file,
|
||||
runtime_option=runtime_option)
|
||||
|
||||
if (len(args.cls_model) != 0):
|
||||
cls_runtime_option = build_cls_option(args)
|
||||
cls_model = fd.vision.ocr.Classifier(
|
||||
cls_model_file, cls_params_file, runtime_option=cls_runtime_option)
|
||||
|
||||
if (len(args.rec_model) != 0):
|
||||
rec_runtime_option = build_rec_option(args)
|
||||
rec_model = fd.vision.ocr.Recognizer(
|
||||
rec_model_file,
|
||||
rec_params_file,
|
||||
rec_label_file,
|
||||
runtime_option=rec_runtime_option)
|
||||
|
||||
ppocrsysv2 = fd.vision.ocr.PPOCRSystemv2(
|
||||
ocr_det=det_model._model,
|
||||
ocr_cls=cls_model._model,
|
||||
ocr_rec=rec_model._model)
|
||||
# 创建OCR系统，串联3个模型，其中cls_model可选，如无需求，可设置为None
|
||||
ocr_system = fd.vision.ocr.PPOCRSystemv2(
|
||||
det_model=det_model, cls_model=cls_model, rec_model=rec_model)
|
||||
|
||||
# 预测图片准备
|
||||
im = cv2.imread(args.image)
|
||||
|
||||
#预测并打印结果
|
||||
result = ppocrsysv2.predict(im)
|
||||
result = ocr_system.predict(im)
|
||||
|
||||
print(result)
|
||||
|
||||
# 可视化结果
|
||||
|
||||
@@ -28,10 +28,9 @@ tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
|
||||
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
|
||||
tar -xvf ch_PP-OCRv3_rec_infer.tar
|
||||
|
||||
wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/doc/imgs/12.jpg
|
||||
|
||||
wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/ppocr/utils/ppocr_keys_v1.txt
|
||||
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
|
||||
|
||||
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
|
||||
|
||||
# CPU推理
|
||||
./infer_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 0
|
||||
@@ -39,8 +38,6 @@ wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/ppocr/
|
||||
./infer_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 1
|
||||
# GPU上TensorRT推理
|
||||
./infer_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 2
|
||||
# OCR还支持det/cls/rec三个模型的组合使用，例如当我们不想使用cls模型的时候，只需要给cls模型路径的位置，传入一个空的字符串, 例子如下
|
||||
./infer_demo ./ch_PP-OCRv3_det_infer "" ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 0
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
@@ -53,12 +50,12 @@ wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/ppocr/
|
||||
### PPOCRSystemv3类
|
||||
|
||||
```
|
||||
fastdeploy::application::ocrsystem::PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* ocr_det = nullptr,
|
||||
fastdeploy::vision::ocr::Classifier* ocr_cls = nullptr,
|
||||
fastdeploy::vision::ocr::Recognizer* ocr_rec = nullptr);
|
||||
fastdeploy::application::ocrsystem::PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* det_model,
|
||||
fastdeploy::vision::ocr::Classifier* cls_model,
|
||||
fastdeploy::vision::ocr::Recognizer* rec_model);
|
||||
```
|
||||
|
||||
PPOCRSystemv3 的初始化，由检测，分类和识别模型串联构成
|
||||
PPOCRSystemv2 的初始化，由检测，分类和识别模型串联构成
|
||||
|
||||
**参数**
|
||||
|
||||
@@ -66,6 +63,17 @@ PPOCRSystemv3 的初始化，由检测，分类和识别模型串联构成
|
||||
> * **Classifier**(model): OCR中的分类模型
|
||||
> * **Recognizer**(model): OCR中的识别模型
|
||||
|
||||
```
|
||||
fastdeploy::application::ocrsystem::PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* det_model,
|
||||
fastdeploy::vision::ocr::Recognizer* rec_model);
|
||||
```
|
||||
PPOCRSystemv2 的初始化，由检测，识别模型串联构成(无分类器)
|
||||
|
||||
**参数**
|
||||
|
||||
> * **DBDetector**(model): OCR中的检测模型
|
||||
> * **Recognizer**(model): OCR中的识别模型
|
||||
|
||||
#### Predict函数
|
||||
|
||||
> ```
|
||||
|
||||
@@ -19,11 +19,7 @@ const char sep = '\\';
|
||||
const char sep = '/';
|
||||
#endif
|
||||
|
||||
void CpuInfer(const std::string& det_model_dir,
|
||||
const std::string& cls_model_dir,
|
||||
const std::string& rec_model_dir,
|
||||
const std::string& rec_label_file,
|
||||
const std::string& image_file) {
|
||||
void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model_dir, const std::string& rec_model_dir, const std::string& rec_label_file, const std::string& image_file, const fastdeploy::RuntimeOption& option) {
|
||||
auto det_model_file = det_model_dir + sep + "inference.pdmodel";
|
||||
auto det_params_file = det_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
@@ -32,235 +28,32 @@ void CpuInfer(const std::string& det_model_dir,
|
||||
|
||||
auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
|
||||
auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
|
||||
auto rec_label = rec_label_file;
|
||||
|
||||
fastdeploy::vision::ocr::DBDetector det_model;
|
||||
fastdeploy::vision::ocr::Classifier cls_model;
|
||||
fastdeploy::vision::ocr::Recognizer rec_model;
|
||||
auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, option);
|
||||
auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, option);
|
||||
auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, option);
|
||||
|
||||
if (!det_model_dir.empty()) {
|
||||
auto det_option = fastdeploy::RuntimeOption();
|
||||
det_option.UseCpu();
|
||||
det_model = fastdeploy::vision::ocr::DBDetector(
|
||||
det_model_file, det_params_file, det_option);
|
||||
assert(det_model.Initialized());
|
||||
assert(cls_model.Initialized());
|
||||
assert(rec_model.Initialized());
|
||||
|
||||
if (!det_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize det_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
if (!cls_model_dir.empty()) {
|
||||
auto cls_option = fastdeploy::RuntimeOption();
|
||||
cls_option.UseCpu();
|
||||
cls_model = fastdeploy::vision::ocr::Classifier(
|
||||
cls_model_file, cls_params_file, cls_option);
|
||||
|
||||
if (!cls_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize cls_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
if (!rec_model_dir.empty()) {
|
||||
auto rec_option = fastdeploy::RuntimeOption();
|
||||
rec_option.UseCpu();
|
||||
rec_model = fastdeploy::vision::ocr::Recognizer(
|
||||
rec_model_file, rec_params_file, rec_label, rec_option);
|
||||
|
||||
if (!rec_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize rec_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
auto ocrv3_app = fastdeploy::application::ocrsystem::PPOCRSystemv3(
|
||||
&det_model, &cls_model, &rec_model);
|
||||
// 其中分类模型可选，因此也可使用如下方式串联OCR系统
|
||||
// auto ocr_system_v3 = fastdeploy::application::ocrsystem::PPOCRSystemv3(&det_model, &rec_model);
|
||||
auto ocr_system_v3 = fastdeploy::application::ocrsystem::PPOCRSystemv3(&det_model, &cls_model, &rec_model);
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
auto im_bak = im.clone();
|
||||
|
||||
fastdeploy::vision::OCRResult res;
|
||||
//开始预测
|
||||
if (!ocrv3_app.Predict(&im, &res)) {
|
||||
|
||||
fastdeploy::vision::OCRResult result;
|
||||
if (!ocr_system_v3.Predict(&im, &result)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
//输出预测信息
|
||||
std::cout << res.Str() << std::endl;
|
||||
std::cout << result.Str() << std::endl;
|
||||
|
||||
//可视化
|
||||
auto vis_img = fastdeploy::vision::Visualize::VisOcr(im_bak, res);
|
||||
|
||||
cv::imwrite("vis_result.jpg", vis_img);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
void GpuInfer(const std::string& det_model_dir,
|
||||
const std::string& cls_model_dir,
|
||||
const std::string& rec_model_dir,
|
||||
const std::string& rec_label_file,
|
||||
const std::string& image_file) {
|
||||
auto det_model_file = det_model_dir + sep + "inference.pdmodel";
|
||||
auto det_params_file = det_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
auto cls_model_file = cls_model_dir + sep + "inference.pdmodel";
|
||||
auto cls_params_file = cls_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
|
||||
auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
|
||||
auto rec_label = rec_label_file;
|
||||
|
||||
fastdeploy::vision::ocr::DBDetector det_model;
|
||||
fastdeploy::vision::ocr::Classifier cls_model;
|
||||
fastdeploy::vision::ocr::Recognizer rec_model;
|
||||
|
||||
//准备模型
|
||||
if (!det_model_dir.empty()) {
|
||||
auto det_option = fastdeploy::RuntimeOption();
|
||||
det_option.UseGpu();
|
||||
det_model = fastdeploy::vision::ocr::DBDetector(
|
||||
det_model_file, det_params_file, det_option);
|
||||
|
||||
if (!det_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize det_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
if (!cls_model_dir.empty()) {
|
||||
auto cls_option = fastdeploy::RuntimeOption();
|
||||
cls_option.UseGpu();
|
||||
cls_model = fastdeploy::vision::ocr::Classifier(
|
||||
cls_model_file, cls_params_file, cls_option);
|
||||
|
||||
if (!cls_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize cls_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
if (!rec_model_dir.empty()) {
|
||||
auto rec_option = fastdeploy::RuntimeOption();
|
||||
rec_option.UseGpu();
|
||||
rec_model = fastdeploy::vision::ocr::Recognizer(
|
||||
rec_model_file, rec_params_file, rec_label, rec_option);
|
||||
|
||||
if (!rec_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize rec_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
auto ocrv3_app = fastdeploy::application::ocrsystem::PPOCRSystemv3(
|
||||
&det_model, &cls_model, &rec_model);
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
auto im_bak = im.clone();
|
||||
|
||||
fastdeploy::vision::OCRResult res;
|
||||
//开始预测
|
||||
if (!ocrv3_app.Predict(&im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
//输出预测信息
|
||||
std::cout << res.Str() << std::endl;
|
||||
|
||||
//可视化
|
||||
auto vis_img = fastdeploy::vision::Visualize::VisOcr(im_bak, res);
|
||||
|
||||
cv::imwrite("vis_result.jpg", vis_img);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
void TrtInfer(const std::string& det_model_dir,
|
||||
const std::string& cls_model_dir,
|
||||
const std::string& rec_model_dir,
|
||||
const std::string& rec_label_file,
|
||||
const std::string& image_file) {
|
||||
auto det_model_file = det_model_dir + sep + "inference.pdmodel";
|
||||
auto det_params_file = det_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
auto cls_model_file = cls_model_dir + sep + "inference.pdmodel";
|
||||
auto cls_params_file = cls_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
|
||||
auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
|
||||
auto rec_label = rec_label_file;
|
||||
|
||||
fastdeploy::vision::ocr::DBDetector det_model;
|
||||
fastdeploy::vision::ocr::Classifier cls_model;
|
||||
fastdeploy::vision::ocr::Recognizer rec_model;
|
||||
|
||||
//准备模型
|
||||
if (!det_model_dir.empty()) {
|
||||
auto det_option = fastdeploy::RuntimeOption();
|
||||
det_option.UseGpu();
|
||||
det_option.UseTrtBackend();
|
||||
det_option.SetTrtInputShape("x", {1, 3, 50, 50}, {1, 3, 640, 640},
|
||||
{1, 3, 960, 960});
|
||||
|
||||
det_model = fastdeploy::vision::ocr::DBDetector(
|
||||
det_model_file, det_params_file, det_option);
|
||||
|
||||
if (!det_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize det_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
if (!cls_model_dir.empty()) {
|
||||
auto cls_option = fastdeploy::RuntimeOption();
|
||||
cls_option.UseGpu();
|
||||
cls_option.UseTrtBackend();
|
||||
cls_option.SetTrtInputShape("x", {1, 3, 48, 192});
|
||||
|
||||
cls_model = fastdeploy::vision::ocr::Classifier(
|
||||
cls_model_file, cls_params_file, cls_option);
|
||||
|
||||
if (!cls_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize cls_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
if (!rec_model_dir.empty()) {
|
||||
auto rec_option = fastdeploy::RuntimeOption();
|
||||
rec_option.UseGpu();
|
||||
rec_option.UseTrtBackend();
|
||||
rec_option.SetTrtInputShape("x", {1, 3, 48, 10}, {1, 3, 48, 320},
|
||||
{1, 3, 48, 2000});
|
||||
|
||||
rec_model = fastdeploy::vision::ocr::Recognizer(
|
||||
rec_model_file, rec_params_file, rec_label, rec_option);
|
||||
|
||||
if (!rec_model.Initialized()) {
|
||||
std::cerr << "Failed to initialize rec_model." << std::endl;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
auto ocrv3_app = fastdeploy::application::ocrsystem::PPOCRSystemv3(
|
||||
&det_model, &cls_model, &rec_model);
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
auto im_bak = im.clone();
|
||||
|
||||
fastdeploy::vision::OCRResult res;
|
||||
//开始预测
|
||||
if (!ocrv3_app.Predict(&im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
//输出预测信息
|
||||
std::cout << res.Str() << std::endl;
|
||||
|
||||
//可视化
|
||||
auto vis_img = fastdeploy::vision::Visualize::VisOcr(im_bak, res);
|
||||
|
||||
cv::imwrite("vis_result.jpg", vis_img);
|
||||
auto vis_im = fastdeploy::vision::Visualize::VisOcr(im_bak, result);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
@@ -279,12 +72,23 @@ int main(int argc, char* argv[]) {
|
||||
return -1;
|
||||
}
|
||||
|
||||
if (std::atoi(argv[6]) == 0) {
|
||||
CpuInfer(argv[1], argv[2], argv[3], argv[4], argv[5]);
|
||||
} else if (std::atoi(argv[6]) == 1) {
|
||||
GpuInfer(argv[1], argv[2], argv[3], argv[4], argv[5]);
|
||||
} else if (std::atoi(argv[6]) == 2) {
|
||||
TrtInfer(argv[1], argv[2], argv[3], argv[4], argv[5]);
|
||||
fastdeploy::RuntimeOption option;
|
||||
int flag = std::atoi(argv[6]);
|
||||
|
||||
if (flag == 0) {
|
||||
option.UseCpu();
|
||||
} else if (flag == 1) {
|
||||
option.UseGpu();
|
||||
} else if (flag == 2) {
|
||||
option.UseGpu();
|
||||
option.UseTrtBackend();
|
||||
}
|
||||
|
||||
std::string det_model_dir = argv[1];
|
||||
std::string cls_model_dir = argv[2];
|
||||
std::string rec_model_dir = argv[3];
|
||||
std::string rec_label_file = argv[4];
|
||||
std::string test_image = argv[5];
|
||||
InitAndInfer(det_model_dir, cls_model_dir, rec_model_dir, rec_label_file, test_image, option);
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -19,10 +19,9 @@ tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
|
||||
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
|
||||
tar xvf ch_PP-OCRv3_rec_infer.tar
|
||||
|
||||
wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/doc/imgs/12.jpg
|
||||
|
||||
wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/ppocr/utils/ppocr_keys_v1.txt
|
||||
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
|
||||
|
||||
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
|
||||
|
||||
#下载部署示例代码
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
@@ -33,9 +32,7 @@ python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2
|
||||
# GPU推理
|
||||
python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu
|
||||
# GPU上使用TensorRT推理
|
||||
python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --det_use_trt True --cls_use_trt True --rec_use_trt True
|
||||
# OCR还支持det/cls/rec三个模型的组合使用，例如当我们不想使用cls模型的时候，只需要给--cls_model传入一个空的字符串, 例子如下：
|
||||
python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model "" --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu
|
||||
python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
|
||||
@@ -1,3 +1,17 @@
|
||||
# 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.
|
||||
|
||||
import fastdeploy as fd
|
||||
import cv2
|
||||
import os
|
||||
@@ -21,7 +35,6 @@ def parse_arguments():
|
||||
"--rec_label_file",
|
||||
required=True,
|
||||
help="Path of Recognization model of PPOCR.")
|
||||
|
||||
parser.add_argument(
|
||||
"--image", type=str, required=True, help="Path of test image file.")
|
||||
parser.add_argument(
|
||||
@@ -30,112 +43,82 @@ def parse_arguments():
|
||||
default='cpu',
|
||||
help="Type of inference device, support 'cpu' or 'gpu'.")
|
||||
parser.add_argument(
|
||||
"--det_use_trt",
|
||||
type=ast.literal_eval,
|
||||
default=False,
|
||||
help="Wether to use tensorrt.")
|
||||
"--backend",
|
||||
type=str,
|
||||
default="default",
|
||||
help="Type of inference backend, support ort/trt/paddle/openvino, default 'openvino' for cpu, 'tensorrt' for gpu"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--cls_use_trt",
|
||||
type=ast.literal_eval,
|
||||
default=False,
|
||||
help="Wether to use tensorrt.")
|
||||
"--device_id",
|
||||
type=int,
|
||||
default=0,
|
||||
help="Define which GPU card used to run model.")
|
||||
parser.add_argument(
|
||||
"--rec_use_trt",
|
||||
type=ast.literal_eval,
|
||||
default=False,
|
||||
help="Wether to use tensorrt.")
|
||||
"--cpu_thread_num",
|
||||
type=int,
|
||||
default=9,
|
||||
help="Number of threads while inference on CPU.")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_det_option(args):
|
||||
def build_option(args):
|
||||
option = fd.RuntimeOption()
|
||||
|
||||
if args.device.lower() == "gpu":
|
||||
option.use_gpu()
|
||||
option.use_gpu(0)
|
||||
|
||||
if args.det_use_trt:
|
||||
option.set_cpu_thread_num(args.cpu_thread_num)
|
||||
|
||||
if args.backend.lower() == "trt":
|
||||
assert args.device.lower(
|
||||
) == "gpu", "TensorRT backend require inference on device GPU."
|
||||
option.use_trt_backend()
|
||||
#det_max_side_len 默认为960,当用户更改DET模型的max_side_len参数时，请将此参数同时更改
|
||||
det_max_side_len = 960
|
||||
option.set_trt_input_shape("x", [1, 3, 50, 50], [1, 3, 640, 640],
|
||||
[1, 3, det_max_side_len, det_max_side_len])
|
||||
|
||||
return option
|
||||
|
||||
|
||||
def build_cls_option(args):
|
||||
option = fd.RuntimeOption()
|
||||
|
||||
if args.device.lower() == "gpu":
|
||||
option.use_gpu()
|
||||
|
||||
if args.cls_use_trt:
|
||||
option.use_trt_backend()
|
||||
option.set_trt_input_shape("x", [1, 3, 32, 100])
|
||||
|
||||
return option
|
||||
|
||||
|
||||
def build_rec_option(args):
|
||||
option = fd.RuntimeOption()
|
||||
|
||||
if args.device.lower() == "gpu":
|
||||
option.use_gpu()
|
||||
|
||||
if args.rec_use_trt:
|
||||
option.use_trt_backend()
|
||||
option.set_trt_input_shape("x", [1, 3, 48, 10], [1, 3, 48, 320],
|
||||
[1, 3, 48, 2000])
|
||||
elif args.backend.lower() == "ort":
|
||||
option.use_ort_backend()
|
||||
elif args.backend.lower() == "paddle":
|
||||
option.use_paddle_backend()
|
||||
elif args.backend.lower() == "openvino":
|
||||
assert args.device.lower(
|
||||
) == "cpu", "OpenVINO backend require inference on device CPU."
|
||||
option.use_openvino_backend()
|
||||
return option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
#Det模型
|
||||
# Detection模型, 检测文字框
|
||||
det_model_file = os.path.join(args.det_model, "inference.pdmodel")
|
||||
det_params_file = os.path.join(args.det_model, "inference.pdiparams")
|
||||
#Cls模型
|
||||
# Classification模型，方向分类，可选
|
||||
cls_model_file = os.path.join(args.cls_model, "inference.pdmodel")
|
||||
cls_params_file = os.path.join(args.cls_model, "inference.pdiparams")
|
||||
#Rec模型
|
||||
# Recognition模型，文字识别模型
|
||||
rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
|
||||
rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
|
||||
rec_label_file = args.rec_label_file
|
||||
|
||||
#默认
|
||||
det_model = fd.vision.ocr.DBDetector()
|
||||
cls_model = fd.vision.ocr.Classifier()
|
||||
rec_model = fd.vision.ocr.Recognizer()
|
||||
# 对于三个模型，均采用同样的部署配置
|
||||
# 用户也可根据自行需求分别配置
|
||||
runtime_option = build_option(args)
|
||||
|
||||
#模型初始化
|
||||
if (len(args.det_model) != 0):
|
||||
det_runtime_option = build_det_option(args)
|
||||
det_model = fd.vision.ocr.DBDetector(
|
||||
det_model_file, det_params_file, runtime_option=det_runtime_option)
|
||||
det_model = fd.vision.ocr.DBDetector(
|
||||
det_model_file, det_params_file, runtime_option=runtime_option)
|
||||
cls_model = fd.vision.ocr.Classifier(
|
||||
cls_model_file, cls_params_file, runtime_option=runtime_option)
|
||||
rec_model = fd.vision.ocr.Recognizer(
|
||||
rec_model_file,
|
||||
rec_params_file,
|
||||
rec_label_file,
|
||||
runtime_option=runtime_option)
|
||||
|
||||
if (len(args.cls_model) != 0):
|
||||
cls_runtime_option = build_cls_option(args)
|
||||
cls_model = fd.vision.ocr.Classifier(
|
||||
cls_model_file, cls_params_file, runtime_option=cls_runtime_option)
|
||||
|
||||
if (len(args.rec_model) != 0):
|
||||
rec_runtime_option = build_rec_option(args)
|
||||
rec_model = fd.vision.ocr.Recognizer(
|
||||
rec_model_file,
|
||||
rec_params_file,
|
||||
rec_label_file,
|
||||
runtime_option=rec_runtime_option)
|
||||
|
||||
ppocrsysv3 = fd.vision.ocr.PPOCRSystemv3(
|
||||
ocr_det=det_model._model,
|
||||
ocr_cls=cls_model._model,
|
||||
ocr_rec=rec_model._model)
|
||||
# 创建OCR系统，串联3个模型，其中cls_model可选，如无需求，可设置为None
|
||||
ocr_system = fd.vision.ocr.PPOCRSystemv3(
|
||||
det_model=det_model, cls_model=cls_model, rec_model=rec_model)
|
||||
|
||||
# 预测图片准备
|
||||
im = cv2.imread(args.image)
|
||||
|
||||
#预测并打印结果
|
||||
result = ppocrsysv3.predict(im)
|
||||
result = ocr_system.predict(im)
|
||||
|
||||
print(result)
|
||||
|
||||
|
||||
7
external/openvino.cmake
vendored
7
external/openvino.cmake
vendored
@@ -124,12 +124,7 @@ else()
|
||||
endif()
|
||||
|
||||
if(UNIX)
|
||||
execute_process(COMMAND sh -c "ls *.so*" WORKING_DIRECTORY ${OPENVINO_INSTALL_DIR}/lib
|
||||
COMMAND sh -c "xargs patchelf --set-rpath '$ORIGIN'" WORKING_DIRECTORY ${OPENVINO_INSTALL_DIR}/lib
|
||||
RESULT_VARIABLE result
|
||||
OUTPUT_VARIABLE curr_out
|
||||
ERROR_VARIABLE curr_out)
|
||||
message(STATUS "result:${result} out:${curr_out}")
|
||||
add_custom_target(patchelf_openvino ALL COMMAND bash -c "sh ${PROJECT_SOURCE_DIR}/build_scripts/patch_lib.sh ${OPENVINO_INSTALL_DIR}/lib" DEPENDS ${LIBRARY_NAME})
|
||||
endif()
|
||||
|
||||
add_library(external_openvino STATIC IMPORTED GLOBAL)
|
||||
|
||||
@@ -64,7 +64,7 @@ class RuntimeOption:
|
||||
def use_cpu(self):
|
||||
return self._option.use_cpu()
|
||||
|
||||
def set_cpu_thread_num(self, thread_num=8):
|
||||
def set_cpu_thread_num(self, thread_num=-1):
|
||||
return self._option.set_cpu_thread_num(thread_num)
|
||||
|
||||
def use_paddle_backend(self):
|
||||
|
||||
@@ -217,18 +217,24 @@ class Recognizer(FastDeployModel):
|
||||
|
||||
|
||||
class PPOCRSystemv3(FastDeployModel):
|
||||
def __init__(self, ocr_det=None, ocr_cls=None, ocr_rec=None):
|
||||
|
||||
self._model = C.vision.ocr.PPOCRSystemv3(ocr_det, ocr_cls, ocr_rec)
|
||||
def __init__(self, det_model=None, cls_model=None, rec_model=None):
|
||||
assert det_model is not None and rec_model is not None, "The det_model and rec_model cannot be None."
|
||||
if cls_model is None:
|
||||
self.system = C.vision.ocr.PPOCRSystemv3(det_model._model, rec_model._model)
|
||||
else:
|
||||
self.system = C.vision.ocr.PPOCRSystemv3(det_model._model, cls_model._model, rec_model._model)
|
||||
|
||||
def predict(self, input_image):
|
||||
return self._model.predict(input_image)
|
||||
return self.system.predict(input_image)
|
||||
|
||||
|
||||
class PPOCRSystemv2(FastDeployModel):
|
||||
def __init__(self, ocr_det=None, ocr_cls=None, ocr_rec=None):
|
||||
|
||||
self._model = C.vision.ocr.PPOCRSystemv2(ocr_det, ocr_cls, ocr_rec)
|
||||
def __init__(self, det_model=None, cls_model=None, rec_model=None):
|
||||
assert det_model is not None and rec_model is not None, "The det_model and rec_model cannot be None."
|
||||
if cls_model is None:
|
||||
self.system = C.vision.ocr.PPOCRSystemv2(det_model._model, rec_model._model)
|
||||
else:
|
||||
self.system = C.vision.ocr.PPOCRSystemv2(det_model._model, cls_model._model, rec_model._model)
|
||||
|
||||
def predict(self, input_image):
|
||||
return self._model.predict(input_image)
|
||||
return self.system.predict(input_image)
|
||||
|
||||
Reference in New Issue
Block a user