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[C API] Add c apis for ppocr models (#1351)
* add batch_predict and initialized api for c * add batch_predict and result str in C api * modify visualization function * add micro to add models batchify * refactor and add models for ppdet others * add base define * add results api * add headers * add ppocr models * add codes for implementing api * add code * add ppocr apis * fix code * add codes * fix codes * add demo
This commit is contained in:
chenjian
59
c_api/fastdeploy_capi/vision/ocr/ppocr/base_define.h
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59
c_api/fastdeploy_capi/vision/ocr/ppocr/base_define.h
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// Copyright (c) 2023 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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#pragma once
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#define OCR_DECLARE_DESTROY_WRAPPER_FUNCTION(model_type, wrapper_var_name) FASTDEPLOY_CAPI_EXPORT extern void \
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FD_C_Destroy##model_type##Wrapper(__fd_take FD_C_##model_type##Wrapper* wrapper_var_name);
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#define OCR_DECLARE_INITIALIZED_FUNCTION(model_type, wrapper_var_name) FASTDEPLOY_CAPI_EXPORT extern FD_C_Bool FD_C_##model_type##WrapperInitialized( \
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__fd_keep FD_C_##model_type##Wrapper* wrapper_var_name)
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#define OCR_IMPLEMENT_DESTROY_WRAPPER_FUNCTION(model_type, wrapper_var_name) delete wrapper_var_name
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#define OCR_DECLARE_AND_IMPLEMENT_DESTROY_WRAPPER_FUNCTION(model_type, wrapper_var_name) void FD_C_Destroy##model_type##Wrapper( \
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__fd_take FD_C_##model_type##Wrapper* wrapper_var_name) { \
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OCR_IMPLEMENT_DESTROY_WRAPPER_FUNCTION(model_type, wrapper_var_name); \
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}
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#define OCR_IMPLEMENT_INITIALIZED_FUNCTION(model_type, wrapper_var_name) auto& model = \
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CHECK_AND_CONVERT_FD_TYPE(model_type##Wrapper, wrapper_var_name); \
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return model->Initialized();
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#define OCR_DECLARE_AND_IMPLEMENT_INITIALIZED_FUNCTION(model_type, wrapper_var_name) FD_C_Bool FD_C_##model_type##WrapperInitialized( \
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FD_C_##model_type##Wrapper* wrapper_var_name) { \
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OCR_IMPLEMENT_INITIALIZED_FUNCTION(model_type, wrapper_var_name); \
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}
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#define PIPELINE_DECLARE_DESTROY_WRAPPER_FUNCTION(model_type, wrapper_var_name) FASTDEPLOY_CAPI_EXPORT extern void \
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FD_C_Destroy##model_type##Wrapper(__fd_take FD_C_##model_type##Wrapper* wrapper_var_name);
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#define PIPELINE_DECLARE_INITIALIZED_FUNCTION(model_type, wrapper_var_name) FASTDEPLOY_CAPI_EXPORT extern FD_C_Bool FD_C_##model_type##WrapperInitialized( \
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__fd_keep FD_C_##model_type##Wrapper* wrapper_var_name)
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#define PIPELINE_IMPLEMENT_DESTROY_WRAPPER_FUNCTION(model_type, wrapper_var_name) delete wrapper_var_name
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#define PIPELINE_DECLARE_AND_IMPLEMENT_DESTROY_WRAPPER_FUNCTION(model_type, wrapper_var_name) void FD_C_Destroy##model_type##Wrapper( \
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__fd_take FD_C_##model_type##Wrapper* wrapper_var_name) { \
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PIPELINE_IMPLEMENT_DESTROY_WRAPPER_FUNCTION(model_type, wrapper_var_name); \
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}
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#define PIPELINE_IMPLEMENT_INITIALIZED_FUNCTION(model_type, wrapper_var_name) auto& model = \
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CHECK_AND_CONVERT_FD_TYPE(model_type##Wrapper, wrapper_var_name); \
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return model->Initialized();
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#define PIPELINE_DECLARE_AND_IMPLEMENT_INITIALIZED_FUNCTION(model_type, wrapper_var_name) FD_C_Bool FD_C_##model_type##WrapperInitialized( \
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FD_C_##model_type##Wrapper* wrapper_var_name) { \
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PIPELINE_IMPLEMENT_INITIALIZED_FUNCTION(model_type, wrapper_var_name); \
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}
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503
c_api/fastdeploy_capi/vision/ocr/ppocr/model.cc
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503
c_api/fastdeploy_capi/vision/ocr/ppocr/model.cc
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@@ -0,0 +1,503 @@
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// Copyright (c) 2023 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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// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy_capi/vision/ocr/ppocr/model.h"
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#include "fastdeploy_capi/types_internal.h"
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#include "fastdeploy_capi/vision/visualize.h"
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#ifdef __cplusplus
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extern "C" {
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#endif
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// Recognizer
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FD_C_RecognizerWrapper* FD_C_CreateRecognizerWrapper(
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const char* model_file, const char* params_file, const char* label_path,
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FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
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const FD_C_ModelFormat model_format) {
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auto& runtime_option = CHECK_AND_CONVERT_FD_TYPE(RuntimeOptionWrapper,
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fd_c_runtime_option_wrapper);
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FD_C_RecognizerWrapper* fd_c_recognizer_wrapper =
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new FD_C_RecognizerWrapper();
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fd_c_recognizer_wrapper->recognizer_model =
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std::unique_ptr<fastdeploy::vision::ocr::Recognizer>(
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new fastdeploy::vision::ocr::Recognizer(
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std::string(model_file), std::string(params_file),
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std::string(label_path), *runtime_option,
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static_cast<fastdeploy::ModelFormat>(model_format)));
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return fd_c_recognizer_wrapper;
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}
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OCR_DECLARE_AND_IMPLEMENT_DESTROY_WRAPPER_FUNCTION(Recognizer,
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fd_c_recognizer_wrapper)
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FD_C_Bool FD_C_RecognizerWrapperPredict(
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FD_C_RecognizerWrapper* fd_c_recognizer_wrapper, FD_C_Mat img,
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FD_C_Cstr* text, float* rec_score) {
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cv::Mat* im = reinterpret_cast<cv::Mat*>(img);
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auto& model =
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CHECK_AND_CONVERT_FD_TYPE(RecognizerWrapper, fd_c_recognizer_wrapper);
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std::string res_string;
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bool successful = model->Predict(*im, &res_string, rec_score);
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if (successful) {
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text->size = res_string.size();
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text->data = new char[res_string.size() + 1];
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strcpy(text->data, res_string.c_str());
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}
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return successful;
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}
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OCR_DECLARE_AND_IMPLEMENT_INITIALIZED_FUNCTION(Recognizer,
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fd_c_recognizer_wrapper)
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FD_C_Bool FD_C_RecognizerWrapperBatchPredict(
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FD_C_RecognizerWrapper* fd_c_recognizer_wrapper, FD_C_OneDimMat imgs,
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FD_C_OneDimArrayCstr* texts, FD_C_OneDimArrayFloat* rec_scores) {
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std::vector<cv::Mat> imgs_vec;
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std::vector<std::string> texts_out;
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std::vector<float> rec_scores_out;
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for (int i = 0; i < imgs.size; i++) {
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imgs_vec.push_back(*(reinterpret_cast<cv::Mat*>(imgs.data[i])));
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}
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auto& model =
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CHECK_AND_CONVERT_FD_TYPE(RecognizerWrapper, fd_c_recognizer_wrapper);
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bool successful = model->BatchPredict(imgs_vec, &texts_out, &rec_scores_out);
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if (successful) {
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// copy results back to FD_C_OneDimArrayCstr and FD_C_OneDimArrayFloat
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texts->size = texts_out.size();
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texts->data = new FD_C_Cstr[texts->size];
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for (int i = 0; i < texts_out.size(); i++) {
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texts->data[i].size = texts_out[i].length();
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texts->data[i].data = new char[texts_out[i].length() + 1];
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strncpy(texts->data[i].data, texts_out[i].c_str(), texts_out[i].length());
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}
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rec_scores->size = rec_scores_out.size();
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rec_scores->data = new float[rec_scores->size];
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memcpy(rec_scores->data, rec_scores_out.data(),
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sizeof(float) * rec_scores->size);
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}
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return successful;
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}
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FD_C_Bool FD_C_RecognizerWrapperBatchPredictWithIndex(
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FD_C_RecognizerWrapper* fd_c_recognizer_wrapper, FD_C_OneDimMat imgs,
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FD_C_OneDimArrayCstr* texts, FD_C_OneDimArrayFloat* rec_scores,
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size_t start_index, size_t end_index, FD_C_OneDimArrayInt32 indices) {
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std::vector<cv::Mat> imgs_vec;
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std::vector<std::string> texts_out;
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std::vector<float> rec_scores_out;
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std::vector<int> indices_in;
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for (int i = 0; i < imgs.size; i++) {
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imgs_vec.push_back(*(reinterpret_cast<cv::Mat*>(imgs.data[i])));
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}
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for (int i = 0; i < indices.size; i++) {
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indices_in.push_back(indices.data[i]);
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}
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auto& model =
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CHECK_AND_CONVERT_FD_TYPE(RecognizerWrapper, fd_c_recognizer_wrapper);
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bool successful = model->BatchPredict(imgs_vec, &texts_out, &rec_scores_out,
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start_index, end_index, indices_in);
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if (successful) {
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// copy results back to FD_C_OneDimArrayCstr and FD_C_OneDimArrayFloat
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texts->size = texts_out.size();
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texts->data = new FD_C_Cstr[texts->size];
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for (int i = 0; i < texts_out.size(); i++) {
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texts->data[i].size = texts_out[i].length();
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texts->data[i].data = new char[texts_out[i].length() + 1];
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strncpy(texts->data[i].data, texts_out[i].c_str(), texts_out[i].length());
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}
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rec_scores->size = rec_scores_out.size();
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rec_scores->data = new float[rec_scores->size];
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memcpy(rec_scores->data, rec_scores_out.data(),
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sizeof(float) * rec_scores->size);
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}
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return successful;
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}
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// Classifier
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FD_C_ClassifierWrapper* FD_C_CreateClassifierWrapper(
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const char* model_file, const char* params_file,
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FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
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const FD_C_ModelFormat model_format) {
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auto& runtime_option = CHECK_AND_CONVERT_FD_TYPE(RuntimeOptionWrapper,
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fd_c_runtime_option_wrapper);
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FD_C_ClassifierWrapper* fd_c_classifier_wrapper =
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new FD_C_ClassifierWrapper();
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fd_c_classifier_wrapper->classifier_model =
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std::unique_ptr<fastdeploy::vision::ocr::Classifier>(
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new fastdeploy::vision::ocr::Classifier(
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std::string(model_file), std::string(params_file),
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*runtime_option,
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static_cast<fastdeploy::ModelFormat>(model_format)));
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return fd_c_classifier_wrapper;
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}
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OCR_DECLARE_AND_IMPLEMENT_DESTROY_WRAPPER_FUNCTION(Classifier,
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fd_c_classifier_wrapper)
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FD_C_Bool FD_C_ClassifierWrapperPredict(
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FD_C_ClassifierWrapper* fd_c_classifier_wrapper, FD_C_Mat img,
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int32_t* cls_label, float* cls_score) {
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cv::Mat* im = reinterpret_cast<cv::Mat*>(img);
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auto& model =
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CHECK_AND_CONVERT_FD_TYPE(ClassifierWrapper, fd_c_classifier_wrapper);
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bool successful = model->Predict(*im, cls_label, cls_score);
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return successful;
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}
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OCR_DECLARE_AND_IMPLEMENT_INITIALIZED_FUNCTION(Classifier,
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fd_c_classifier_wrapper)
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FD_C_Bool FD_C_ClassifierWrapperBatchPredict(
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FD_C_ClassifierWrapper* fd_c_classifier_wrapper, FD_C_OneDimMat imgs,
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FD_C_OneDimArrayInt32* cls_labels, FD_C_OneDimArrayFloat* cls_scores) {
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std::vector<cv::Mat> imgs_vec;
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std::vector<int> cls_labels_out;
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std::vector<float> cls_scores_out;
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for (int i = 0; i < imgs.size; i++) {
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imgs_vec.push_back(*(reinterpret_cast<cv::Mat*>(imgs.data[i])));
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}
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auto& model =
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CHECK_AND_CONVERT_FD_TYPE(ClassifierWrapper, fd_c_classifier_wrapper);
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bool successful =
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model->BatchPredict(imgs_vec, &cls_labels_out, &cls_scores_out);
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if (successful) {
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// copy results back to FD_C_OneDimArrayInt32 and FD_C_OneDimArrayFloat
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cls_labels->size = cls_labels_out.size();
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cls_labels->data = new int[cls_labels->size];
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memcpy(cls_labels->data, cls_labels_out.data(),
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sizeof(int) * cls_labels->size);
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cls_scores->size = cls_scores_out.size();
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cls_scores->data = new float[cls_scores->size];
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memcpy(cls_scores->data, cls_scores_out.data(),
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sizeof(int) * cls_scores->size);
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}
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return successful;
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}
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FD_C_Bool FD_C_ClassifierWrapperBatchPredictWithIndex(
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FD_C_ClassifierWrapper* fd_c_classifier_wrapper, FD_C_OneDimMat imgs,
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FD_C_OneDimArrayInt32* cls_labels, FD_C_OneDimArrayFloat* cls_scores,
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size_t start_index, size_t end_index) {
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std::vector<cv::Mat> imgs_vec;
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std::vector<int> cls_labels_out;
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std::vector<float> cls_scores_out;
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for (int i = 0; i < imgs.size; i++) {
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imgs_vec.push_back(*(reinterpret_cast<cv::Mat*>(imgs.data[i])));
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}
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auto& model =
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CHECK_AND_CONVERT_FD_TYPE(ClassifierWrapper, fd_c_classifier_wrapper);
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bool successful = model->BatchPredict(
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imgs_vec, &cls_labels_out, &cls_scores_out, start_index, end_index);
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if (successful) {
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// copy results back to FD_C_OneDimArrayInt32 and FD_C_OneDimArrayFloat
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cls_labels->size = cls_labels_out.size();
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cls_labels->data = new int[cls_labels->size];
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memcpy(cls_labels->data, cls_labels_out.data(),
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sizeof(int) * cls_labels->size);
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cls_scores->size = cls_scores_out.size();
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cls_scores->data = new float[cls_scores->size];
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memcpy(cls_scores->data, cls_scores_out.data(),
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sizeof(int) * cls_scores->size);
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}
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return successful;
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}
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// DBDetector
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FD_C_DBDetectorWrapper* FD_C_CreateDBDetectorWrapper(
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const char* model_file, const char* params_file,
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FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
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const FD_C_ModelFormat model_format) {
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auto& runtime_option = CHECK_AND_CONVERT_FD_TYPE(RuntimeOptionWrapper,
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fd_c_runtime_option_wrapper);
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FD_C_DBDetectorWrapper* fd_c_dbdetector_wrapper =
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new FD_C_DBDetectorWrapper();
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fd_c_dbdetector_wrapper->dbdetector_model =
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std::unique_ptr<fastdeploy::vision::ocr::DBDetector>(
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new fastdeploy::vision::ocr::DBDetector(
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std::string(model_file), std::string(params_file),
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*runtime_option,
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static_cast<fastdeploy::ModelFormat>(model_format)));
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return fd_c_dbdetector_wrapper;
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}
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OCR_DECLARE_AND_IMPLEMENT_DESTROY_WRAPPER_FUNCTION(DBDetector,
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fd_c_dbdetector_wrapper)
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FD_C_Bool FD_C_DBDetectorWrapperPredict(
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FD_C_DBDetectorWrapper* fd_c_dbdetector_wrapper, FD_C_Mat img,
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||||
FD_C_TwoDimArrayInt32* boxes_result) {
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cv::Mat* im = reinterpret_cast<cv::Mat*>(img);
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std::vector<std::array<int, 8>> boxes_result_out;
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||||
auto& model =
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CHECK_AND_CONVERT_FD_TYPE(DBDetectorWrapper, fd_c_dbdetector_wrapper);
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bool successful = model->Predict(*im, &boxes_result_out);
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if (successful) {
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// copy boxes
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const int boxes_coordinate_dim = 8;
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boxes_result->size = boxes_result_out.size();
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boxes_result->data = new FD_C_OneDimArrayInt32[boxes_result->size];
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||||
for (size_t i = 0; i < boxes_result_out.size(); i++) {
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boxes_result->data[i].size = boxes_coordinate_dim;
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boxes_result->data[i].data = new int[boxes_coordinate_dim];
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||||
for (size_t j = 0; j < boxes_coordinate_dim; j++) {
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boxes_result->data[i].data[j] = boxes_result_out[i][j];
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}
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||||
}
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||||
}
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||||
return successful;
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||||
}
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||||
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||||
OCR_DECLARE_AND_IMPLEMENT_INITIALIZED_FUNCTION(DBDetector,
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||||
fd_c_dbdetector_wrapper)
|
||||
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||||
FD_C_Bool FD_C_DBDetectorWrapperBatchPredict(
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||||
FD_C_DBDetectorWrapper* fd_c_dbdetector_wrapper, FD_C_OneDimMat imgs,
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||||
FD_C_ThreeDimArrayInt32* det_results) {
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||||
std::vector<cv::Mat> imgs_vec;
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||||
std::vector<std::vector<std::array<int, 8>>> det_results_out;
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||||
for (int i = 0; i < imgs.size; i++) {
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||||
imgs_vec.push_back(*(reinterpret_cast<cv::Mat*>(imgs.data[i])));
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||||
}
|
||||
auto& model =
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CHECK_AND_CONVERT_FD_TYPE(DBDetectorWrapper, fd_c_dbdetector_wrapper);
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||||
bool successful = model->BatchPredict(imgs_vec, &det_results_out);
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||||
if (successful) {
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||||
// copy results back to FD_C_ThreeDimArrayInt32
|
||||
det_results->size = det_results_out.size();
|
||||
det_results->data = new FD_C_TwoDimArrayInt32[det_results->size];
|
||||
for (int batch_indx = 0; batch_indx < det_results->size; batch_indx++) {
|
||||
const int boxes_coordinate_dim = 8;
|
||||
det_results->data[batch_indx].size = det_results_out[batch_indx].size();
|
||||
det_results->data[batch_indx].data =
|
||||
new FD_C_OneDimArrayInt32[det_results->data[batch_indx].size];
|
||||
for (size_t i = 0; i < det_results_out[batch_indx].size(); i++) {
|
||||
det_results->data[batch_indx].data[i].size = boxes_coordinate_dim;
|
||||
det_results->data[batch_indx].data[i].data =
|
||||
new int[boxes_coordinate_dim];
|
||||
for (size_t j = 0; j < boxes_coordinate_dim; j++) {
|
||||
det_results->data[batch_indx].data[i].data[j] =
|
||||
det_results_out[batch_indx][i][j];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
return successful;
|
||||
}
|
||||
|
||||
// PPOCRv2
|
||||
|
||||
FD_C_PPOCRv2Wrapper* FD_C_CreatePPOCRv2Wrapper(
|
||||
FD_C_DBDetectorWrapper* fd_c_det_model_wrapper,
|
||||
FD_C_ClassifierWrapper* fd_c_cls_model_wrapper,
|
||||
FD_C_RecognizerWrapper* fd_c_rec_model_wrapper) {
|
||||
FD_C_PPOCRv2Wrapper* fd_c_ppocrv2_wrapper = new FD_C_PPOCRv2Wrapper();
|
||||
auto& det_model =
|
||||
CHECK_AND_CONVERT_FD_TYPE(DBDetectorWrapper, fd_c_det_model_wrapper);
|
||||
auto& cls_model =
|
||||
CHECK_AND_CONVERT_FD_TYPE(ClassifierWrapper, fd_c_cls_model_wrapper);
|
||||
auto& rec_model =
|
||||
CHECK_AND_CONVERT_FD_TYPE(RecognizerWrapper, fd_c_rec_model_wrapper);
|
||||
fd_c_ppocrv2_wrapper->ppocrv2_model =
|
||||
std::unique_ptr<fastdeploy::pipeline::PPOCRv2>(
|
||||
new fastdeploy::pipeline::PPOCRv2(det_model.get(), cls_model.get(),
|
||||
rec_model.get()));
|
||||
return fd_c_ppocrv2_wrapper;
|
||||
}
|
||||
|
||||
PIPELINE_DECLARE_AND_IMPLEMENT_DESTROY_WRAPPER_FUNCTION(PPOCRv2,
|
||||
fd_c_ppocrv2_wrapper)
|
||||
|
||||
FD_C_Bool FD_C_PPOCRv2WrapperPredict(FD_C_PPOCRv2Wrapper* fd_c_ppocrv2_wrapper,
|
||||
FD_C_Mat img,
|
||||
FD_C_OCRResult* fd_c_ocr_result) {
|
||||
cv::Mat* im = reinterpret_cast<cv::Mat*>(img);
|
||||
auto& model = CHECK_AND_CONVERT_FD_TYPE(PPOCRv2Wrapper, fd_c_ppocrv2_wrapper);
|
||||
FD_C_OCRResultWrapper* fd_c_ocr_result_wrapper =
|
||||
FD_C_CreateOCRResultWrapper();
|
||||
auto& ocr_result =
|
||||
CHECK_AND_CONVERT_FD_TYPE(OCRResultWrapper, fd_c_ocr_result_wrapper);
|
||||
|
||||
bool successful = model->Predict(im, ocr_result.get());
|
||||
if (successful) {
|
||||
FD_C_OCRResult* res = FD_C_OCRResultWrapperGetData(fd_c_ocr_result_wrapper);
|
||||
*fd_c_ocr_result = *res;
|
||||
}
|
||||
return successful;
|
||||
}
|
||||
|
||||
PIPELINE_DECLARE_AND_IMPLEMENT_INITIALIZED_FUNCTION(PPOCRv2,
|
||||
fd_c_ppocrv2_wrapper)
|
||||
|
||||
FD_C_OCRResult* FD_C_OCRResultToC(fastdeploy::vision::OCRResult* ocr_result) {
|
||||
// Internal use, transfer fastdeploy::vision::OCRResult to
|
||||
// FD_C_OCRResult
|
||||
FD_C_OCRResult* fd_c_ocr_result = new FD_C_OCRResult();
|
||||
// copy boxes
|
||||
const int boxes_coordinate_dim = 8;
|
||||
fd_c_ocr_result->boxes.size = ocr_result->boxes.size();
|
||||
fd_c_ocr_result->boxes.data =
|
||||
new FD_C_OneDimArrayInt32[fd_c_ocr_result->boxes.size];
|
||||
for (size_t i = 0; i < ocr_result->boxes.size(); i++) {
|
||||
fd_c_ocr_result->boxes.data[i].size = boxes_coordinate_dim;
|
||||
fd_c_ocr_result->boxes.data[i].data = new int[boxes_coordinate_dim];
|
||||
for (size_t j = 0; j < boxes_coordinate_dim; j++) {
|
||||
fd_c_ocr_result->boxes.data[i].data[j] = ocr_result->boxes[i][j];
|
||||
}
|
||||
}
|
||||
// copy text
|
||||
fd_c_ocr_result->text.size = ocr_result->text.size();
|
||||
fd_c_ocr_result->text.data = new FD_C_Cstr[fd_c_ocr_result->text.size];
|
||||
for (size_t i = 0; i < ocr_result->text.size(); i++) {
|
||||
fd_c_ocr_result->text.data[i].size = ocr_result->text[i].length();
|
||||
fd_c_ocr_result->text.data[i].data =
|
||||
new char[ocr_result->text[i].length() + 1];
|
||||
strncpy(fd_c_ocr_result->text.data[i].data, ocr_result->text[i].c_str(),
|
||||
ocr_result->text[i].length());
|
||||
}
|
||||
|
||||
// copy rec_scores
|
||||
fd_c_ocr_result->rec_scores.size = ocr_result->rec_scores.size();
|
||||
fd_c_ocr_result->rec_scores.data =
|
||||
new float[fd_c_ocr_result->rec_scores.size];
|
||||
memcpy(fd_c_ocr_result->rec_scores.data, ocr_result->rec_scores.data(),
|
||||
sizeof(float) * fd_c_ocr_result->rec_scores.size);
|
||||
// copy cls_scores
|
||||
fd_c_ocr_result->cls_scores.size = ocr_result->cls_scores.size();
|
||||
fd_c_ocr_result->cls_scores.data =
|
||||
new float[fd_c_ocr_result->cls_scores.size];
|
||||
memcpy(fd_c_ocr_result->cls_scores.data, ocr_result->cls_scores.data(),
|
||||
sizeof(float) * fd_c_ocr_result->cls_scores.size);
|
||||
// copy cls_labels
|
||||
fd_c_ocr_result->cls_labels.size = ocr_result->cls_labels.size();
|
||||
fd_c_ocr_result->cls_labels.data =
|
||||
new int32_t[fd_c_ocr_result->cls_labels.size];
|
||||
memcpy(fd_c_ocr_result->cls_labels.data, ocr_result->cls_labels.data(),
|
||||
sizeof(int32_t) * fd_c_ocr_result->cls_labels.size);
|
||||
// copy type
|
||||
fd_c_ocr_result->type = static_cast<FD_C_ResultType>(ocr_result->type);
|
||||
return fd_c_ocr_result;
|
||||
}
|
||||
|
||||
FD_C_Bool FD_C_PPOCRv2WrapperBatchPredict(
|
||||
FD_C_PPOCRv2Wrapper* fd_c_ppocrv2_wrapper, FD_C_OneDimMat imgs,
|
||||
FD_C_OneDimOCRResult* results) {
|
||||
std::vector<cv::Mat> imgs_vec;
|
||||
std::vector<fastdeploy::vision::OCRResult> results_out;
|
||||
for (int i = 0; i < imgs.size; i++) {
|
||||
imgs_vec.push_back(*(reinterpret_cast<cv::Mat*>(imgs.data[i])));
|
||||
}
|
||||
auto& model = CHECK_AND_CONVERT_FD_TYPE(PPOCRv2Wrapper, fd_c_ppocrv2_wrapper);
|
||||
bool successful = model->BatchPredict(imgs_vec, &results_out);
|
||||
if (successful) {
|
||||
// copy results back to FD_C_OneDimOCRResult
|
||||
results->size = results_out.size();
|
||||
results->data = new FD_C_OCRResult[results->size];
|
||||
for (int i = 0; i < results_out.size(); i++) {
|
||||
results->data[i] = *FD_C_OCRResultToC(&results_out[i]);
|
||||
}
|
||||
}
|
||||
return successful;
|
||||
}
|
||||
|
||||
// PPOCRv3
|
||||
|
||||
FD_C_PPOCRv3Wrapper* FD_C_CreatePPOCRv3Wrapper(
|
||||
FD_C_DBDetectorWrapper* fd_c_det_model_wrapper,
|
||||
FD_C_ClassifierWrapper* fd_c_cls_model_wrapper,
|
||||
FD_C_RecognizerWrapper* fd_c_rec_model_wrapper) {
|
||||
FD_C_PPOCRv3Wrapper* fd_c_ppocrv3_wrapper = new FD_C_PPOCRv3Wrapper();
|
||||
auto& det_model =
|
||||
CHECK_AND_CONVERT_FD_TYPE(DBDetectorWrapper, fd_c_det_model_wrapper);
|
||||
auto& cls_model =
|
||||
CHECK_AND_CONVERT_FD_TYPE(ClassifierWrapper, fd_c_cls_model_wrapper);
|
||||
auto& rec_model =
|
||||
CHECK_AND_CONVERT_FD_TYPE(RecognizerWrapper, fd_c_rec_model_wrapper);
|
||||
fd_c_ppocrv3_wrapper->ppocrv3_model =
|
||||
std::unique_ptr<fastdeploy::pipeline::PPOCRv3>(
|
||||
new fastdeploy::pipeline::PPOCRv3(det_model.get(), cls_model.get(),
|
||||
rec_model.get()));
|
||||
return fd_c_ppocrv3_wrapper;
|
||||
}
|
||||
|
||||
PIPELINE_DECLARE_AND_IMPLEMENT_DESTROY_WRAPPER_FUNCTION(PPOCRv3,
|
||||
fd_c_ppocrv3_wrapper)
|
||||
|
||||
FD_C_Bool FD_C_PPOCRv3WrapperPredict(FD_C_PPOCRv3Wrapper* fd_c_ppocrv3_wrapper,
|
||||
FD_C_Mat img,
|
||||
FD_C_OCRResult* fd_c_ocr_result) {
|
||||
cv::Mat* im = reinterpret_cast<cv::Mat*>(img);
|
||||
auto& model = CHECK_AND_CONVERT_FD_TYPE(PPOCRv3Wrapper, fd_c_ppocrv3_wrapper);
|
||||
FD_C_OCRResultWrapper* fd_c_ocr_result_wrapper =
|
||||
FD_C_CreateOCRResultWrapper();
|
||||
auto& ocr_result =
|
||||
CHECK_AND_CONVERT_FD_TYPE(OCRResultWrapper, fd_c_ocr_result_wrapper);
|
||||
|
||||
bool successful = model->Predict(im, ocr_result.get());
|
||||
if (successful) {
|
||||
FD_C_OCRResult* res = FD_C_OCRResultWrapperGetData(fd_c_ocr_result_wrapper);
|
||||
*fd_c_ocr_result = *res;
|
||||
}
|
||||
return successful;
|
||||
}
|
||||
|
||||
PIPELINE_DECLARE_AND_IMPLEMENT_INITIALIZED_FUNCTION(PPOCRv3,
|
||||
fd_c_ppocrv3_wrapper)
|
||||
|
||||
FD_C_Bool FD_C_PPOCRv3WrapperBatchPredict(
|
||||
FD_C_PPOCRv3Wrapper* fd_c_ppocrv3_wrapper, FD_C_OneDimMat imgs,
|
||||
FD_C_OneDimOCRResult* results) {
|
||||
std::vector<cv::Mat> imgs_vec;
|
||||
std::vector<fastdeploy::vision::OCRResult> results_out;
|
||||
for (int i = 0; i < imgs.size; i++) {
|
||||
imgs_vec.push_back(*(reinterpret_cast<cv::Mat*>(imgs.data[i])));
|
||||
}
|
||||
auto& model = CHECK_AND_CONVERT_FD_TYPE(PPOCRv3Wrapper, fd_c_ppocrv3_wrapper);
|
||||
bool successful = model->BatchPredict(imgs_vec, &results_out);
|
||||
if (successful) {
|
||||
// copy results back to FD_C_OneDimOCRResult
|
||||
results->size = results_out.size();
|
||||
results->data = new FD_C_OCRResult[results->size];
|
||||
for (int i = 0; i < results_out.size(); i++) {
|
||||
results->data[i] = *FD_C_OCRResultToC(&results_out[i]);
|
||||
}
|
||||
}
|
||||
return successful;
|
||||
}
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
348
c_api/fastdeploy_capi/vision/ocr/ppocr/model.h
Normal file
348
c_api/fastdeploy_capi/vision/ocr/ppocr/model.h
Normal file
@@ -0,0 +1,348 @@
|
||||
// Copyright (c) 2023 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.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "fastdeploy_capi/fd_common.h"
|
||||
#include "fastdeploy_capi/fd_type.h"
|
||||
#include "fastdeploy_capi/runtime_option.h"
|
||||
#include "fastdeploy_capi/vision/result.h"
|
||||
#include "fastdeploy_capi/vision/ocr/ppocr/base_define.h"
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
// Recognizer
|
||||
|
||||
typedef struct FD_C_RecognizerWrapper FD_C_RecognizerWrapper;
|
||||
|
||||
/** \brief Create a new FD_C_RecognizerWrapper object
|
||||
*
|
||||
* \param[in] model_file Path of model file, e.g ./ch_PP-OCRv3_rec_infer/model.pdmodel.
|
||||
* \param[in] params_file Path of parameter file, e.g ./ch_PP-OCRv3_rec_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
|
||||
* \param[in] label_path Path of label file used by OCR recognition model. e.g ./ppocr_keys_v1.txt
|
||||
* \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`.
|
||||
* \param[in] model_format Model format of the loaded model, default is Paddle format.
|
||||
*
|
||||
* \return Return a pointer to FD_C_RecognizerWrapper object
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern __fd_give FD_C_RecognizerWrapper*
|
||||
FD_C_CreateRecognizerWrapper(
|
||||
const char* model_file, const char* params_file, const char* label_path,
|
||||
FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
|
||||
const FD_C_ModelFormat model_format);
|
||||
|
||||
/** \brief Destroy a FD_C_RecognizerWrapper object
|
||||
*
|
||||
* \param[in] fd_c_recognizer_wrapper pointer to FD_C_RecognizerWrapper object
|
||||
*/
|
||||
|
||||
OCR_DECLARE_DESTROY_WRAPPER_FUNCTION(Recognizer, fd_c_recognizer_wrapper);
|
||||
|
||||
/** \brief Predict the ocr result for an input image
|
||||
*
|
||||
* \param[in] fd_c_recognizer_wrapper pointer to FD_C_RecognizerWrapper object
|
||||
* \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
|
||||
* \param[in] text The text result of rec model will be written into this parameter.
|
||||
* \param[in] rec_score The sccore result of rec model will be written into this parameter.
|
||||
* \return true if the prediction is successed, otherwise false.
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern FD_C_Bool FD_C_RecognizerWrapperPredict(
|
||||
__fd_keep FD_C_RecognizerWrapper* fd_c_recognizer_wrapper, FD_C_Mat img,
|
||||
FD_C_Cstr* text, float* rec_score);
|
||||
|
||||
/** \brief Check if the model is initialized successfully
|
||||
*
|
||||
* \param[in] fd_c_recognizer_wrapper pointer to FD_C_RecognizerWrapper object
|
||||
*
|
||||
* \return Return a bool of value true if initialized successfully
|
||||
*/
|
||||
|
||||
OCR_DECLARE_INITIALIZED_FUNCTION(Recognizer, fd_c_recognizer_wrapper);
|
||||
|
||||
/** \brief Predict the ocr results for a batch of input images
|
||||
*
|
||||
* \param[in] fd_c_recognizer_wrapper pointer to FD_C_RecognizerWrapper object
|
||||
* \param[in] imgs The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
|
||||
* \param[in] texts The list of text results of rec model will be written into this vector.
|
||||
* \param[in] rec_scores The list of sccore result of rec model will be written into this vector.
|
||||
*
|
||||
* \return true if the prediction successed, otherwise false
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern FD_C_Bool FD_C_RecognizerWrapperBatchPredict(
|
||||
__fd_keep FD_C_RecognizerWrapper* fd_c_recognizer_wrapper, FD_C_OneDimMat imgs,
|
||||
FD_C_OneDimArrayCstr* texts, FD_C_OneDimArrayFloat* rec_scores);
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern FD_C_Bool FD_C_RecognizerWrapperBatchPredictWithIndex(
|
||||
__fd_keep FD_C_RecognizerWrapper* fd_c_recognizer_wrapper, FD_C_OneDimMat imgs,
|
||||
FD_C_OneDimArrayCstr* texts, FD_C_OneDimArrayFloat* rec_scores,
|
||||
size_t start_index, size_t end_index,
|
||||
FD_C_OneDimArrayInt32 indices);
|
||||
|
||||
|
||||
// Classifier
|
||||
|
||||
typedef struct FD_C_ClassifierWrapper FD_C_ClassifierWrapper;
|
||||
|
||||
/** \brief Create a new FD_C_ClassifierWrapper object
|
||||
*
|
||||
* \param[in] model_file Path of model file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdmodel.
|
||||
* \param[in] params_file Path of parameter file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
|
||||
* \param[in] fd_c_runtime_option_wrapper RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`.
|
||||
* \param[in] model_format Model format of the loaded model, default is Paddle format.
|
||||
*
|
||||
* \return Return a pointer to FD_C_ClassifierWrapper object
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern __fd_give FD_C_ClassifierWrapper*
|
||||
FD_C_CreateClassifierWrapper(
|
||||
const char* model_file, const char* params_file,
|
||||
FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
|
||||
const FD_C_ModelFormat model_format);
|
||||
|
||||
/** \brief Destroy a FD_C_ClassifierWrapper object
|
||||
*
|
||||
* \param[in] fd_c_classifier_wrapper pointer to FD_C_ClassifierWrapper object
|
||||
*/
|
||||
|
||||
OCR_DECLARE_DESTROY_WRAPPER_FUNCTION(Classifier, fd_c_classifier_wrapper);
|
||||
|
||||
/** \brief Predict the input image and get OCR classification model cls_result.
|
||||
*
|
||||
* \param[in] fd_c_classifier_wrapper pointer to FD_C_ClassifierWrapper object
|
||||
* \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
|
||||
* \param[in] cls_label The label result of cls model will be written in to this param.
|
||||
* \param[in] cls_score The score result of cls model will be written in to this param.
|
||||
* \return true if the prediction is successed, otherwise false.
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern FD_C_Bool FD_C_ClassifierWrapperPredict(
|
||||
__fd_keep FD_C_ClassifierWrapper* fd_c_classifier_wrapper, FD_C_Mat img,
|
||||
int32_t* cls_label, float* cls_score);
|
||||
|
||||
/** \brief Check if the model is initialized successfully
|
||||
*
|
||||
* \param[in] fd_c_classifier_wrapper pointer to FD_C_ClassifierWrapper object
|
||||
*
|
||||
* \return Return a bool of value true if initialized successfully
|
||||
*/
|
||||
|
||||
OCR_DECLARE_INITIALIZED_FUNCTION(Classifier, fd_c_classifier_wrapper);
|
||||
|
||||
/** \brief BatchPredict the input image and get OCR classification model cls_result.
|
||||
*
|
||||
* \param[in] fd_c_classifier_wrapper pointer to FD_C_ClassifierWrapper object
|
||||
* \param[in] imgs The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
|
||||
* \param[in] cls_labels The label results of cls model will be written in to this vector.
|
||||
* \param[in] cls_scores The score results of cls model will be written in to this vector.
|
||||
* \return true if the prediction is successed, otherwise false.
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern FD_C_Bool FD_C_ClassifierWrapperBatchPredict(
|
||||
__fd_keep FD_C_ClassifierWrapper* fd_c_classifier_wrapper, FD_C_OneDimMat imgs,
|
||||
FD_C_OneDimArrayInt32* cls_labels, FD_C_OneDimArrayFloat* cls_scores);
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern FD_C_Bool FD_C_ClassifierWrapperBatchPredictWithIndex(
|
||||
__fd_keep FD_C_ClassifierWrapper* fd_c_classifier_wrapper, FD_C_OneDimMat imgs,
|
||||
FD_C_OneDimArrayInt32* cls_labels, FD_C_OneDimArrayFloat* cls_scores,
|
||||
size_t start_index, size_t end_index);
|
||||
|
||||
|
||||
// DBDetector
|
||||
|
||||
typedef struct FD_C_DBDetectorWrapper FD_C_DBDetectorWrapper;
|
||||
|
||||
/** \brief Create a new FD_C_DBDetectorWrapper object
|
||||
*
|
||||
* \param[in] model_file Path of model file, e.g ./ch_PP-OCRv3_det_infer/model.pdmodel.
|
||||
* \param[in] params_file Path of parameter file, e.g ./ch_PP-OCRv3_det_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
|
||||
* \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`.
|
||||
* \param[in] model_format Model format of the loaded model, default is Paddle format.
|
||||
*
|
||||
* \return Return a pointer to FD_C_DBDetectorWrapper object
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern __fd_give FD_C_DBDetectorWrapper*
|
||||
FD_C_CreateDBDetectorWrapper(
|
||||
const char* model_file, const char* params_file,
|
||||
FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
|
||||
const FD_C_ModelFormat model_format);
|
||||
|
||||
/** \brief Destroy a FD_C_DBDetectorWrapper object
|
||||
*
|
||||
* \param[in] fd_c_dbdetector_wrapper pointer to FD_C_DBDetectorWrapper object
|
||||
*/
|
||||
|
||||
OCR_DECLARE_DESTROY_WRAPPER_FUNCTION(DBDetector, fd_c_dbdetector_wrapper);
|
||||
|
||||
/** \brief Predict the input image and get OCR detection model result.
|
||||
*
|
||||
* \param[in] fd_c_dbdetector_wrapper pointer to FD_C_DBDetectorWrapper object
|
||||
* \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
|
||||
* \param[in] boxes_result The output of OCR detection model result will be writen to this structure.
|
||||
* \return true if the prediction is successed, otherwise false.
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern FD_C_Bool FD_C_DBDetectorWrapperPredict(
|
||||
__fd_keep FD_C_DBDetectorWrapper* fd_c_dbdetector_wrapper, FD_C_Mat img,
|
||||
FD_C_TwoDimArrayInt32* boxes_result);
|
||||
|
||||
/** \brief Check if the model is initialized successfully
|
||||
*
|
||||
* \param[in] fd_c_dbdetector_wrapper pointer to FD_C_DBDetectorWrapper object
|
||||
*
|
||||
* \return Return a bool of value true if initialized successfully
|
||||
*/
|
||||
|
||||
OCR_DECLARE_INITIALIZED_FUNCTION(DBDetector, fd_c_dbdetector_wrapper);
|
||||
|
||||
/** \brief BatchPredict the input image and get OCR detection model result.
|
||||
*
|
||||
* \param[in] fd_c_dbdetector_wrapper pointer to FD_C_DBDetectorWrapper object
|
||||
* \param[in] imgs The list input of image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
|
||||
* \param[in] det_results The output of OCR detection model result will be writen to this structure.
|
||||
*
|
||||
* \return true if the prediction is successed, otherwise false.
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern FD_C_Bool FD_C_DBDetectorWrapperBatchPredict(
|
||||
__fd_keep FD_C_DBDetectorWrapper* fd_c_dbdetector_wrapper, FD_C_OneDimMat imgs,
|
||||
FD_C_ThreeDimArrayInt32* det_results);
|
||||
|
||||
|
||||
// PPOCRv2
|
||||
|
||||
|
||||
typedef struct FD_C_PPOCRv2Wrapper FD_C_PPOCRv2Wrapper;
|
||||
|
||||
/** \brief Set up the detection model path, classification model path and recognition model path respectively.
|
||||
*
|
||||
* \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv2_det_infer
|
||||
* \param[in] cls_model Path of classification model, e.g ./ch_ppocr_mobile_v2.0_cls_infer
|
||||
* \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv2_rec_infer
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern __fd_give FD_C_PPOCRv2Wrapper*
|
||||
FD_C_CreatePPOCRv2Wrapper(
|
||||
FD_C_DBDetectorWrapper* det_model,
|
||||
FD_C_ClassifierWrapper* cls_model,
|
||||
FD_C_RecognizerWrapper* rec_model);
|
||||
|
||||
/** \brief Destroy a FD_C_PPOCRv2Wrapper object
|
||||
*
|
||||
* \param[in] fd_c_ppocrv2_wrapper pointer to FD_C_PPOCRv2Wrapper object
|
||||
*/
|
||||
|
||||
OCR_DECLARE_DESTROY_WRAPPER_FUNCTION(PPOCRv2, fd_c_ppocrv2_wrapper);
|
||||
|
||||
/** \brief Predict the input image and get OCR result.
|
||||
*
|
||||
* \param[in] fd_c_ppocrv2_wrapper pointer to FD_C_PPOCRv2Wrapper object
|
||||
* \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
|
||||
* \param[in] result The output OCR result will be writen to this structure.
|
||||
* \return true if the prediction successed, otherwise false.
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern FD_C_Bool FD_C_PPOCRv2WrapperPredict(
|
||||
__fd_keep FD_C_PPOCRv2Wrapper* fd_c_ppocrv2_wrapper, FD_C_Mat img,
|
||||
FD_C_OCRResult* result);
|
||||
|
||||
/** \brief Check if the model is initialized successfully
|
||||
*
|
||||
* \param[in] fd_c_ppocrv2_wrapper pointer to FD_C_PPOCRv2Wrapper object
|
||||
*
|
||||
* \return Return a bool of value true if initialized successfully
|
||||
*/
|
||||
|
||||
OCR_DECLARE_INITIALIZED_FUNCTION(PPOCRv2, fd_c_ppocrv2_wrapper);
|
||||
|
||||
/** \brief BatchPredict the input image and get OCR result.
|
||||
*
|
||||
* \param[in] fd_c_ppocrv2_wrapper pointer to FD_C_PPOCRv2Wrapper object
|
||||
* \param[in] imgs The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
|
||||
* \param[in] batch_result The output list of OCR result will be writen to this structure.
|
||||
* \return true if the prediction successed, otherwise false.
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern FD_C_Bool FD_C_PPOCRv2WrapperBatchPredict(
|
||||
__fd_keep FD_C_PPOCRv2Wrapper* fd_c_ppocrv2_wrapper, FD_C_OneDimMat imgs,
|
||||
FD_C_OneDimOCRResult* batch_result);
|
||||
|
||||
|
||||
|
||||
// PPOCRv3
|
||||
|
||||
typedef struct FD_C_PPOCRv3Wrapper FD_C_PPOCRv3Wrapper;
|
||||
|
||||
/** \brief Set up the detection model path, classification model path and recognition model path respectively.
|
||||
*
|
||||
* \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv2_det_infer
|
||||
* \param[in] cls_model Path of classification model, e.g ./ch_ppocr_mobile_v2.0_cls_infer
|
||||
* \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv2_rec_infer
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern __fd_give FD_C_PPOCRv3Wrapper*
|
||||
FD_C_CreatePPOCRv3Wrapper(
|
||||
FD_C_DBDetectorWrapper* det_model,
|
||||
FD_C_ClassifierWrapper* cls_model,
|
||||
FD_C_RecognizerWrapper* rec_model);
|
||||
|
||||
/** \brief Destroy a FD_C_PPOCRv3Wrapper object
|
||||
*
|
||||
* \param[in] fd_c_ppocrv3_wrapper pointer to FD_C_PPOCRv3Wrapper object
|
||||
*/
|
||||
|
||||
OCR_DECLARE_DESTROY_WRAPPER_FUNCTION(PPOCRv3, fd_c_ppocrv3_wrapper);
|
||||
|
||||
/** \brief Predict the input image and get OCR result.
|
||||
*
|
||||
* \param[in] fd_c_ppocrv3_wrapper pointer to FD_C_PPOCRv3Wrapper object
|
||||
* \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
|
||||
* \param[in] result The output OCR result will be writen to this structure.
|
||||
* \return true if the prediction successed, otherwise false.
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern FD_C_Bool FD_C_PPOCRv3WrapperPredict(
|
||||
__fd_keep FD_C_PPOCRv3Wrapper* fd_c_ppocrv3_wrapper, FD_C_Mat img,
|
||||
FD_C_OCRResult* result);
|
||||
|
||||
/** \brief Check if the model is initialized successfully
|
||||
*
|
||||
* \param[in] fd_c_ppocrv3_wrapper pointer to FD_C_PPOCRv3Wrapper object
|
||||
*
|
||||
* \return Return a bool of value true if initialized successfully
|
||||
*/
|
||||
|
||||
OCR_DECLARE_INITIALIZED_FUNCTION(PPOCRv3, fd_c_ppocrv3_wrapper);
|
||||
|
||||
/** \brief BatchPredict the input image and get OCR result.
|
||||
*
|
||||
* \param[in] fd_c_ppocrv3_wrapper pointer to FD_C_PPOCRv3Wrapper object
|
||||
* \param[in] imgs The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
|
||||
* \param[in] batch_result The output list of OCR result will be writen to this structure.
|
||||
* \return true if the prediction successed, otherwise false.
|
||||
*/
|
||||
|
||||
FASTDEPLOY_CAPI_EXPORT extern FD_C_Bool FD_C_PPOCRv3WrapperBatchPredict(
|
||||
__fd_keep FD_C_PPOCRv3Wrapper* fd_c_ppocrv3_wrapper, FD_C_OneDimMat imgs,
|
||||
FD_C_OneDimOCRResult* batch_result);
|
||||
|
||||
|
||||
#ifdef __cplusplus
|
||||
} // extern "C"
|
||||
#endif
|
||||
Reference in New Issue
Block a user