mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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db9739a76e
* 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 * fix * refine predict api
120 lines
4.9 KiB
C++
120 lines
4.9 KiB
C++
// 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/classification/ppcls/model.h"
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#include "fastdeploy_capi/types_internal.h"
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#ifdef __cplusplus
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extern "C" {
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#endif
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FD_C_PaddleClasModelWrapper* FD_C_CreatePaddleClasModelWrapper(
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const char* model_file, const char* params_file, const char* config_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_PaddleClasModelWrapper* fd_c_paddleclas_model_wrapper =
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new FD_C_PaddleClasModelWrapper();
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fd_c_paddleclas_model_wrapper->paddleclas_model =
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std::unique_ptr<fastdeploy::vision::classification::PaddleClasModel>(
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new fastdeploy::vision::classification::PaddleClasModel(
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std::string(model_file), std::string(params_file),
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std::string(config_file), *runtime_option,
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static_cast<fastdeploy::ModelFormat>(model_format)));
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return fd_c_paddleclas_model_wrapper;
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}
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void FD_C_DestroyPaddleClasModelWrapper(
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FD_C_PaddleClasModelWrapper* fd_c_paddleclas_model_wrapper) {
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delete fd_c_paddleclas_model_wrapper;
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}
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FD_C_Bool FD_C_PaddleClasModelWrapperPredict(
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FD_C_PaddleClasModelWrapper* fd_c_paddleclas_model_wrapper, FD_C_Mat img,
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FD_C_ClassifyResult* fd_c_classify_result) {
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cv::Mat* im = reinterpret_cast<cv::Mat*>(img);
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auto& paddleclas_model = CHECK_AND_CONVERT_FD_TYPE(
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PaddleClasModelWrapper, fd_c_paddleclas_model_wrapper);
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FD_C_ClassifyResultWrapper* fd_c_classify_result_wrapper =
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FD_C_CreateClassifyResultWrapper();
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auto& classify_result = CHECK_AND_CONVERT_FD_TYPE(
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ClassifyResultWrapper, fd_c_classify_result_wrapper);
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bool successful = paddleclas_model->Predict(im, classify_result.get());
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if (successful) {
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FD_C_ClassifyResult* res =
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FD_C_ClassifyResultWrapperGetData(fd_c_classify_result_wrapper);
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*fd_c_classify_result = *res;
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}
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return successful;
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}
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FD_C_Bool FD_C_PaddleClasModelWrapperInitialized(
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FD_C_PaddleClasModelWrapper* fd_c_paddleclas_model_wrapper) {
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auto& paddleclas_model = CHECK_AND_CONVERT_FD_TYPE(
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PaddleClasModelWrapper, fd_c_paddleclas_model_wrapper);
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return paddleclas_model->Initialized();
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}
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FD_C_ClassifyResult* FD_C_ClassifyResultToC(
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fastdeploy::vision::ClassifyResult* classify_result) {
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// Internal use, transfer fastdeploy::vision::ClassifyResult to
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// FD_C_ClassifyResult
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FD_C_ClassifyResult* fd_c_classify_result_data = new FD_C_ClassifyResult();
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// copy label_ids
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fd_c_classify_result_data->label_ids.size = classify_result->label_ids.size();
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fd_c_classify_result_data->label_ids.data =
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new int32_t[fd_c_classify_result_data->label_ids.size];
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memcpy(fd_c_classify_result_data->label_ids.data,
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classify_result->label_ids.data(),
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sizeof(int32_t) * fd_c_classify_result_data->label_ids.size);
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// copy scores
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fd_c_classify_result_data->scores.size = classify_result->scores.size();
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fd_c_classify_result_data->scores.data =
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new float[fd_c_classify_result_data->scores.size];
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memcpy(fd_c_classify_result_data->scores.data, classify_result->scores.data(),
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sizeof(float) * fd_c_classify_result_data->scores.size);
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fd_c_classify_result_data->type =
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static_cast<FD_C_ResultType>(classify_result->type);
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return fd_c_classify_result_data;
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}
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FD_C_Bool FD_C_PaddleClasModelWrapperBatchPredict(
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FD_C_PaddleClasModelWrapper* fd_c_paddleclas_model_wrapper,
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FD_C_OneDimMat imgs, FD_C_OneDimClassifyResult* results) {
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std::vector<cv::Mat> imgs_vec;
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std::vector<fastdeploy::vision::ClassifyResult> 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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}
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auto& paddleclas_model = CHECK_AND_CONVERT_FD_TYPE(
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PaddleClasModelWrapper, fd_c_paddleclas_model_wrapper);
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bool successful = paddleclas_model->BatchPredict(imgs_vec, &results_out);
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if (successful) {
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// copy results back to FD_C_OneDimClassifyResult
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results->size = results_out.size();
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results->data = new FD_C_ClassifyResult[results->size];
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for (int i = 0; i < results_out.size(); i++) {
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results->data[i] = *FD_C_ClassifyResultToC(&results_out[i]);
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}
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}
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return successful;
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}
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#ifdef __cplusplus
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}
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#endif
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