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dbdetector.cc
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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/dbdetector.h"
#include "fastdeploy/utils/perf.h"
#include "fastdeploy/vision/ocr/ppocr/utils/ocr_utils.h"
namespace fastdeploy {
namespace vision {
namespace ocr {
DBDetector::DBDetector() {}
DBDetector::DBDetector(const std::string& model_file,
const std::string& params_file,
const RuntimeOption& custom_option,
const ModelFormat& model_format) {
if (model_format == ModelFormat::ONNX) {
valid_cpu_backends = {Backend::ORT, Backend::OPENVINO};
valid_gpu_backends = {Backend::ORT, Backend::TRT};
} else {
valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::OPENVINO,
Backend::LITE};
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
valid_kunlunxin_backends = {Backend::LITE};
valid_ascend_backends = {Backend::LITE};
valid_sophgonpu_backends = {Backend::SOPHGOTPU};
valid_rknpu_backends = {Backend::RKNPU2};
}
runtime_option = custom_option;
runtime_option.model_format = model_format;
runtime_option.model_file = model_file;
runtime_option.params_file = params_file;
initialized = Initialize();
}
// Init
bool DBDetector::Initialize() {
if (!InitRuntime()) {
FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
return false;
}
return true;
}
std::unique_ptr<DBDetector> DBDetector::Clone() const {
std::unique_ptr<DBDetector> clone_model =
utils::make_unique<DBDetector>(DBDetector(*this));
clone_model->SetRuntime(clone_model->CloneRuntime());
return clone_model;
}
bool DBDetector::Predict(const cv::Mat& img,
std::vector<std::vector<std::array<int, 2>>>* boxes_result) {
std::vector<std::vector<std::vector<std::array<int, 2>>>> det_results;
if (!BatchPredict({img}, &det_results)) {
return false;
}
*boxes_result = std::move(det_results[0]);
return true;
}
bool DBDetector::Predict(const cv::Mat& img, vision::OCRResult* ocr_result) {
if (!Predict(img, &(ocr_result->boxes))) {
return false;
}
return true;
}
bool DBDetector::BatchPredict(const std::vector<cv::Mat>& images,
std::vector<vision::OCRResult>* ocr_results) {
std::vector<std::vector<std::vector<std::array<int, 2>>>> det_results;
if (!BatchPredict(images, &det_results)) {
return false;
}
ocr_results->resize(det_results.size());
for (int i = 0; i < det_results.size(); i++) {
(*ocr_results)[i].boxes = std::move(det_results[i]);
}
return true;
}
bool DBDetector::BatchPredict(
const std::vector<cv::Mat>& images,
std::vector<std::vector<std::vector<std::array<int, 2>>>>* det_results) {
std::vector<FDMat> fd_images = WrapMat(images);
if (!preprocessor_.Run(&fd_images, &reused_input_tensors_)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
}
auto batch_det_img_info = preprocessor_.GetBatchImgInfo();
reused_input_tensors_[0].name = InputInfoOfRuntime(0).name;
if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
FDERROR << "Failed to inference by runtime." << std::endl;
return false;
}
if (!postprocessor_.Run(reused_output_tensors_, det_results,
*batch_det_img_info)) {
FDERROR << "Failed to postprocess the inference cls_results by runtime."
<< std::endl;
return false;
}
return true;
}
} // namespace ocr
} // namespace vision
} // namespace fastdeploy