Files
FastDeploy/fastdeploy/vision/ocr/ppocr/recognizer.cc
huangjianhui 6c4a08e416 [Other] PPOCR models support model clone function (#1072)
* Refactor PaddleSeg with preprocessor && postprocessor

* Fix bugs

* Delete redundancy code

* Modify by comments

* Refactor according to comments

* Add batch evaluation

* Add single test script

* Add ppliteseg single test script && fix eval(raise) error

* fix bug

* Fix evaluation segmentation.py batch predict

* Fix segmentation evaluation bug

* Fix evaluation segmentation bugs

* Update segmentation result docs

* Update old predict api and DisableNormalizeAndPermute

* Update resize segmentation label map with cv::INTER_NEAREST

* Add Model Clone function for PaddleClas && PaddleDet && PaddleSeg

* Add multi thread demo

* Add python model clone function

* Add multi thread python && C++ example

* Fix bug

* Update python && cpp multi_thread examples

* Add cpp && python directory

* Add README.md for examples

* Delete redundant code

* Create README_CN.md

* Rename README_CN.md to README.md

* Update README.md

* Update README.md

* Update VERSION_NUMBER

* Update requirements.txt

* Update README.md

* update version in doc:

* [Serving]Update Dockerfile (#1037)

Update Dockerfile

* Add license notice for RVM onnx model file (#1060)

* [Model] Add GPL-3.0 license (#1065)

Add GPL-3.0 license

* PPOCR model support model clone

* Update README.md

* Update PPOCRv2 && PPOCRv3 clone code

* Update PPOCR python __init__

* Add multi thread ocr example code

* Update README.md

* Update README.md

* Update ResNet50_vd_infer multi process code

* Add PPOCR multi process && thread example

* Update README.md

* Update README.md

* Update multi-thread docs

Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: leiqing <54695910+leiqing1@users.noreply.github.com>
Co-authored-by: heliqi <1101791222@qq.com>
Co-authored-by: WJJ1995 <wjjisloser@163.com>
2023-01-17 15:16:41 +08:00

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4.0 KiB
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Executable File

// 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/recognizer.h"
#include "fastdeploy/utils/perf.h"
#include "fastdeploy/vision/ocr/ppocr/utils/ocr_utils.h"
namespace fastdeploy {
namespace vision {
namespace ocr {
Recognizer::Recognizer() {}
Recognizer::Recognizer(const std::string& model_file,
const std::string& params_file,
const std::string& label_path,
const RuntimeOption& custom_option,
const ModelFormat& model_format):postprocessor_(label_path) {
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};
}
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 Recognizer::Initialize() {
if (!InitRuntime()) {
FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
return false;
}
return true;
}
std::unique_ptr<Recognizer> Recognizer::Clone() const {
std::unique_ptr<Recognizer> clone_model = utils::make_unique<Recognizer>(Recognizer(*this));
clone_model->SetRuntime(clone_model->CloneRuntime());
return clone_model;
}
bool Recognizer::Predict(const cv::Mat& img, std::string* text, float* rec_score) {
std::vector<std::string> texts(1);
std::vector<float> rec_scores(1);
bool success = BatchPredict({img}, &texts, &rec_scores);
if (!success) {
return success;
}
*text = std::move(texts[0]);
*rec_score = rec_scores[0];
return true;
}
bool Recognizer::BatchPredict(const std::vector<cv::Mat>& images,
std::vector<std::string>* texts, std::vector<float>* rec_scores) {
return BatchPredict(images, texts, rec_scores, 0, images.size(), {});
}
bool Recognizer::BatchPredict(const std::vector<cv::Mat>& images,
std::vector<std::string>* texts, std::vector<float>* rec_scores,
size_t start_index, size_t end_index, const std::vector<int>& indices) {
size_t total_size = images.size();
if (indices.size() != 0 && indices.size() != total_size) {
FDERROR << "indices.size() should be 0 or images.size()." << std::endl;
return false;
}
std::vector<FDMat> fd_images = WrapMat(images);
if (!preprocessor_.Run(&fd_images, &reused_input_tensors_, start_index, end_index, indices)) {
FDERROR << "Failed to preprocess the input image." << std::endl;
return false;
}
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_, texts, rec_scores, start_index, total_size, indices)) {
FDERROR << "Failed to postprocess the inference cls_results by runtime." << std::endl;
return false;
}
return true;
}
} // namesapce ocr
} // namespace vision
} // namespace fastdeploy