mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-04 16:22:57 +08:00

* Add Huawei Ascend NPU deploy through PaddleLite CANN * Add NNAdapter interface for paddlelite * Modify Huawei Ascend Cmake * Update way for compiling Huawei Ascend NPU deployment * remove UseLiteBackend in UseCANN * Support compile python whlee * Change names of nnadapter API * Add nnadapter pybind and remove useless API * Support Python deployment on Huawei Ascend NPU * Add models suppor for ascend * Add PPOCR rec reszie for ascend * fix conflict for ascend * Rename CANN to Ascend * Rename CANN to Ascend * Improve ascend * fix ascend bug * improve ascend docs * improve ascend docs * improve ascend docs * Improve Ascend * Improve Ascend * Move ascend python demo * Imporve ascend * Improve ascend * Improve ascend * Improve ascend * Improve ascend * Imporve ascend * Imporve ascend * Improve ascend
103 lines
3.7 KiB
C++
Executable File
103 lines
3.7 KiB
C++
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_ascend_backends = {Backend::LITE};
|
|
}
|
|
|
|
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;
|
|
}
|
|
|
|
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
|