111 lines
3.6 KiB
C++
111 lines
3.6 KiB
C++
// Copyright (c) 2020 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 <include/ocr_cls.h>
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namespace PaddleOCR {
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cv::Mat Classifier::Run(cv::Mat &img) {
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cv::Mat src_img;
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img.copyTo(src_img);
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cv::Mat resize_img;
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std::vector<int> rec_image_shape = {3, 48, 192};
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int index = 0;
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float wh_ratio = float(img.cols) / float(img.rows);
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this->resize_op_.Run(img, resize_img, rec_image_shape);
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this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
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this->is_scale_);
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std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
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this->permute_op_.Run(&resize_img, input.data());
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// Inference.
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if (this->use_zero_copy_run_) {
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auto input_names = this->predictor_->GetInputNames();
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auto input_t = this->predictor_->GetInputTensor(input_names[0]);
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input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
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input_t->copy_from_cpu(input.data());
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this->predictor_->ZeroCopyRun();
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} else {
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paddle::PaddleTensor input_t;
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input_t.shape = {1, 3, resize_img.rows, resize_img.cols};
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input_t.data =
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paddle::PaddleBuf(input.data(), input.size() * sizeof(float));
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input_t.dtype = PaddleDType::FLOAT32;
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std::vector<paddle::PaddleTensor> outputs;
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this->predictor_->Run({input_t}, &outputs, 1);
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}
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std::vector<float> softmax_out;
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std::vector<int64_t> label_out;
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auto output_names = this->predictor_->GetOutputNames();
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auto softmax_out_t = this->predictor_->GetOutputTensor(output_names[0]);
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auto label_out_t = this->predictor_->GetOutputTensor(output_names[1]);
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auto softmax_shape_out = softmax_out_t->shape();
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auto label_shape_out = label_out_t->shape();
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int softmax_out_num =
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std::accumulate(softmax_shape_out.begin(), softmax_shape_out.end(), 1,
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std::multiplies<int>());
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int label_out_num =
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std::accumulate(label_shape_out.begin(), label_shape_out.end(), 1,
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std::multiplies<int>());
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softmax_out.resize(softmax_out_num);
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label_out.resize(label_out_num);
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softmax_out_t->copy_to_cpu(softmax_out.data());
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label_out_t->copy_to_cpu(label_out.data());
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int label = label_out[0];
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float score = softmax_out[label];
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// std::cout << "\nlabel "<<label<<" score: "<<score;
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if (label % 2 == 1 && score > this->cls_thresh) {
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cv::rotate(src_img, src_img, 1);
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}
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return src_img;
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}
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void Classifier::LoadModel(const std::string &model_dir) {
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AnalysisConfig config;
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config.SetModel(model_dir + "/model", model_dir + "/params");
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if (this->use_gpu_) {
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config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
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} else {
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config.DisableGpu();
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if (this->use_mkldnn_) {
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config.EnableMKLDNN();
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}
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config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
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}
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// false for zero copy tensor
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config.SwitchUseFeedFetchOps(!this->use_zero_copy_run_);
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// true for multiple input
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config.SwitchSpecifyInputNames(true);
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config.SwitchIrOptim(true);
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config.EnableMemoryOptim();
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config.DisableGlogInfo();
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this->predictor_ = CreatePaddlePredictor(config);
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}
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} // namespace PaddleOCR
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