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// Copyright (c) 2020 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 <include/ocr_cls.h>
namespace PaddleOCR {
cv::Mat Classifier::Run(cv::Mat &img) {
cv::Mat src_img;
img.copyTo(src_img);
cv::Mat resize_img;
std::vector<int> cls_image_shape = {3, 48, 192};
int index = 0;
float wh_ratio = float(img.cols) / float(img.rows);
this->resize_op_.Run(img, resize_img, cls_image_shape);
this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
this->is_scale_);
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
this->permute_op_.Run(&resize_img, input.data());
// Inference.
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auto input_names = this->predictor_->GetInputNames();
auto input_t = this->predictor_->GetInputHandle(input_names[0]);
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
input_t->CopyFromCpu(input.data());
this->predictor_->Run();
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std::vector<float> softmax_out;
std::vector<int64_t> label_out;
auto output_names = this->predictor_->GetOutputNames();
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auto softmax_out_t = this->predictor_->GetOutputHandle(output_names[0]);
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auto softmax_shape_out = softmax_out_t->shape();
int softmax_out_num =
std::accumulate(softmax_shape_out.begin(), softmax_shape_out.end(), 1,
std::multiplies<int>());
softmax_out.resize(softmax_out_num);
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softmax_out_t->CopyToCpu(softmax_out.data());
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float score = 0;
int label = 0;
for (int i = 0; i < softmax_out_num; i++) {
if (softmax_out[i] > score) {
score = softmax_out[i];
label = i;
}
}
if (label % 2 == 1 && score > this->cls_thresh) {
cv::rotate(src_img, src_img, 1);
}
return src_img;
}
void Classifier::LoadModel(const std::string &model_dir) {
AnalysisConfig config;
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config.SetModel(model_dir + "/inference.pdmodel",
model_dir + "/inference.pdiparams");
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if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
if (this->use_tensorrt_) {
config.EnableTensorRtEngine(
1 << 20, 10, 3,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
false, false);
}
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} else {
config.DisableGpu();
if (this->use_mkldnn_) {
config.EnableMKLDNN();
}
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_)
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}
// false for zero copy tensor
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config.SwitchUseFeedFetchOps(false);
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// true for multiple input
config.SwitchSpecifyInputNames(true);
config.SwitchIrOptim(true);
config.EnableMemoryOptim();
config.DisableGlogInfo();
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this->predictor_ = CreatePredictor(config);
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}
} // namespace PaddleOCR