// 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 namespace PaddleOCR { cv::Mat Classifier::Run(cv::Mat &img) { cv::Mat src_img; img.copyTo(src_img); cv::Mat resize_img; std::vector 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, this->use_tensorrt_, cls_image_shape); this->normalize_op_.Run(&resize_img, this->mean_, this->scale_, this->is_scale_); std::vector input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f); this->permute_op_.Run(&resize_img, input.data()); // Inference. 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(); std::vector softmax_out; std::vector label_out; auto output_names = this->predictor_->GetOutputNames(); auto softmax_out_t = this->predictor_->GetOutputHandle(output_names[0]); 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()); softmax_out.resize(softmax_out_num); softmax_out_t->CopyToCpu(softmax_out.data()); 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; config.SetModel(model_dir + "/inference.pdmodel", model_dir + "/inference.pdiparams"); if (this->use_gpu_) { config.EnableUseGpu(this->gpu_mem_, this->gpu_id_); if (this->use_tensorrt_) { auto precision = paddle_infer::Config::Precision::kFloat32; if (this->precision_ == "fp16") { precision = paddle_infer::Config::Precision::kHalf; } if (this->precision_ == "int8") { precision = paddle_infer::Config::Precision::kInt8; } config.EnableTensorRtEngine( 1 << 20, 10, 3, precision, false, false); } } else { config.DisableGpu(); if (this->use_mkldnn_) { config.EnableMKLDNN(); } config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_); } // false for zero copy tensor config.SwitchUseFeedFetchOps(false); // true for multiple input config.SwitchSpecifyInputNames(true); config.SwitchIrOptim(true); config.EnableMemoryOptim(); config.DisableGlogInfo(); this->predictor_ = CreatePredictor(config); } } // namespace PaddleOCR