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127 lines
4.3 KiB
C++
127 lines
4.3 KiB
C++
// Copyright (c) 2022 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 "fastdeploy/vision.h"
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void InitAndInfer(const std::string &det_model_file,
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const std::string &cls_model_file,
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const std::string &rec_model_file,
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const std::string &rec_label_file,
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const std::string &image_file,
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const fastdeploy::RuntimeOption &option,
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const fastdeploy::ModelFormat &format) {
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auto det_params_file = "";
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auto cls_params_file = "";
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auto rec_params_file = "";
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auto det_option = option;
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auto cls_option = option;
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auto rec_option = option;
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if (format == fastdeploy::ONNX) {
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std::cout << "ONNX Model" << std::endl;
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}
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auto det_model = fastdeploy::vision::ocr::DBDetector(
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det_model_file, det_params_file, det_option, format);
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auto cls_model = fastdeploy::vision::ocr::Classifier(
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cls_model_file, cls_params_file, cls_option, format);
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auto rec_model = fastdeploy::vision::ocr::Recognizer(
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rec_model_file, rec_params_file, rec_label_file, rec_option, format);
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if (format == fastdeploy::RKNN) {
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cls_model.GetPreprocessor().DisableNormalize();
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cls_model.GetPreprocessor().DisablePermute();
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det_model.GetPreprocessor().DisableNormalize();
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det_model.GetPreprocessor().DisablePermute();
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rec_model.GetPreprocessor().DisableNormalize();
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rec_model.GetPreprocessor().DisablePermute();
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}
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det_model.GetPreprocessor().SetStaticShapeInfer(true);
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rec_model.GetPreprocessor().SetStaticShapeInfer(true);
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assert(det_model.Initialized());
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assert(cls_model.Initialized());
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assert(rec_model.Initialized());
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// The classification model is optional, so the PP-OCR can also be connected
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// in series as follows auto ppocr_v3 =
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// fastdeploy::pipeline::PPOCRv3(&det_model, &rec_model);
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auto ppocr_v3 =
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fastdeploy::pipeline::PPOCRv3(&det_model, &cls_model, &rec_model);
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// When users enable static shape infer for rec model, the batch size of cls
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// and rec model must to be set to 1.
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ppocr_v3.SetClsBatchSize(1);
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ppocr_v3.SetRecBatchSize(1);
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if (!ppocr_v3.Initialized()) {
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std::cerr << "Failed to initialize PP-OCR." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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fastdeploy::vision::OCRResult result;
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if (!ppocr_v3.Predict(im, &result)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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std::cout << result.Str() << std::endl;
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auto vis_im = fastdeploy::vision::VisOcr(im, result);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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int main(int argc, char *argv[]) {
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if (argc < 7) {
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std::cout << "Usage: infer_demo path/to/det_model path/to/cls_model "
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"path/to/rec_model path/to/rec_label_file path/to/image "
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"run_option, "
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"e.g ./infer_demo ./ch_PP-OCRv3_det_infer "
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"./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer "
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"./ppocr_keys_v1.txt ./12.jpg 0"
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<< std::endl;
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std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
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"with ascend."
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<< std::endl;
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return -1;
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}
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fastdeploy::RuntimeOption option;
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fastdeploy::ModelFormat format;
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int flag = std::atoi(argv[6]);
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if (flag == 0) {
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option.UseCpu();
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format = fastdeploy::ONNX;
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} else if (flag == 1) {
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option.UseRKNPU2();
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format = fastdeploy::RKNN;
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}
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std::string det_model_dir = argv[1];
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std::string cls_model_dir = argv[2];
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std::string rec_model_dir = argv[3];
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std::string rec_label_file = argv[4];
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std::string test_image = argv[5];
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InitAndInfer(det_model_dir, cls_model_dir, rec_model_dir, rec_label_file,
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test_image, option, format);
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return 0;
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}
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