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Add PaddleOCRv3 & PaddleOCRv2 Support (#139)
* Add PaddleOCR Support * Add PaddleOCR Support * Add PaddleOCRv3 Support * Add PaddleOCRv3 Support * Update README.md * Update README.md * Update README.md * Update README.md * Add PaddleOCRv3 Support * Add PaddleOCRv3 Supports * Add PaddleOCRv3 Suport * Fix Rec diff * Remove useless functions * Remove useless comments * Add PaddleOCRv2 Support
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yunyaoXYY
290
examples/vision/ocr/PPOCRSystemv3/cpp/infer.cc
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290
examples/vision/ocr/PPOCRSystemv3/cpp/infer.cc
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// 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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#ifdef WIN32
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const char sep = '\\';
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#else
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const char sep = '/';
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#endif
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void CpuInfer(const std::string& det_model_dir,
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const std::string& cls_model_dir,
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const std::string& rec_model_dir,
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const std::string& rec_label_file,
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const std::string& image_file) {
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auto det_model_file = det_model_dir + sep + "inference.pdmodel";
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auto det_params_file = det_model_dir + sep + "inference.pdiparams";
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auto cls_model_file = cls_model_dir + sep + "inference.pdmodel";
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auto cls_params_file = cls_model_dir + sep + "inference.pdiparams";
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auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
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auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
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auto rec_label = rec_label_file;
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fastdeploy::vision::ocr::DBDetector det_model;
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fastdeploy::vision::ocr::Classifier cls_model;
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fastdeploy::vision::ocr::Recognizer rec_model;
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if (!det_model_dir.empty()) {
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auto det_option = fastdeploy::RuntimeOption();
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det_option.UseCpu();
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det_model = fastdeploy::vision::ocr::DBDetector(
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det_model_file, det_params_file, det_option);
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if (!det_model.Initialized()) {
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std::cerr << "Failed to initialize det_model." << std::endl;
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return;
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}
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}
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if (!cls_model_dir.empty()) {
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auto cls_option = fastdeploy::RuntimeOption();
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cls_option.UseCpu();
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cls_model = fastdeploy::vision::ocr::Classifier(
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cls_model_file, cls_params_file, cls_option);
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if (!cls_model.Initialized()) {
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std::cerr << "Failed to initialize cls_model." << std::endl;
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return;
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}
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}
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if (!rec_model_dir.empty()) {
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auto rec_option = fastdeploy::RuntimeOption();
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rec_option.UseCpu();
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rec_model = fastdeploy::vision::ocr::Recognizer(
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rec_model_file, rec_params_file, rec_label, rec_option);
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if (!rec_model.Initialized()) {
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std::cerr << "Failed to initialize rec_model." << std::endl;
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return;
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}
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}
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auto ocrv3_app = fastdeploy::application::ocrsystem::PPOCRSystemv3(
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&det_model, &cls_model, &rec_model);
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auto im = cv::imread(image_file);
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auto im_bak = im.clone();
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fastdeploy::vision::OCRResult res;
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//开始预测
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if (!ocrv3_app.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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//输出预测信息
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std::cout << res.Str() << std::endl;
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//可视化
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auto vis_img = fastdeploy::vision::Visualize::VisOcr(im_bak, res);
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cv::imwrite("vis_result.jpg", vis_img);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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void GpuInfer(const std::string& det_model_dir,
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const std::string& cls_model_dir,
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const std::string& rec_model_dir,
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const std::string& rec_label_file,
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const std::string& image_file) {
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auto det_model_file = det_model_dir + sep + "inference.pdmodel";
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auto det_params_file = det_model_dir + sep + "inference.pdiparams";
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auto cls_model_file = cls_model_dir + sep + "inference.pdmodel";
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auto cls_params_file = cls_model_dir + sep + "inference.pdiparams";
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auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
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auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
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auto rec_label = rec_label_file;
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fastdeploy::vision::ocr::DBDetector det_model;
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fastdeploy::vision::ocr::Classifier cls_model;
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fastdeploy::vision::ocr::Recognizer rec_model;
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//准备模型
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if (!det_model_dir.empty()) {
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auto det_option = fastdeploy::RuntimeOption();
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det_option.UseGpu();
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det_model = fastdeploy::vision::ocr::DBDetector(
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det_model_file, det_params_file, det_option);
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if (!det_model.Initialized()) {
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std::cerr << "Failed to initialize det_model." << std::endl;
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return;
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}
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}
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if (!cls_model_dir.empty()) {
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auto cls_option = fastdeploy::RuntimeOption();
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cls_option.UseGpu();
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cls_model = fastdeploy::vision::ocr::Classifier(
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cls_model_file, cls_params_file, cls_option);
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if (!cls_model.Initialized()) {
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std::cerr << "Failed to initialize cls_model." << std::endl;
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return;
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}
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}
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if (!rec_model_dir.empty()) {
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auto rec_option = fastdeploy::RuntimeOption();
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rec_option.UseGpu();
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rec_model = fastdeploy::vision::ocr::Recognizer(
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rec_model_file, rec_params_file, rec_label, rec_option);
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if (!rec_model.Initialized()) {
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std::cerr << "Failed to initialize rec_model." << std::endl;
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return;
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}
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}
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auto ocrv3_app = fastdeploy::application::ocrsystem::PPOCRSystemv3(
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&det_model, &cls_model, &rec_model);
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auto im = cv::imread(image_file);
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auto im_bak = im.clone();
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fastdeploy::vision::OCRResult res;
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//开始预测
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if (!ocrv3_app.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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//输出预测信息
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std::cout << res.Str() << std::endl;
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//可视化
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auto vis_img = fastdeploy::vision::Visualize::VisOcr(im_bak, res);
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cv::imwrite("vis_result.jpg", vis_img);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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void TrtInfer(const std::string& det_model_dir,
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const std::string& cls_model_dir,
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const std::string& rec_model_dir,
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const std::string& rec_label_file,
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const std::string& image_file) {
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auto det_model_file = det_model_dir + sep + "inference.pdmodel";
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auto det_params_file = det_model_dir + sep + "inference.pdiparams";
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auto cls_model_file = cls_model_dir + sep + "inference.pdmodel";
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auto cls_params_file = cls_model_dir + sep + "inference.pdiparams";
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auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
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auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
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auto rec_label = rec_label_file;
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fastdeploy::vision::ocr::DBDetector det_model;
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fastdeploy::vision::ocr::Classifier cls_model;
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fastdeploy::vision::ocr::Recognizer rec_model;
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//准备模型
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if (!det_model_dir.empty()) {
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auto det_option = fastdeploy::RuntimeOption();
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det_option.UseGpu();
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det_option.UseTrtBackend();
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det_option.SetTrtInputShape("x", {1, 3, 50, 50}, {1, 3, 640, 640},
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{1, 3, 960, 960});
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det_model = fastdeploy::vision::ocr::DBDetector(
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det_model_file, det_params_file, det_option);
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if (!det_model.Initialized()) {
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std::cerr << "Failed to initialize det_model." << std::endl;
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return;
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}
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}
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if (!cls_model_dir.empty()) {
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auto cls_option = fastdeploy::RuntimeOption();
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cls_option.UseGpu();
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cls_option.UseTrtBackend();
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cls_option.SetTrtInputShape("x", {1, 3, 48, 192});
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cls_model = fastdeploy::vision::ocr::Classifier(
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cls_model_file, cls_params_file, cls_option);
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if (!cls_model.Initialized()) {
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std::cerr << "Failed to initialize cls_model." << std::endl;
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return;
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}
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}
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if (!rec_model_dir.empty()) {
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auto rec_option = fastdeploy::RuntimeOption();
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rec_option.UseGpu();
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rec_option.UseTrtBackend();
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rec_option.SetTrtInputShape("x", {1, 3, 48, 10}, {1, 3, 48, 320},
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{1, 3, 48, 2000});
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rec_model = fastdeploy::vision::ocr::Recognizer(
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rec_model_file, rec_params_file, rec_label, rec_option);
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if (!rec_model.Initialized()) {
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std::cerr << "Failed to initialize rec_model." << std::endl;
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return;
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}
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}
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auto ocrv3_app = fastdeploy::application::ocrsystem::PPOCRSystemv3(
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&det_model, &cls_model, &rec_model);
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auto im = cv::imread(image_file);
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auto im_bak = im.clone();
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fastdeploy::vision::OCRResult res;
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//开始预测
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if (!ocrv3_app.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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//输出预测信息
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std::cout << res.Str() << std::endl;
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//可视化
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auto vis_img = fastdeploy::vision::Visualize::VisOcr(im_bak, res);
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cv::imwrite("vis_result.jpg", vis_img);
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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 gpu; 2: run with gpu and use tensorrt backend."
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<< std::endl;
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return -1;
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}
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if (std::atoi(argv[6]) == 0) {
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CpuInfer(argv[1], argv[2], argv[3], argv[4], argv[5]);
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} else if (std::atoi(argv[6]) == 1) {
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GpuInfer(argv[1], argv[2], argv[3], argv[4], argv[5]);
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} else if (std::atoi(argv[6]) == 2) {
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TrtInfer(argv[1], argv[2], argv[3], argv[4], argv[5]);
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}
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return 0;
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}
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