80 lines
2.4 KiB
C++
80 lines
2.4 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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#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 InitAndInfer(const std::string &cls_model_dir,
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const std::string &image_file,
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const fastdeploy::RuntimeOption &option) {
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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 cls_option = option;
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auto cls_model = fastdeploy::vision::ocr::Classifier(
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cls_model_file, cls_params_file, cls_option);
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assert(cls_model.Initialized());
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// Parameters settings for pre and post processing of Cls Model.
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cls_model.GetPostprocessor().SetClsThresh(0.9);
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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 result;
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if (!cls_model.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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// User can infer a batch of images by following code.
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// if (!cls_model.BatchPredict({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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}
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int main(int argc, char *argv[]) {
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if (argc < 4) {
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std::cout << "Usage: infer_demo path/to/cls_model path/to/image "
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"run_option, "
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"e.g ./infer_demo ./ch_ppocr_mobile_v2.0_cls_infer ./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;."
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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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int flag = std::atoi(argv[3]);
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if (flag == 0) {
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option.UseCpu();
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} else if (flag == 1) {
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option.UseGpu();
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}
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std::string cls_model_dir = argv[1];
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std::string test_image = argv[2];
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InitAndInfer(cls_model_dir, test_image, option);
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return 0;
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}
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