93 lines
3.0 KiB
C++
93 lines
3.0 KiB
C++
// Copyright (c) 2020 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 "glog/logging.h"
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#include "omp.h"
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#include "opencv2/core.hpp"
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/imgproc.hpp"
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#include <chrono>
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#include <iomanip>
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#include <iostream>
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#include <ostream>
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#include <vector>
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#include <cstring>
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#include <fstream>
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#include <numeric>
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#include <include/config.h>
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#include <include/ocr_det.h>
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#include <include/ocr_rec.h>
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using namespace std;
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using namespace cv;
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using namespace PaddleOCR;
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int main(int argc, char **argv) {
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if (argc < 3) {
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std::cerr << "[ERROR] usage: " << argv[0]
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<< " configure_filepath image_path\n";
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exit(1);
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}
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OCRConfig config(argv[1]);
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config.PrintConfigInfo();
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std::string img_path(argv[2]);
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cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
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if (!srcimg.data) {
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std::cerr << "[ERROR] image read failed! image path: " << img_path << "\n";
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exit(1);
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}
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DBDetector det(config.det_model_dir, config.use_gpu, config.gpu_id,
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config.gpu_mem, config.cpu_math_library_num_threads,
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config.use_mkldnn, config.max_side_len, config.det_db_thresh,
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config.det_db_box_thresh, config.det_db_unclip_ratio,
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config.visualize, config.use_tensorrt, config.use_fp16);
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Classifier *cls = nullptr;
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if (config.use_angle_cls == true) {
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cls = new Classifier(config.cls_model_dir, config.use_gpu, config.gpu_id,
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config.gpu_mem, config.cpu_math_library_num_threads,
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config.use_mkldnn, config.cls_thresh,
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config.use_tensorrt, config.use_fp16);
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}
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CRNNRecognizer rec(config.rec_model_dir, config.use_gpu, config.gpu_id,
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config.gpu_mem, config.cpu_math_library_num_threads,
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config.use_mkldnn, config.char_list_file,
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config.use_tensorrt, config.use_fp16);
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auto start = std::chrono::system_clock::now();
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std::vector<std::vector<std::vector<int>>> boxes;
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det.Run(srcimg, boxes);
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rec.Run(boxes, srcimg, cls);
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auto end = std::chrono::system_clock::now();
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auto duration =
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std::chrono::duration_cast<std::chrono::microseconds>(end - start);
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std::cout << "Cost "
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<< double(duration.count()) *
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std::chrono::microseconds::period::num /
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std::chrono::microseconds::period::den
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<< "s" << std::endl;
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return 0;
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}
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