mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-06-03 21:53:39 +08:00
216 lines
7.9 KiB
C++
216 lines
7.9 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 <glog/logging.h>
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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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// #include <include/utility.h>
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#include <sys/stat.h>
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#include <gflags/gflags.h>
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DEFINE_bool(use_gpu, false, "Infering with GPU or CPU.");
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DEFINE_int32(gpu_id, 0, "Device id of GPU to execute.");
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DEFINE_int32(gpu_mem, 4000, "GPU id when infering with GPU.");
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DEFINE_int32(cpu_math_library_num_threads, 10, "Num of threads with CPU.");
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DEFINE_bool(use_mkldnn, false, "Whether use mkldnn with CPU.");
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DEFINE_string(image_dir, "", "Dir of input image.");
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DEFINE_string(det_model_dir, "", "Path of det inference model.");
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DEFINE_int32(max_side_len, 960, "max_side_len of input image.");
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DEFINE_double(det_db_thresh, 0.3, "Threshold of det_db_thresh.");
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DEFINE_double(det_db_box_thresh, 0.5, "Threshold of det_db_box_thresh.");
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DEFINE_double(det_db_unclip_ratio, 1.6, "Threshold of det_db_unclip_ratio.");
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DEFINE_bool(use_polygon_score, false, "Whether use polygon score.");
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DEFINE_bool(visualize, true, "Whether show the detection results.");
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DEFINE_bool(use_angle_cls, false, "Whether use use_angle_cls.");
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DEFINE_string(cls_model_dir, "", "Path of cls inference model.");
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DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh.");
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DEFINE_string(rec_model_dir, "", "Path of rec inference model.");
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DEFINE_string(char_list_file, "../../ppocr/utils/ppocr_keys_v1.txt", "Path of dictionary.");
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DEFINE_bool(use_tensorrt, false, "Whether use tensorrt.");
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DEFINE_bool(use_fp16, false, "Whether use fp16 when use tensorrt.");
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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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static bool PathExists(const std::string& path){
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#ifdef _WIN32
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struct _stat buffer;
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return (_stat(path.c_str(), &buffer) == 0);
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#else
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struct stat buffer;
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return (stat(path.c_str(), &buffer) == 0);
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#endif // !_WIN32
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}
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cv::Mat GetRotateCropImage(const cv::Mat &srcimage,
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std::vector<std::vector<int>> box) {
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cv::Mat image;
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srcimage.copyTo(image);
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std::vector<std::vector<int>> points = box;
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int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
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int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
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int left = int(*std::min_element(x_collect, x_collect + 4));
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int right = int(*std::max_element(x_collect, x_collect + 4));
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int top = int(*std::min_element(y_collect, y_collect + 4));
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int bottom = int(*std::max_element(y_collect, y_collect + 4));
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cv::Mat img_crop;
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image(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
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for (int i = 0; i < points.size(); i++) {
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points[i][0] -= left;
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points[i][1] -= top;
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}
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int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
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pow(points[0][1] - points[1][1], 2)));
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int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
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pow(points[0][1] - points[3][1], 2)));
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cv::Point2f pts_std[4];
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pts_std[0] = cv::Point2f(0., 0.);
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pts_std[1] = cv::Point2f(img_crop_width, 0.);
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pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
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pts_std[3] = cv::Point2f(0.f, img_crop_height);
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cv::Point2f pointsf[4];
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pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
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pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
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pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
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pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
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cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
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cv::Mat dst_img;
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cv::warpPerspective(img_crop, dst_img, M,
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cv::Size(img_crop_width, img_crop_height),
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cv::BORDER_REPLICATE);
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if (float(dst_img.rows) >= float(dst_img.cols) * 1.5) {
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cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth());
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cv::transpose(dst_img, srcCopy);
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cv::flip(srcCopy, srcCopy, 0);
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return srcCopy;
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} else {
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return dst_img;
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}
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}
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int main(int argc, char **argv) {
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// Parsing command-line
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google::ParseCommandLineFlags(&argc, &argv, true);
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if ((FLAGS_det_model_dir.empty() || FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) ||
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(FLAGS_use_angle_cls && FLAGS_cls_model_dir.empty())) {
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std::cout << "Usage[default]: ./ocr_system --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
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<< "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
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<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
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std::cout << "Usage[use angle cls]: ./ocr_system --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
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<< "--use_angle_cls=true "
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<< "--cls_model_dir=/PATH/TO/CLS_INFERENCE_MODEL/ "
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<< "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
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<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
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return -1;
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}
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if (!PathExists(FLAGS_image_dir)) {
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std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir << endl;
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exit(1);
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}
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std::vector<cv::String> cv_all_img_names;
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cv::glob(FLAGS_image_dir, cv_all_img_names);
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std::cout << "total images num: " << cv_all_img_names.size() << endl;
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DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
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FLAGS_gpu_mem, FLAGS_cpu_math_library_num_threads,
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FLAGS_use_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh,
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FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
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FLAGS_use_polygon_score, FLAGS_visualize,
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FLAGS_use_tensorrt, FLAGS_use_fp16);
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Classifier *cls = nullptr;
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if (FLAGS_use_angle_cls) {
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cls = new Classifier(FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
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FLAGS_gpu_mem, FLAGS_cpu_math_library_num_threads,
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FLAGS_use_mkldnn, FLAGS_cls_thresh,
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FLAGS_use_tensorrt, FLAGS_use_fp16);
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}
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CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
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FLAGS_gpu_mem, FLAGS_cpu_math_library_num_threads,
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FLAGS_use_mkldnn, FLAGS_char_list_file,
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FLAGS_use_tensorrt, FLAGS_use_fp16);
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auto start = std::chrono::system_clock::now();
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for (int i = 0; i < cv_all_img_names.size(); ++i) {
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LOG(INFO) << "The predict img: " << cv_all_img_names[i];
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cv::Mat srcimg = cv::imread(FLAGS_image_dir, cv::IMREAD_COLOR);
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if (!srcimg.data) {
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std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl;
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exit(1);
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}
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std::vector<std::vector<std::vector<int>>> boxes;
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det.Run(srcimg, boxes);
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cv::Mat crop_img;
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for (int j = 0; j < boxes.size(); j++) {
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crop_img = GetRotateCropImage(srcimg, boxes[j]);
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if (cls != nullptr) {
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crop_img = cls->Run(crop_img);
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}
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rec.Run(crop_img);
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}
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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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}
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return 0;
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}
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