main.cpp

// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "glog/logging.h"
#include "omp.h"
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <vector>

#include <cstring>
#include <fstream>
#include <numeric>

#include <glog/logging.h>
#include <include/ocr_cls.h>
#include <include/ocr_det.h>
#include <include/ocr_rec.h>
#include <include/utility.h>
#include <sys/stat.h>

#include "auto_log/autolog.h"
#include <gflags/gflags.h>

DEFINE_bool(use_gpu, false, "Infering with GPU or CPU.");
DEFINE_int32(gpu_id, 0, "Device id of GPU to execute.");
DEFINE_int32(gpu_mem, 4000, "GPU id when infering with GPU.");
DEFINE_int32(cpu_threads, 10, "Num of threads with CPU.");
DEFINE_bool(enable_mkldnn, false, "Whether use mkldnn with CPU.");
DEFINE_bool(use_tensorrt, false, "Whether use tensorrt.");
DEFINE_string(precision, "fp32", "Precision be one of fp32/fp16/int8");
DEFINE_bool(benchmark, false, "Whether use benchmark.");
DEFINE_string(save_log_path, "./log_output/", "Save benchmark log path.");
// detection related
DEFINE_string(image_dir, "", "Dir of input image.");
DEFINE_string(det_model_dir, "", "Path of det inference model.");
DEFINE_int32(max_side_len, 960, "max_side_len of input image.");
DEFINE_double(det_db_thresh, 0.3, "Threshold of det_db_thresh.");
DEFINE_double(det_db_box_thresh, 0.6, "Threshold of det_db_box_thresh.");
DEFINE_double(det_db_unclip_ratio, 1.5, "Threshold of det_db_unclip_ratio.");
DEFINE_bool(use_polygon_score, false, "Whether use polygon score.");
DEFINE_bool(visualize, true, "Whether show the detection results.");
// classification related
DEFINE_bool(use_angle_cls, false, "Whether use use_angle_cls.");
DEFINE_string(cls_model_dir, "", "Path of cls inference model.");
DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh.");
// recognition related
DEFINE_string(rec_model_dir, "", "Path of rec inference model.");
DEFINE_int32(rec_batch_num, 6, "rec_batch_num.");
DEFINE_string(char_list_file, "../../ppocr/utils/ppocr_keys_v1.txt",
              "Path of dictionary.");

using namespace std;
using namespace cv;
using namespace PaddleOCR;

static bool PathExists(const std::string &path) {
#ifdef _WIN32
  struct _stat buffer;
  return (_stat(path.c_str(), &buffer) == 0);
#else
  struct stat buffer;
  return (stat(path.c_str(), &buffer) == 0);
#endif // !_WIN32
}

int main_det(std::vector<cv::String> cv_all_img_names) {
  std::vector<double> time_info = {0, 0, 0};
  DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
                 FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
                 FLAGS_max_side_len, FLAGS_det_db_thresh,
                 FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
                 FLAGS_use_polygon_score, FLAGS_visualize, FLAGS_use_tensorrt,
                 FLAGS_precision);

  for (int i = 0; i < cv_all_img_names.size(); ++i) {
    //       LOG(INFO) << "The predict img: " << cv_all_img_names[i];

    cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
    if (!srcimg.data) {
      std::cerr << "[ERROR] image read failed! image path: "
                << cv_all_img_names[i] << endl;
      exit(1);
    }
    std::vector<std::vector<std::vector<int>>> boxes;
    std::vector<double> det_times;

    det.Run(srcimg, boxes, &det_times);

    time_info[0] += det_times[0];
    time_info[1] += det_times[1];
    time_info[2] += det_times[2];

    if (FLAGS_benchmark) {
      cout << cv_all_img_names[i] << '\t';
      for (int n = 0; n < boxes.size(); n++) {
        for (int m = 0; m < boxes[n].size(); m++) {
          cout << boxes[n][m][0] << ' ' << boxes[n][m][1] << ' ';
        }
      }
      cout << endl;
    }
  }

  if (FLAGS_benchmark) {
    AutoLogger autolog("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
                       FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
                       FLAGS_precision, time_info, cv_all_img_names.size());
    autolog.report();
  }
  return 0;
}

int main_rec(std::vector<cv::String> cv_all_img_names) {
  std::vector<double> time_info = {0, 0, 0};

  std::string char_list_file = FLAGS_char_list_file;
  if (FLAGS_benchmark)
    char_list_file = FLAGS_char_list_file.substr(6);
  cout << "label file: " << char_list_file << endl;

  CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
                     FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
                     char_list_file, FLAGS_use_tensorrt, FLAGS_precision,
                     FLAGS_rec_batch_num);

  std::vector<cv::Mat> img_list;
  for (int i = 0; i < cv_all_img_names.size(); ++i) {
    LOG(INFO) << "The predict img: " << cv_all_img_names[i];

    cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
    if (!srcimg.data) {
      std::cerr << "[ERROR] image read failed! image path: "
                << cv_all_img_names[i] << endl;
      exit(1);
    }
    img_list.push_back(srcimg);
  }
  std::vector<double> rec_times;
  rec.Run(img_list, &rec_times);
  time_info[0] += rec_times[0];
  time_info[1] += rec_times[1];
  time_info[2] += rec_times[2];

  if (FLAGS_benchmark) {
    AutoLogger autolog("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt,
                       FLAGS_enable_mkldnn, FLAGS_cpu_threads,
                       FLAGS_rec_batch_num, "dynamic", FLAGS_precision,
                       time_info, cv_all_img_names.size());
    autolog.report();
  }
  return 0;
}

int main_system(std::vector<cv::String> cv_all_img_names) {
  std::vector<double> time_info_det = {0, 0, 0};
  std::vector<double> time_info_rec = {0, 0, 0};

  DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
                 FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
                 FLAGS_max_side_len, FLAGS_det_db_thresh,
                 FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
                 FLAGS_use_polygon_score, FLAGS_visualize, FLAGS_use_tensorrt,
                 FLAGS_precision);

  Classifier *cls = nullptr;
  if (FLAGS_use_angle_cls) {
    cls = new Classifier(FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
                         FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
                         FLAGS_cls_thresh, FLAGS_use_tensorrt, FLAGS_precision);
  }

  std::string char_list_file = FLAGS_char_list_file;
  if (FLAGS_benchmark)
    char_list_file = FLAGS_char_list_file.substr(6);
  cout << "label file: " << char_list_file << endl;

  CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
                     FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
                     char_list_file, FLAGS_use_tensorrt, FLAGS_precision,
                     FLAGS_rec_batch_num);

  for (int i = 0; i < cv_all_img_names.size(); ++i) {
    LOG(INFO) << "The predict img: " << cv_all_img_names[i];

    cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
    if (!srcimg.data) {
      std::cerr << "[ERROR] image read failed! image path: "
                << cv_all_img_names[i] << endl;
      exit(1);
    }
    std::vector<std::vector<std::vector<int>>> boxes;
    std::vector<double> det_times;
    std::vector<double> rec_times;

    det.Run(srcimg, boxes, &det_times);
    time_info_det[0] += det_times[0];
    time_info_det[1] += det_times[1];
    time_info_det[2] += det_times[2];

    std::vector<cv::Mat> img_list;
    for (int j = 0; j < boxes.size(); j++) {
      cv::Mat crop_img;
      crop_img = Utility::GetRotateCropImage(srcimg, boxes[j]);
      if (cls != nullptr) {
        crop_img = cls->Run(crop_img);
      }
      img_list.push_back(crop_img);
    }

    rec.Run(img_list, &rec_times);
    time_info_rec[0] += rec_times[0];
    time_info_rec[1] += rec_times[1];
    time_info_rec[2] += rec_times[2];
  }

  if (FLAGS_benchmark) {
    AutoLogger autolog_det("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
                           FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
                           FLAGS_precision, time_info_det,
                           cv_all_img_names.size());
    AutoLogger autolog_rec("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt,
                           FLAGS_enable_mkldnn, FLAGS_cpu_threads,
                           FLAGS_rec_batch_num, "dynamic", FLAGS_precision,
                           time_info_rec, cv_all_img_names.size());
    autolog_det.report();
    std::cout << endl;
    autolog_rec.report();
  }
  return 0;
}

void check_params(char *mode) {
  if (strcmp(mode, "det") == 0) {
    if (FLAGS_det_model_dir.empty() || FLAGS_image_dir.empty()) {
      std::cout << "Usage[det]: ./ppocr "
                   "--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
                << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
      exit(1);
    }
  }
  if (strcmp(mode, "rec") == 0) {
    if (FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) {
      std::cout << "Usage[rec]: ./ppocr "
                   "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
                << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
      exit(1);
    }
  }
  if (strcmp(mode, "system") == 0) {
    if ((FLAGS_det_model_dir.empty() || FLAGS_rec_model_dir.empty() ||
         FLAGS_image_dir.empty()) ||
        (FLAGS_use_angle_cls && FLAGS_cls_model_dir.empty())) {
      std::cout << "Usage[system without angle cls]: ./ppocr "
                   "--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
                << "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
                << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
      std::cout << "Usage[system with angle cls]: ./ppocr "
                   "--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
                << "--use_angle_cls=true "
                << "--cls_model_dir=/PATH/TO/CLS_INFERENCE_MODEL/ "
                << "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
                << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
      exit(1);
    }
  }
  if (FLAGS_precision != "fp32" && FLAGS_precision != "fp16" &&
      FLAGS_precision != "int8") {
    cout << "precison should be 'fp32'(default), 'fp16' or 'int8'. " << endl;
    exit(1);
  }
}

int main(int argc, char **argv) {
  if (argc <= 1 ||
      (strcmp(argv[1], "det") != 0 && strcmp(argv[1], "rec") != 0 &&
       strcmp(argv[1], "system") != 0)) {
    std::cout << "Please choose one mode of [det, rec, system] !" << std::endl;
    return -1;
  }
  std::cout << "mode: " << argv[1] << endl;

  // Parsing command-line
  google::ParseCommandLineFlags(&argc, &argv, true);
  check_params(argv[1]);

  if (!PathExists(FLAGS_image_dir)) {
    std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir
              << endl;
    exit(1);
  }

  std::vector<cv::String> cv_all_img_names;
  cv::glob(FLAGS_image_dir, cv_all_img_names);
  std::cout << "total images num: " << cv_all_img_names.size() << endl;

  if (strcmp(argv[1], "det") == 0) {
    return main_det(cv_all_img_names);
  }
  if (strcmp(argv[1], "rec") == 0) {
    return main_rec(cv_all_img_names);
  }
  if (strcmp(argv[1], "system") == 0) {
    return main_system(cv_all_img_names);
  }
}