FastDeploy/tutorials/multi_thread/cpp/pipeline/multi_thread_ocr.cc

// Copyright (c) 2022 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 <thread>
#include "fastdeploy/vision.h"
#ifdef WIN32
const char sep = '\\';
#else
const char sep = '/';
#endif

void Predict(fastdeploy::pipeline::PPOCRv3 *model, int thread_id, const std::vector<std::string>& images) {
  for (auto const &image_file : images) {
      auto im = cv::imread(image_file);

      fastdeploy::vision::OCRResult res;
      if (!model->Predict(im, &res)) {
        std::cerr << "Failed to predict." << std::endl;
        return;
      }

      // print res
      std::cout << "Thread Id: " << thread_id << std::endl;
      std::cout << res.Str() << std::endl;
  }
}

void GetImageList(std::vector<std::vector<std::string>>* image_list, const std::string& image_file_path, int thread_num){
  std::vector<cv::String> images;
  cv::glob(image_file_path, images, false);
  // number of image files in images folder
  size_t count = images.size(); 
  size_t num = count / thread_num;
  for (int i = 0; i < thread_num; i++) {
    std::vector<std::string> temp_list;
    if (i == thread_num - 1) {
      for (size_t j = i*num; j < count; j++){
        temp_list.push_back(images[j]);
      }
    } else {
      for (size_t j = 0; j < num; j++){
        temp_list.push_back(images[i * num + j]);
      }
    }
    (*image_list)[i] = temp_list;
  }
}

void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model_dir, const std::string& rec_model_dir, const std::string& rec_label_file, const std::string& image_file_path, const fastdeploy::RuntimeOption& option, int thread_num) {
  auto det_model_file = det_model_dir + sep + "inference.pdmodel";
  auto det_params_file = det_model_dir + sep + "inference.pdiparams";

  auto cls_model_file = cls_model_dir + sep + "inference.pdmodel";
  auto cls_params_file = cls_model_dir + sep + "inference.pdiparams";

  auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
  auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";

  auto det_option = option;
  auto cls_option = option;
  auto rec_option = option;

  // The cls and rec model can inference a batch of images now.
  // User could initialize the inference batch size and set them after create PP-OCR model.
  int cls_batch_size = 1;
  int rec_batch_size = 6;

  // If use TRT backend, the dynamic shape will be set as follow.
  // We recommend that users set the length and height of the detection model to a multiple of 32.
  // We also recommend that users set the Trt input shape as follow.
  det_option.SetTrtInputShape("x", {1, 3, 64,64}, {1, 3, 640, 640},
                                {1, 3, 960, 960});
  cls_option.SetTrtInputShape("x", {1, 3, 48, 10}, {cls_batch_size, 3, 48, 320}, {cls_batch_size, 3, 48, 1024});
  rec_option.SetTrtInputShape("x", {1, 3, 48, 10}, {rec_batch_size, 3, 48, 320},
                                {rec_batch_size, 3, 48, 2304});
  
  // Users could save TRT cache file to disk as follow. 
  // det_option.SetTrtCacheFile(det_model_dir + sep + "det_trt_cache.trt");
  // cls_option.SetTrtCacheFile(cls_model_dir + sep + "cls_trt_cache.trt");
  // rec_option.SetTrtCacheFile(rec_model_dir + sep + "rec_trt_cache.trt");

  auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, det_option);
  auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, cls_option);
  auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, rec_option);

  assert(det_model.Initialized());
  assert(cls_model.Initialized());
  assert(rec_model.Initialized());

  // The classification model is optional, so the PP-OCR can also be connected in series as follows
  // auto ppocr_v3 = fastdeploy::pipeline::PPOCRv3(&det_model, &rec_model);
  auto ppocr_v3 = fastdeploy::pipeline::PPOCRv3(&det_model, &cls_model, &rec_model);
  
  // Set inference batch size for cls model and rec model, the value could be -1 and 1 to positive infinity.
  // When inference batch size is set to -1, it means that the inference batch size 
  // of the cls and rec models will be the same as the number of boxes detected by the det model. 
  ppocr_v3.SetClsBatchSize(cls_batch_size);
  ppocr_v3.SetRecBatchSize(rec_batch_size); 

  if(!ppocr_v3.Initialized()){
    std::cerr << "Failed to initialize PP-OCR." << std::endl;
    return;
  }


  std::vector<decltype(ppocr_v3.Clone())> models;
  for (int i = 0; i < thread_num; ++i) {
    models.emplace_back(std::move(ppocr_v3.Clone()));
  }

  std::vector<std::vector<std::string>> image_list(thread_num);
  GetImageList(&image_list, image_file_path, thread_num);

  std::vector<std::thread> threads;
  for (int i = 0; i < thread_num; ++i) {
    threads.emplace_back(Predict, models[i].get(), i, image_list[i]);
  }

  for (int i = 0; i < thread_num; ++i) {
    threads[i].join();
  }
}

int main(int argc, char* argv[]) {
  if (argc < 7) {
    std::cout << "Usage: infer_demo path/to/det_model path/to/cls_model "
                 "path/to/rec_model path/to/rec_label_file path/to/image "
                 "run_option thread_num,"
                 "e.g ./infer_demo ./ch_PP-OCRv3_det_infer "
                 "./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer "
                 "./ppocr_keys_v1.txt ./12.jpg 0 3"
              << std::endl;
    std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with gpu and use Paddle-TRT; 4: run with kunlunxin."
              << std::endl;
    return -1;
  }

  fastdeploy::RuntimeOption option;
  int flag = std::atoi(argv[6]);

  if (flag == 0) {
    option.UseCpu(); 
  } else if (flag == 1) {
    option.UseGpu();
  } else if (flag == 2) {
    option.UseGpu();
    option.UseTrtBackend();
  } else if (flag == 3) {
    option.UseGpu();
    option.UseTrtBackend();
    option.EnablePaddleTrtCollectShape();
    option.EnablePaddleToTrt();
  } else if (flag == 4) {
    option.UseKunlunXin();
  }

  std::string det_model_dir = argv[1];
  std::string cls_model_dir = argv[2];
  std::string rec_model_dir = argv[3];
  std::string rec_label_file = argv[4];
  std::string image_file_path = argv[5];
  int thread_num = std::atoi(argv[7]);
  InitAndInfer(det_model_dir, cls_model_dir, rec_model_dir, rec_label_file, image_file_path, option, thread_num);
  return 0;
}