PaddleX/deploy/cpp/demo/detector.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 <algorithm>
#include <chrono>
#include <fstream>
#include <iostream>
#include <string>
#include <vector>
#include <utility>
#include <omp.h>

#include "include/paddlex/paddlex.h"
#include "include/paddlex/visualize.h"

using namespace std::chrono;

DEFINE_string(model_dir, "", "Path of inference model");
DEFINE_bool(use_gpu, false, "Infering with GPU or CPU");
DEFINE_bool(use_trt, false, "Infering with TensorRT");
DEFINE_int32(gpu_id, 0, "GPU card id");
DEFINE_string(key, "", "key of encryption");
DEFINE_string(image, "", "Path of test image file");
DEFINE_string(image_list, "", "Path of test image list file");
DEFINE_string(save_dir, "output", "Path to save visualized image");
DEFINE_int32(batch_size, 1, "Batch size of infering");
DEFINE_double(threshold, 0.5, "The minimum scores of target boxes which are shown");
DEFINE_int32(thread_num, omp_get_num_procs(), "Number of preprocessing threads");

int main(int argc, char** argv) {
  // 解析命令行参数
  google::ParseCommandLineFlags(&argc, &argv, true);
  
  if (FLAGS_model_dir == "") {
    std::cerr << "--model_dir need to be defined" << std::endl;
    return -1;
  }
  if (FLAGS_image == "" & FLAGS_image_list == "") {
    std::cerr << "--image or --image_list need to be defined" << std::endl;
    return -1;
  }
  std::cout << "Thread num: " << FLAGS_thread_num << std::endl;
  // 加载模型
  PaddleX::Model model;
  model.Init(FLAGS_model_dir, FLAGS_use_gpu, FLAGS_use_trt, FLAGS_gpu_id, FLAGS_key, FLAGS_batch_size);

  double total_running_time_s = 0.0;
  double total_imread_time_s = 0.0;
  int imgs = 1;
  auto colormap = PaddleX::GenerateColorMap(model.labels.size());
  std::string save_dir = "output";
  // 进行预测
  if (FLAGS_image_list != "") {
    std::ifstream inf(FLAGS_image_list);
    if (!inf) {
      std::cerr << "Fail to open file " << FLAGS_image_list << std::endl;
      return -1;
    }
    std::string image_path;
    std::vector<std::string> image_paths;
    while (getline(inf, image_path)) {
      image_paths.push_back(image_path);
    }
    imgs = image_paths.size();
    for(int i = 0; i < image_paths.size(); i += FLAGS_batch_size) {
      auto start = system_clock::now();
      int im_vec_size = std::min((int)image_paths.size(), i + FLAGS_batch_size);
      std::vector<cv::Mat> im_vec(im_vec_size - i);
      std::vector<PaddleX::DetResult> results(im_vec_size - i, PaddleX::DetResult());
      int thread_num = std::min(FLAGS_thread_num, im_vec_size - i);
      #pragma omp parallel for num_threads(thread_num)
      for(int j = i; j < im_vec_size; ++j){
        im_vec[j - i] = std::move(cv::imread(image_paths[j], 1));
      }
      auto imread_end = system_clock::now();
      model.predict(im_vec, results, thread_num);
      auto imread_duration = duration_cast<microseconds>(imread_end - start);
      total_imread_time_s += double(imread_duration.count()) * microseconds::period::num / microseconds::period::den;
      auto end = system_clock::now();
      auto duration = duration_cast<microseconds>(end - start);
      total_running_time_s += double(duration.count()) * microseconds::period::num / microseconds::period::den;
      //输出结果目标框
      for(int j = 0; j < im_vec_size - i; ++j) {
        for(int k = 0; k < results[j].boxes.size(); ++k) {
          std::cout << "image file: " << image_paths[i + j] << ", ";// << std::endl;          
          std::cout << "predict label: " << results[j].boxes[k].category
                    << ", label_id:" << results[j].boxes[k].category_id
                    << ", score: " << results[j].boxes[k].score << ", box(xmin, ymin, w, h):("
                    << results[j].boxes[k].coordinate[0] << ", "
                    << results[j].boxes[k].coordinate[1] << ", "
                    << results[j].boxes[k].coordinate[2] << ", "
                    << results[j].boxes[k].coordinate[3] << ")" << std::endl;
          
        }
      }
      // 可视化
      for(int j = 0; j < im_vec_size - i; ++j) {
        cv::Mat vis_img =
            PaddleX::Visualize(im_vec[j], results[j], model.labels, colormap, FLAGS_threshold);
        std::string save_path =
            PaddleX::generate_save_path(FLAGS_save_dir, image_paths[i + j]);
        cv::imwrite(save_path, vis_img);
        std::cout << "Visualized output saved as " << save_path << std::endl;
      }
    }
  } else {
    PaddleX::DetResult result;
    cv::Mat im = cv::imread(FLAGS_image, 1);
    model.predict(im, &result);
    for (int i = 0; i < result.boxes.size(); ++i) {
      std::cout << "image file: " << FLAGS_image << std::endl;          
      std::cout << ", predict label: " << result.boxes[i].category
                << ", label_id:" << result.boxes[i].category_id
                << ", score: " << result.boxes[i].score << ", box(xmin, ymin, w, h):("
                << result.boxes[i].coordinate[0] << ", "
                << result.boxes[i].coordinate[1] << ", "
                << result.boxes[i].coordinate[2] << ", "
                << result.boxes[i].coordinate[3] << ")" << std::endl;
    }

    // 可视化
    cv::Mat vis_img =
        PaddleX::Visualize(im, result, model.labels, colormap, FLAGS_threshold);
    std::string save_path =
        PaddleX::generate_save_path(FLAGS_save_dir, FLAGS_image);
    cv::imwrite(save_path, vis_img);
    result.clear();
    std::cout << "Visualized output saved as " << save_path << std::endl;
  }
  
  std::cout << "Total running time: " 
	    << total_running_time_s
      << " s, average running time: "
      << total_running_time_s / imgs
	    << " s/img, total read img time: " 
	    << total_imread_time_s
      << " s, average read img time: "
      << total_imread_time_s / imgs
	    << " s, batch_size = " 
	    << FLAGS_batch_size 
	    << std::endl;

  return 0;
}