FastDeploy/examples/vision/segmentation/paddleseg/matting/cpu-gpu/cpp/infer.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 "fastdeploy/vision.h"

#ifdef WIN32
const char sep = '\\';
#else
const char sep = '/';
#endif

void CpuInfer(const std::string& model_dir, const std::string& image_file,
              const std::string& background_file) {
  auto model_file = model_dir + sep + "model.pdmodel";
  auto params_file = model_dir + sep + "model.pdiparams";
  auto config_file = model_dir + sep + "deploy.yaml";
  auto option = fastdeploy::RuntimeOption();
  option.UseCpu();
  auto model = fastdeploy::vision::matting::PPMatting(model_file, params_file,
                                                      config_file, option);
  if (!model.Initialized()) {
    std::cerr << "Failed to initialize." << std::endl;
    return;
  }

  auto im = cv::imread(image_file);
  cv::Mat bg = cv::imread(background_file);
  fastdeploy::vision::MattingResult res;
  if (!model.Predict(&im, &res)) {
    std::cerr << "Failed to predict." << std::endl;
    return;
  }
  auto vis_im = fastdeploy::vision::VisMatting(im, res);
  auto vis_im_with_bg = fastdeploy::vision::SwapBackground(im, bg, res);
  cv::imwrite("visualized_result.jpg", vis_im_with_bg);
  cv::imwrite("visualized_result_fg.png", vis_im);
  std::cout << "Visualized result save in ./visualized_result_replaced_bg.jpg "
               "and ./visualized_result_fg.jpg"
            << std::endl;
}

void KunlunXinInfer(const std::string& model_dir, const std::string& image_file,
                    const std::string& background_file) {
  auto model_file = model_dir + sep + "model.pdmodel";
  auto params_file = model_dir + sep + "model.pdiparams";
  auto config_file = model_dir + sep + "deploy.yaml";
  auto option = fastdeploy::RuntimeOption();
  option.UseKunlunXin();
  auto model = fastdeploy::vision::matting::PPMatting(model_file, params_file,
                                                      config_file, option);
  if (!model.Initialized()) {
    std::cerr << "Failed to initialize." << std::endl;
    return;
  }

  auto im = cv::imread(image_file);
  cv::Mat bg = cv::imread(background_file);
  fastdeploy::vision::MattingResult res;
  if (!model.Predict(&im, &res)) {
    std::cerr << "Failed to predict." << std::endl;
    return;
  }
  auto vis_im = fastdeploy::vision::VisMatting(im, res);
  auto vis_im_with_bg = fastdeploy::vision::SwapBackground(im, bg, res);
  cv::imwrite("visualized_result.jpg", vis_im_with_bg);
  cv::imwrite("visualized_result_fg.png", vis_im);
  std::cout << "Visualized result save in ./visualized_result_replaced_bg.jpg "
               "and ./visualized_result_fg.jpg"
            << std::endl;
}

void GpuInfer(const std::string& model_dir, const std::string& image_file,
              const std::string& background_file) {
  auto model_file = model_dir + sep + "model.pdmodel";
  auto params_file = model_dir + sep + "model.pdiparams";
  auto config_file = model_dir + sep + "deploy.yaml";

  auto option = fastdeploy::RuntimeOption();
  option.UseGpu();
  option.UsePaddleInferBackend();
  auto model = fastdeploy::vision::matting::PPMatting(model_file, params_file,
                                                      config_file, option);
  if (!model.Initialized()) {
    std::cerr << "Failed to initialize." << std::endl;
    return;
  }

  auto im = cv::imread(image_file);
  cv::Mat bg = cv::imread(background_file);
  fastdeploy::vision::MattingResult res;
  if (!model.Predict(&im, &res)) {
    std::cerr << "Failed to predict." << std::endl;
    return;
  }
  auto vis_im = fastdeploy::vision::VisMatting(im, res);
  auto vis_im_with_bg = fastdeploy::vision::SwapBackground(im, bg, res);
  cv::imwrite("visualized_result.jpg", vis_im_with_bg);
  cv::imwrite("visualized_result_fg.png", vis_im);
  std::cout << "Visualized result save in ./visualized_result_replaced_bg.jpg "
               "and ./visualized_result_fg.png"
            << std::endl;
}

void TrtInfer(const std::string& model_dir, const std::string& image_file,
              const std::string& background_file) {
  auto model_file = model_dir + sep + "model.pdmodel";
  auto params_file = model_dir + sep + "model.pdiparams";
  auto config_file = model_dir + sep + "deploy.yaml";

  auto option = fastdeploy::RuntimeOption();
  option.UseGpu();
  option.UseTrtBackend();
  // If use original Tensorrt, not Paddle-TensorRT,
  // comment the following two lines
  option.EnablePaddleToTrt();
  option.EnablePaddleTrtCollectShape();
  option.SetTrtInputShape("img", {1, 3, 512, 512});
  auto model = fastdeploy::vision::matting::PPMatting(model_file, params_file,
                                                      config_file, option);
  if (!model.Initialized()) {
    std::cerr << "Failed to initialize." << std::endl;
    return;
  }

  auto im = cv::imread(image_file);
  cv::Mat bg = cv::imread(background_file);
  fastdeploy::vision::MattingResult res;
  if (!model.Predict(&im, &res)) {
    std::cerr << "Failed to predict." << std::endl;
    return;
  }
  auto vis_im = fastdeploy::vision::VisMatting(im, res);
  auto vis_im_with_bg = fastdeploy::vision::SwapBackground(im, bg, res);
  cv::imwrite("visualized_result.jpg", vis_im_with_bg);
  cv::imwrite("visualized_result_fg.png", vis_im);
  std::cout << "Visualized result save in ./visualized_result_replaced_bg.jpg "
               "and ./visualized_result_fg.jpg"
            << std::endl;
}

int main(int argc, char* argv[]) {
  if (argc < 5) {
    std::cout
        << "Usage: infer_demo path/to/model_dir path/to/image run_option, "
           "e.g ./infer_model ./PP-Matting-512 ./test.jpg ./test_bg.jpg 0"
        << 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 kunlunxin."
              << std::endl;
    return -1;
  }
  if (std::atoi(argv[4]) == 0) {
    CpuInfer(argv[1], argv[2], argv[3]);
  } else if (std::atoi(argv[4]) == 1) {
    GpuInfer(argv[1], argv[2], argv[3]);
  } else if (std::atoi(argv[4]) == 2) {
    TrtInfer(argv[1], argv[2], argv[3]);
  } else if (std::atoi(argv[4]) == 3) {
    KunlunXinInfer(argv[1], argv[2], argv[3]);
  }
  return 0;
}