//   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 <algorithm>
#include <chrono>  // NOLINT
#include <fstream>
#include <iostream>
#include <string>
#include <vector>
#include <utility>

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

#if defined(__arm__) || defined(__aarch64__)
#include <opencv2/videoio/legacy/constants_c.h>
#endif

using namespace std::chrono;  // NOLINT

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_bool(use_camera, false, "Infering with Camera");
DEFINE_int32(camera_id, 0, "Camera id");
DEFINE_string(video_path, "", "Path of input video");
DEFINE_bool(show_result, false, "show the result of each frame with a window");
DEFINE_bool(save_result, true, "save the result of each frame to a video");
DEFINE_string(save_dir, "output", "Path to save visualized image");

int main(int argc, char** argv) {
  // Parsing command-line
  google::ParseCommandLineFlags(&argc, &argv, true);

  if (FLAGS_model_dir == "") {
    std::cerr << "--model_dir need to be defined" << std::endl;
    return -1;
  }
  if (FLAGS_video_path == "" & FLAGS_use_camera == false) {
    std::cerr << "--video_path or --use_camera need to be defined" << std::endl;
    return -1;
  }

  // Load model
  PaddleX::Model model;
  model.Init(FLAGS_model_dir,
             FLAGS_use_gpu,
             FLAGS_use_trt,
             FLAGS_gpu_id,
             FLAGS_key);

  // Open video
  cv::VideoCapture capture;
  if (FLAGS_use_camera) {
    capture.open(FLAGS_camera_id);
    if (!capture.isOpened()) {
      std::cout << "Can not open the camera "
                << FLAGS_camera_id << "."
                << std::endl;
      return -1;
    }
  } else {
    capture.open(FLAGS_video_path);
    if (!capture.isOpened()) {
      std::cout << "Can not open the video "
                << FLAGS_video_path << "."
                << std::endl;
      return -1;
    }
  }

  // Create a VideoWriter
  cv::VideoWriter video_out;
  std::string video_out_path;
  if (FLAGS_save_result) {
    // Get video information: resolution, fps
    int video_width = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_WIDTH));
    int video_height = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_HEIGHT));
    int video_fps = static_cast<int>(capture.get(CV_CAP_PROP_FPS));
    int video_fourcc;
    if (FLAGS_use_camera) {
      video_fourcc = 828601953;
    } else {
      video_fourcc = static_cast<int>(capture.get(CV_CAP_PROP_FOURCC));
    }

    if (FLAGS_use_camera) {
      time_t now = time(0);
      video_out_path =
        PaddleX::generate_save_path(FLAGS_save_dir,
                                    std::to_string(now) + ".mp4");
    } else {
      video_out_path =
        PaddleX::generate_save_path(FLAGS_save_dir, FLAGS_video_path);
    }
    video_out.open(video_out_path.c_str(),
                   video_fourcc,
                   video_fps,
                   cv::Size(video_width, video_height),
                   true);
    if (!video_out.isOpened()) {
      std::cout << "Create video writer failed!" << std::endl;
      return -1;
    }
  }

  PaddleX::ClsResult result;
  cv::Mat frame;
  int key;
  while (capture.read(frame)) {
    if (FLAGS_show_result || FLAGS_use_camera) {
     key = cv::waitKey(1);
     // When pressing `ESC`, then exit program and result video is saved
     if (key == 27) {
       break;
     }
    } else if (frame.empty()) {
      break;
    }
    // Begin to predict
    model.predict(frame, &result);
    // Visualize results
    cv::Mat vis_img = frame.clone();
    auto colormap = PaddleX::GenerateColorMap(model.labels.size());
    int c1 = colormap[3 * result.category_id + 0];
    int c2 = colormap[3 * result.category_id + 1];
    int c3 = colormap[3 * result.category_id + 2];
    cv::Scalar text_color = cv::Scalar(c1, c2, c3);
    std::string text = result.category;
    text += std::to_string(static_cast<int>(result.score * 100)) + "%";
    int font_face = cv::FONT_HERSHEY_SIMPLEX;
    double font_scale = 0.5f;
    float thickness = 0.5;
    cv::Size text_size =
        cv::getTextSize(text, font_face, font_scale, thickness, nullptr);
    cv::Point origin;
    origin.x = frame.cols / 2;
    origin.y = frame.rows / 2;
    cv::Rect text_back = cv::Rect(origin.x,
                                  origin.y - text_size.height,
                                  text_size.width,
                                  text_size.height);
    cv::rectangle(vis_img, text_back, text_color, -1);
    cv::putText(vis_img,
                text,
                origin,
                font_face,
                font_scale,
                cv::Scalar(255, 255, 255),
                thickness);
    if (FLAGS_show_result || FLAGS_use_camera) {
      cv::imshow("video_classifier", vis_img);
    }
    if (FLAGS_save_result) {
      video_out.write(vis_img);
    }
    std::cout << "Predict label: " << result.category
              << ", label_id:" << result.category_id
              << ", score: " << result.score << std::endl;
  }
  capture.release();
  if (FLAGS_save_result) {
    video_out.release();
    std::cout << "Visualized output saved as " << video_out_path << std::endl;
  }
  if (FLAGS_show_result || FLAGS_use_camera) {
    cv::destroyAllWindows();
  }
  return 0;
}