text: /*
   * Licensed to the Apache Software Foundation (ASF) under one
   * or more contributor license agreements.  See the NOTICE file
   * distributed with this work for additional information
   * regarding copyright ownership.  The ASF licenses this file
   * to you 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.
   */
  
  /*
   * Copyright (c) 2020, OPEN AI LAB
   * Author: qtang@openailab.com
   * Author: sudolewis@gmail.com
   * 
   * original model: https://github.com/milesial/Pytorch-UNet
   */
  
  #include <stdlib.h>
  #include <stdio.h>
  #include <fstream>
  #include <limits>
  #include <opencv2/opencv.hpp>
  
  #include "common.h"
  #include "tengine/c_api.h"
  #include "tengine_operations.h"
  
  #define DEFAULT_IMG_H 512 
  #define DEFAULT_IMG_W 512
  #define DEFAULT_SCALE1 (1.f/255.f)
  #define DEFAULT_SCALE2 (1.f/255.f)
  #define DEFAULT_SCALE3 (1.f/255.f)
  #define DEFAULT_MEAN1 0
  #define DEFAULT_MEAN2 0
  #define DEFAULT_MEAN3 0
  #define DEFAULT_LOOP_COUNT 1
  #define DEFAULT_THREAD_COUNT 1
  #define DEFAULT_CPU_AFFINITY 255
  #define DEFAULT_CONF_THRESHOLD 0.5f
  
  /**
   * unet model are tested based on https://github.com/milesial/Pytorch-UNet
   * pretrained model can be downloaded from https://github.com/milesial/Pytorch-UNet/releases/tag/v1.0
   * tmfile can be converted using the pretrained model
   * because of the onnx->tmfile convertion problem, keep the network input size dividable by 16 (256,512) 
   */
  
  int draw_segmentation(const int32_t* data, int h, int w) {
      static std::map<int32_t, cv::Vec3b> color_table = {{0, cv::Vec3b(0,0,0)},
                                                         {1, cv::Vec3b(20,59,255)},
                                                         {2, cv::Vec3b(120,59,200)},
                                                         {3, cv::Vec3b(80,29,129)},
                                                         {4, cv::Vec3b(210,99,12)}, // add more color if needed
                                                         {-1, cv::Vec3b(255,255,255)} // other type
                                                         };
      cv::Mat img = cv::Mat::zeros(h, w, CV_8UC3);
      for (int i = 0; i < h; ++i) {
          for (int j = 0; j < w; ++j) {
              cv::Vec3b color;
              int32_t value = data[i * w + j];
              if (color_table.count(value) > 0) {
                  color = color_table.at(value);
              } else {
                  color = color_table.at(-1);
              }
              img.at<cv::Vec3b>(i, j) = color;
          }
      }
      cv::imwrite("unet_out.png", img);
      return 0;
  }
  
  int tengine_segment(const char* model_file, const char* image_file, int img_h, int img_w, const float* mean,
                       const float* scale, int loop_count, int num_thread, int affinity, float conf_thresh)
  {
      /* set runtime options */
      struct options opt;
      opt.num_thread = num_thread;
      opt.cluster = TENGINE_CLUSTER_ALL;
      opt.precision = TENGINE_MODE_FP32;
      opt.affinity = affinity;
  
      /* inital tengine */
      if (init_tengine() != 0)
      {
          fprintf(stderr, "Initial tengine failed.\n");
          return -1;
      }
      fprintf(stderr, "tengine-lite library version: %s\n", get_tengine_version());
  
      /* create graph, load tengine model xxx.tmfile */
      graph_t graph = create_graph(NULL, "tengine", model_file);
      if (NULL == graph)
      {
          fprintf(stderr, "Create graph failed.\n");
          return -1;
      }
  
      /* set the shape, data buffer of input_tensor of the graph */
      int img_size = img_h * img_w * 3;
      int dims[] = {1, 3, img_h, img_w};    // nchw
      float* input_data = ( float* )malloc(img_size * sizeof(float));
  
      tensor_t input_tensor = get_graph_input_tensor(graph, 0, 0);
      if (input_tensor == NULL)
      {
          fprintf(stderr, "Get input tensor failed\n");
          return -1;
      }
  
      if (set_tensor_shape(input_tensor, dims, 4) < 0)
      {
          fprintf(stderr, "Set input tensor shape failed\n");
          return -1;
      }
  
      if (set_tensor_buffer(input_tensor, input_data, img_size * 4) < 0)
      {
          fprintf(stderr, "Set input tensor buffer failed\n");
          return -1;
      }    
  
      /* prerun graph, set work options(num_thread, cluster, precision) */
      if (prerun_graph_multithread(graph, opt) < 0)
      {
          fprintf(stderr, "Prerun multithread graph failed.\n");
          return -1;
      }
  
      /* prepare process input data, set the data mem to input tensor */
      get_input_data(image_file, input_data, img_h, img_w, mean, scale);
  
      /* run graph */
      double min_time = DBL_MAX;
      double max_time = DBL_MIN;
      double total_time = 0.;
      for (int i = 0; i < loop_count; i++)
      {
          double start = get_current_time();
          if (run_graph(graph, 1) < 0)
          {
              fprintf(stderr, "Run graph failed\n");
              return -1;
          }
          double end = get_current_time();
          double cur = end - start;
          total_time += cur;
          if (min_time > cur)
              min_time = cur;
          if (max_time < cur)
              max_time = cur;
      }
      fprintf(stderr, "\nmodel file : %s\n", model_file);
      fprintf(stderr, "image file : %s\n", image_file);
      fprintf(stderr, "img_h, img_w, scale[3], mean[3] : %d %d , %.3f %.3f %.3f, %.1f %.1f %.1f\n", img_h, img_w,
              scale[0], scale[1], scale[2], mean[0], mean[1], mean[2]);
      fprintf(stderr, "Repeat %d times, thread %d, avg time %.2f ms, max_time %.2f ms, min_time %.2f ms\n", loop_count,
              num_thread, total_time / loop_count, max_time, min_time);
      fprintf(stderr, "--------------------------------------\n");
  
      /* get the result of classification */
      tensor_t output_tensor = get_graph_output_tensor(graph, 0, 0);
      float* output_data = ( float* )get_tensor_buffer(output_tensor);
      int output_size = get_tensor_buffer_size(output_tensor) / sizeof(float);
  
      int channel = output_size / img_h / img_w;
      int res = output_size % (img_h * img_w);
      if (res != 0) {
        fprintf(stderr, "output shape is not supported.\n");
      } else {
        int* label_data = new int[img_h * img_w];
        /* single class segmentation */
        if (channel == 1) {
          for (int i=0; i < img_h; ++i) {
            for (int j=0; j < img_w; ++j) {
                float conf = 1/(1+std::exp(-output_data[i*img_w + j]));
                label_data[i*img_w + j] = conf > conf_thresh ? 1 : 0;
            }
          }
        }
        /* multi-class segmentation */
        else {
          for (int i=0; i < img_h; ++i) {
            for (int j=0; j < img_w; ++j) {
                int argmax_id = -1;
                float max_conf = std::numeric_limits<float>::min();
                for (int k=0; k < channel; ++k) {
                    float out_value = output_data[k * img_w * img_h + i * img_w + j];
                    if (out_value > max_conf) {
                        argmax_id = k;
                        max_conf = out_value;
                    }
                }
                label_data[i*img_w + j] = argmax_id;
            }
          }
        }
        /* visualization */
        draw_segmentation(label_data, img_h, img_w);
        fprintf(stderr, "segmentatation result is save as unet_out.png\n");
        delete[] label_data;
      }
  
      /* release tengine */
      free(input_data);
      postrun_graph(graph);
      destroy_graph(graph);
      release_tengine();
  
      return 0;
  }
  
  void show_usage()
  {
      fprintf(
          stderr,
          "[Usage]:  [-h]\n    [-m model_file] [-i image_file]\n [-g img_h,img_w] [-s scale[0],scale[1],scale[2]] [-w "
          "mean[0],mean[1],mean[2]] [-r loop_count] [-t thread_count] [-a cpu_affinity] [-c conf_thresh]\n");
      fprintf(
          stderr,
          "\nmobilenet example: \n    ./classification -m /path/to/mobilenet.tmfile -i /path/to/img.jpg -g 224,224 -s "
          "0.017,0.017,0.017 -w 104.007,116.669,122.679\n");
  }
  
  int main(int argc, char* argv[])
  {
      int loop_count = DEFAULT_LOOP_COUNT;
      int num_thread = DEFAULT_THREAD_COUNT;
      int cpu_affinity = DEFAULT_CPU_AFFINITY;
      float conf_thresh = DEFAULT_CONF_THRESHOLD;
      char* model_file = NULL;
      char* image_file = NULL;
      float img_hw[2] = {0.f};
      int img_h = 0;
      int img_w = 0;
      float mean[3] = {0.f, 0.f, 0.f};
      float scale[3] = {0.f, 0.f, 0.f};
  
      int res;
      while ((res = getopt(argc, argv, "m:i:l:g:s:w:r:t:a:c:h")) != -1)
      {
          switch (res)
          {
              case 'm':
                  model_file = optarg;
                  break;
              case 'i':
                  image_file = optarg;
                  break;
              case 'g':
                  split(img_hw, optarg, ",");
                  img_h = ( int )img_hw[0];
                  img_w = ( int )img_hw[1];
                  break;
              case 's':
                  split(scale, optarg, ",");
                  break;
              case 'w':
                  split(mean, optarg, ",");
                  break;
              case 'r':
                  loop_count = atoi(optarg);
                  break;
              case 't':
                  num_thread = atoi(optarg);
                  break;
              case 'a':
                  cpu_affinity = atoi(optarg);
                  break;
              case 'c':
                  conf_thresh = atof(optarg);
                  break;
              case 'h':
                  show_usage();
                  return 0;
              default:
                  break;
          }
      }
  
      /* check files */
      if (model_file == NULL)
      {
          fprintf(stderr, "Error: Tengine model file not specified!\n");
          show_usage();
          return -1;
      }
  
      if (image_file == NULL)
      {
          fprintf(stderr, "Error: Image file not specified!\n");
          show_usage();
          return -1;
      }
  
      if (!check_file_exist(model_file) || !check_file_exist(image_file))
          return -1;
  
      if (img_h == 0)
      {
          img_h = DEFAULT_IMG_H;
          fprintf(stderr, "Image height not specified, use default %d\n", img_h);
      }
  
      if (img_w == 0)
      {
          img_w = DEFAULT_IMG_W;
          fprintf(stderr, "Image width not specified, use default  %d\n", img_w);
      }
  
      if (scale[0] == 0.f || scale[1] == 0.f || scale[2] == 0.f)
      {
          scale[0] = DEFAULT_SCALE1;
          scale[1] = DEFAULT_SCALE2;
          scale[2] = DEFAULT_SCALE3;
          fprintf(stderr, "Scale value not specified, use default  %.5f, %.5f, %.5f\n", scale[0], scale[1], scale[2]);
      }
  
      if (mean[0] == -1.0 || mean[1] == -1.0 || mean[2] == -1.0)
      {
          mean[0] = DEFAULT_MEAN1;
          mean[1] = DEFAULT_MEAN2;
          mean[2] = DEFAULT_MEAN3;
          fprintf(stderr, "Mean value not specified, use default   %.5f, %.5f, %.5f\n", mean[0], mean[1], mean[2]);
      }
  
      if (tengine_segment(model_file, image_file, img_h, img_w, mean, scale, loop_count, num_thread, cpu_affinity, conf_thresh) < 0)
          return -1;
  
      return 0;
  }
  