text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import cv2
  import numpy as np
  from progress.bar import Bar
  import time
  import torch
  
  try:
    from external.nms import soft_nms
  except:
    print('NMS not imported! If you need it,'
          ' do \n cd $CenterNet_ROOT/src/lib/external \n make')
  from models.decode import ctdet_decode
  from models.utils import flip_tensor
  from utils.image import get_affine_transform
  from utils.post_process import ctdet_post_process
  from utils.debugger import Debugger
  
  from .base_detector_double import BaseDetectorDouble
  
  class CtdetDetectorDouble(BaseDetectorDouble):
    def __init__(self, opt):
      super(CtdetDetectorDouble, self).__init__(opt)
    
    def process(self, images, return_time=False):
      with torch.no_grad():
        output = self.model(images)[-1]
        hm = output['hm'].sigmoid_()
        wh = output['wh']
        reg = output['reg'] if self.opt.reg_offset else None
        if self.opt.flip_test:
          hm = (hm[0:1] + flip_tensor(hm[1:2])) / 2
          wh = (wh[0:1] + flip_tensor(wh[1:2])) / 2
          reg = reg[0:1] if reg is not None else None
        torch.cuda.synchronize()
        forward_time = time.time()
        dets = ctdet_decode(hm, wh, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K)
      if return_time:
        return output, dets, forward_time
      else:
        return output, dets
  
    def post_process(self, dets, meta, scale=1):
      dets = dets.detach().cpu().numpy()
      dets = dets.reshape(1, -1, dets.shape[2])
      
      dets = ctdet_post_process(
          dets.copy(), [meta['c']], [meta['s']],
          meta['out_height'], meta['out_width'], self.opt.num_classes)
      for j in range(1, self.num_classes + 1):
        dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape(-1, 5)
        dets[0][j][:, :4] /= scale
      return dets[0]
  
    def merge_outputs(self, detections):
      results = {}
      for j in range(1, self.num_classes + 1):
        results[j] = np.concatenate(
          [detection[j] for detection in detections], axis=0).astype(np.float32)
        if len(self.scales) > 1 or self.opt.nms:
           soft_nms(results[j], Nt=0.5, method=2)
      scores = np.hstack(
        [results[j][:, 4] for j in range(1, self.num_classes + 1)])
      if len(scores) > self.max_per_image:
        kth = len(scores) - self.max_per_image
        thresh = np.partition(scores, kth)[kth]
        for j in range(1, self.num_classes + 1):
          keep_inds = (results[j][:, 4] >= thresh)
          results[j] = results[j][keep_inds]
      return results
  
    def debug(self, debugger, images, dets, output, scale=1):
      detection = dets.detach().cpu().numpy().copy()
      detection[:, :, :4] *= self.opt.down_ratio
      for i in range(1):
        img = images[i].detach().cpu().numpy().transpose(1, 2, 0)
        img = ((img * self.std + self.mean) * 255).astype(np.uint8)
        pred = debugger.gen_colormap(output['hm'][i].detach().cpu().numpy())
        debugger.add_blend_img(img, pred, 'pred_hm_{:.1f}'.format(scale))
        debugger.add_img(img, img_id='out_pred_{:.1f}'.format(scale))
        for k in range(len(dets[i])):
          if detection[i, k, 4] > self.opt.center_thresh:
            debugger.add_coco_bbox(detection[i, k, :4], detection[i, k, -1],
                                   detection[i, k, 4], 
                                   img_id='out_pred_{:.1f}'.format(scale))
  
    def show_results(self, debugger, image, results):
      debugger.add_img(image, img_id='ctdet')
      for j in range(1, self.num_classes + 1):
        for bbox in results[j]:
          if bbox[4] > self.opt.vis_thresh:
            debugger.add_coco_bbox(bbox[:4], j - 1, bbox[4], img_id='ctdet')
      debugger.save_all_imgs()
  