docs: def loadRes(self, resFile):
          """
          Load result file and return a result api object.
          :param   resFile (str)     : file name of result file
          :return: res (obj)         : result api object
          """
          res = COCO()
          res.dataset['images'] = [img for img in self.dataset['images']]
  
          print('Loading and preparing results...')
          tic = time.time()
          if type(resFile) == str: #or type(resFile) == unicode:
              anns = json.load(open(resFile))
          elif type(resFile) == np.ndarray:
              anns = self.loadNumpyAnnotations(resFile)
          else:
              anns = resFile
          assert type(anns) == list, 'results in not an array of objects'
          annsImgIds = [ann['image_id'] for ann in anns]
          assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \
                 'Results do not correspond to current coco set'
          if 'caption' in anns[0]:
              imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns])
              res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds]
              for id, ann in enumerate(anns):
                  ann['id'] = id+1
          elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
              res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
              for id, ann in enumerate(anns):
                  bb = ann['bbox']
                  x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]
                  if not 'segmentation' in ann:
                      ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
                  ann['area'] = bb[2]*bb[3]
                  ann['id'] = id+1
                  ann['iscrowd'] = 0
          elif 'segmentation' in anns[0]:
              res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
              for id, ann in enumerate(anns):
                  # now only support compressed RLE format as segmentation results
                  ann['area'] = maskUtils.area(ann['segmentation'])
                  if not 'bbox' in ann:
                      ann['bbox'] = maskUtils.toBbox(ann['segmentation'])
                  ann['id'] = id+1
                  ann['iscrowd'] = 0
          elif 'keypoints' in anns[0]:
              res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
              for id, ann in enumerate(anns):
                  s = ann['keypoints']
                  x = s[0::3]
                  y = s[1::3]
                  x0,x1,y0,y1 = np.min(x), np.max(x), np.min(y), np.max(y)
                  ann['area'] = (x1-x0)*(y1-y0)
                  ann['id'] = id + 1
                  ann['bbox'] = [x0,y0,x1-x0,y1-y0]
          print('DONE (t={:0.2f}s)'.format(time.time()- tic))
  
          res.dataset['annotations'] = anns
          res.createIndex()
          return res