docs: def showAnns(self, anns):
          """
          Display the specified annotations.
          :param anns (array of object): annotations to display
          :return: None
          """
          if len(anns) == 0:
              return 0
          if 'segmentation' in anns[0] or 'keypoints' in anns[0]:
              datasetType = 'instances'
          elif 'caption' in anns[0]:
              datasetType = 'captions'
          else:
              raise Exception('datasetType not supported')
          if datasetType == 'instances':
              ax = plt.gca()
              ax.set_autoscale_on(False)
              polygons = []
              color = []
              for ann in anns:
                  c = (np.random.random((1, 3))*0.6+0.4).tolist()[0]
                  if 'segmentation' in ann:
                      if type(ann['segmentation']) == list:
                          # polygon
                          for seg in ann['segmentation']:
                              poly = np.array(seg).reshape((int(len(seg)/2), 2))
                              polygons.append(Polygon(poly))
                              color.append(c)
                      else:
                          # mask
                          t = self.imgs[ann['image_id']]
                          if type(ann['segmentation']['counts']) == list:
                              rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width'])
                          else:
                              rle = [ann['segmentation']]
                          m = maskUtils.decode(rle)
                          img = np.ones( (m.shape[0], m.shape[1], 3) )
                          if ann['iscrowd'] == 1:
                              color_mask = np.array([2.0,166.0,101.0])/255
                          if ann['iscrowd'] == 0:
                              color_mask = np.random.random((1, 3)).tolist()[0]
                          for i in range(3):
                              img[:,:,i] = color_mask[i]
                          ax.imshow(np.dstack( (img, m*0.5) ))
                  if 'keypoints' in ann and type(ann['keypoints']) == list:
                      # turn skeleton into zero-based index
                      sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1
                      kp = np.array(ann['keypoints'])
                      x = kp[0::3]
                      y = kp[1::3]
                      v = kp[2::3]
                      for sk in sks:
                          if np.all(v[sk]>0):
                              plt.plot(x[sk],y[sk], linewidth=3, color=c)
                      plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2)
                      plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2)
              p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
              ax.add_collection(p)
              p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2)
              ax.add_collection(p)
          elif datasetType == 'captions':
              for ann in anns:
                  print(ann['caption'])