parameters: (self, anns)
docstring: Display the specified annotations.
          :param anns (array of object): annotations to display
          :return: None
docstring_summary: Display the specified annotations.
          :param anns (array of object): annotations to display
          :return: None
docstring_tokens: Display the specified annotations . : param anns ( array of object ) : annotations to display : return : None
function: 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]:
              datasetType = 'instances'
          elif 'caption' in anns[0]:
              datasetType = 'captions'
          if datasetType == 'instances':
              ax = plt.gca()
              polygons = []
              color = []
              for ann in anns:
                  c = np.random.random((1, 3)).tolist()[0]
                  if type(ann['segmentation']) == list:
                      # polygon
                      for seg in ann['segmentation']:
                          poly = np.array(seg).reshape((len(seg)/2, 2))
                          polygons.append(Polygon(poly, True,alpha=0.4))
                          color.append(c)
                  else:
                      # mask
                      t = self.imgs[ann['image_id']]
                      if type(ann['segmentation']['counts']) == list:
                          rle = mask.frPyObjects([ann['segmentation']], t['height'], t['width'])
                      else:
                          rle = [ann['segmentation']]
                      m = mask.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) ))
              p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
              ax.add_collection(p)
          elif datasetType == 'captions':
              for ann in anns:
                  print ann['caption']
function_tokens: def showAnns ( self , anns ) : if len ( anns ) == 0 : return 0 if 'segmentation' in anns [ 0 ] : datasetType = 'instances' elif 'caption' in anns [ 0 ] : datasetType = 'captions' if datasetType == 'instances' : ax = plt . gca ( ) polygons = [ ] color = [ ] for ann in anns : c = np . random . random ( ( 1 , 3 ) ) . tolist ( ) [ 0 ] if type ( ann [ 'segmentation' ] ) == list : # polygon for seg in ann [ 'segmentation' ] : poly = np . array ( seg ) . reshape ( ( len ( seg ) / 2 , 2 ) ) polygons . append ( Polygon ( poly , True , alpha = 0.4 ) ) color . append ( c ) else : # mask t = self . imgs [ ann [ 'image_id' ] ] if type ( ann [ 'segmentation' ] [ 'counts' ] ) == list : rle = mask . frPyObjects ( [ ann [ 'segmentation' ] ] , t [ 'height' ] , t [ 'width' ] ) else : rle = [ ann [ 'segmentation' ] ] m = mask . 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 ) ) ) p = PatchCollection ( polygons , facecolors = color , edgecolors = ( 0 , 0 , 0 , 1 ) , linewidths = 3 , alpha = 0.4 ) ax . add_collection ( p ) elif datasetType == 'captions' : for ann in anns : print ann [ 'caption' ]