parameters: (self, roidb, scale_inds, im_names)
return_statement: return blob, im_scales
docstring: Builds an input blob from the images in the roidb at the specified
          scales.
docstring_summary: Builds an input blob from the images in the roidb at the specified
          scales.
docstring_tokens: Builds an input blob from the images in the roidb at the specified scales .
function: def _get_image_blob(self, roidb, scale_inds, im_names):
          """Builds an input blob from the images in the roidb at the specified
          scales.
          """
          num_images = len(roidb)
          processed_ims = []
          im_scales = []
          for i in xrange(num_images):
              im = cv2.imread(im_names[i])
              # here [0][0] is due to the nature of scipy.io.savemat
              # since it will change True/False to [[1]] or [[0]] with shape (1,1)
              # so we judge whether flip image in this un-normal way
              if roidb[i]['Flip'][0][0]:
                  im = im[:, ::-1, :]
              target_size = cfg.TRAIN.SCALES[scale_inds[i]]
              im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size,
                                              cfg.TRAIN.MAX_SIZE)
              im_scales.append(im_scale)
              processed_ims.append(im)
          # Create a blob to hold the input images
          blob = im_list_to_blob(processed_ims)
          return blob, im_scales
function_tokens: def _get_image_blob ( self , roidb , scale_inds , im_names ) : num_images = len ( roidb ) processed_ims = [ ] im_scales = [ ] for i in xrange ( num_images ) : im = cv2 . imread ( im_names [ i ] ) # here [0][0] is due to the nature of scipy.io.savemat # since it will change True/False to [[1]] or [[0]] with shape (1,1) # so we judge whether flip image in this un-normal way if roidb [ i ] [ 'Flip' ] [ 0 ] [ 0 ] : im = im [ : , : : - 1 , : ] target_size = cfg . TRAIN . SCALES [ scale_inds [ i ] ] im , im_scale = prep_im_for_blob ( im , cfg . PIXEL_MEANS , target_size , cfg . TRAIN . MAX_SIZE ) im_scales . append ( im_scale ) processed_ims . append ( im ) # Create a blob to hold the input images blob = im_list_to_blob ( processed_ims ) return blob , im_scales