def pad_image_data(list_of_blobs): max_shape = blob_utils.get_max_shape([blobs['data'].shape[1:] for blobs in list_of_blobs]) output_list = [] for blobs in list_of_blobs: data_padded = np.zeros((3, max_shape[0], max_shape[1]), dtype=np.float32) _, h, w = blobs['data'].shape data_padded[:, :h, :w] = blobs['data'] blobs['data'] = data_padded output_list.append(blobs) return output_list
def _flo_to_blob(flo, target_size): """ """ flo, flo_scale = blob_utils.prep_im_for_blob(flo, (0, 0), [target_size], cfg.TRAIN.MAX_SIZE) flo = flo[0]*flo_scale # scale the value. flo_scale = flo_scale[0] max_shape = blob_utils.get_max_shape([flo.shape[:2]]) blob = np.zeros((1, max_shape[0], max_shape[1], 2), dtype=np.float32) blob[0, 0:flo.shape[0], 0:flo.shape[1], :] = flo channel_swap = (0, 3, 1, 2) blob = blob.transpose(channel_swap) return blob
def pad_image_data(list_of_blobs): max_shape = blob_utils.get_max_shape( [blobs['data'].shape[-2:] for blobs in list_of_blobs]) output_list = [] for blobs in list_of_blobs: c, h, w = blobs['data'].shape[-3:] if cfg.MODEL.LR_VIEW_ON or cfg.MODEL.GIF_ON or cfg.MODEL.LRASY_MAHA_ON: data_padded = np.zeros((2, 3, max_shape[0], max_shape[1]), dtype=np.float32) data_padded[:, :, :h, :w] = blobs['data'] else: data_padded = np.zeros((c, max_shape[0], max_shape[1]), dtype=np.float32) data_padded[:c, :h, :w] = blobs['data'] blobs['data'] = data_padded output_list.append(blobs) return output_list