// Copyright 2019 The MediaPipe Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
syntax = "proto2";
package mediapipe;
import "mediapipe/framework/calculator.proto";
message LappedTensorBufferCalculatorOptions {
extend mediapipe.CalculatorOptions {
optional LappedTensorBufferCalculatorOptions ext = 175222228;
}
// The output tensor will have this many tensors concatenated together along
// their first dimension.
optional int32 buffer_size = 1;
// The overlap determines how many input tensors are shared between frames.
// Because the input tensors may have a non-singleton first dimension, this
// is not necessarily the number of overlapping entries in the first
// dimension.
optional int32 overlap = 2;
// If true, inserts a singleton first dimension before concatenating the
// tensors together.
optional bool add_batch_dim_to_tensors = 3 [default = false];
// Timestamp offset for output batch. The valid range is [0, buffer_size).
// The timestamp of the output tensor will match the timestamp of the input
// correspeonding to the offset. For example, setting to 0 will output at the
// timestamp matching the first input tensor. Setting the timestamp_offset to
// int((N-1) / 2) output at the timestamp matching the middle input tensor.
// This is useful for aligning the timestamp to be centered on the input
// range.
optional int32 timestamp_offset = 4 [default = 0];
// Amount of padding (repeating of first/last value) to add to the beginning
// and end of the input stream.
optional int32 padding = 5;
}