pose_tracking_opengles.txt

# 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.

# Copied from mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt
#
# CHANGES:
#   - Add ImageTransformationCalculator and rotate the input
#   - Remove PoseRendererGpu

# MediaPipe graph that performs upper-body pose tracking with TensorFlow Lite on GPU.

input_stream: "input_video"

output_stream: "pose_landmarks"
output_stream: "pose_world_landmarks"
output_stream: "segmentation_mask"
output_stream: "pose_detection"
output_stream: "roi_from_landmarks"

# Throttles the images flowing downstream for flow control. It passes through
# the very first incoming image unaltered, and waits for downstream nodes
# (calculators and subgraphs) in the graph to finish their tasks before it
# passes through another image. All images that come in while waiting are
# dropped, limiting the number of in-flight images in most part of the graph to
# 1. This prevents the downstream nodes from queuing up incoming images and data
# excessively, which leads to increased latency and memory usage, unwanted in
# real-time mobile applications. It also eliminates unnecessarily computation,
# e.g., the output produced by a node may get dropped downstream if the
# subsequent nodes are still busy processing previous inputs.
node {
  calculator: "FlowLimiterCalculator"
  input_stream: "input_video"
  input_stream: "FINISHED:pose_landmarks"
  input_stream_info: {
    tag_index: "FINISHED"
    back_edge: true
  }
  output_stream: "throttled_input_video"
}

node: {
  calculator: "ImageTransformationCalculator"
  input_stream: "IMAGE_GPU:throttled_input_video"
  input_side_packet: "ROTATION_DEGREES:input_rotation"
  input_side_packet: "FLIP_HORIZONTALLY:input_horizontally_flipped"
  input_side_packet: "FLIP_VERTICALLY:input_vertically_flipped"
  output_stream: "IMAGE_GPU:transformed_input_video"
}

# Subgraph that detects poses and corresponding landmarks.
node {
  calculator: "PoseLandmarkGpu"
  input_stream: "IMAGE:transformed_input_video"
  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
  input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
  input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
  output_stream: "LANDMARKS:pose_landmarks"
  output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
  output_stream: "SEGMENTATION_MASK:segmentation_mask_gpu"
  output_stream: "DETECTION:pose_detection"
  output_stream: "ROI_FROM_LANDMARKS:roi_from_landmarks"
}

node: {
  calculator: "ImageTransformationCalculator"
  input_stream: "IMAGE_GPU:segmentation_mask_gpu"
  input_side_packet: "ROTATION_DEGREES:output_rotation"
  input_side_packet: "FLIP_HORIZONTALLY:output_horizontally_flipped"
  input_side_packet: "FLIP_VERTICALLY:output_vertically_flipped"
  output_stream: "IMAGE_GPU:segmentation_mask_unrotated_gpu"
}

node: {
  calculator: "GpuBufferToImageFrameCalculator"
  input_stream: "segmentation_mask_unrotated_gpu"
  output_stream: "segmentation_mask"
}