# MediaPipe graph that performs multi-hand tracking with TensorFlow Lite on GPU.
# Used in the examples in
# mediapipe/examples/android/src/java/com/mediapipe/apps/handtrackinggpu.

# GPU image. (GpuBuffer)
input_stream: "input_video"

# GPU image. (GpuBuffer)
output_stream: "output_video"
# Collection of detected/predicted hands, each represented as a list of
# landmarks. (std::vector<NormalizedLandmarkList>)
output_stream: "hand_landmarks"

# Generates side packet cotaining max number of hands to detect/track.
node {
  calculator: "ConstantSidePacketCalculator"
  output_side_packet: "PACKET:num_hands"
  node_options: {
    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
      packet { int_value: 2 }
    }
  }
}

# Detects/tracks hand landmarks.
node {
  calculator: "HandLandmarkTrackingGpu"
  input_stream: "IMAGE:input_video"
  input_side_packet: "NUM_HANDS:num_hands"
  output_stream: "LANDMARKS:hand_landmarks"
  output_stream: "HANDEDNESS:handedness"
  output_stream: "PALM_DETECTIONS:palm_detections"
  output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects_from_landmarks"
  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
}

# Subgraph that renders annotations and overlays them on top of the input
# images (see hand_renderer_gpu.pbtxt).
node {
  calculator: "HandRendererSubgraph"
  input_stream: "IMAGE:input_video"
  input_stream: "DETECTIONS:palm_detections"
  input_stream: "LANDMARKS:hand_landmarks"
  input_stream: "HANDEDNESS:handedness"
  input_stream: "NORM_RECTS:0:hand_rects_from_palm_detections"
  input_stream: "NORM_RECTS:1:hand_rects_from_landmarks"
  output_stream: "IMAGE:output_video"
}