multi_hand_tracking_desktop_live.pbtxt

# MediaPipe graph that performs multi-hand tracking on desktop with TensorFlow
# Lite on CPU.
# Used in the example in
# mediapipie/examples/desktop/hand_tracking:multi_hand_tracking_cpu.

# Images coming into and out of the graph.
input_stream: "input_video"
output_stream: "output_video"

# Determines if an input vector of NormalizedRect has a size greater than or
# equal to the provided min_size.
node {
  calculator: "NormalizedRectVectorHasMinSizeCalculator"
  input_stream: "ITERABLE:prev_multi_hand_rects_from_landmarks"
  output_stream: "prev_has_enough_hands"
  node_options: {
    [type.googleapis.com/mediapipe.CollectionHasMinSizeCalculatorOptions] {
      # This value can be changed to support tracking arbitrary number of hands.
      # Please also remember to modify max_vec_size in
      # ClipVectorSizeCalculatorOptions in
      # mediapipe/graphs/hand_tracking/subgraphs/multi_hand_detection_gpu.pbtxt
      min_size: 2
    }
  }
}

# Drops the incoming image if the previous frame had at least N hands.
# Otherwise, passes the incoming image through to trigger a new round of hand
# detection in MultiHandDetectionSubgraph.
node {
  calculator: "GateCalculator"
  input_stream: "input_video"
  input_stream: "DISALLOW:prev_has_enough_hands"
  output_stream: "multi_hand_detection_input_video"
  node_options: {
    [type.googleapis.com/mediapipe.GateCalculatorOptions] {
      empty_packets_as_allow: true
    }
  }
}

# Subgraph that detections hands (see multi_hand_detection_cpu.pbtxt).
node {
  calculator: "MultiHandDetectionSubgraph"
  input_stream: "multi_hand_detection_input_video"
  output_stream: "DETECTIONS:multi_palm_detections"
  output_stream: "NORM_RECTS:multi_palm_rects"
}

# Subgraph that localizes hand landmarks for multiple hands (see
# multi_hand_landmark.pbtxt).
node {
  calculator: "MultiHandLandmarkSubgraph"
  input_stream: "IMAGE:input_video"
  input_stream: "NORM_RECTS:multi_hand_rects"
  output_stream: "LANDMARKS:multi_hand_landmarks"
  output_stream: "NORM_RECTS:multi_hand_rects_from_landmarks"
}

# Caches a hand rectangle fed back from MultiHandLandmarkSubgraph, and upon the
# arrival of the next input image sends out the cached rectangle with the
# timestamp replaced by that of the input image, essentially generating a packet
# that carries the previous hand rectangle. Note that upon the arrival of the
# very first input image, an empty packet is sent out to jump start the
# feedback loop.
node {
  calculator: "PreviousLoopbackCalculator"
  input_stream: "MAIN:input_video"
  input_stream: "LOOP:multi_hand_rects_from_landmarks"
  input_stream_info: {
    tag_index: "LOOP"
    back_edge: true
  }
  output_stream: "PREV_LOOP:prev_multi_hand_rects_from_landmarks"
}

# Performs association between NormalizedRect vector elements from previous
# frame and those from the current frame if MultiHandDetectionSubgraph runs.
# This calculator ensures that the output multi_hand_rects vector doesn't
# contain overlapping regions based on the specified min_similarity_threshold.
node {
  calculator: "AssociationNormRectCalculator"
  input_stream: "prev_multi_hand_rects_from_landmarks"
  input_stream: "multi_palm_rects"
  output_stream: "multi_hand_rects"
  node_options: {
    [type.googleapis.com/mediapipe.AssociationCalculatorOptions] {
      min_similarity_threshold: 0.5
    }
  }
}

# Subgraph that renders annotations and overlays them on top of the input
# images (see multi_hand_renderer_cpu.pbtxt).
node {
  calculator: "MultiHandRendererSubgraph"
  input_stream: "IMAGE:input_video"
  input_stream: "LANDMARKS:multi_hand_landmarks"
  output_stream: "IMAGE:output_video"
}