face_mesh_desktop_live.pbtxt

# MediaPipe graph that performs face mesh with TensorFlow Lite on CPU.

# Input image. (ImageFrame)
input_stream: "input_video"

# Output image with rendered results. (ImageFrame)
output_stream: "output_video"
# Collection of detected/processed faces, each represented as a list of
# landmarks. (std::vector<NormalizedLandmarkList>)
output_stream: "multi_face_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:output_video"
  input_stream_info: {
    tag_index: "FINISHED"
    back_edge: true
  }
  output_stream: "throttled_input_video"
}

# Defines side packets for further use in the graph.
node {
  calculator: "ConstantSidePacketCalculator"
  output_side_packet: "PACKET:num_faces"
  node_options: {
    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
      packet { int_value: 1 }
    }
  }
}


# 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: "HandLandmarkTrackingCpu"
#  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:multi_palm_detections"
#  output_stream: "HAND_ROIS_FROM_LANDMARKS:multi_hand_rects"
#  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:multi_palm_rects"
#}

# Subgraph that detects faces and corresponding landmarks.
node {
  calculator: "FaceLandmarkFrontCpu"
  input_stream: "IMAGE:throttled_input_video"
  input_side_packet: "NUM_FACES:num_faces"
  output_stream: "LANDMARKS:multi_face_landmarks"
  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
  output_stream: "DETECTIONS:face_detections"
  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
}

# Subgraph that renders face-landmark annotation onto the input image.
node {
  calculator: "RendererCustom"
# input_stream: "IMAGE:throttled_input_video"
  input_stream: "LANDMARKS:multi_face_landmarks"
  #input_stream: "LANDMARKS:hand_landmarks"

  input_stream: "IMAGE:throttled_input_video"
  
  input_stream: "NORM_RECTS:0:face_rects_from_landmarks"
  #input_stream: "NORM_RECTS:1:multi_palm_rects"
  #input_stream: "NORM_RECTS:2:multi_hand_rects"

  input_stream: "DETECTIONS:face_detections"
  output_stream: "IMAGE:output_video"
}

renderer.pbtxt

# MediaPipe face mesh rendering subgraph.

type: "RendererCustom"

# CPU image. (ImageFrame)
input_stream: "IMAGE:input_image"
# Collection of detected/predicted faces, each represented as a list of
# landmarks. (std::vector<NormalizedLandmarkList>)
input_stream: "LANDMARKS:multi_face_landmarks"
# Regions of interest calculated based on palm detections.
# (std::vector<NormalizedRect>)
input_stream: "NORM_RECTS:rects"
# Detected palms. (std::vector<Detection>)
input_stream: "DETECTIONS:detections"

# CPU image with rendered data. (ImageFrame)
output_stream: "IMAGE:output_image"

# Converts detections to drawing primitives for annotation overlay.
node {
  calculator: "DetectionsToRenderDataCalculator"
  input_stream: "DETECTIONS:detections"
  output_stream: "RENDER_DATA:detections_render_data"
  node_options: {
    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
      thickness: 4.0
      color { r: 0 g: 255 b: 0 }
    }
  }
}

# Outputs each element of multi_face_landmarks at a fake timestamp for the rest
# of the graph to process. At the end of the loop, outputs the BATCH_END
# timestamp for downstream calculators to inform them that all elements in the
# vector have been processed.
node {
  calculator: "BeginLoopNormalizedLandmarkListVectorCalculator"
  input_stream: "ITERABLE:multi_face_landmarks"
  output_stream: "ITEM:face_landmarks"
  output_stream: "BATCH_END:landmark_timestamp"
}

# Converts landmarks to drawing primitives for annotation overlay.
node {
  calculator: "FaceLandmarksToRenderDataCalculator"
  input_stream: "NORM_LANDMARKS:face_landmarks"
  output_stream: "RENDER_DATA:landmarks_render_data"
  node_options: {
    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
      landmark_color { r: 234 g: 5 b: 255 }
      connection_color { r: 100 g: 100 b: 100 }
      thickness: 2
      visualize_landmark_depth: false
    }
  }
}

# Collects a RenderData object for each hand into a vector. Upon receiving the
# BATCH_END timestamp, outputs the vector of RenderData at the BATCH_END
# timestamp.
node {
  calculator: "EndLoopRenderDataCalculator"
  input_stream: "ITEM:landmarks_render_data"
  input_stream: "BATCH_END:landmark_timestamp"
  output_stream: "ITERABLE:multi_face_landmarks_render_data"
}

# Converts normalized rects to drawing primitives for annotation overlay.
node {
  calculator: "RectToRenderDataCalculator"
  input_stream: "NORM_RECTS:rects"
  output_stream: "RENDER_DATA:rects_render_data"
  node_options: {
    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
      filled: false
      color { r: 255 g: 0 b: 0 }
      thickness: 4.0
    }
  }
}

# Draws annotations and overlays them on top of the input images.
node {
  calculator: "AnnotationOverlayCalculator"
 # input_stream: "IMAGE:input_image"
  input_stream: "detections_render_data"
  input_stream: "VECTOR:multi_face_landmarks_render_data"
  input_stream: "rects_render_data"
  output_stream: "IMAGE:output_image"
  node_options: {
    [type.googleapis.com/mediapipe.AnnotationOverlayCalculatorOptions] {
      canvas_width_px: 1600
      canvas_height_px: 1200
    }
  }