mediapipe/mediapipe/graphs/hand_tracking/subgraphs/hand_detection_gpu.pbtxt

# MediaPipe hand detection subgraph.

type: "HandDetectionSubgraph"

input_stream: "input_video"
output_stream: "DETECTIONS:palm_detections"
output_stream: "NORM_RECT:hand_rect_from_palm_detections"

# Transforms the input image on GPU to a 256x256 image. To scale the input
# image, the scale_mode option is set to FIT to preserve the aspect ratio,
# resulting in potential letterboxing in the transformed image.
node: {
  calculator: "ImageTransformationCalculator"
  input_stream: "IMAGE_GPU:input_video"
  output_stream: "IMAGE_GPU:transformed_input_video"
  output_stream: "LETTERBOX_PADDING:letterbox_padding"
  node_options: {
    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
      output_width: 256
      output_height: 256
      scale_mode: FIT
    }
  }
}

# Generates a single side packet containing a TensorFlow Lite op resolver that
# supports custom ops needed by the model used in this graph.
node {
  calculator: "TfLiteCustomOpResolverCalculator"
  output_side_packet: "opresolver"
  node_options: {
    [type.googleapis.com/mediapipe.TfLiteCustomOpResolverCalculatorOptions] {
      use_gpu: true
    }
  }
}

# Converts the transformed input image on GPU into an image tensor stored as a
# TfLiteTensor.
node {
  calculator: "TfLiteConverterCalculator"
  input_stream: "IMAGE_GPU:transformed_input_video"
  output_stream: "TENSORS_GPU:image_tensor"
}

# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
# vector of tensors representing, for instance, detection boxes/keypoints and
# scores.
node {
  calculator: "TfLiteInferenceCalculator"
  input_stream: "TENSORS_GPU:image_tensor"
  output_stream: "TENSORS_GPU:detection_tensors"
  input_side_packet: "CUSTOM_OP_RESOLVER:opresolver"
  node_options: {
    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
      model_path: "mediapipe/models/palm_detection.tflite"
      use_gpu: true
    }
  }
}

# Generates a single side packet containing a vector of SSD anchors based on
# the specification in the options.
node {
  calculator: "SsdAnchorsCalculator"
  output_side_packet: "anchors"
  node_options: {
    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
      num_layers: 5
      min_scale: 0.1171875
      max_scale: 0.75
      input_size_height: 256
      input_size_width: 256
      anchor_offset_x: 0.5
      anchor_offset_y: 0.5
      strides: 8
      strides: 16
      strides: 32
      strides: 32
      strides: 32
      aspect_ratios: 1.0
      fixed_anchor_size: true
    }
  }
}

# Decodes the detection tensors generated by the TensorFlow Lite model, based on
# the SSD anchors and the specification in the options, into a vector of
# detections. Each detection describes a detected object.
node {
  calculator: "TfLiteTensorsToDetectionsCalculator"
  input_stream: "TENSORS_GPU:detection_tensors"
  input_side_packet: "ANCHORS:anchors"
  output_stream: "DETECTIONS:detections"
  node_options: {
    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
      num_classes: 1
      num_boxes: 2944
      num_coords: 18
      box_coord_offset: 0
      keypoint_coord_offset: 4
      num_keypoints: 7
      num_values_per_keypoint: 2
      sigmoid_score: true
      score_clipping_thresh: 100.0
      reverse_output_order: true

      x_scale: 256.0
      y_scale: 256.0
      h_scale: 256.0
      w_scale: 256.0
      min_score_thresh: 0.7
    }
  }
}

# Performs non-max suppression to remove excessive detections.
node {
  calculator: "NonMaxSuppressionCalculator"
  input_stream: "detections"
  output_stream: "filtered_detections"
  node_options: {
    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
      min_suppression_threshold: 0.3
      overlap_type: INTERSECTION_OVER_UNION
      algorithm: WEIGHTED
      return_empty_detections: true
    }
  }
}

# Maps detection label IDs to the corresponding label text ("Palm"). The label
# map is provided in the label_map_path option.
node {
  calculator: "DetectionLabelIdToTextCalculator"
  input_stream: "filtered_detections"
  output_stream: "labeled_detections"
  node_options: {
    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
      label_map_path: "mediapipe/models/palm_detection_labelmap.txt"
    }
  }
}

# Adjusts detection locations (already normalized to [0.f, 1.f]) on the
# letterboxed image (after image transformation with the FIT scale mode) to the
# corresponding locations on the same image with the letterbox removed (the
# input image to the graph before image transformation).
node {
  calculator: "DetectionLetterboxRemovalCalculator"
  input_stream: "DETECTIONS:labeled_detections"
  input_stream: "LETTERBOX_PADDING:letterbox_padding"
  output_stream: "DETECTIONS:palm_detections"
}

# Extracts image size from the input images.
node {
  calculator: "ImagePropertiesCalculator"
  input_stream: "IMAGE_GPU:input_video"
  output_stream: "SIZE:image_size"
}

# Converts results of palm detection into a rectangle (normalized by image size)
# that encloses the palm and is rotated such that the line connecting center of
# the wrist and MCP of the middle finger is aligned with the Y-axis of the
# rectangle.
node {
  calculator: "DetectionsToRectsCalculator"
  input_stream: "DETECTIONS:palm_detections"
  input_stream: "IMAGE_SIZE:image_size"
  output_stream: "NORM_RECT:palm_rect"
  node_options: {
    [type.googleapis.com/mediapipe.DetectionsToRectsCalculatorOptions] {
      rotation_vector_start_keypoint_index: 0  # Center of wrist.
      rotation_vector_end_keypoint_index: 2  # MCP of middle finger.
      rotation_vector_target_angle_degrees: 90
      output_zero_rect_for_empty_detections: true
    }
  }
}

# Expands and shifts the rectangle that contains the palm so that it's likely
# to cover the entire hand.
node {
  calculator: "RectTransformationCalculator"
  input_stream: "NORM_RECT:palm_rect"
  input_stream: "IMAGE_SIZE:image_size"
  output_stream: "hand_rect_from_palm_detections"
  node_options: {
    [type.googleapis.com/mediapipe.RectTransformationCalculatorOptions] {
      scale_x: 2.6
      scale_y: 2.6
      shift_y: -0.5
      square_long: true
    }
  }
}