# MediaPipe graph performing common processing to detect faces using
# face_detection_full_range_sparse.tflite model, currently consisting of tensor
# post processing.
#
# EXAMPLE:
#   node {
#     calculator: "FaceDetectionFullRangeCommon"
#     input_stream: "TENSORS:detection_tensors"
#     input_stream: "MATRIX:transform_matrix"
#     output_stream: "DETECTIONS:detections"
#   }

type: "FaceDetectionShortRangeCommon"

# Detection tensors. (std::vector<Tensor>)
input_stream: "TENSORS:detection_tensors"

# A 4x4 row-major-order matrix that maps a point represented in the detection
# tensors to a desired coordinate system, e.g., in the original input image
# before scaling/cropping. (std::array<float, 16>)
input_stream: "MATRIX:transform_matrix"

# Detected faces. (std::vector<Detection>)
# NOTE: there will not be an output packet in the DETECTIONS stream for this
# particular timestamp if none of faces detected. However, the MediaPipe
# framework will internally inform the downstream calculators of the absence of
# this packet so that they don't wait for it unnecessarily.
output_stream: "DETECTIONS:detections"

# 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"
  options: {
    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
      num_layers: 1
      min_scale: 0.1484375
      max_scale: 0.75
      input_size_height: 192
      input_size_width: 192
      anchor_offset_x: 0.5
      anchor_offset_y: 0.5
      strides: 4
      aspect_ratios: 1.0
      fixed_anchor_size: true
      interpolated_scale_aspect_ratio: 0.0
    }
  }
}

# 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: "TensorsToDetectionsCalculator"
  input_stream: "TENSORS:detection_tensors"
  input_side_packet: "ANCHORS:anchors"
  output_stream: "DETECTIONS:unfiltered_detections"
  options: {
    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
      num_classes: 1
      num_boxes: 2304
      num_coords: 16
      box_coord_offset: 0
      keypoint_coord_offset: 4
      num_keypoints: 6
      num_values_per_keypoint: 2
      sigmoid_score: true
      score_clipping_thresh: 100.0
      reverse_output_order: true
      x_scale: 192.0
      y_scale: 192.0
      h_scale: 192.0
      w_scale: 192.0
      min_score_thresh: 0.6
    }
  }
}

# Performs non-max suppression to remove excessive detections.
node {
  calculator: "NonMaxSuppressionCalculator"
  input_stream: "unfiltered_detections"
  output_stream: "filtered_detections"
  options: {
    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
      min_suppression_threshold: 0.3
      overlap_type: INTERSECTION_OVER_UNION
      algorithm: WEIGHTED
    }
  }
}

# Projects the detections from input tensor to the corresponding locations on
# the original image (input to the graph).
node {
  calculator: "DetectionProjectionCalculator"
  input_stream: "DETECTIONS:filtered_detections"
  input_stream: "PROJECTION_MATRIX:transform_matrix"
  output_stream: "DETECTIONS:detections"
}