# MediaPipe graph to detect/predict face landmarks. (GPU input, and inference is
# executed on GPU.) This graph tries to skip face detection as much as possible
# by using previously detected/predicted landmarks for new images.
#
# It is required that "face_detection_short_range.tflite" is available at
# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
# path during execution.
#
# It is required that "face_landmark.tflite" is available at
# "mediapipe/modules/face_landmark/face_landmark.tflite"
# path during execution if `with_attention` is not set or set to `false`.
#
# It is required that "face_landmark_with_attention.tflite" is available at
# "mediapipe/modules/face_landmark/face_landmark_with_attention.tflite"
# path during execution if `with_attention` is set to `true`.
#
# EXAMPLE:
#   node {
#     calculator: "FaceLandmarkFrontGpu"
#     input_stream: "IMAGE:image"
#     input_side_packet: "NUM_FACES:num_faces"
#     input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
#     input_side_packet: "WITH_ATTENTION:with_attention"
#     output_stream: "LANDMARKS:multi_face_landmarks"
#   }

type: "FaceLandmarkFrontGpu"

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

# Max number of faces to detect/track. (int)
input_side_packet: "NUM_FACES:num_faces"

# Whether landmarks on the previous image should be used to help localize
# landmarks on the current image. (bool)
input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"

# Whether to run face mesh model with attention on lips and eyes. (bool)
# Attention provides more accuracy on lips and eye regions as well as iris
# landmarks.
input_side_packet: "WITH_ATTENTION:with_attention"

# Collection of detected/predicted faces, each represented as a list of 468 face
# landmarks. (std::vector<NormalizedLandmarkList>)
# NOTE: there will not be an output packet in the LANDMARKS 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: "LANDMARKS:multi_face_landmarks"

# Extra outputs (for debugging, for instance).
# Detected faces. (std::vector<Detection>)
output_stream: "DETECTIONS:face_detections"
# Regions of interest calculated based on landmarks.
# (std::vector<NormalizedRect>)
output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
# Regions of interest calculated based on face detections.
# (std::vector<NormalizedRect>)
output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"

# When the optional input side packet "use_prev_landmarks" is either absent or
# set to true, uses the landmarks on the previous image to help localize
# landmarks on the current image.
node {
  calculator: "GateCalculator"
  input_side_packet: "ALLOW:use_prev_landmarks"
  input_stream: "prev_face_rects_from_landmarks"
  output_stream: "gated_prev_face_rects_from_landmarks"
  options: {
    [mediapipe.GateCalculatorOptions.ext] {
      allow: true
    }
  }
}

# Determines if an input vector of NormalizedRect has a size greater than or
# equal to the provided num_faces.
node {
  calculator: "NormalizedRectVectorHasMinSizeCalculator"
  input_stream: "ITERABLE:gated_prev_face_rects_from_landmarks"
  input_side_packet: "num_faces"
  output_stream: "prev_has_enough_faces"
}

# Drops the incoming image if enough faces have already been identified from the
# previous image. Otherwise, passes the incoming image through to trigger a new
# round of face detection.
node {
  calculator: "GateCalculator"
  input_stream: "image"
  input_stream: "DISALLOW:prev_has_enough_faces"
  output_stream: "gated_image"
  options: {
    [mediapipe.GateCalculatorOptions.ext] {
      empty_packets_as_allow: true
    }
  }
}

# Detects faces.
node {
  calculator: "FaceDetectionShortRangeGpu"
  input_stream: "IMAGE:gated_image"
  output_stream: "DETECTIONS:all_face_detections"
}

# Makes sure there are no more detections than the provided num_faces.
node {
  calculator: "ClipDetectionVectorSizeCalculator"
  input_stream: "all_face_detections"
  output_stream: "face_detections"
  input_side_packet: "num_faces"
}

# Calculate size of the image.
node {
  calculator: "ImagePropertiesCalculator"
  input_stream: "IMAGE_GPU:gated_image"
  output_stream: "SIZE:gated_image_size"
}

# Outputs each element of face_detections at a fake timestamp for the rest of
# the graph to process. Clones the image size packet for each face_detection at
# the fake timestamp. 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: "BeginLoopDetectionCalculator"
  input_stream: "ITERABLE:face_detections"
  input_stream: "CLONE:gated_image_size"
  output_stream: "ITEM:face_detection"
  output_stream: "CLONE:detections_loop_image_size"
  output_stream: "BATCH_END:detections_loop_end_timestamp"
}

# Calculates region of interest based on face detections, so that can be used
# to detect landmarks.
node {
  calculator: "FaceDetectionFrontDetectionToRoi"
  input_stream: "DETECTION:face_detection"
  input_stream: "IMAGE_SIZE:detections_loop_image_size"
  output_stream: "ROI:face_rect_from_detection"
}

# Collects a NormalizedRect for each face into a vector. Upon receiving the
# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
# timestamp.
node {
  calculator: "EndLoopNormalizedRectCalculator"
  input_stream: "ITEM:face_rect_from_detection"
  input_stream: "BATCH_END:detections_loop_end_timestamp"
  output_stream: "ITERABLE:face_rects_from_detections"
}

# Performs association between NormalizedRect vector elements from previous
# image and rects based on face detections from the current image. This
# calculator ensures that the output face_rects vector doesn't contain
# overlapping regions based on the specified min_similarity_threshold.
node {
  calculator: "AssociationNormRectCalculator"
  input_stream: "face_rects_from_detections"
  input_stream: "gated_prev_face_rects_from_landmarks"
  output_stream: "face_rects"
  options: {
    [mediapipe.AssociationCalculatorOptions.ext] {
      min_similarity_threshold: 0.5
    }
  }
}

# Calculate size of the image.
node {
  calculator: "ImagePropertiesCalculator"
  input_stream: "IMAGE_GPU:image"
  output_stream: "SIZE:image_size"
}

# Outputs each element of face_rects at a fake timestamp for the rest of the
# graph to process. Clones image and image size packets for each
# single_face_rect at the fake timestamp. 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: "BeginLoopNormalizedRectCalculator"
  input_stream: "ITERABLE:face_rects"
  input_stream: "CLONE:0:image"
  input_stream: "CLONE:1:image_size"
  output_stream: "ITEM:face_rect"
  output_stream: "CLONE:0:landmarks_loop_image"
  output_stream: "CLONE:1:landmarks_loop_image_size"
  output_stream: "BATCH_END:landmarks_loop_end_timestamp"
}

# Detects face landmarks within specified region of interest of the image.
node {
  calculator: "FaceLandmarkGpu"
  input_stream: "IMAGE:landmarks_loop_image"
  input_stream: "ROI:face_rect"
  input_side_packet: "WITH_ATTENTION:with_attention"
  output_stream: "LANDMARKS:face_landmarks"
}

# Calculates region of interest based on face landmarks, so that can be reused
# for subsequent image.
node {
  calculator: "FaceLandmarkLandmarksToRoi"
  input_stream: "LANDMARKS:face_landmarks"
  input_stream: "IMAGE_SIZE:landmarks_loop_image_size"
  output_stream: "ROI:face_rect_from_landmarks"
}

# Collects a set of landmarks for each face into a vector. Upon receiving the
# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
# timestamp.
node {
  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
  input_stream: "ITEM:face_landmarks"
  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
  output_stream: "ITERABLE:multi_face_landmarks"
}

# Collects a NormalizedRect for each face into a vector. Upon receiving the
# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
# timestamp.
node {
  calculator: "EndLoopNormalizedRectCalculator"
  input_stream: "ITEM:face_rect_from_landmarks"
  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
  output_stream: "ITERABLE:face_rects_from_landmarks"
}

# Caches face rects calculated from landmarks, and upon the arrival of the next
# input image, sends out the cached rects with timestamps replaced by that of
# the input image, essentially generating a packet that carries the previous
# face rects. Note that upon the arrival of the very first input image, a
# timestamp bound update occurs to jump start the feedback loop.
node {
  calculator: "PreviousLoopbackCalculator"
  input_stream: "MAIN:image"
  input_stream: "LOOP:face_rects_from_landmarks"
  input_stream_info: {
    tag_index: "LOOP"
    back_edge: true
  }
  output_stream: "PREV_LOOP:prev_face_rects_from_landmarks"
}