mediapipe-rs/mediapipe/modules/face_landmark/face_landmark_front_gpu_image.pbtxt

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2022-03-01 13:04:01 +01:00
# MediaPipe graph to detect/predict face landmarks on GPU.
type: "FaceLandmarkFrontGpuImage"
# Input image. (Image)
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"
# The throttled input image. (Image)
output_stream: "IMAGE:throttled_image"
# 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"
node {
calculator: "FlowLimiterCalculator"
input_stream: "image"
input_stream: "FINISHED:multi_face_landmarks"
input_stream_info: {
tag_index: "FINISHED"
back_edge: true
}
output_stream: "throttled_image"
options: {
[mediapipe.FlowLimiterCalculatorOptions.ext] {
max_in_flight: 1
max_in_queue: 1
}
}
}
# Converts Image to GpuBuffer for FaceLandmarkFrontGpu to consume.
node {
calculator: "FromImageCalculator"
input_stream: "IMAGE:throttled_image"
output_stream: "IMAGE_GPU:raw_gpu_buffer"
output_stream: "SOURCE_ON_GPU:is_gpu_image"
}
# TODO: Remove the extra flipping once adopting MlImage.
# If the source images are on gpu, flip the data vertically before sending them
# into FaceLandmarkFrontGpu. This maybe needed because OpenGL represents images
# assuming the image origin is at the bottom-left corner, whereas MediaPipe in
# general assumes the image origin is at the top-left corner.
node: {
calculator: "ImageTransformationCalculator"
input_stream: "IMAGE_GPU:raw_gpu_buffer"
input_stream: "FLIP_VERTICALLY:is_gpu_image"
output_stream: "IMAGE_GPU:gpu_buffer"
}
node {
calculator: "FaceLandmarkFrontGpu"
input_stream: "IMAGE:gpu_buffer"
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"
output_stream: "DETECTIONS:face_detections"
output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
}