Files
mediapipe/mediapipe/modules/face_landmark/face_landmark_front_cpu_image.pbtxt
T
MediaPipe Teamandchuoling 50c92c6623 Project import generated by Copybara.
GitOrigin-RevId: 27c70b5fe62ab71189d358ca122ee4b19c817a8f
2021-07-27 19:36:32 -04:00

45 lines
1.6 KiB
Protocol Buffer Text Format

# MediaPipe graph to detect/predict face landmarks on CPU.
type: "FaceLandmarkFrontCpuImage"
# Input image. (Image)
input_stream: "IMAGE:image"
# Max number of faces to detect/track. (int)
input_side_packet: "NUM_FACES:num_faces"
# 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"
# Converts Image to ImageFrame for FaceLandmarkFrontCpu to consume.
node {
calculator: "FromImageCalculator"
input_stream: "IMAGE:image"
output_stream: "IMAGE_CPU:image_frame"
}
node {
calculator: "FaceLandmarkFrontCpu"
input_stream: "IMAGE:image_frame"
input_side_packet: "NUM_FACES:num_faces"
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"
}