Files
mediapipe/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu_image.pbtxt
T
MediaPipe Teamandjqtang 710fb3de58 Project import generated by Copybara.
GitOrigin-RevId: 1610e588e497817fae2d9a458093ab6a370e2972
2021-08-18 17:45:46 -07:00

73 lines
2.9 KiB
Protocol Buffer Text Format

# MediaPipe graph to detect/predict hand landmarks on GPU.
#
# The procedure is done in two steps:
# - locate palms/hands
# - detect landmarks for each palm/hand.
# This graph tries to skip palm detection as much as possible by reusing
# previously detected/predicted landmarks for new images.
type: "HandLandmarkTrackingGpuImage"
# Input image. (Image)
input_stream: "IMAGE:image"
# Max number of hands to detect/track. (int)
input_side_packet: "NUM_HANDS:num_hands"
# Collection of detected/predicted hands, each represented as a list of
# landmarks. (std::vector<NormalizedLandmarkList>)
# NOTE: there will not be an output packet in the LANDMARKS stream for this
# particular timestamp if none of hands 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_hand_landmarks"
# Collection of handedness of the detected hands (i.e. is hand left or right),
# each represented as a Classification proto.
# Note that handedness is determined assuming the input image is mirrored,
# i.e., taken with a front-facing/selfie camera with images flipped
# horizontally.
output_stream: "HANDEDNESS:multi_handedness"
# The original input image. (Image)
output_stream: "IMAGE:image"
# Extra outputs (for debugging, for instance).
# Detected palms. (std::vector<Detection>)
output_stream: "PALM_DETECTIONS:palm_detections"
# Regions of interest calculated based on landmarks.
# (std::vector<NormalizedRect>)
output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
# Regions of interest calculated based on palm detections.
# (std::vector<NormalizedRect>)
output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
# Converts Image to GpuBuffer for HandLandmarkTrackingGpu to consume.
node {
calculator: "FromImageCalculator"
input_stream: "IMAGE: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 HandLandmarkTrackingGpu. 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: "HandLandmarkTrackingGpu"
input_stream: "IMAGE:gpu_buffer"
input_side_packet: "NUM_HANDS:num_hands"
output_stream: "LANDMARKS:multi_hand_landmarks"
output_stream: "HANDEDNESS:multi_handedness"
output_stream: "PALM_DETECTIONS:palm_detections"
output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
}