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mediapipe/mediapipe/graphs/hand_tracking/hand_tracking_desktop.pbtxt
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MediaPipe Teamandchuoling b133b0f200 Project import generated by Copybara.
GitOrigin-RevId: afeb9cf5a8c069c0a566d16e1622bbb086170e4d
2020-05-21 13:37:51 -04:00

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# MediaPipe graph that performs hand tracking on desktop with TensorFlow Lite
# on CPU.
# Used in the example in
# mediapipe/examples/desktop/hand_tracking:hand_tracking_tflite.
# max_queue_size limits the number of packets enqueued on any input stream
# by throttling inputs to the graph. This makes the graph only process one
# frame per time.
max_queue_size: 1
# Decodes an input video file into images and a video header.
node {
calculator: "OpenCvVideoDecoderCalculator"
input_side_packet: "INPUT_FILE_PATH:input_video_path"
output_stream: "VIDEO:input_video"
output_stream: "VIDEO_PRESTREAM:input_video_header"
}
# Caches a hand-presence decision fed back from HandLandmarkSubgraph, and upon
# the arrival of the next input image sends out the cached decision with the
# timestamp replaced by that of the input image, essentially generating a packet
# that carries the previous hand-presence decision. Note that upon the arrival
# of the very first input image, an empty packet is sent out to jump start the
# feedback loop.
node {
calculator: "PreviousLoopbackCalculator"
input_stream: "MAIN:input_video"
input_stream: "LOOP:hand_presence"
input_stream_info: {
tag_index: "LOOP"
back_edge: true
}
output_stream: "PREV_LOOP:prev_hand_presence"
}
# Drops the incoming image if HandLandmarkSubgraph was able to identify hand
# presence in the previous image. Otherwise, passes the incoming image through
# to trigger a new round of hand detection in HandDetectionSubgraph.
node {
calculator: "GateCalculator"
input_stream: "input_video"
input_stream: "DISALLOW:prev_hand_presence"
output_stream: "hand_detection_input_video"
node_options: {
[type.googleapis.com/mediapipe.GateCalculatorOptions] {
empty_packets_as_allow: true
}
}
}
# Subgraph that detections hands (see hand_detection_cpu.pbtxt).
node {
calculator: "HandDetectionSubgraph"
input_stream: "hand_detection_input_video"
output_stream: "DETECTIONS:palm_detections"
output_stream: "NORM_RECT:hand_rect_from_palm_detections"
}
# Subgraph that localizes hand landmarks (see hand_landmark_cpu.pbtxt).
node {
calculator: "HandLandmarkSubgraph"
input_stream: "IMAGE:input_video"
input_stream: "NORM_RECT:hand_rect"
output_stream: "LANDMARKS:hand_landmarks"
output_stream: "NORM_RECT:hand_rect_from_landmarks"
output_stream: "HANDEDNESS:handedness"
output_stream: "PRESENCE:hand_presence"
}
# Caches a hand rectangle fed back from HandLandmarkSubgraph, and upon the
# arrival of the next input image sends out the cached rectangle with the
# timestamp replaced by that of the input image, essentially generating a packet
# that carries the previous hand rectangle. Note that upon the arrival of the
# very first input image, an empty packet is sent out to jump start the
# feedback loop.
node {
calculator: "PreviousLoopbackCalculator"
input_stream: "MAIN:input_video"
input_stream: "LOOP:hand_rect_from_landmarks"
input_stream_info: {
tag_index: "LOOP"
back_edge: true
}
output_stream: "PREV_LOOP:prev_hand_rect_from_landmarks"
}
# Merges a stream of hand rectangles generated by HandDetectionSubgraph and that
# generated by HandLandmarkSubgraph into a single output stream by selecting
# between one of the two streams. The former is selected if the incoming packet
# is not empty, i.e., hand detection is performed on the current image by
# HandDetectionSubgraph (because HandLandmarkSubgraph could not identify hand
# presence in the previous image). Otherwise, the latter is selected, which is
# never empty because HandLandmarkSubgraphs processes all images (that went
# through FlowLimiterCaculator).
node {
calculator: "MergeCalculator"
input_stream: "hand_rect_from_palm_detections"
input_stream: "prev_hand_rect_from_landmarks"
output_stream: "hand_rect"
}
# Subgraph that renders annotations and overlays them on top of the input
# images (see renderer_cpu.pbtxt).
node {
calculator: "RendererSubgraph"
input_stream: "IMAGE:input_video"
input_stream: "LANDMARKS:hand_landmarks"
input_stream: "NORM_RECT:hand_rect"
input_stream: "DETECTIONS:palm_detections"
input_stream: "HANDEDNESS:handedness"
output_stream: "IMAGE:output_video"
}
# Encodes the annotated images into a video file, adopting properties specified
# in the input video header, e.g., video framerate.
node {
calculator: "OpenCvVideoEncoderCalculator"
input_stream: "VIDEO:output_video"
input_stream: "VIDEO_PRESTREAM:input_video_header"
input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
node_options: {
[type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
codec: "avc1"
video_format: "mp4"
}
}
}