67 lines
2.5 KiB
Plaintext
67 lines
2.5 KiB
Plaintext
|
# MediaPipe graph that performs face mesh with TensorFlow Lite on GPU.
|
||
|
|
|
||
|
|
# Input image. (GpuBuffer)
|
||
|
|
input_stream: "input_video"
|
||
|
|
|
||
|
|
# Output image with rendered results. (GpuBuffer)
|
||
|
|
output_stream: "output_video"
|
||
|
|
# Collection of detected/processed faces, each represented as a list of
|
||
|
|
# landmarks. (std::vector<NormalizedLandmarkList>)
|
||
|
|
output_stream: "multi_face_landmarks"
|
||
|
|
|
||
|
|
# Throttles the images flowing downstream for flow control. It passes through
|
||
|
|
# the very first incoming image unaltered, and waits for downstream nodes
|
||
|
|
# (calculators and subgraphs) in the graph to finish their tasks before it
|
||
|
|
# passes through another image. All images that come in while waiting are
|
||
|
|
# dropped, limiting the number of in-flight images in most part of the graph to
|
||
|
|
# 1. This prevents the downstream nodes from queuing up incoming images and data
|
||
|
|
# excessively, which leads to increased latency and memory usage, unwanted in
|
||
|
|
# real-time mobile applications. It also eliminates unnecessarily computation,
|
||
|
|
# e.g., the output produced by a node may get dropped downstream if the
|
||
|
|
# subsequent nodes are still busy processing previous inputs.
|
||
|
|
node {
|
||
|
|
calculator: "FlowLimiterCalculator"
|
||
|
|
input_stream: "input_video"
|
||
|
|
input_stream: "FINISHED:output_video"
|
||
|
|
input_stream_info: {
|
||
|
|
tag_index: "FINISHED"
|
||
|
|
back_edge: true
|
||
|
|
}
|
||
|
|
output_stream: "throttled_input_video"
|
||
|
|
}
|
||
|
|
|
||
|
|
# Defines side packets for further use in the graph.
|
||
|
|
node {
|
||
|
|
calculator: "ConstantSidePacketCalculator"
|
||
|
|
output_side_packet: "PACKET:0:num_faces"
|
||
|
|
output_side_packet: "PACKET:1:with_attention"
|
||
|
|
node_options: {
|
||
|
|
[type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
|
||
|
|
packet { int_value: 1 }
|
||
|
|
packet { bool_value: true }
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
# Subgraph that detects faces and corresponding landmarks.
|
||
|
|
node {
|
||
|
|
calculator: "FaceLandmarkFrontGpu"
|
||
|
|
input_stream: "IMAGE:throttled_input_video"
|
||
|
|
input_side_packet: "NUM_FACES:num_faces"
|
||
|
|
input_side_packet: "WITH_ATTENTION:with_attention"
|
||
|
|
output_stream: "LANDMARKS:multi_face_landmarks"
|
||
|
|
output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
|
||
|
|
output_stream: "DETECTIONS:face_detections"
|
||
|
|
output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
|
||
|
|
}
|
||
|
|
|
||
|
|
# Subgraph that renders face-landmark annotation onto the input image.
|
||
|
|
node {
|
||
|
|
calculator: "FaceRendererGpu"
|
||
|
|
input_stream: "IMAGE:throttled_input_video"
|
||
|
|
input_stream: "LANDMARKS:multi_face_landmarks"
|
||
|
|
input_stream: "NORM_RECTS:face_rects_from_landmarks"
|
||
|
|
input_stream: "DETECTIONS:face_detections"
|
||
|
|
output_stream: "IMAGE:output_video"
|
||
|
|
}
|