mediapipe/mediapipe/graphs/pose_tracking/pose_tracking_cpu.pbtxt
2021-07-29 02:04:57 -04:00

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# MediaPipe graph that performs pose tracking with TensorFlow Lite on CPU.
# CPU buffer. (ImageFrame)
input_stream: "input_video"
# Transfers the input image from GPU to CPU memory for the purpose of
# demonstrating a CPU-based pipeline. Note that the input image on GPU has the
# origin defined at the bottom-left corner (OpenGL convention). As a result,
# the transferred image on CPU also shares the same representation.
node: {
calculator: "GpuBufferToImageFrameCalculator"
input_stream: "input_video"
output_stream: "input_video_cpu"
}
# Output image with rendered results. (ImageFrame)
output_stream: "output_video"
# Pose landmarks. (NormalizedLandmarkList)
output_stream: "pose_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_cpu"
input_stream: "FINISHED:output_video"
input_stream_info: {
tag_index: "FINISHED"
back_edge: true
}
output_stream: "throttled_input_video"
}
# Subgraph that detects poses and corresponding landmarks.
node {
calculator: "PoseLandmarkCpu"
input_stream: "IMAGE:throttled_input_video"
output_stream: "LANDMARKS:pose_landmarks"
output_stream: "DETECTION:pose_detection"
output_stream: "ROI_FROM_LANDMARKS:roi_from_landmarks"
}
# Subgraph that renders pose-landmark annotation onto the input image.
node {
calculator: "PoseRendererCpu"
input_stream: "IMAGE:throttled_input_video"
input_stream: "LANDMARKS:pose_landmarks"
input_stream: "ROI:roi_from_landmarks"
input_stream: "DETECTION:pose_detection"
output_stream: "IMAGE:output_video_cpu"
}
# Transfers the annotated image from CPU back to GPU memory, to be sent out of
# the graph.
node: {
calculator: "ImageFrameToGpuBufferCalculator"
input_stream: "output_video_cpu"
output_stream: "output_video"
}