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MediaPipe Team
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# MediaPipe graph to detect/predict upper-body pose landmarks. (CPU input, and
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# inference is executed on CPU.)
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#
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# It is required that "pose_landmark_full_body.tflite" or
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# "pose_landmark_upper_body.tflite" is available at
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# "mediapipe/modules/pose_landmark/pose_landmark_full_body.tflite"
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# or
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# "mediapipe/modules/pose_landmark/pose_landmark_upper_body.tflite"
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# path respectively during execution, depending on the specification in the
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# UPPER_BODY_ONLY input side packet.
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#
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# EXAMPLE:
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# node {
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# calculator: "PoseLandmarkByRoiCpu"
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# input_side_packet: "UPPER_BODY_ONLY:upper_body_only"
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# input_stream: "IMAGE:image"
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# input_stream: "ROI:roi"
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# output_stream: "LANDMARKS:landmarks"
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# }
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type: "PoseLandmarkByRoiCpu"
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# CPU image. (ImageFrame)
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input_stream: "IMAGE:image"
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# ROI (region of interest) within the given image where a pose is located.
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# (NormalizedRect)
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input_stream: "ROI:roi"
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# Whether to detect/predict the full set of pose landmarks (see below), or only
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# those on the upper body. If unspecified, functions as set to false. (bool)
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# Note that upper-body-only prediction may be more accurate for use cases where
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# the lower-body parts are mostly out of view.
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input_side_packet: "UPPER_BODY_ONLY:upper_body_only"
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# Pose landmarks within the given ROI. (NormalizedLandmarkList)
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# We have 33 landmarks (see pose_landmark_full_body_topology.svg) with the
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# first 25 fall on the upper body (see pose_landmark_upper_body_topology.svg),
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# and there are other auxiliary key points.
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# 0 - nose
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# 1 - left eye (inner)
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# 2 - left eye
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# 3 - left eye (outer)
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# 4 - right eye (inner)
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# 5 - right eye
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# 6 - right eye (outer)
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# 7 - left ear
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# 8 - right ear
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# 9 - mouth (left)
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# 10 - mouth (right)
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# 11 - left shoulder
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# 12 - right shoulder
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# 13 - left elbow
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# 14 - right elbow
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# 15 - left wrist
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# 16 - right wrist
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# 17 - left pinky
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# 18 - right pinky
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# 19 - left index
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# 20 - right index
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# 21 - left thumb
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# 22 - right thumb
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# 23 - left hip
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# 24 - right hip
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# 25 - left knee
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# 26 - right knee
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# 27 - left ankle
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# 28 - right ankle
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# 29 - left heel
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# 30 - right heel
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# 31 - left foot index
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# 32 - right foot index
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#
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# NOTE: if a pose is not present within the given ROI, for this particular
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# timestamp there will not be an output packet in the LANDMARKS stream. However,
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# the MediaPipe framework will internally inform the downstream calculators of
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# the absence of this packet so that they don't wait for it unnecessarily.
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output_stream: "LANDMARKS:landmarks"
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# Auxiliary landmarks for deriving the ROI in the subsequent image.
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# (NormalizedLandmarkList)
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output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
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# Transforms the input image into a 256x256 tensor while keeping the aspect
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# ratio (what is expected by the corresponding model), resulting in potential
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# letterboxing in the transformed image.
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node: {
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calculator: "ImageToTensorCalculator"
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input_stream: "IMAGE:image"
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input_stream: "NORM_RECT:roi"
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output_stream: "TENSORS:input_tensors"
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output_stream: "LETTERBOX_PADDING:letterbox_padding"
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options: {
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[mediapipe.ImageToTensorCalculatorOptions.ext] {
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output_tensor_width: 256
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output_tensor_height: 256
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keep_aspect_ratio: true
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output_tensor_float_range {
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min: 0.0
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max: 1.0
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}
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}
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}
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}
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# Loads the pose landmark TF Lite model.
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node {
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calculator: "PoseLandmarkModelLoader"
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input_side_packet: "UPPER_BODY_ONLY:upper_body_only"
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output_side_packet: "MODEL:model"
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}
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# Runs model inference on CPU.
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node {
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calculator: "InferenceCalculator"
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input_side_packet: "MODEL:model"
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input_stream: "TENSORS:input_tensors"
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output_stream: "TENSORS:output_tensors"
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options: {
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[mediapipe.InferenceCalculatorOptions.ext] {
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delegate { xnnpack {} }
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}
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}
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}
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# Splits a vector of TFLite tensors to multiple vectors according to the ranges
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# specified in option.
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node {
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calculator: "SplitTensorVectorCalculator"
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input_stream: "output_tensors"
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output_stream: "landmark_tensors"
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output_stream: "pose_flag_tensor"
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options: {
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[mediapipe.SplitVectorCalculatorOptions.ext] {
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ranges: { begin: 0 end: 1 }
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ranges: { begin: 1 end: 2 }
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}
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}
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}
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# Converts the pose-flag tensor into a float that represents the confidence
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# score of pose presence.
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node {
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calculator: "TensorsToFloatsCalculator"
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input_stream: "TENSORS:pose_flag_tensor"
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output_stream: "FLOAT:pose_presence_score"
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}
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# Applies a threshold to the confidence score to determine whether a pose is
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# present.
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node {
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calculator: "ThresholdingCalculator"
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input_stream: "FLOAT:pose_presence_score"
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output_stream: "FLAG:pose_presence"
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options: {
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[mediapipe.ThresholdingCalculatorOptions.ext] {
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threshold: 0.5
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}
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}
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}
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# Drops landmark tensors if pose is not present.
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node {
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calculator: "GateCalculator"
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input_stream: "landmark_tensors"
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input_stream: "ALLOW:pose_presence"
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output_stream: "ensured_landmark_tensors"
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}
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# Decodes the landmark tensors into a vector of landmarks, where the landmark
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# coordinates are normalized by the size of the input image to the model.
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node {
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calculator: "SwitchContainer"
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input_side_packet: "ENABLE:upper_body_only"
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input_stream: "TENSORS:ensured_landmark_tensors"
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output_stream: "NORM_LANDMARKS:raw_landmarks"
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options: {
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[mediapipe.SwitchContainerOptions.ext] {
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contained_node: {
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calculator: "TensorsToLandmarksCalculator"
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options: {
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[mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
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num_landmarks: 35
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input_image_width: 256
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input_image_height: 256
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visibility_activation: SIGMOID
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presence_activation: SIGMOID
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}
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}
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}
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contained_node: {
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calculator: "TensorsToLandmarksCalculator"
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options: {
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[mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
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num_landmarks: 27
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input_image_width: 256
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input_image_height: 256
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visibility_activation: SIGMOID
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presence_activation: SIGMOID
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}
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}
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}
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}
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}
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}
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# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed pose
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# image (after image transformation with the FIT scale mode) to the
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# corresponding locations on the same image with the letterbox removed (pose
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# image before image transformation).
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node {
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calculator: "LandmarkLetterboxRemovalCalculator"
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input_stream: "LANDMARKS:raw_landmarks"
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input_stream: "LETTERBOX_PADDING:letterbox_padding"
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output_stream: "LANDMARKS:adjusted_landmarks"
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}
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# Projects the landmarks from the cropped pose image to the corresponding
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# locations on the full image before cropping (input to the graph).
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node {
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calculator: "LandmarkProjectionCalculator"
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input_stream: "NORM_LANDMARKS:adjusted_landmarks"
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input_stream: "NORM_RECT:roi"
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output_stream: "NORM_LANDMARKS:all_landmarks"
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}
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# Splits the landmarks into two sets: the actual pose landmarks and the
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# auxiliary landmarks.
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node {
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calculator: "SwitchContainer"
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input_side_packet: "ENABLE:upper_body_only"
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input_stream: "all_landmarks"
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output_stream: "landmarks"
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output_stream: "auxiliary_landmarks"
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options: {
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[mediapipe.SwitchContainerOptions.ext] {
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contained_node: {
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calculator: "SplitNormalizedLandmarkListCalculator"
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options: {
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[mediapipe.SplitVectorCalculatorOptions.ext] {
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ranges: { begin: 0 end: 33 }
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ranges: { begin: 33 end: 35 }
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}
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}
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}
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contained_node: {
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calculator: "SplitNormalizedLandmarkListCalculator"
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options: {
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[mediapipe.SplitVectorCalculatorOptions.ext] {
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ranges: { begin: 0 end: 25 }
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ranges: { begin: 25 end: 27 }
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}
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}
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}
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}
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}
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}
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