88 lines
3.1 KiB
Plaintext
88 lines
3.1 KiB
Plaintext
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# MediaPipe graph to detect/predict face landmarks on CPU.
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type: "FaceLandmarkFrontCpuImage"
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# Input image. (Image)
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input_stream: "IMAGE:image"
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# Max number of faces to detect/track. (int)
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input_side_packet: "NUM_FACES:num_faces"
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# Whether landmarks on the previous image should be used to help localize
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# landmarks on the current image. (bool)
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input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
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# Whether to run face mesh model with attention on lips and eyes. (bool)
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# Attention provides more accuracy on lips and eye regions as well as iris
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# landmarks.
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input_side_packet: "WITH_ATTENTION:with_attention"
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# The throttled input image. (Image)
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output_stream: "IMAGE:throttled_image"
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# Collection of detected/predicted faces, each represented as a list of 468 face
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# landmarks. (std::vector<NormalizedLandmarkList>)
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# NOTE: there will not be an output packet in the LANDMARKS stream for this
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# particular timestamp if none of faces detected. However, the MediaPipe
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# framework will internally inform the downstream calculators of the absence of
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# this packet so that they don't wait for it unnecessarily.
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output_stream: "LANDMARKS:multi_face_landmarks"
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# Extra outputs (for debugging, for instance).
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# Detected faces. (std::vector<Detection>)
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output_stream: "DETECTIONS:face_detections"
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# Regions of interest calculated based on landmarks.
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# (std::vector<NormalizedRect>)
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output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
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# Regions of interest calculated based on face detections.
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# (std::vector<NormalizedRect>)
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output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
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node {
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calculator: "FlowLimiterCalculator"
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input_stream: "image"
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input_stream: "FINISHED:multi_face_landmarks"
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input_stream_info: {
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tag_index: "FINISHED"
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back_edge: true
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}
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output_stream: "throttled_image"
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options: {
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[mediapipe.FlowLimiterCalculatorOptions.ext] {
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max_in_flight: 1
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max_in_queue: 1
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}
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}
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}
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# Converts Image to ImageFrame for FaceLandmarkFrontCpu to consume.
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node {
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calculator: "FromImageCalculator"
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input_stream: "IMAGE:throttled_image"
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output_stream: "IMAGE_CPU:raw_image_frame"
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output_stream: "SOURCE_ON_GPU:is_gpu_image"
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}
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# TODO: Remove the extra flipping once adopting MlImage.
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# If the source images are on gpu, flip the data vertically before sending them
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# into FaceLandmarkFrontCpu. This maybe needed because OpenGL represents images
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# assuming the image origin is at the bottom-left corner, whereas MediaPipe in
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# general assumes the image origin is at the top-left corner.
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node: {
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calculator: "ImageTransformationCalculator"
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input_stream: "IMAGE:raw_image_frame"
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input_stream: "FLIP_VERTICALLY:is_gpu_image"
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output_stream: "IMAGE:image_frame"
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}
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node {
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calculator: "FaceLandmarkFrontCpu"
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input_stream: "IMAGE:image_frame"
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input_side_packet: "NUM_FACES:num_faces"
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input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
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input_side_packet: "WITH_ATTENTION:with_attention"
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output_stream: "LANDMARKS:multi_face_landmarks"
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output_stream: "DETECTIONS:face_detections"
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output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
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output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
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}
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