b7dd4cfe72
Change List: - added graphs for running face mesh dll example with face_detections and face_landmarks models paths saved in side pockets (these pathed can be configured in `MPFaceMeshDetector` constructor - added possibility to set maximum nuber of faces to detect (by default 1)
144 lines
4.2 KiB
Plaintext
144 lines
4.2 KiB
Plaintext
# MediaPipe graph to detect/predict face landmarks. (CPU input, and inference is
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# executed on CPU.)
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#
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# It is required that "face_landmark.tflite" is available at
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# "mediapipe/modules/face_landmark/face_landmark.tflite"
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# path during execution.
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#
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# EXAMPLE:
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# node {
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# calculator: "FaceLandmarkCpu"
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# input_stream: "IMAGE:image"
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# input_stream: "ROI:face_roi"
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# input_side_packet: "MODEL:face_landmark_model"
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# output_stream: "LANDMARKS:face_landmarks"
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# }
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type: "FaceLandmarkCpu"
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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 face is located.
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# (NormalizedRect)
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input_stream: "ROI:roi"
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# TfLite model to detect face landmarks.
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# (std::unique_ptr<tflite::FlatBufferModel,
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# std::function<void(tflite::FlatBufferModel*)>>)
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# NOTE: mediapipe/modules/face_landmark/face_landmark.tflite model
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# only, can be passed here, otherwise - results are undefined.
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input_side_packet: "MODEL:face_landmark_model"
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# 468 face landmarks within the given ROI. (NormalizedLandmarkList)
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# NOTE: if a face 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:face_landmarks"
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# Transforms the input image into a 192x192 tensor.
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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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options: {
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[mediapipe.ImageToTensorCalculatorOptions.ext] {
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output_tensor_width: 192
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output_tensor_height: 192
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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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# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
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# vector of tensors representing, for instance, detection boxes/keypoints and
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# scores.
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node {
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calculator: "InferenceCalculator"
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input_stream: "TENSORS:input_tensors"
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output_stream: "TENSORS:output_tensors"
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input_side_packet: "MODEL:face_landmark_model"
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options {
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[mediapipe.InferenceCalculatorOptions.ext] {
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delegate { tflite {} }
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}
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}
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}
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# Splits a vector of tensors into multiple vectors.
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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: "face_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 face-flag tensor into a float that represents the confidence
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# score of face presence.
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node {
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calculator: "TensorsToFloatsCalculator"
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input_stream: "TENSORS:face_flag_tensor"
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output_stream: "FLOAT:face_presence_score"
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options {
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[mediapipe.TensorsToFloatsCalculatorOptions.ext] {
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activation: SIGMOID
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}
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}
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}
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# Applies a threshold to the confidence score to determine whether a face is
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# present.
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node {
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calculator: "ThresholdingCalculator"
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input_stream: "FLOAT:face_presence_score"
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output_stream: "FLAG:face_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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# Drop landmarks tensors if face 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:face_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: "TensorsToLandmarksCalculator"
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input_stream: "TENSORS:ensured_landmark_tensors"
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output_stream: "NORM_LANDMARKS:landmarks"
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options: {
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[mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
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num_landmarks: 468
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input_image_width: 192
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input_image_height: 192
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}
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}
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}
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# Projects the landmarks from the cropped face 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:landmarks"
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input_stream: "NORM_RECT:roi"
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output_stream: "NORM_LANDMARKS:face_landmarks"
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}
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