157 lines
4.8 KiB
Plaintext
157 lines
4.8 KiB
Plaintext
# MediaPipe subgraph to calculate iris landmarks and eye contour landmarks for
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# a single eye. (CPU input, and inference is executed on CPU.)
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#
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# It is required that "iris_landmark.tflite" is available at
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# "mediapipe/modules/iris_landmark/iris_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: "IrisLandmarkCpu"
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# input_stream: "IMAGE:image"
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# input_stream: "ROI:eye_roi"
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# input_stream: "IS_RIGHT_EYE:is_right_eye"
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# output_stream: "EYE_CONTOUR_LANDMARKS:eye_contour_landmarks"
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# output_stream: "IRIS_LANDMARKS:iris_landmarks"
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# }
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type: "IrisLandmarkCpu"
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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 an eye is located.
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# (NormalizedRect)
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input_stream: "ROI:roi"
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# Is right eye. (bool)
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# (Model is trained to detect left eye landmarks only, hence for right eye,
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# flipping is required to immitate left eye.)
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input_stream: "IS_RIGHT_EYE:is_right_eye"
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# 71 refined normalized eye contour landmarks. (NormalizedLandmarkList)
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output_stream: "EYE_CONTOUR_LANDMARKS:projected_eye_landmarks"
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# 5 normalized iris landmarks. (NormalizedLandmarkList)
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output_stream: "IRIS_LANDMARKS:projected_iris_landmarks"
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node {
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calculator: "ImageCroppingCalculator"
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input_stream: "IMAGE:image"
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input_stream: "NORM_RECT:roi"
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output_stream: "IMAGE:eye_image"
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options: {
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[mediapipe.ImageCroppingCalculatorOptions.ext] {
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border_mode: BORDER_REPLICATE
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}
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}
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}
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node {
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calculator: "ImageTransformationCalculator"
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input_stream: "IMAGE:eye_image"
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input_stream: "FLIP_HORIZONTALLY:is_right_eye"
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output_stream: "IMAGE:transformed_eye_image"
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output_stream: "LETTERBOX_PADDING:eye_letterbox_padding"
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options: {
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[mediapipe.ImageTransformationCalculatorOptions.ext] {
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output_width: 64
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output_height: 64
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scale_mode: FIT
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}
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}
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}
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# Converts the transformed input image on CPU into an image tensor stored as a
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# TfLiteTensor.
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node {
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calculator: "TfLiteConverterCalculator"
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input_stream: "IMAGE:transformed_eye_image"
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output_stream: "TENSORS:image_tensor"
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options: {
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[mediapipe.TfLiteConverterCalculatorOptions.ext] {
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zero_center: false
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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: "TfLiteInferenceCalculator"
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input_stream: "TENSORS:image_tensor"
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output_stream: "TENSORS:output_tensors"
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options: {
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[mediapipe.TfLiteInferenceCalculatorOptions.ext] {
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model_path: "mediapipe/modules/iris_landmark/iris_landmark.tflite"
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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: "SplitTfLiteTensorVectorCalculator"
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input_stream: "output_tensors"
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output_stream: "eye_landmarks_tensor"
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output_stream: "iris_landmarks_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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# 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: "TfLiteTensorsToLandmarksCalculator"
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input_stream: "TENSORS:iris_landmarks_tensor"
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input_stream: "FLIP_HORIZONTALLY:is_right_eye"
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output_stream: "NORM_LANDMARKS:iris_landmarks"
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options: {
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[mediapipe.TfLiteTensorsToLandmarksCalculatorOptions.ext] {
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num_landmarks: 5
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input_image_width: 64
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input_image_height: 64
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}
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}
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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: "TfLiteTensorsToLandmarksCalculator"
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input_stream: "TENSORS:eye_landmarks_tensor"
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input_stream: "FLIP_HORIZONTALLY:is_right_eye"
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output_stream: "NORM_LANDMARKS:eye_landmarks"
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options: {
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[mediapipe.TfLiteTensorsToLandmarksCalculatorOptions.ext] {
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num_landmarks: 71
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input_image_width: 64
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input_image_height: 64
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}
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}
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}
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node {
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calculator: "LandmarkLetterboxRemovalCalculator"
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input_stream: "LANDMARKS:0:iris_landmarks"
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input_stream: "LANDMARKS:1:eye_landmarks"
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input_stream: "LETTERBOX_PADDING:eye_letterbox_padding"
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output_stream: "LANDMARKS:0:padded_iris_landmarks"
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output_stream: "LANDMARKS:1:padded_eye_landmarks"
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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:0:padded_iris_landmarks"
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input_stream: "NORM_LANDMARKS:1:padded_eye_landmarks"
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input_stream: "NORM_RECT:roi"
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output_stream: "NORM_LANDMARKS:0:projected_iris_landmarks"
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output_stream: "NORM_LANDMARKS:1:projected_eye_landmarks"
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}
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