Fixes multiple typos in the calculator's internal files.
PiperOrigin-RevId: 579718764
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@ -109,7 +109,7 @@ bool IsValidFftSize(int size) {
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// Non-streaming mode: when "stream_mode" is set to false in the calculator
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// options, the calculators treats the packets in the input audio stream as
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// a batch of unrelated audio buffers. In each Process() call, the input
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// buffer will be frist resampled, and framed as fixed-sized, possibly
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// buffer will be first resampled, and framed as fixed-sized, possibly
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// overlapping tensors. The last tensor produced by a Process() invocation
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// will be zero-padding if the remaining samples are insufficient. As the
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// calculator treats the input packets as unrelated, all samples will be
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@ -159,7 +159,7 @@ class AudioToTensorCalculator : public Node {
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public:
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static constexpr Input<Matrix> kAudioIn{"AUDIO"};
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// TODO: Removes this optional input stream when the "AUDIO" stream
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// uses the new mediapipe audio data containers that carry audio metatdata,
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// uses the new mediapipe audio data containers that carry audio metadata,
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// such as sample rate.
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static constexpr Input<double>::Optional kAudioSampleRateIn{"SAMPLE_RATE"};
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static constexpr Output<std::vector<Tensor>> kTensorsOut{"TENSORS"};
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@ -37,7 +37,7 @@ message AudioToTensorCalculatorOptions {
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// will be converted into tensors.
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optional double target_sample_rate = 4;
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// Whether to treat the input audio stream as a continous stream or a batch
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// Whether to treat the input audio stream as a continuous stream or a batch
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// of unrelated audio buffers.
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optional bool stream_mode = 5 [default = true];
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@ -206,7 +206,7 @@ mediapipe::ImageFormat::Format GetImageFormat(int image_channels) {
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} else if (image_channels == 1) {
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return ImageFormat::GRAY8;
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}
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ABSL_CHECK(false) << "Unsupported input image channles: " << image_channels;
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ABSL_CHECK(false) << "Unsupported input image channels: " << image_channels;
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}
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Packet MakeImageFramePacket(cv::Mat input) {
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@ -124,7 +124,7 @@ absl::Status TensorsToLandmarksCalculator::Open(CalculatorContext* cc) {
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kFlipVertically(cc).IsConnected())) {
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RET_CHECK(options_.has_input_image_height() &&
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options_.has_input_image_width())
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<< "Must provide input width/height for using flipping when outputing "
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<< "Must provide input width/height for using flipping when outputting "
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"landmarks in absolute coordinates.";
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}
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return absl::OkStatus();
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@ -79,7 +79,7 @@ namespace mpms = mediapipe::mediasequence;
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// and label and label_id are optional but at least one of them should be set.
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// "IMAGE_${NAME}", "BBOX_${NAME}", and "KEYPOINTS_${NAME}" will also store
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// prefixed versions of each stream, which allows for multiple image streams to
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// be included. However, the default names are suppored by more tools.
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// be included. However, the default names are supported by more tools.
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//
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// Example config:
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// node {
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@ -67,8 +67,8 @@ absl::Status FillTimeSeriesHeaderIfValid(const Packet& header_packet,
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// -- 1-D or 2-D Tensor
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// Output:
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// -- Matrix with the same values as the Tensor
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// If input tensor is 1 dimensional, the ouput Matrix is of (1xn) shape.
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// If input tensor is 2 dimensional (batched), the ouput Matrix is (mxn) shape.
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// If input tensor is 1 dimensional, the output Matrix is of (1xn) shape.
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// If input tensor is 2 dimensional (batched), the output Matrix is (mxn) shape.
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//
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// Example Config
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// node: {
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@ -111,8 +111,8 @@ class InferenceState {
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// input_side_packet.
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//
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// The input and output streams are TensorFlow tensors labeled by tags. The tags
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// for the streams are matched to feeds and fetchs in a TensorFlow session using
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// a named_signature.generic_signature in the ModelManifest. The
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// for the streams are matched to feeds and fetches in a TensorFlow session
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// using a named_signature.generic_signature in the ModelManifest. The
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// generic_signature is used as key-value pairs between the MediaPipe tag and
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// the TensorFlow tensor. The signature_name in the options proto determines
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// which named_signature is used. The keys in the generic_signature must be
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@ -128,7 +128,7 @@ class InferenceState {
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// addition. Once batch_size inputs have been provided, the batch will be run
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// and the output tensors sent out on the output streams with timestamps
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// corresponding to the input stream packets. Setting the batch_size to 1
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// completely disables batching, but is indepdent of add_batch_dim_to_tensors.
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// completely disables batching, but is independent of add_batch_dim_to_tensors.
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//
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// The TensorFlowInferenceCalculator also support feeding states recurrently for
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// RNNs and LSTMs. Simply set the recurrent_tag_pair options to define the
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@ -42,7 +42,7 @@ message TensorFlowInferenceCalculatorOptions {
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// If the 0th dimension is the batch dimension, then the tensors are
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// concatenated on that dimension. If the 0th is a data dimension, then a 0th
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// dimension is added before concatenating. If added, the extra dimension is
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// removed before outputing the tensor. Examples of each case: If you want
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// removed before outputting the tensor. Examples of each case: If you want
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// to batch spectra of audio over time for an LSTM, a time-frequency
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// representation has a 0th dimension as the batch dimension. If you want to
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// batch frames of video that are [width, height, channels], the batch
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