Fixes multiple typos in the calculator's internal files.

PiperOrigin-RevId: 579718764
This commit is contained in:
MediaPipe Team 2023-11-05 20:45:18 -08:00 committed by Copybara-Service
parent e22b7d5dd4
commit 0b53c9752f
8 changed files with 12 additions and 12 deletions

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@ -109,7 +109,7 @@ bool IsValidFftSize(int size) {
// Non-streaming mode: when "stream_mode" is set to false in the calculator
// options, the calculators treats the packets in the input audio stream as
// a batch of unrelated audio buffers. In each Process() call, the input
// buffer will be frist resampled, and framed as fixed-sized, possibly
// buffer will be first resampled, and framed as fixed-sized, possibly
// overlapping tensors. The last tensor produced by a Process() invocation
// will be zero-padding if the remaining samples are insufficient. As the
// calculator treats the input packets as unrelated, all samples will be
@ -159,7 +159,7 @@ class AudioToTensorCalculator : public Node {
public:
static constexpr Input<Matrix> kAudioIn{"AUDIO"};
// TODO: Removes this optional input stream when the "AUDIO" stream
// uses the new mediapipe audio data containers that carry audio metatdata,
// uses the new mediapipe audio data containers that carry audio metadata,
// such as sample rate.
static constexpr Input<double>::Optional kAudioSampleRateIn{"SAMPLE_RATE"};
static constexpr Output<std::vector<Tensor>> kTensorsOut{"TENSORS"};

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@ -37,7 +37,7 @@ message AudioToTensorCalculatorOptions {
// will be converted into tensors.
optional double target_sample_rate = 4;
// Whether to treat the input audio stream as a continous stream or a batch
// Whether to treat the input audio stream as a continuous stream or a batch
// of unrelated audio buffers.
optional bool stream_mode = 5 [default = true];

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@ -206,7 +206,7 @@ mediapipe::ImageFormat::Format GetImageFormat(int image_channels) {
} else if (image_channels == 1) {
return ImageFormat::GRAY8;
}
ABSL_CHECK(false) << "Unsupported input image channles: " << image_channels;
ABSL_CHECK(false) << "Unsupported input image channels: " << image_channels;
}
Packet MakeImageFramePacket(cv::Mat input) {

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@ -124,7 +124,7 @@ absl::Status TensorsToLandmarksCalculator::Open(CalculatorContext* cc) {
kFlipVertically(cc).IsConnected())) {
RET_CHECK(options_.has_input_image_height() &&
options_.has_input_image_width())
<< "Must provide input width/height for using flipping when outputing "
<< "Must provide input width/height for using flipping when outputting "
"landmarks in absolute coordinates.";
}
return absl::OkStatus();

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@ -79,7 +79,7 @@ namespace mpms = mediapipe::mediasequence;
// and label and label_id are optional but at least one of them should be set.
// "IMAGE_${NAME}", "BBOX_${NAME}", and "KEYPOINTS_${NAME}" will also store
// prefixed versions of each stream, which allows for multiple image streams to
// be included. However, the default names are suppored by more tools.
// be included. However, the default names are supported by more tools.
//
// Example config:
// node {

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@ -67,8 +67,8 @@ absl::Status FillTimeSeriesHeaderIfValid(const Packet& header_packet,
// -- 1-D or 2-D Tensor
// Output:
// -- Matrix with the same values as the Tensor
// If input tensor is 1 dimensional, the ouput Matrix is of (1xn) shape.
// If input tensor is 2 dimensional (batched), the ouput Matrix is (mxn) shape.
// If input tensor is 1 dimensional, the output Matrix is of (1xn) shape.
// If input tensor is 2 dimensional (batched), the output Matrix is (mxn) shape.
//
// Example Config
// node: {

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@ -111,8 +111,8 @@ class InferenceState {
// input_side_packet.
//
// The input and output streams are TensorFlow tensors labeled by tags. The tags
// for the streams are matched to feeds and fetchs in a TensorFlow session using
// a named_signature.generic_signature in the ModelManifest. The
// for the streams are matched to feeds and fetches in a TensorFlow session
// using a named_signature.generic_signature in the ModelManifest. The
// generic_signature is used as key-value pairs between the MediaPipe tag and
// the TensorFlow tensor. The signature_name in the options proto determines
// which named_signature is used. The keys in the generic_signature must be
@ -128,7 +128,7 @@ class InferenceState {
// addition. Once batch_size inputs have been provided, the batch will be run
// and the output tensors sent out on the output streams with timestamps
// corresponding to the input stream packets. Setting the batch_size to 1
// completely disables batching, but is indepdent of add_batch_dim_to_tensors.
// completely disables batching, but is independent of add_batch_dim_to_tensors.
//
// The TensorFlowInferenceCalculator also support feeding states recurrently for
// RNNs and LSTMs. Simply set the recurrent_tag_pair options to define the

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@ -42,7 +42,7 @@ message TensorFlowInferenceCalculatorOptions {
// If the 0th dimension is the batch dimension, then the tensors are
// concatenated on that dimension. If the 0th is a data dimension, then a 0th
// dimension is added before concatenating. If added, the extra dimension is
// removed before outputing the tensor. Examples of each case: If you want
// removed before outputting the tensor. Examples of each case: If you want
// to batch spectra of audio over time for an LSTM, a time-frequency
// representation has a 0th dimension as the batch dimension. If you want to
// batch frames of video that are [width, height, channels], the batch