Fixes multiple typos in the calculator's internal files.

PiperOrigin-RevId: 579718764

mediapipe/calculators/tensor/audio_to_tensor_calculator.cc

@@ -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"};

mediapipe/calculators/tensor/audio_to_tensor_calculator.proto

@@ -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];
 

mediapipe/calculators/tensor/image_to_tensor_calculator_test.cc

@@ -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) {

mediapipe/calculators/tensor/tensors_to_landmarks_calculator.cc

@@ -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();

mediapipe/calculators/tensorflow/pack_media_sequence_calculator.cc

@@ -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 {

mediapipe/calculators/tensorflow/tensor_to_matrix_calculator.cc

@@ -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: {

mediapipe/calculators/tensorflow/tensorflow_inference_calculator.cc

@@ -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

mediapipe/calculators/tensorflow/tensorflow_inference_calculator.proto

@@ -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