Holds the base options that is used for creation of any type of task. It has fields with important information acceleration configuration, TFLite model source etc.

  class 
 BaseOptions 
 : 
 NSObject 
 , 
 NSCopying 
 

Category is a util class that contains a label, its display name, a float value as score, and the index of the label in the corresponding label file. Typically it’s used as the result of classification tasks.

  class 
 ResultCategory 
 : 
 NSObject 
 

Represents the list of classification for a given classifier head. Typically used as a result for classification tasks.

  class 
 Classifications 
 : 
 NSObject 
 

Represents the classification results of a model. Typically used as a result for classification tasks.

  class 
 ClassificationResult 
 : 
 NSObject 
 

Classifier options shared across MediaPipe iOS classification tasks.

  class 
 ClassifierOptions 
 : 
 NSObject 
 , 
 NSCopying 
 

The value class representing a landmark connection.

  class 
 Connection 
 : 
 NSObject 
 

Normalized keypoint represents a point in 2D space with x, y coordinates. x and y are normalized to [0.0, 1.0] by the image width and height respectively.

  class 
 NormalizedKeypoint 
 : 
 NSObject 
 

Represents one detected object in the results of ObjectDetector .

  class 
 Detection 
 : 
 NSObject 
 

Represents the embedding for a given embedder head. Typically used in embedding tasks.

One and only one of the two ‘floatEmbedding’ and ‘quantizedEmbedding’ will contain data, based on whether or not the embedder was configured to perform scala quantization.

  class 
 Embedding 
 : 
 NSObject 
 

Represents the embedding results of a model. Typically used as a result for embedding tasks.

  class 
 EmbeddingResult 
 : 
 NSObject 
 

@brief Class that performs face detection on images.

The API expects a TFLite model with mandatory TFLite Model Metadata.

The API supports models with one image input tensor and one or more output tensors. To be more specific, here are the requirements:

Input tensor (kTfLiteUInt8/kTfLiteFloat32)

• image input of size [batch x height x width x channels] .
• batch inference is not supported ( batch is required to be 1).
• only RGB inputs are supported ( channels is required to be 3).
• if type is kTfLiteFloat32, NormalizationOptions are required to be attached to the metadata for input normalization.

Output tensors must be the 4 outputs of a DetectionPostProcess op, i.e:(kTfLiteFloat32) (kTfLiteUInt8/kTfLiteFloat32)

• locations tensor of size [num_results x 4] , the inner array representing bounding boxes in the form [top, left, right, bottom].
• BoundingBoxProperties are required to be attached to the metadata and must specify type=BOUNDARIES and coordinate_type=RATIO. (kTfLiteFloat32)
• classes tensor of size [num_results] , each value representing the integer index of a class.
• scores tensor of size [num_results] , each value representing the score of the detected face.
• optional score calibration can be attached using ScoreCalibrationOptions and an AssociatedFile with type TENSOR_AXIS_SCORE_CALIBRATION. See metadata_schema.fbs [1] for more details. (kTfLiteFloat32)
• integer num_results as a tensor of size [1]

  class 
 FaceDetector 
 : 
 NSObject 
 

Options for setting up a FaceDetector .

  class 
 FaceDetectorOptions 
 : 
  TaskOptions 
 
 , 
 NSCopying 
 

Represents the detection results generated by FaceDetector .

  class 
 FaceDetectorResult 
 : 
  TaskResult 
 
 

@brief Class that performs face landmark detection on images.

The API expects a TFLite model with mandatory TFLite Model Metadata.

  class 
 FaceLandmarker 
 : 
 NSObject 
 

Options for setting up a FaceLandmarker .

  class 
 FaceLandmarkerOptions 
 : 
  TaskOptions 
 
 , 
 NSCopying 
 

A matrix that can be used for tansformations.

  class 
 TransformMatrix 
 : 
 NSObject 
 

Represents the detection results generated by FaceLandmarker .

  class 
 FaceLandmarkerResult 
 : 
  TaskResult 
 
 

Class that performs face stylization on images.

  class 
 FaceStylizer 
 : 
 NSObject 
 

Options for setting up a FaceStylizer .

  class 
 FaceStylizerOptions 
 : 
  TaskOptions 
 
 , 
 NSCopying 
 

Represents the stylized image generated by FaceStylizer .

  class 
 FaceStylizerResult 
 : 
  TaskResult 
 
 

@brief Performs gesture recognition on images.

This API expects a pre-trained TFLite hand gesture recognizer model or a custom one created using MediaPipe Solutions Model Maker. See https://developers.google.com/mediapipe/solutions/model_maker .

  class 
 GestureRecognizer 
 : 
 NSObject 
 

Options for setting up a GestureRecognizer .

  class 
 GestureRecognizerOptions 
 : 
  TaskOptions 
 
 , 
 NSCopying 
 

Represents the gesture recognition results generated by GestureRecognizer .

  class 
 GestureRecognizerResult 
 : 
  TaskResult 
 
 

@brief Performs hand landmarks detection on images.

This API expects a pre-trained hand landmarks model asset bundle.

  class 
 HandLandmarker 
 : 
 NSObject 
 

Options for setting up a HandLandmarker .

  class 
 HandLandmarkerOptions 
 : 
  TaskOptions 
 
 , 
 NSCopying 
 

Represents the hand landmarker results generated by HandLandmarker .

  class 
 HandLandmarkerResult 
 : 
  TaskResult 
 
 

An image used in on-device machine learning using MediaPipe Task library.

  class 
 MPImage 
 : 
 NSObject 
 

@brief Performs classification on images.

The API expects a TFLite model with optional, but strongly recommended, TFLite Model Metadata. .

The API supports models with one image input tensor and one or more output tensors. To be more specific, here are the requirements.

Input tensor (kTfLiteUInt8/kTfLiteFloat32)

• image input of size [batch x height x width x channels] .
• batch inference is not supported ( batch is required to be 1).
• only RGB inputs are supported ( channels is required to be 3).
• if type is kTfLiteFloat32, NormalizationOptions are required to be attached to the metadata for input normalization.

At least one output tensor with: (kTfLiteUInt8/kTfLiteFloat32)

• N classes and either 2 or 4 dimensions, i.e. [1 x N] or [1 x 1 x 1 x N]
• optional (but recommended) label map(s) as AssociatedFiles with type TENSOR_AXIS_LABELS, containing one label per line. The first such AssociatedFile (if any) is used to fill the class_name field of the results. The display_name field is filled from the AssociatedFile (if any) whose locale matches the display_names_locale field of the ImageClassifierOptions used at creation time (“en” by default, i.e. English). If none of these are available, only the index field of the results will be filled.
• optional score calibration can be attached using ScoreCalibrationOptions and an AssociatedFile with type TENSOR_AXIS_SCORE_CALIBRATION. See metadata_schema.fbs [1] for more details.

  class 
 ImageClassifier 
 : 
 NSObject 
 

Options for setting up a ImageClassifier .

  class 
 ImageClassifierOptions 
 : 
  TaskOptions 
 
 , 
 NSCopying 
 

Represents the classification results generated by ImageClassifier . *

  class 
 ImageClassifierResult 
 : 
  TaskResult 
 
 

@brief Performs embedding extraction on images.

The API expects a TFLite model with optional, but strongly recommended, TFLite Model Metadata. .

The API supports models with one image input tensor and one or more output tensors. To be more specific, here are the requirements.

Input image tensor (kTfLiteUInt8/kTfLiteFloat32)

• image input of size [batch x height x width x channels] .
• batch inference is not supported ( batch is required to be 1).
• only RGB inputs are supported ( channels is required to be 3).
• if type is kTfLiteFloat32, NormalizationOptions are required to be attached to the metadata for input normalization.

At least one output tensor (kTfLiteUInt8/kTfLiteFloat32) with shape [1 x N] where N is the number of dimensions in the produced embeddings.

  class 
 ImageEmbedder 
 : 
 NSObject 
 

Options for setting up a ImageEmbedder .

  class 
 ImageEmbedderOptions 
 : 
  TaskOptions 
 
 , 
 NSCopying 
 

Represents the embedding results generated by ImageEmbedder . *

  class 
 ImageEmbedderResult 
 : 
  TaskResult 
 
 

@brief Class that performs segmentation on images.

The API expects a TFLite model with mandatory TFLite Model Metadata.

  class 
 ImageSegmenter 
 : 
 NSObject 
 

Options for setting up a ImageSegmenter .

  class 
 ImageSegmenterOptions 
 : 
  TaskOptions 
 
 , 
 NSCopying 
 

Represents the segmentation results generated by ImageSegmenter .

  class 
 ImageSegmenterResult 
 : 
  TaskResult 
 
 

@brief Class that performs interactive segmentation on images.

Users can represent user interaction through RegionOfInterest , which gives a hint to InteractiveSegmenter to perform segmentation focusing on the given region of interest.

The API expects a TFLite model with mandatory TFLite Model Metadata.

Input tensor: (kTfLiteUInt8/kTfLiteFloat32)

• image input of size [batch x height x width x channels] .
• batch inference is not supported ( batch is required to be 1).
• RGB and greyscale inputs are supported ( channels is required to be 1 or 3).
• if type is kTfLiteFloat32, NormalizationOptions are required to be attached to the metadata for input normalization. Output tensors: (kTfLiteUInt8/kTfLiteFloat32)
• list of segmented masks.
• if output_type is CATEGORY_MASK, uint8 Image, Image vector of size 1.
• if output_type is CONFIDENCE_MASK, float32 Image list of size channels .
• batch is always 1.

An example of such model can be found at: https://tfhub.dev/tensorflow/lite-model/deeplabv3/1/metadata/2

  class 
 InteractiveSegmenter 
 : 
 NSObject 
 

Options for setting up a InteractiveSegmenter .

  class 
 InteractiveSegmenterOptions 
 : 
  TaskOptions 
 
 , 
 NSCopying 
 

Represents the segmentation results generated by ImageSegmenter .

  class 
 InteractiveSegmenterResult 
 : 
  TaskResult 
 
 

Landmark represents a point in 3D space with x, y, z coordinates. The landmark coordinates are in meters. z represents the landmark depth, and the smaller the value the closer the world landmark is to the camera.

  class 
 Landmark 
 : 
 NSObject 
 

Normalized Landmark represents a point in 3D space with x, y, z coordinates. x and y are normalized to [0.0, 1.0] by the image width and height respectively. z represents the landmark depth, and the smaller the value the closer the landmark is to the camera. The magnitude of z uses roughly the same scale as x.

  class 
 NormalizedLandmark 
 : 
 NSObject 
 

The wrapper class for MediaPipe segmentation masks.

Masks are stored as UInt8 * or float * objects. Every mask has an underlying type which can be accessed using dataType . You can access the mask as any other type using the appropriate properties. For example, if the underlying type is uInt8 , in addition to accessing the mask using uint8Data , you can access float32Data to get the 32 bit float data (with values ranging from 0.0 to 1.0). The first time you access the data as a type different from the underlying type, an expensive type conversion is performed. Subsequent accesses return a pointer to the memory location for the same type converted array. As type conversions can be expensive, it is recommended to limit the accesses to data of types different from the underlying type.

Masks that are returned from a MediaPipe Tasks are owned by by the underlying C++ Task. If you need to extend the lifetime of these objects, you can invoke the copy() method.

  class 
 Mask 
 : 
 NSObject 
 , 
 NSCopying 
 

@brief Class that performs object detection on images.

The API expects a TFLite model with mandatory TFLite Model Metadata.

The API supports models with one image input tensor and one or more output tensors. To be more specific, here are the requirements:

Input tensor (kTfLiteUInt8/kTfLiteFloat32)

• image input of size [batch x height x width x channels] .
• batch inference is not supported ( batch is required to be 1).
• only RGB inputs are supported ( channels is required to be 3).
• if type is kTfLiteFloat32, NormalizationOptions are required to be attached to the metadata for input normalization.

Output tensors must be the 4 outputs of a DetectionPostProcess op, i.e:(kTfLiteFloat32) (kTfLiteUInt8/kTfLiteFloat32)

• locations tensor of size [num_results x 4] , the inner array representing bounding boxes in the form [top, left, right, bottom].
• BoundingBoxProperties are required to be attached to the metadata and must specify type=BOUNDARIES and coordinate_type=RATIO. (kTfLiteFloat32)
• classes tensor of size [num_results] , each value representing the integer index of a class.
• optional (but recommended) label map(s) can be attached as AssociatedFiles with type TENSOR_VALUE_LABELS, containing one label per line. The first such AssociatedFile (if any) is used to fill the class_name field of the results. The display_name field is filled from the AssociatedFile (if any) whose locale matches the display_names_locale field of the ObjectDetectorOptions used at creation time (“en” by default, i.e. English). If none of these are available, only the index field of the results will be filled. (kTfLiteFloat32)
• scores tensor of size [num_results] , each value representing the score of the detected object.
• optional score calibration can be attached using ScoreCalibrationOptions and an AssociatedFile with type TENSOR_AXIS_SCORE_CALIBRATION. See metadata_schema.fbs [1] for more details. (kTfLiteFloat32)
• integer num_results as a tensor of size [1]

  class 
 ObjectDetector 
 : 
 NSObject 
 

Options for setting up a ObjectDetector .

  class 
 ObjectDetectorOptions 
 : 
  TaskOptions 
 
 , 
 NSCopying 
 

Represents the detection results generated by ObjectDetector .

  class 
 ObjectDetectorResult 
 : 
  TaskResult 
 
 

@brief Performs pose landmarks detection on images.

This API expects a pre-trained pose landmarks model asset bundle.

  class 
 PoseLandmarker 
 : 
 NSObject 
 

Options for setting up a PoseLandmarker .

  class 
 PoseLandmarkerOptions 
 : 
  TaskOptions 
 
 , 
 NSCopying 
 

Represents the pose landmarks deection results generated by PoseLandmarker .

  class 
 PoseLandmarkerResult 
 : 
  TaskResult 
 
 

The Region-Of-Interest (ROI) to interact with in an interactive segmentation inference.

An instance can contain erither contain a single normalized point pointing to the object that the user wants to segment or array of normalized key points that make up scribbles over the object that the user wants to segment.

  class 
 RegionOfInterest 
 : 
 NSObject 
 

MediaPipe Tasks options base class. Any MediaPipe task-specific options class should extend this class.

  class 
 TaskOptions 
 : 
 NSObject 
 , 
 NSCopying 
 

MediaPipe Tasks result base class. Any MediaPipe task result class should extend this class.

  class 
 TaskResult 
 : 
 NSObject 
 , 
 NSCopying