Classes
The following classes are available globally.
-
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.
Declaration
Swift
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.
Declaration
Swift
class ResultCategory : NSObject
-
Represents the list of classification for a given classifier head. Typically used as a result for classification tasks.
Declaration
Swift
class Classifications : NSObject
-
Represents the classification results of a model. Typically used as a result for classification tasks.
Declaration
Swift
class ClassificationResult : NSObject
-
Classifier options shared across MediaPipe iOS classification tasks.
Declaration
Swift
class ClassifierOptions : NSObject, NSCopying
-
The value class representing a landmark connection.
Declaration
Swift
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.
Declaration
Swift
class NormalizedKeypoint : NSObject
-
Represents one detected object in the results of
ObjectDetector
.Declaration
Swift
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.
Declaration
Swift
class Embedding : NSObject
-
Represents the embedding results of a model. Typically used as a result for embedding tasks.
Declaration
Swift
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]
Declaration
Swift
class FaceDetector : NSObject
- image input of size
-
Options for setting up a
FaceDetector
.Declaration
Swift
class FaceDetectorOptions : TaskOptions, NSCopying
-
Represents the detection results generated by
FaceDetector
.Declaration
Swift
class FaceDetectorResult : TaskResult
-
@brief Class that performs face landmark detection on images.
The API expects a TFLite model with mandatory TFLite Model Metadata.
Declaration
Swift
class FaceLandmarker : NSObject
-
Options for setting up a
FaceLandmarker
.Declaration
Swift
class FaceLandmarkerOptions : TaskOptions, NSCopying
-
A matrix that can be used for tansformations.
Declaration
Swift
class TransformMatrix : NSObject
-
Represents the detection results generated by
FaceLandmarker
.Declaration
Swift
class FaceLandmarkerResult : TaskResult
-
Class that performs face stylization on images.
Declaration
Swift
class FaceStylizer : NSObject
-
Options for setting up a
FaceStylizer
.Declaration
Swift
class FaceStylizerOptions : TaskOptions, NSCopying