Class DISK

java.lang.Object
org.opencv.core.Algorithm
org.opencv.features.Feature2D
org.opencv.features.DISK
public class DISK extends Feature2D
DISK feature detector and descriptor, based on a DNN model. DISK (Deep Image Structure and Keypoints) is a learned local-feature pipeline that produces keypoints and 128-D L2-normalized descriptors via a single forward pass through a fully convolutional network. This class wraps an ONNX export of the pre-trained DISK model through cv::dnn::Net and exposes it under the standard cv::Feature2D interface so it can be used as a drop-in alternative to SIFT/ORB. The class assumes the ONNX model has a single input named image taking an N×3×H×W float32 tensor in [0, 1] (RGB channel order) and three outputs named keypoints (N×2), scores (N) and descriptors (N×128).

  • Constructor Details

    • DISK

      protected DISK(long addr)

  • Method Details

    • __fromPtr__

      public static DISK __fromPtr__(long addr)

    • create

      public static DISK create(String modelPath, int maxKeypoints, float scoreThreshold, Size imageSize, int backendId, int targetId)
      Creates a DISK detector.
      Parameters:
      modelPath - Path to the DISK ONNX model.
      maxKeypoints - Maximum number of keypoints to return per image. The strongest responses (by network score) are kept; -1 keeps all detections.
      scoreThreshold - Discard keypoints with network score strictly below this value.
      imageSize - Target input size (width, height) fed to the network. Use Size() (the default) to fall back to the network's expected fixed input shape of 1024x1024. When overriding, both dimensions must be positive multiples of 16, since DISK downsamples by a factor of 16.
      backendId - DNN backend identifier (see cv::dnn::Backend); 0 = DNN_BACKEND_DEFAULT.
      targetId - DNN target identifier (see cv::dnn::Target); 0 = DNN_TARGET_CPU.
      Returns:
      automatically generated

    • create

      public static DISK create(String modelPath, int maxKeypoints, float scoreThreshold, Size imageSize, int backendId)
      Creates a DISK detector.
      Parameters:
      modelPath - Path to the DISK ONNX model.
      maxKeypoints - Maximum number of keypoints to return per image. The strongest responses (by network score) are kept; -1 keeps all detections.
      scoreThreshold - Discard keypoints with network score strictly below this value.
      imageSize - Target input size (width, height) fed to the network. Use Size() (the default) to fall back to the network's expected fixed input shape of 1024x1024. When overriding, both dimensions must be positive multiples of 16, since DISK downsamples by a factor of 16.
      backendId - DNN backend identifier (see cv::dnn::Backend); 0 = DNN_BACKEND_DEFAULT.
      Returns:
      automatically generated

    • create

      public static DISK create(String modelPath, int maxKeypoints, float scoreThreshold, Size imageSize)
      Creates a DISK detector.
      Parameters:
      modelPath - Path to the DISK ONNX model.
      maxKeypoints - Maximum number of keypoints to return per image. The strongest responses (by network score) are kept; -1 keeps all detections.
      scoreThreshold - Discard keypoints with network score strictly below this value.
      imageSize - Target input size (width, height) fed to the network. Use Size() (the default) to fall back to the network's expected fixed input shape of 1024x1024. When overriding, both dimensions must be positive multiples of 16, since DISK downsamples by a factor of 16.
      Returns:
      automatically generated

    • create

      public static DISK create(String modelPath, int maxKeypoints, float scoreThreshold)
      Creates a DISK detector.
      Parameters:
      modelPath - Path to the DISK ONNX model.
      maxKeypoints - Maximum number of keypoints to return per image. The strongest responses (by network score) are kept; -1 keeps all detections.
      scoreThreshold - Discard keypoints with network score strictly below this value. (the default) to fall back to the network's expected fixed input shape of 1024x1024. When overriding, both dimensions must be positive multiples of 16, since DISK downsamples by a factor of 16.
      Returns:
      automatically generated

    • create

      public static DISK create(String modelPath, int maxKeypoints)
      Creates a DISK detector.
      Parameters:
      modelPath - Path to the DISK ONNX model.
      maxKeypoints - Maximum number of keypoints to return per image. The strongest responses (by network score) are kept; -1 keeps all detections. (the default) to fall back to the network's expected fixed input shape of 1024x1024. When overriding, both dimensions must be positive multiples of 16, since DISK downsamples by a factor of 16.
      Returns:
      automatically generated

    • create

      public static DISK create(String modelPath)
      Creates a DISK detector.
      Parameters:
      modelPath - Path to the DISK ONNX model. responses (by network score) are kept; -1 keeps all detections. (the default) to fall back to the network's expected fixed input shape of 1024x1024. When overriding, both dimensions must be positive multiples of 16, since DISK downsamples by a factor of 16.
      Returns:
      automatically generated

    • createFromMemory

      public static DISK createFromMemory(MatOfByte bufferModel, int maxKeypoints, float scoreThreshold, Size imageSize, int backendId, int targetId)
      Creates a DISK detector from an in-memory model buffer. This overload loads the DISK ONNX model from a buffer instead of a file on disk. It is intended for cases where the model is read from application resources (for example Android assets) and is not available as a path on the filesystem.
      Parameters:
      bufferModel - A buffer containing the contents of the DISK ONNX model.
      maxKeypoints - Maximum number of keypoints to return per image. The strongest responses (by network score) are kept; -1 keeps all detections.
      scoreThreshold - Discard keypoints with network score strictly below this value.
      imageSize - Target input size (width, height) fed to the network. Use Size() (the default) to fall back to the network's expected fixed input shape of 1024x1024. When overriding, both dimensions must be positive multiples of 16, since DISK downsamples by a factor of 16.
      backendId - DNN backend identifier (see cv::dnn::Backend); 0 = DNN_BACKEND_DEFAULT.
      targetId - DNN target identifier (see cv::dnn::Target); 0 = DNN_TARGET_CPU. Note: In C++ this is an overload of REF: create. The Python/Java/Objective-C bindings expose it as createFromMemory, because Objective-C selectors are not disambiguated by argument type and would otherwise clash with the file-path REF: create.
      Returns:
      automatically generated

    • createFromMemory

      public static DISK createFromMemory(MatOfByte bufferModel, int maxKeypoints, float scoreThreshold, Size imageSize, int backendId)
      Creates a DISK detector from an in-memory model buffer. This overload loads the DISK ONNX model from a buffer instead of a file on disk. It is intended for cases where the model is read from application resources (for example Android assets) and is not available as a path on the filesystem.
      Parameters:
      bufferModel - A buffer containing the contents of the DISK ONNX model.
      maxKeypoints - Maximum number of keypoints to return per image. The strongest responses (by network score) are kept; -1 keeps all detections.
      scoreThreshold - Discard keypoints with network score strictly below this value.
      imageSize - Target input size (width, height) fed to the network. Use Size() (the default) to fall back to the network's expected fixed input shape of 1024x1024. When overriding, both dimensions must be positive multiples of 16, since DISK downsamples by a factor of 16.
      backendId - DNN backend identifier (see cv::dnn::Backend); 0 = DNN_BACKEND_DEFAULT. Note: In C++ this is an overload of REF: create. The Python/Java/Objective-C bindings expose it as createFromMemory, because Objective-C selectors are not disambiguated by argument type and would otherwise clash with the file-path REF: create.
      Returns:
      automatically generated

    • createFromMemory

      public static DISK createFromMemory(MatOfByte bufferModel, int maxKeypoints, float scoreThreshold, Size imageSize)
      Creates a DISK detector from an in-memory model buffer. This overload loads the DISK ONNX model from a buffer instead of a file on disk. It is intended for cases where the model is read from application resources (for example Android assets) and is not available as a path on the filesystem.
      Parameters:
      bufferModel - A buffer containing the contents of the DISK ONNX model.
      maxKeypoints - Maximum number of keypoints to return per image. The strongest responses (by network score) are kept; -1 keeps all detections.
      scoreThreshold - Discard keypoints with network score strictly below this value.
      imageSize - Target input size (width, height) fed to the network. Use Size() (the default) to fall back to the network's expected fixed input shape of 1024x1024. When overriding, both dimensions must be positive multiples of 16, since DISK downsamples by a factor of 16. Note: In C++ this is an overload of REF: create. The Python/Java/Objective-C bindings expose it as createFromMemory, because Objective-C selectors are not disambiguated by argument type and would otherwise clash with the file-path REF: create.
      Returns:
      automatically generated

    • createFromMemory

      public static DISK createFromMemory(MatOfByte bufferModel, int maxKeypoints, float scoreThreshold)
      Creates a DISK detector from an in-memory model buffer. This overload loads the DISK ONNX model from a buffer instead of a file on disk. It is intended for cases where the model is read from application resources (for example Android assets) and is not available as a path on the filesystem.
      Parameters:
      bufferModel - A buffer containing the contents of the DISK ONNX model.
      maxKeypoints - Maximum number of keypoints to return per image. The strongest responses (by network score) are kept; -1 keeps all detections.
      scoreThreshold - Discard keypoints with network score strictly below this value. (the default) to fall back to the network's expected fixed input shape of 1024x1024. When overriding, both dimensions must be positive multiples of 16, since DISK downsamples by a factor of 16. Note: In C++ this is an overload of REF: create. The Python/Java/Objective-C bindings expose it as createFromMemory, because Objective-C selectors are not disambiguated by argument type and would otherwise clash with the file-path REF: create.
      Returns:
      automatically generated

    • createFromMemory

      public static DISK createFromMemory(MatOfByte bufferModel, int maxKeypoints)
      Creates a DISK detector from an in-memory model buffer. This overload loads the DISK ONNX model from a buffer instead of a file on disk. It is intended for cases where the model is read from application resources (for example Android assets) and is not available as a path on the filesystem.
      Parameters:
      bufferModel - A buffer containing the contents of the DISK ONNX model.
      maxKeypoints - Maximum number of keypoints to return per image. The strongest responses (by network score) are kept; -1 keeps all detections. (the default) to fall back to the network's expected fixed input shape of 1024x1024. When overriding, both dimensions must be positive multiples of 16, since DISK downsamples by a factor of 16. Note: In C++ this is an overload of REF: create. The Python/Java/Objective-C bindings expose it as createFromMemory, because Objective-C selectors are not disambiguated by argument type and would otherwise clash with the file-path REF: create.
      Returns:
      automatically generated

    • createFromMemory

      public static DISK createFromMemory(MatOfByte bufferModel)
      Creates a DISK detector from an in-memory model buffer. This overload loads the DISK ONNX model from a buffer instead of a file on disk. It is intended for cases where the model is read from application resources (for example Android assets) and is not available as a path on the filesystem.
      Parameters:
      bufferModel - A buffer containing the contents of the DISK ONNX model. responses (by network score) are kept; -1 keeps all detections. (the default) to fall back to the network's expected fixed input shape of 1024x1024. When overriding, both dimensions must be positive multiples of 16, since DISK downsamples by a factor of 16. Note: In C++ this is an overload of REF: create. The Python/Java/Objective-C bindings expose it as createFromMemory, because Objective-C selectors are not disambiguated by argument type and would otherwise clash with the file-path REF: create.
      Returns:
      automatically generated

    • setMaxKeypoints

      public void setMaxKeypoints(int maxKeypoints)

    • getMaxKeypoints

      public int getMaxKeypoints()

    • setScoreThreshold

      public void setScoreThreshold(float threshold)

    • getScoreThreshold

      public float getScoreThreshold()

    • setImageSize

      public void setImageSize(Size size)

    • getImageSize

      public Size getImageSize()

    • getDefaultName

      public String getDefaultName()
      Description copied from class: Algorithm
      Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.
      Overrides:
      getDefaultName in class Feature2D
      Returns:
      automatically generated