Struct DISK 

pub struct DISK { /* private fields */ }

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).

Implementations

impl DISK

pub fn create( model_path: &str, max_keypoints: i32, score_threshold: f32, image_size: Size, backend_id: i32, target_id: i32, ) -> Result<Ptr<DISK>>

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.

C++ default parameters

• max_keypoints: -1
• score_threshold: 0.0f
• image_size: Size()
• backend_id: 0
• target_id: 0

pub fn create_def(model_path: &str) -> Result<Ptr<DISK>>

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.

Note

This alternative version of DISK::create function uses the following default values for its arguments:

• max_keypoints: -1
• score_threshold: 0.0f
• image_size: Size()
• backend_id: 0
• target_id: 0

pub fn create_from_memory( buffer_model: &Vector<u8>, max_keypoints: i32, score_threshold: f32, image_size: Size, backend_id: i32, target_id: i32, ) -> Result<Ptr<DISK>>

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 [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 [create].

C++ default parameters

• max_keypoints: -1
• score_threshold: 0.0f
• image_size: Size()
• backend_id: 0
• target_id: 0

pub fn create_from_memory_def(buffer_model: &Vector<u8>) -> Result<Ptr<DISK>>

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 [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 [create].

Note

This alternative version of DISK::create_from_memory function uses the following default values for its arguments:

• max_keypoints: -1
• score_threshold: 0.0f
• image_size: Size()
• backend_id: 0
• target_id: 0

Trait Implementations

impl AlgorithmTrait for DISK

fn as_raw_mut_Algorithm(&mut self) -> *mut c_void

fn clear(&mut self) -> Result<()>

Clears the algorithm state

fn read(&mut self, fn_: &impl FileNodeTraitConst) -> Result<()>

Reads algorithm parameters from a file storage

impl AlgorithmTraitConst for DISK

fn as_raw_Algorithm(&self) -> *const c_void

fn write(&self, fs: &mut impl FileStorageTrait) -> Result<()>

Stores algorithm parameters in a file storage

fn write_1(&self, fs: &mut impl FileStorageTrait, name: &str) -> Result<()>

Stores algorithm parameters in a file storage

fn empty(&self) -> Result<bool>

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

fn save(&self, filename: &str) -> Result<()>

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

fn get_default_name(&self) -> Result<String>

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.

impl Boxed for DISK

unsafe fn from_raw(ptr: <DISK as OpenCVFromExtern>::ExternReceive) -> Self

Wrap the specified raw pointer

fn into_raw(self) -> <DISK as OpenCVTypeExternContainer>::ExternSendMut

Return the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> <DISK as OpenCVTypeExternContainer>::ExternSend

Return the underlying raw pointer.

fn as_raw_mut(&mut self) -> <DISK as OpenCVTypeExternContainer>::ExternSendMut

Return the underlying mutable raw pointer

impl DISKTrait for DISK

fn as_raw_mut_DISK(&mut self) -> *mut c_void

fn set_max_keypoints(&mut self, max_keypoints: i32) -> Result<()>

fn set_score_threshold(&mut self, threshold: f32) -> Result<()>

fn set_image_size(&mut self, size: Size) -> Result<()>

impl DISKTraitConst for DISK

fn as_raw_DISK(&self) -> *const c_void

fn get_max_keypoints(&self) -> Result<i32>

fn get_score_threshold(&self) -> Result<f32>

fn get_image_size(&self) -> Result<Size>

fn get_default_name(&self) -> Result<String>

impl Debug for DISK

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Drop for DISK

fn drop(&mut self)

Executes the destructor for this type.

fn pin_drop(self: Pin<&mut Self>)

🔬This is a nightly-only experimental API. (pin_ergonomics)

Execute the destructor for this type, but different to Drop::drop, it requires self to be pinned.

impl Feature2DTrait for DISK

fn as_raw_mut_Feature2D(&mut self) -> *mut c_void

fn detect( &mut self, image: &impl ToInputArray, keypoints: &mut Vector<KeyPoint>, mask: &impl ToInputArray, ) -> Result<()>

Detects keypoints in an image (first variant) or image set (second variant).

fn detect_def( &mut self, image: &impl ToInputArray, keypoints: &mut Vector<KeyPoint>, ) -> Result<()>

Detects keypoints in an image (first variant) or image set (second variant).

fn detect_multiple( &mut self, images: &impl ToInputArray, keypoints: &mut Vector<Vector<KeyPoint>>, masks: &impl ToInputArray, ) -> Result<()>

Detects keypoints in an image (first variant) or image set (second variant).

fn detect_multiple_def( &mut self, images: &impl ToInputArray, keypoints: &mut Vector<Vector<KeyPoint>>, ) -> Result<()>

Detects keypoints in an image (first variant) or image set (second variant).

fn compute( &mut self, image: &impl ToInputArray, keypoints: &mut Vector<KeyPoint>, descriptors: &mut impl ToOutputArray, ) -> Result<()>

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

fn compute_multiple( &mut self, images: &impl ToInputArray, keypoints: &mut Vector<Vector<KeyPoint>>, descriptors: &mut impl ToOutputArray, ) -> Result<()>

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

fn detect_and_compute( &mut self, image: &impl ToInputArray, mask: &impl ToInputArray, keypoints: &mut Vector<KeyPoint>, descriptors: &mut impl ToOutputArray, use_provided_keypoints: bool, ) -> Result<()>

Detects keypoints and computes the descriptors

fn detect_and_compute_def( &mut self, image: &impl ToInputArray, mask: &impl ToInputArray, keypoints: &mut Vector<KeyPoint>, descriptors: &mut impl ToOutputArray, ) -> Result<()>

Detects keypoints and computes the descriptors

fn read(&mut self, file_name: &str) -> Result<()>

fn read_from_node(&mut self, unnamed: &impl FileNodeTraitConst) -> Result<()>

impl Feature2DTraitConst for DISK

fn as_raw_Feature2D(&self) -> *const c_void

fn descriptor_size(&self) -> Result<i32>

fn descriptor_type(&self) -> Result<i32>

fn default_norm(&self) -> Result<i32>

fn write(&self, file_name: &str) -> Result<()>

fn write_to_storage(&self, unnamed: &mut impl FileStorageTrait) -> Result<()>

fn empty(&self) -> Result<bool>

Return true if detector object is empty

fn get_default_name(&self) -> Result<String>

fn write_to_storage_with_name( &self, fs: &mut impl FileStorageTrait, name: &str, ) -> Result<()>

impl From<DISK> for Algorithm

fn from(s: DISK) -> Self

Converts to this type from the input type.

impl From<DISK> for Feature2D

fn from(s: DISK) -> Self

Converts to this type from the input type.

impl Send for DISK

impl TryFrom<Feature2D> for DISK

type Error = Error

The type returned in the event of a conversion error.

fn try_from(s: Feature2D) -> Result<Self>

Performs the conversion.