Struct opencv::features2d::SIFT

pub struct SIFT { /* private fields */ }

Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe Lowe04 .

Implementations

impl SIFT

pub fn create( nfeatures: i32, n_octave_layers: i32, contrast_threshold: f64, edge_threshold: f64, sigma: f64, enable_precise_upscale: bool ) -> Result<Ptr<SIFT>>

Parameters

• nfeatures: The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)

• nOctaveLayers: The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.

• contrastThreshold: The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.

Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.

• edgeThreshold: The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).

• sigma: The sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.

• enable_precise_upscale: Whether to enable precise upscaling in the scale pyramid, which maps index to . This prevents localization bias. The option is disabled by default.

C++ default parameters

• nfeatures: 0
• n_octave_layers: 3
• contrast_threshold: 0.04
• edge_threshold: 10
• sigma: 1.6
• enable_precise_upscale: false

pub fn create_def() -> Result<Ptr<SIFT>>

Parameters

• nfeatures: The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)

• nOctaveLayers: The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.

• contrastThreshold: The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.

Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.

• edgeThreshold: The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).

• sigma: The sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.

• enable_precise_upscale: Whether to enable precise upscaling in the scale pyramid, which maps index to . This prevents localization bias. The option is disabled by default.

Note

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

• nfeatures: 0
• n_octave_layers: 3
• contrast_threshold: 0.04
• edge_threshold: 10
• sigma: 1.6
• enable_precise_upscale: false

pub fn create_1( nfeatures: i32, n_octave_layers: i32, contrast_threshold: f64, edge_threshold: f64, sigma: f64, descriptor_type: i32, enable_precise_upscale: bool ) -> Result<Ptr<SIFT>>

Create SIFT with specified descriptorType.

Parameters

• nfeatures: The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)

• nOctaveLayers: The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.

• contrastThreshold: The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.

Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.

• edgeThreshold: The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).

• sigma: The sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.

• descriptorType: The type of descriptors. Only CV_32F and CV_8U are supported.

• enable_precise_upscale: Whether to enable precise upscaling in the scale pyramid, which maps index to . This prevents localization bias. The option is disabled by default.

C++ default parameters

• enable_precise_upscale: false

pub fn create_def_1( nfeatures: i32, n_octave_layers: i32, contrast_threshold: f64, edge_threshold: f64, sigma: f64, descriptor_type: i32 ) -> Result<Ptr<SIFT>>

Create SIFT with specified descriptorType.

Parameters

• nfeatures: The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)

• nOctaveLayers: The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.

• contrastThreshold: The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.

Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.

• edgeThreshold: The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).

• sigma: The sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.

• descriptorType: The type of descriptors. Only CV_32F and CV_8U are supported.

• enable_precise_upscale: Whether to enable precise upscaling in the scale pyramid, which maps index to . This prevents localization bias. The option is disabled by default.

Note

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

• enable_precise_upscale: false

Trait Implementations

impl AlgorithmTrait for SIFT

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

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

Clears the algorithm state

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

Reads algorithm parameters from a file storage

impl AlgorithmTraitConst for SIFT

fn as_raw_Algorithm(&self) -> *const c_void

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

Stores algorithm parameters in a file storage

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

Stores algorithm parameters in a file storage

fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>

@deprecated

fn write_with_name_def(&self, fs: &Ptr<FileStorage>) -> Result<()>

👎Deprecated:

Note

Deprecated: ## Note This alternative version of AlgorithmTraitConst::write_with_name function uses the following default values for its arguments:

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 SIFT

unsafe fn from_raw(ptr: *mut c_void) -> Self

Wrap the specified raw pointer

fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

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

Return the underlying mutable raw pointer

impl Debug for SIFT

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

Formats the value using the given formatter.

impl Drop for SIFT

fn drop(&mut self)

Executes the destructor for this type.

impl Feature2DTrait for SIFT

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<()>

@overload

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_1(&mut self, unnamed: &FileNode) -> Result<()>

impl Feature2DTraitConst for SIFT

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_1(&self, unnamed: &mut FileStorage) -> Result<()>

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

Return true if detector object is empty

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

fn write_2(&self, fs: &mut FileStorage, name: &str) -> Result<()>

fn write_3(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>

impl From<SIFT> for Algorithm

fn from(s: SIFT) -> Self

Converts to this type from the input type.

impl From<SIFT> for Feature2D

fn from(s: SIFT) -> Self

Converts to this type from the input type.

impl SIFTTrait for SIFT

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

fn set_n_features(&mut self, max_features: i32) -> Result<()>

fn set_n_octave_layers(&mut self, n_octave_layers: i32) -> Result<()>

fn set_contrast_threshold(&mut self, contrast_threshold: f64) -> Result<()>

fn set_edge_threshold(&mut self, edge_threshold: f64) -> Result<()>

fn set_sigma(&mut self, sigma: f64) -> Result<()>

impl SIFTTraitConst for SIFT

fn as_raw_SIFT(&self) -> *const c_void

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

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

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

fn get_contrast_threshold(&self) -> Result<f64>

fn get_edge_threshold(&self) -> Result<f64>

fn get_sigma(&self) -> Result<f64>

impl TryFrom<Feature2D> for SIFT

type Error = Error

The type returned in the event of a conversion error.

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

Performs the conversion.

impl Send for SIFT