Trait opencv::prelude::SIFT

pub trait SIFT: Feature2DTrait + SIFTConst {
    // Required method
    fn as_raw_mut_SIFT(&mut self) -> *mut c_void;

    // Provided methods
    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<()> { ... }
}

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

Required Methods

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

Provided Methods

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<()>

Implementations

impl dyn SIFT + '_

pub fn create( nfeatures: i32, n_octave_layers: i32, contrast_threshold: f64, edge_threshold: f64, sigma: f64 ) -> Result<Ptr<dyn 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.

C++ default parameters

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

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

Implementors

impl SIFT for Ptr<dyn SIFT>