Struct SACSegmentation 

pub struct SACSegmentation { /* private fields */ }

Sample Consensus algorithm segmentation of 3D point cloud model.

Example of segmenting plane from a 3D point cloud using the RANSAC algorithm: planeSegmentationUsingRANSAC

See also

1. Supported algorithms: enum SacMethod in ptcloud.hpp.
2. Supported models: enum SacModelType in ptcloud.hpp.

Implementations

impl SACSegmentation

pub fn create( sac_model_type: SacModelType, sac_method: SacMethod, threshold: f64, max_iterations: i32, ) -> Result<Ptr<SACSegmentation>>

C++ default parameters

• sac_model_type: SAC_MODEL_PLANE
• sac_method: SAC_METHOD_RANSAC
• threshold: 0.5
• max_iterations: 1000

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

Note

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

• sac_model_type: SAC_MODEL_PLANE
• sac_method: SAC_METHOD_RANSAC
• threshold: 0.5
• max_iterations: 1000

Trait Implementations

impl Boxed for SACSegmentation

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

Wrap the specified raw pointer

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

Return the underlying raw pointer while consuming this wrapper.

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

Return the underlying raw pointer.

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

Return the underlying mutable raw pointer

impl Debug for SACSegmentation

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

Formats the value using the given formatter.

impl Drop for SACSegmentation

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 SACSegmentationTrait for SACSegmentation

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

fn segment( &mut self, input_pts: &impl ToInputArray, labels: &mut impl ToOutputArray, models_coefficients: &mut impl ToOutputArray, ) -> Result<i32>

Execute segmentation using the sample consensus method.

fn set_sac_model_type(&mut self, sac_model_type: SacModelType) -> Result<()>

Set the type of sample consensus model to use.

fn set_sac_method_type(&mut self, sac_method: SacMethod) -> Result<()>

Set the type of sample consensus method to use.

fn set_distance_threshold(&mut self, threshold: f64) -> Result<()>

Set the distance to the model threshold. Considered as inlier point if distance to the model less than threshold.

fn set_radius_limits(&mut self, radius_min: f64, radius_max: f64) -> Result<()>

Set the minimum and maximum radius limits for the model. Only used for models whose model parameters include a radius.

fn set_max_iterations(&mut self, max_iterations: i32) -> Result<()>

Set the maximum number of iterations to attempt.

fn set_confidence(&mut self, confidence: f64) -> Result<()>

Set the confidence that ensure at least one of selections is an error-free set of data points.

fn set_number_of_models_expected( &mut self, number_of_models_expected: i32, ) -> Result<()>

Set the number of models expected.

fn set_parallel(&mut self, is_parallel: bool) -> Result<()>

Set whether to use parallelism or not. The number of threads is set by cv::setNumThreads(int nthreads).

fn set_random_generator_state(&mut self, rng_state: u64) -> Result<()>

Set state used to initialize the RNG(Random Number Generator).

impl SACSegmentationTraitConst for SACSegmentation

fn as_raw_SACSegmentation(&self) -> *const c_void

fn get_sac_model_type(&self) -> Result<SacModelType>

Get the type of sample consensus model used.

fn get_sac_method_type(&self) -> Result<SacMethod>

Get the type of sample consensus method used.

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

Get the distance to the model threshold.

fn get_radius_limits( &self, radius_min: &mut f64, radius_max: &mut f64, ) -> Result<()>

Get the minimum and maximum radius limits for the model.

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

Get the maximum number of iterations to attempt.

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

Get the confidence that ensure at least one of selections is an error-free set of data points.

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

Get the expected number of models.

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

Get whether to use parallelism or not.

fn get_random_generator_state(&self) -> Result<u64>

Get state used to initialize the RNG(Random Number Generator).

impl Send for SACSegmentation