Struct opencv::dnn::SegmentationModel

pub struct SegmentationModel { /* fields omitted */ }

This class represents high-level API for segmentation models

SegmentationModel allows to set params for preprocessing input image. SegmentationModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and returns the class prediction for each pixel.

Implementations

impl SegmentationModel

pub fn as_raw_SegmentationModel(&self) -> *const c_void

pub fn as_raw_mut_SegmentationModel(&mut self) -> *mut c_void

impl SegmentationModel

pub fn new(model: &str, config: &str) -> Result<SegmentationModel>

Create segmentation model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.

Parameters

• model: Binary file contains trained weights.
• config: Text file contains network configuration.

C++ default parameters

• config: ""

pub fn new_1(network: &Net) -> Result<SegmentationModel>

Create model from deep learning network.

Parameters

• network: Net object.

Trait Implementations

impl Boxed for SegmentationModel

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

Wrap the specified raw pointer

pub fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

pub fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

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

Return the underlying mutable raw pointer

impl Drop for SegmentationModel

pub fn drop(&mut self)

Executes the destructor for this type.

impl ModelTrait for SegmentationModel

pub fn as_raw_Model(&self) -> *const c_void

pub fn as_raw_mut_Model(&mut self) -> *mut c_void

pub fn set_input_size(&mut self, size: Size) -> Result<Model>

Set input size for frame.

pub fn set_input_size_1(&mut self, width: i32, height: i32) -> Result<Model>

Set input size for frame.

pub fn set_input_mean(&mut self, mean: Scalar) -> Result<Model>

Set mean value for frame.

pub fn set_input_scale(&mut self, scale: f64) -> Result<Model>

Set scalefactor value for frame.

pub fn set_input_crop(&mut self, crop: bool) -> Result<Model>

Set flag crop for frame.

pub fn set_input_swap_rb(&mut self, swap_rb: bool) -> Result<Model>

Set flag swapRB for frame.

pub fn set_input_params(
    &mut self,
    scale: f64,
    size: Size,
    mean: Scalar,
    swap_rb: bool,
    crop: bool
) -> Result<()>

Set preprocessing parameters for frame.

pub fn predict(
    &self,
    frame: &dyn ToInputArray,
    outs: &mut dyn ToOutputArray
) -> Result<()>

Given the @p input frame, create input blob, run net and return the output @p blobs.

pub fn set_preferable_backend(&mut self, backend_id: Backend) -> Result<Model>

See also

pub fn set_preferable_target(&mut self, target_id: Target) -> Result<Model>

See also

pub fn get_network_(&self) -> Result<Net>

pub fn get_network__1(&mut self) -> Result<Net>

impl SegmentationModelTrait for SegmentationModel

pub fn as_raw_SegmentationModel(&self) -> *const c_void

pub fn as_raw_mut_SegmentationModel(&mut self) -> *mut c_void

pub fn segment(
    &mut self,
    frame: &dyn ToInputArray,
    mask: &mut dyn ToOutputArray
) -> Result<()>

Given the @p input frame, create input blob, run net

impl Send for SegmentationModel