Struct opencv::dnn::KeypointsModel

pub struct KeypointsModel { /* private fields */ }

This class represents high-level API for keypoints models

KeypointsModel allows to set params for preprocessing input image. KeypointsModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and returns the x and y coordinates of each detected keypoint

Implementations

impl KeypointsModel

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

Create keypoints 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<KeypointsModel>

Create model from deep learning network.

Parameters

• network: Net object.

Trait Implementations

impl Boxed for KeypointsModel

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 Clone for KeypointsModel

fn clone(&self) -> Self

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Drop for KeypointsModel

fn drop(&mut self)

Executes the destructor for this type.

impl From<KeypointsModel> for Model

fn from(s: KeypointsModel) -> Self

Converts to this type from the input type.

impl KeypointsModelTrait for KeypointsModel

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

fn estimate( &mut self, frame: &dyn ToInputArray, thresh: f32 ) -> Result<Vector<Point2f>>

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

impl KeypointsModelTraitConst for KeypointsModel

fn as_raw_KeypointsModel(&self) -> *const c_void

impl ModelTrait for KeypointsModel

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

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

Set input size for frame.

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

Set input size for frame.

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

Set mean value for frame.

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

Set scalefactor value for frame.

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

Set flag crop for frame.

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

Set flag swapRB for frame.

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

Set preprocessing parameters for frame.

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

See also

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

See also

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

impl ModelTraitConst for KeypointsModel

fn as_raw_Model(&self) -> *const c_void

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.

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

impl Send for KeypointsModel