Struct opencv::dnn::KeypointsModel

pub struct KeypointsModel { /* fields omitted */ }

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 as_raw_KeypointsModel(&self) -> *const c_void

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

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

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 KeypointsModel

pub fn drop(&mut self)

Executes the destructor for this type.

impl KeypointsModelTrait for KeypointsModel

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

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

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

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

impl ModelTrait for KeypointsModel

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 Send for KeypointsModel