Struct opencv::dnn::Model

pub struct Model { /* private fields */ }

This class is presented high-level API for neural networks.

Model allows to set params for preprocessing input image. Model creates net from file with trained weights and config, sets preprocessing input and runs forward pass.

Implementations

impl Model

pub fn default() -> Result<Model>

pub fn copy(unnamed: &Model) -> Model

pub fn copy_mut(unnamed: &mut Model) -> Model

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

Create model from deep learning 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<Model>

Create model from deep learning network.

Parameters

• network: Net object.

Trait Implementations

impl Boxed for Model

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 Drop for Model

fn drop(&mut self)

Executes the destructor for this type.

impl From<ClassificationModel> for Model

fn from(s: ClassificationModel) -> Self

Converts to this type from the input type.

impl From<DetectionModel> for Model

fn from(s: DetectionModel) -> Self

Converts to this type from the input type.

impl From<KeypointsModel> for Model

fn from(s: KeypointsModel) -> Self

Converts to this type from the input type.

impl From<SegmentationModel> for Model

fn from(s: SegmentationModel) -> Self

Converts to this type from the input type.

impl From<TextDetectionModel> for Model

fn from(s: TextDetectionModel) -> Self

Converts to this type from the input type.

impl From<TextDetectionModel_DB> for Model

fn from(s: TextDetectionModel_DB) -> Self

Converts to this type from the input type.

impl From<TextDetectionModel_EAST> for Model

fn from(s: TextDetectionModel_EAST) -> Self

Converts to this type from the input type.

impl From<TextRecognitionModel> for Model

fn from(s: TextRecognitionModel) -> Self

Converts to this type from the input type.

impl ModelTrait for Model

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 Model

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 Model