Struct opencv::dnn::DetectionModel

pub struct DetectionModel { /* private fields */ }

This class represents high-level API for object detection networks.

DetectionModel allows to set params for preprocessing input image. DetectionModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and return result detections. For DetectionModel SSD, Faster R-CNN, YOLO topologies are supported.

Implementations

impl DetectionModel

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

Create detection 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<DetectionModel>

Create model from deep learning network.

Parameters

• network: Net object.

pub fn default() -> Result<DetectionModel>

Trait Implementations

impl Boxed for DetectionModel

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 DetectionModelTrait for DetectionModel

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

fn set_nms_across_classes(&mut self, value: bool) -> Result<DetectionModel>

nmsAcrossClasses defaults to false, such that when non max suppression is used during the detect() function, it will do so per-class. This function allows you to toggle this behaviour.

fn get_nms_across_classes(&mut self) -> Result<bool>

Getter for nmsAcrossClasses. This variable defaults to false, such that when non max suppression is used during the detect() function, it will do so only per-class

fn detect(
    &mut self,
    frame: &dyn ToInputArray,
    class_ids: &mut Vector<i32>,
    confidences: &mut Vector<f32>,
    boxes: &mut Vector<Rect>,
    conf_threshold: f32,
    nms_threshold: f32
) -> Result<()>

Given the @p input frame, create input blob, run net and return result detections.

impl DetectionModelTraitConst for DetectionModel

fn as_raw_DetectionModel(&self) -> *const c_void

impl Drop for DetectionModel

fn drop(&mut self)

Executes the destructor for this type.

impl From<DetectionModel> for Model

fn from(s: DetectionModel) -> Self

Converts to this type from the input type.

impl ModelTrait for DetectionModel

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 DetectionModel

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 DetectionModel