Struct opencv::dnn::DetectionModel

pub struct DetectionModel { /* fields omitted */ }

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 as_raw_DetectionModel(&self) -> *const c_void

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

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

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 DetectionModelTrait for DetectionModel

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

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

pub 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.

pub 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

pub 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 Drop for DetectionModel

pub fn drop(&mut self)

Executes the destructor for this type.

impl ModelTrait for DetectionModel

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 DetectionModel