Trait opencv::cudaobjdetect::HOG[−][src]

pub trait HOG: AlgorithmTrait + HOGConst {

Show 18 methods    fn as_raw_mut_HOG(&mut self) -> *mut c_void;

    fn set_win_sigma(&mut self, win_sigma: f64) -> Result<()> { ... }

    fn set_l2_hys_threshold(&mut self, threshold_l2hys: f64) -> Result<()> { ... }

    fn set_gamma_correction(&mut self, gamma_correction: bool) -> Result<()> { ... }

    fn set_num_levels(&mut self, nlevels: i32) -> Result<()> { ... }

    fn set_hit_threshold(&mut self, hit_threshold: f64) -> Result<()> { ... }

    fn set_win_stride(&mut self, win_stride: Size) -> Result<()> { ... }

    fn set_scale_factor(&mut self, scale0: f64) -> Result<()> { ... }

    fn set_group_threshold(&mut self, group_threshold: i32) -> Result<()> { ... }

    fn set_descriptor_format(
        &mut self, 
        descr_format: HOGDescriptor_DescriptorStorageFormat
    ) -> Result<()> { ... }

    fn set_svm_detector(&mut self, detector: &dyn ToInputArray) -> Result<()> { ... }

    fn detect(
        &mut self, 
        img: &dyn ToInputArray, 
        found_locations: &mut Vector<Point>, 
        confidences: &mut Vector<f64>
    ) -> Result<()> { ... }

    fn detect_1(
        &mut self, 
        img: &dyn ToInputArray, 
        found_locations: &mut Vector<Point>, 
        confidences: &mut Vector<f64>
    ) -> Result<()> { ... }

    fn detect_without_conf(
        &mut self, 
        img: &dyn ToInputArray, 
        found_locations: &mut Vector<Point>
    ) -> Result<()> { ... }

    fn detect_multi_scale(
        &mut self, 
        img: &dyn ToInputArray, 
        found_locations: &mut Vector<Rect>, 
        confidences: &mut Vector<f64>
    ) -> Result<()> { ... }

    fn detect_multi_scale_1(
        &mut self, 
        img: &dyn ToInputArray, 
        found_locations: &mut Vector<Rect>, 
        confidences: &mut Vector<f64>
    ) -> Result<()> { ... }

    fn detect_multi_scale_without_conf(
        &mut self, 
        img: &dyn ToInputArray, 
        found_locations: &mut Vector<Rect>
    ) -> Result<()> { ... }

    fn compute(
        &mut self, 
        img: &dyn ToInputArray, 
        descriptors: &mut dyn ToOutputArray, 
        stream: &mut Stream
    ) -> Result<()> { ... }

}

Required methods

Provided methods

Gaussian smoothing window parameter.

L2-Hys normalization method shrinkage.

Flag to specify whether the gamma correction preprocessing is required or not.

Maximum number of detection window increases.

Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

Window stride. It must be a multiple of block stride.

Coefficient of the detection window increase.

Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping. See groupRectangles.

Descriptor storage format:

  • DESCR_FORMAT_ROW_BY_ROW - Row-major order.
  • DESCR_FORMAT_COL_BY_COL - Column-major order.

Sets coefficients for the linear SVM classifier.

Performs object detection without a multi-scale window.

Parameters
  • img: Source image. CV_8UC1 and CV_8UC4 types are supported for now.
  • found_locations: Left-top corner points of detected objects boundaries.
  • confidences: Optional output array for confidences.
C++ default parameters
  • confidences: NULL

Performs object detection without a multi-scale window.

Parameters
  • img: Source image. CV_8UC1 and CV_8UC4 types are supported for now.
  • found_locations: Left-top corner points of detected objects boundaries.

Performs object detection with a multi-scale window.

Parameters
  • img: Source image. See cuda::HOGDescriptor::detect for type limitations.
  • found_locations: Detected objects boundaries.
  • confidences: Optional output array for confidences.
C++ default parameters
  • confidences: NULL

Performs object detection with a multi-scale window.

Parameters
  • img: Source image. See cuda::HOGDescriptor::detect for type limitations.
  • found_locations: Detected objects boundaries.

Returns block descriptors computed for the whole image.

Parameters
  • img: Source image. See cuda::HOGDescriptor::detect for type limitations.
  • descriptors: 2D array of descriptors.
  • stream: CUDA stream.
C++ default parameters
  • stream: Stream::Null()

Implementations

Creates the HOG descriptor and detector.

Parameters
  • win_size: Detection window size. Align to block size and block stride.
  • block_size: Block size in pixels. Align to cell size. Only (16,16) is supported for now.
  • block_stride: Block stride. It must be a multiple of cell size.
  • cell_size: Cell size. Only (8, 8) is supported for now.
  • nbins: Number of bins. Only 9 bins per cell are supported for now.
C++ default parameters
  • win_size: Size(64,128)
  • block_size: Size(16,16)
  • block_stride: Size(8,8)
  • cell_size: Size(8,8)
  • nbins: 9

Implementors