Trait HOGDescriptorTraitConst 

pub trait HOGDescriptorTraitConst {
    // Required method
    fn as_raw_HOGDescriptor(&self) -> *const c_void;

    // Provided methods
    fn win_size(&self) -> Size { ... }
    fn block_size(&self) -> Size { ... }
    fn block_stride(&self) -> Size { ... }
    fn cell_size(&self) -> Size { ... }
    fn nbins(&self) -> i32 { ... }
    fn deriv_aperture(&self) -> i32 { ... }
    fn win_sigma(&self) -> f64 { ... }
    fn histogram_norm_type(&self) -> HOGDescriptor_HistogramNormType { ... }
    fn l2_hys_threshold(&self) -> f64 { ... }
    fn gamma_correction(&self) -> bool { ... }
    fn svm_detector(&self) -> Vector<f32> { ... }
    fn ocl_svm_detector(&self) -> UMat { ... }
    fn free_coef(&self) -> f32 { ... }
    fn nlevels(&self) -> i32 { ... }
    fn signed_gradient(&self) -> bool { ... }
    fn get_descriptor_size(&self) -> Result<size_t> { ... }
    fn check_detector_size(&self) -> Result<bool> { ... }
    fn get_win_sigma(&self) -> Result<f64> { ... }
    fn write(&self, fs: &mut impl FileStorageTrait, objname: &str) -> Result<()> { ... }
    fn save(&self, filename: &str, objname: &str) -> Result<()> { ... }
    fn save_def(&self, filename: &str) -> Result<()> { ... }
    fn copy_to(&self, c: &mut impl HOGDescriptorTrait) -> Result<()> { ... }
    fn compute(
        &self,
        img: &impl ToInputArray,
        descriptors: &mut Vector<f32>,
        win_stride: Size,
        padding: Size,
        locations: &Vector<Point>,
    ) -> Result<()> { ... }
    fn compute_def(
        &self,
        img: &impl ToInputArray,
        descriptors: &mut Vector<f32>,
    ) -> Result<()> { ... }
    fn detect_weights(
        &self,
        img: &impl ToInputArray,
        found_locations: &mut Vector<Point>,
        weights: &mut Vector<f64>,
        hit_threshold: f64,
        win_stride: Size,
        padding: Size,
        search_locations: &Vector<Point>,
    ) -> Result<()> { ... }
    fn detect_weights_def(
        &self,
        img: &impl ToInputArray,
        found_locations: &mut Vector<Point>,
        weights: &mut Vector<f64>,
    ) -> Result<()> { ... }
    fn detect(
        &self,
        img: &impl ToInputArray,
        found_locations: &mut Vector<Point>,
        hit_threshold: f64,
        win_stride: Size,
        padding: Size,
        search_locations: &Vector<Point>,
    ) -> Result<()> { ... }
    fn detect_def(
        &self,
        img: &impl ToInputArray,
        found_locations: &mut Vector<Point>,
    ) -> Result<()> { ... }
    fn detect_multi_scale_weights(
        &self,
        img: &impl ToInputArray,
        found_locations: &mut Vector<Rect>,
        found_weights: &mut Vector<f64>,
        hit_threshold: f64,
        win_stride: Size,
        padding: Size,
        scale: f64,
        group_threshold: f64,
        use_meanshift_grouping: bool,
    ) -> Result<()> { ... }
    fn detect_multi_scale_weights_def(
        &self,
        img: &impl ToInputArray,
        found_locations: &mut Vector<Rect>,
        found_weights: &mut Vector<f64>,
    ) -> Result<()> { ... }
    fn detect_multi_scale(
        &self,
        img: &impl ToInputArray,
        found_locations: &mut Vector<Rect>,
        hit_threshold: f64,
        win_stride: Size,
        padding: Size,
        scale: f64,
        group_threshold: f64,
        use_meanshift_grouping: bool,
    ) -> Result<()> { ... }
    fn detect_multi_scale_def(
        &self,
        img: &impl ToInputArray,
        found_locations: &mut Vector<Rect>,
    ) -> Result<()> { ... }
    fn compute_gradient(
        &self,
        img: &impl ToInputArray,
        grad: &mut impl ToInputOutputArray,
        angle_ofs: &mut impl ToInputOutputArray,
        padding_tl: Size,
        padding_br: Size,
    ) -> Result<()> { ... }
    fn compute_gradient_def(
        &self,
        img: &impl ToInputArray,
        grad: &mut impl ToInputOutputArray,
        angle_ofs: &mut impl ToInputOutputArray,
    ) -> Result<()> { ... }
    fn detect_roi(
        &self,
        img: &impl ToInputArray,
        locations: &Vector<Point>,
        found_locations: &mut Vector<Point>,
        confidences: &mut Vector<f64>,
        hit_threshold: f64,
        win_stride: Size,
        padding: Size,
    ) -> Result<()> { ... }
    fn detect_roi_def(
        &self,
        img: &impl ToInputArray,
        locations: &Vector<Point>,
        found_locations: &mut Vector<Point>,
        confidences: &mut Vector<f64>,
    ) -> Result<()> { ... }
    fn detect_multi_scale_roi(
        &self,
        img: &impl ToInputArray,
        found_locations: &mut Vector<Rect>,
        locations: &mut Vector<DetectionROI>,
        hit_threshold: f64,
        group_threshold: i32,
    ) -> Result<()> { ... }
    fn detect_multi_scale_roi_def(
        &self,
        img: &impl ToInputArray,
        found_locations: &mut Vector<Rect>,
        locations: &mut Vector<DetectionROI>,
    ) -> Result<()> { ... }
    fn group_rectangles(
        &self,
        rect_list: &mut Vector<Rect>,
        weights: &mut Vector<f64>,
        group_threshold: i32,
        eps: f64,
    ) -> Result<()> { ... }
}

Constant methods for crate::objdetect::HOGDescriptor

Required Methods

fn as_raw_HOGDescriptor(&self) -> *const c_void

Provided Methods

fn win_size(&self) -> Size

Detection window size. Align to block size and block stride. Default value is Size(64,128).

fn block_size(&self) -> Size

Block size in pixels. Align to cell size. Default value is Size(16,16).

fn block_stride(&self) -> Size

Block stride. It must be a multiple of cell size. Default value is Size(8,8).

fn cell_size(&self) -> Size

Cell size. Default value is Size(8,8).

fn nbins(&self) -> i32

Number of bins used in the calculation of histogram of gradients. Default value is 9.

fn deriv_aperture(&self) -> i32

not documented

fn win_sigma(&self) -> f64

Gaussian smoothing window parameter.

fn histogram_norm_type(&self) -> HOGDescriptor_HistogramNormType

histogramNormType

fn l2_hys_threshold(&self) -> f64

L2-Hys normalization method shrinkage.

fn gamma_correction(&self) -> bool

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

fn svm_detector(&self) -> Vector<f32>

coefficients for the linear SVM classifier.

fn ocl_svm_detector(&self) -> UMat

coefficients for the linear SVM classifier used when OpenCL is enabled

fn free_coef(&self) -> f32

not documented

fn nlevels(&self) -> i32

Maximum number of detection window increases. Default value is 64

fn signed_gradient(&self) -> bool

Indicates signed gradient will be used or not

fn get_descriptor_size(&self) -> Result<size_t>

Returns the number of coefficients required for the classification.

fn check_detector_size(&self) -> Result<bool>

Checks if detector size equal to descriptor size.

fn get_win_sigma(&self) -> Result<f64>

Returns winSigma value

fn write(&self, fs: &mut impl FileStorageTrait, objname: &str) -> Result<()>

Stores HOGDescriptor parameters and coefficients for the linear SVM classifier in a file storage.

Parameters

• fs: File storage
• objname: Object name

fn save(&self, filename: &str, objname: &str) -> Result<()>

saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file

Parameters

• filename: File name
• objname: Object name

C++ default parameters

• objname: String()

fn save_def(&self, filename: &str) -> Result<()>

saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file

Parameters

• filename: File name
• objname: Object name

Note

This alternative version of HOGDescriptorTraitConst::save function uses the following default values for its arguments:

• objname: String()

fn copy_to(&self, c: &mut impl HOGDescriptorTrait) -> Result<()>

clones the HOGDescriptor

Parameters

• c: cloned HOGDescriptor

fn compute( &self, img: &impl ToInputArray, descriptors: &mut Vector<f32>, win_stride: Size, padding: Size, locations: &Vector<Point>, ) -> Result<()>

@example samples/cpp/train_HOG.cpp / Computes HOG descriptors of given image.

Parameters

• img: Matrix of the type CV_8U containing an image where HOG features will be calculated.
• descriptors: Matrix of the type CV_32F
• winStride: Window stride. It must be a multiple of block stride.
• padding: Padding
• locations: Vector of Point

C++ default parameters

• win_stride: Size()
• padding: Size()
• locations: std::vector()

fn compute_def( &self, img: &impl ToInputArray, descriptors: &mut Vector<f32>, ) -> Result<()>

@example samples/cpp/train_HOG.cpp / Computes HOG descriptors of given image.

Parameters

• img: Matrix of the type CV_8U containing an image where HOG features will be calculated.
• descriptors: Matrix of the type CV_32F
• winStride: Window stride. It must be a multiple of block stride.
• padding: Padding
• locations: Vector of Point

Note

This alternative version of HOGDescriptorTraitConst::compute function uses the following default values for its arguments:

• win_stride: Size()
• padding: Size()
• locations: std::vector()

fn detect_weights( &self, img: &impl ToInputArray, found_locations: &mut Vector<Point>, weights: &mut Vector<f64>, hit_threshold: f64, win_stride: Size, padding: Size, search_locations: &Vector<Point>, ) -> Result<()>

Performs object detection without a multi-scale window.

Parameters

• img: Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
• foundLocations: Vector of point where each point contains left-top corner point of detected object boundaries.
• weights: Vector that will contain confidence values for each detected object.
• hitThreshold: 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.
• winStride: Window stride. It must be a multiple of block stride.
• padding: Padding
• searchLocations: Vector of Point includes set of requested locations to be evaluated.

C++ default parameters

• hit_threshold: 0
• win_stride: Size()
• padding: Size()
• search_locations: std::vector()

fn detect_weights_def( &self, img: &impl ToInputArray, found_locations: &mut Vector<Point>, weights: &mut Vector<f64>, ) -> Result<()>

Performs object detection without a multi-scale window.

Parameters

• img: Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
• foundLocations: Vector of point where each point contains left-top corner point of detected object boundaries.
• weights: Vector that will contain confidence values for each detected object.
• hitThreshold: 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.
• winStride: Window stride. It must be a multiple of block stride.
• padding: Padding
• searchLocations: Vector of Point includes set of requested locations to be evaluated.

Note

This alternative version of HOGDescriptorTraitConst::detect_weights function uses the following default values for its arguments:

• hit_threshold: 0
• win_stride: Size()
• padding: Size()
• search_locations: std::vector()

fn detect( &self, img: &impl ToInputArray, found_locations: &mut Vector<Point>, hit_threshold: f64, win_stride: Size, padding: Size, search_locations: &Vector<Point>, ) -> Result<()>

Performs object detection without a multi-scale window.

Parameters

• img: Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
• foundLocations: Vector of point where each point contains left-top corner point of detected object boundaries.
• hitThreshold: 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.
• winStride: Window stride. It must be a multiple of block stride.
• padding: Padding
• searchLocations: Vector of Point includes locations to search.

C++ default parameters

• hit_threshold: 0
• win_stride: Size()
• padding: Size()
• search_locations: std::vector()

fn detect_def( &self, img: &impl ToInputArray, found_locations: &mut Vector<Point>, ) -> Result<()>

Performs object detection without a multi-scale window.

Parameters

• img: Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
• foundLocations: Vector of point where each point contains left-top corner point of detected object boundaries.
• hitThreshold: 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.
• winStride: Window stride. It must be a multiple of block stride.
• padding: Padding
• searchLocations: Vector of Point includes locations to search.

Note

This alternative version of HOGDescriptorTraitConst::detect function uses the following default values for its arguments:

• hit_threshold: 0
• win_stride: Size()
• padding: Size()
• search_locations: std::vector()

fn detect_multi_scale_weights( &self, img: &impl ToInputArray, found_locations: &mut Vector<Rect>, found_weights: &mut Vector<f64>, hit_threshold: f64, win_stride: Size, padding: Size, scale: f64, group_threshold: f64, use_meanshift_grouping: bool, ) -> Result<()>

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Parameters

• img: Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
• foundLocations: Vector of rectangles where each rectangle contains the detected object.
• foundWeights: Vector that will contain confidence values for each detected object.
• hitThreshold: 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.
• winStride: Window stride. It must be a multiple of block stride.
• padding: Padding
• scale: Coefficient of the detection window increase.
• groupThreshold: Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping.
• useMeanshiftGrouping: indicates grouping algorithm

C++ default parameters

• hit_threshold: 0
• win_stride: Size()
• padding: Size()
• scale: 1.05
• group_threshold: 2.0
• use_meanshift_grouping: false

fn detect_multi_scale_weights_def( &self, img: &impl ToInputArray, found_locations: &mut Vector<Rect>, found_weights: &mut Vector<f64>, ) -> Result<()>

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Parameters

• img: Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
• foundLocations: Vector of rectangles where each rectangle contains the detected object.
• foundWeights: Vector that will contain confidence values for each detected object.
• hitThreshold: 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.
• winStride: Window stride. It must be a multiple of block stride.
• padding: Padding
• scale: Coefficient of the detection window increase.
• groupThreshold: Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping.
• useMeanshiftGrouping: indicates grouping algorithm

Note

This alternative version of HOGDescriptorTraitConst::detect_multi_scale_weights function uses the following default values for its arguments:

• hit_threshold: 0
• win_stride: Size()
• padding: Size()
• scale: 1.05
• group_threshold: 2.0
• use_meanshift_grouping: false

fn detect_multi_scale( &self, img: &impl ToInputArray, found_locations: &mut Vector<Rect>, hit_threshold: f64, win_stride: Size, padding: Size, scale: f64, group_threshold: f64, use_meanshift_grouping: bool, ) -> Result<()>

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Parameters

• img: Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
• foundLocations: Vector of rectangles where each rectangle contains the detected object.
• hitThreshold: 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.
• winStride: Window stride. It must be a multiple of block stride.
• padding: Padding
• scale: Coefficient of the detection window increase.
• groupThreshold: Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping.
• useMeanshiftGrouping: indicates grouping algorithm

C++ default parameters

• hit_threshold: 0
• win_stride: Size()
• padding: Size()
• scale: 1.05
• group_threshold: 2.0
• use_meanshift_grouping: false

fn detect_multi_scale_def( &self, img: &impl ToInputArray, found_locations: &mut Vector<Rect>, ) -> Result<()>

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Parameters

• img: Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
• foundLocations: Vector of rectangles where each rectangle contains the detected object.
• hitThreshold: 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.
• winStride: Window stride. It must be a multiple of block stride.
• padding: Padding
• scale: Coefficient of the detection window increase.
• groupThreshold: Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping.
• useMeanshiftGrouping: indicates grouping algorithm

Note

This alternative version of HOGDescriptorTraitConst::detect_multi_scale function uses the following default values for its arguments:

• hit_threshold: 0
• win_stride: Size()
• padding: Size()
• scale: 1.05
• group_threshold: 2.0
• use_meanshift_grouping: false

fn compute_gradient( &self, img: &impl ToInputArray, grad: &mut impl ToInputOutputArray, angle_ofs: &mut impl ToInputOutputArray, padding_tl: Size, padding_br: Size, ) -> Result<()>

Computes gradients and quantized gradient orientations.

Parameters

• img: Matrix contains the image to be computed
• grad: Matrix of type CV_32FC2 contains computed gradients
• angleOfs: Matrix of type CV_8UC2 contains quantized gradient orientations
• paddingTL: Padding from top-left
• paddingBR: Padding from bottom-right

C++ default parameters

• padding_tl: Size()
• padding_br: Size()

fn compute_gradient_def( &self, img: &impl ToInputArray, grad: &mut impl ToInputOutputArray, angle_ofs: &mut impl ToInputOutputArray, ) -> Result<()>

Computes gradients and quantized gradient orientations.

Parameters

• img: Matrix contains the image to be computed
• grad: Matrix of type CV_32FC2 contains computed gradients
• angleOfs: Matrix of type CV_8UC2 contains quantized gradient orientations
• paddingTL: Padding from top-left
• paddingBR: Padding from bottom-right

Note

This alternative version of HOGDescriptorTraitConst::compute_gradient function uses the following default values for its arguments:

• padding_tl: Size()
• padding_br: Size()

fn detect_roi( &self, img: &impl ToInputArray, locations: &Vector<Point>, found_locations: &mut Vector<Point>, confidences: &mut Vector<f64>, hit_threshold: f64, win_stride: Size, padding: Size, ) -> Result<()>

evaluate specified ROI and return confidence value for each location

Parameters

• img: Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
• locations: Vector of Point
• foundLocations: Vector of Point where each Point is detected object’s top-left point.
• confidences: confidences
• hitThreshold: 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
• winStride: winStride
• padding: padding

C++ default parameters

• hit_threshold: 0
• win_stride: Size()
• padding: Size()

fn detect_roi_def( &self, img: &impl ToInputArray, locations: &Vector<Point>, found_locations: &mut Vector<Point>, confidences: &mut Vector<f64>, ) -> Result<()>

evaluate specified ROI and return confidence value for each location

Parameters

• img: Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
• locations: Vector of Point
• foundLocations: Vector of Point where each Point is detected object’s top-left point.
• confidences: confidences
• hitThreshold: 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
• winStride: winStride
• padding: padding

Note

This alternative version of HOGDescriptorTraitConst::detect_roi function uses the following default values for its arguments:

• hit_threshold: 0
• win_stride: Size()
• padding: Size()

fn detect_multi_scale_roi( &self, img: &impl ToInputArray, found_locations: &mut Vector<Rect>, locations: &mut Vector<DetectionROI>, hit_threshold: f64, group_threshold: i32, ) -> Result<()>

evaluate specified ROI and return confidence value for each location in multiple scales

Parameters

• img: Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
• foundLocations: Vector of rectangles where each rectangle contains the detected object.
• locations: Vector of DetectionROI
• hitThreshold: 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.
• groupThreshold: Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.

C++ default parameters

• hit_threshold: 0
• group_threshold: 0

fn detect_multi_scale_roi_def( &self, img: &impl ToInputArray, found_locations: &mut Vector<Rect>, locations: &mut Vector<DetectionROI>, ) -> Result<()>

evaluate specified ROI and return confidence value for each location in multiple scales

Parameters

• img: Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
• foundLocations: Vector of rectangles where each rectangle contains the detected object.
• locations: Vector of DetectionROI
• hitThreshold: 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.
• groupThreshold: Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.

Note

This alternative version of HOGDescriptorTraitConst::detect_multi_scale_roi function uses the following default values for its arguments:

• hit_threshold: 0
• group_threshold: 0

fn group_rectangles( &self, rect_list: &mut Vector<Rect>, weights: &mut Vector<f64>, group_threshold: i32, eps: f64, ) -> Result<()>

Groups the object candidate rectangles.

Parameters

• rectList: Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.)
• weights: Input/output vector of weights of rectangles. Output vector includes weights of retained and grouped rectangles. (The Python list is not modified in place.)
• groupThreshold: Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.
• eps: Relative difference between sides of the rectangles to merge them into a group.

Dyn Compatibility

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementors

impl HOGDescriptorTraitConst for BoxedRef<'_, HOGDescriptor>

impl HOGDescriptorTraitConst for BoxedRefMut<'_, HOGDescriptor>

impl HOGDescriptorTraitConst for HOGDescriptor