Trait opencv::objdetect::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 FileStorage, objname: &str) -> Result<()> { ... }
    fn save(&self, filename: &str, objname: &str) -> Result<()> { ... }
    fn copy_to(&self, c: &mut HOGDescriptor) -> Result<()> { ... }
    fn compute(
        &self,
        img: &impl ToInputArray,
        descriptors: &mut Vector<f32>,
        win_stride: Size,
        padding: Size,
        locations: &Vector<Point>
    ) -> 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(
        &self,
        img: &impl ToInputArray,
        found_locations: &mut Vector<Point>,
        hit_threshold: f64,
        win_stride: Size,
        padding: Size,
        search_locations: &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(
        &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 compute_gradient(
        &self,
        img: &impl ToInputArray,
        grad: &mut impl ToInputOutputArray,
        angle_ofs: &mut impl ToInputOutputArray,
        padding_tl: Size,
        padding_br: Size
    ) -> 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_multi_scale_roi(
        &self,
        img: &impl ToInputArray,
        found_locations: &mut Vector<Rect>,
        locations: &mut Vector<DetectionROI>,
        hit_threshold: f64,
        group_threshold: i32
    ) -> 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 FileStorage, 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 copy_to(&self, c: &mut HOGDescriptor) -> 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 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( &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_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( &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 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 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_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 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.

Implementors

impl HOGDescriptorTraitConst for HOGDescriptor