Trait IntelligentScissorsMBTrait

pub trait IntelligentScissorsMBTrait: IntelligentScissorsMBTraitConst {
    // Required method
    fn as_raw_mut_IntelligentScissorsMB(&mut self) -> *mut c_void;

    // Provided methods
    fn set_weights(
        &mut self,
        weight_non_edge: f32,
        weight_gradient_direction: f32,
        weight_gradient_magnitude: f32,
    ) -> Result<IntelligentScissorsMB> { ... }
    fn set_gradient_magnitude_max_limit(
        &mut self,
        gradient_magnitude_threshold_max: f32,
    ) -> Result<IntelligentScissorsMB> { ... }
    fn set_gradient_magnitude_max_limit_def(
        &mut self,
    ) -> Result<IntelligentScissorsMB> { ... }
    fn set_edge_feature_zero_crossing_parameters(
        &mut self,
        gradient_magnitude_min_value: f32,
    ) -> Result<IntelligentScissorsMB> { ... }
    fn set_edge_feature_zero_crossing_parameters_def(
        &mut self,
    ) -> Result<IntelligentScissorsMB> { ... }
    fn set_edge_feature_canny_parameters(
        &mut self,
        threshold1: f64,
        threshold2: f64,
        aperture_size: i32,
        l2gradient: bool,
    ) -> Result<IntelligentScissorsMB> { ... }
    fn set_edge_feature_canny_parameters_def(
        &mut self,
        threshold1: f64,
        threshold2: f64,
    ) -> Result<IntelligentScissorsMB> { ... }
    fn apply_image(
        &mut self,
        image: &impl ToInputArray,
    ) -> Result<IntelligentScissorsMB> { ... }
    fn apply_image_features(
        &mut self,
        non_edge: &impl ToInputArray,
        gradient_direction: &impl ToInputArray,
        gradient_magnitude: &impl ToInputArray,
        image: &impl ToInputArray,
    ) -> Result<IntelligentScissorsMB> { ... }
    fn apply_image_features_def(
        &mut self,
        non_edge: &impl ToInputArray,
        gradient_direction: &impl ToInputArray,
        gradient_magnitude: &impl ToInputArray,
    ) -> Result<IntelligentScissorsMB> { ... }
    fn build_map(&mut self, source_pt: Point) -> Result<()> { ... }
}

Mutable methods for crate::imgproc::IntelligentScissorsMB

Required Methods

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

Provided Methods

fn set_weights( &mut self, weight_non_edge: f32, weight_gradient_direction: f32, weight_gradient_magnitude: f32, ) -> Result<IntelligentScissorsMB>

Specify weights of feature functions

Consider keeping weights normalized (sum of weights equals to 1.0) Discrete dynamic programming (DP) goal is minimization of costs between pixels.

Parameters

• weight_non_edge: Specify cost of non-edge pixels (default: 0.43f)
• weight_gradient_direction: Specify cost of gradient direction function (default: 0.43f)
• weight_gradient_magnitude: Specify cost of gradient magnitude function (default: 0.14f)

fn set_gradient_magnitude_max_limit( &mut self, gradient_magnitude_threshold_max: f32, ) -> Result<IntelligentScissorsMB>

Specify gradient magnitude max value threshold

Zero limit value is used to disable gradient magnitude thresholding (default behavior, as described in original article). Otherwize pixels with gradient magnitude >= threshold have zero cost.

Note: Thresholding should be used for images with irregular regions (to avoid stuck on parameters from high-contract areas, like embedded logos).

Parameters

• gradient_magnitude_threshold_max: Specify gradient magnitude max value threshold (default: 0, disabled)

C++ default parameters

• gradient_magnitude_threshold_max: 0.0f

fn set_gradient_magnitude_max_limit_def( &mut self, ) -> Result<IntelligentScissorsMB>

Specify gradient magnitude max value threshold

Zero limit value is used to disable gradient magnitude thresholding (default behavior, as described in original article). Otherwize pixels with gradient magnitude >= threshold have zero cost.

Note: Thresholding should be used for images with irregular regions (to avoid stuck on parameters from high-contract areas, like embedded logos).

Parameters

• gradient_magnitude_threshold_max: Specify gradient magnitude max value threshold (default: 0, disabled)

Note

This alternative version of IntelligentScissorsMBTrait::set_gradient_magnitude_max_limit function uses the following default values for its arguments:

• gradient_magnitude_threshold_max: 0.0f

fn set_edge_feature_zero_crossing_parameters( &mut self, gradient_magnitude_min_value: f32, ) -> Result<IntelligentScissorsMB>

Switch to “Laplacian Zero-Crossing” edge feature extractor and specify its parameters

This feature extractor is used by default according to article.

Implementation has additional filtering for regions with low-amplitude noise. This filtering is enabled through parameter of minimal gradient amplitude (use some small value 4, 8, 16).

Note: Current implementation of this feature extractor is based on processing of grayscale images (color image is converted to grayscale image first).

Note: Canny edge detector is a bit slower, but provides better results (especially on color images): use setEdgeFeatureCannyParameters().

Parameters

• gradient_magnitude_min_value: Minimal gradient magnitude value for edge pixels (default: 0, check is disabled)

C++ default parameters

• gradient_magnitude_min_value: 0.0f

fn set_edge_feature_zero_crossing_parameters_def( &mut self, ) -> Result<IntelligentScissorsMB>

Switch to “Laplacian Zero-Crossing” edge feature extractor and specify its parameters

This feature extractor is used by default according to article.

Implementation has additional filtering for regions with low-amplitude noise. This filtering is enabled through parameter of minimal gradient amplitude (use some small value 4, 8, 16).

Note: Current implementation of this feature extractor is based on processing of grayscale images (color image is converted to grayscale image first).

Note: Canny edge detector is a bit slower, but provides better results (especially on color images): use setEdgeFeatureCannyParameters().

Parameters

• gradient_magnitude_min_value: Minimal gradient magnitude value for edge pixels (default: 0, check is disabled)

Note

This alternative version of IntelligentScissorsMBTrait::set_edge_feature_zero_crossing_parameters function uses the following default values for its arguments:

• gradient_magnitude_min_value: 0.0f

fn set_edge_feature_canny_parameters( &mut self, threshold1: f64, threshold2: f64, aperture_size: i32, l2gradient: bool, ) -> Result<IntelligentScissorsMB>

Switch edge feature extractor to use Canny edge detector

Note: “Laplacian Zero-Crossing” feature extractor is used by default (following to original article)

See also

Canny

C++ default parameters

• aperture_size: 3
• l2gradient: false

fn set_edge_feature_canny_parameters_def( &mut self, threshold1: f64, threshold2: f64, ) -> Result<IntelligentScissorsMB>

Switch edge feature extractor to use Canny edge detector

Note: “Laplacian Zero-Crossing” feature extractor is used by default (following to original article)

See also

Canny

Note

This alternative version of IntelligentScissorsMBTrait::set_edge_feature_canny_parameters function uses the following default values for its arguments:

• aperture_size: 3
• l2gradient: false

fn apply_image( &mut self, image: &impl ToInputArray, ) -> Result<IntelligentScissorsMB>

Specify input image and extract image features

Parameters

• image: input image. Type is [CV_8UC1] / #CV_8UC3

fn apply_image_features( &mut self, non_edge: &impl ToInputArray, gradient_direction: &impl ToInputArray, gradient_magnitude: &impl ToInputArray, image: &impl ToInputArray, ) -> Result<IntelligentScissorsMB>

Specify custom features of input image

Customized advanced variant of applyImage() call.

Parameters

• non_edge: Specify cost of non-edge pixels. Type is CV_8UC1. Expected values are {0, 1}.
• gradient_direction: Specify gradient direction feature. Type is CV_32FC2. Values are expected to be normalized: x^2 + y^2 == 1
• gradient_magnitude: Specify cost of gradient magnitude function: Type is CV_32FC1. Values should be in range [0, 1].
• image: Optional parameter. Must be specified if subset of features is specified (non-specified features are calculated internally)

C++ default parameters

• image: noArray()

fn apply_image_features_def( &mut self, non_edge: &impl ToInputArray, gradient_direction: &impl ToInputArray, gradient_magnitude: &impl ToInputArray, ) -> Result<IntelligentScissorsMB>

Specify custom features of input image

Customized advanced variant of applyImage() call.

Parameters

• non_edge: Specify cost of non-edge pixels. Type is CV_8UC1. Expected values are {0, 1}.
• gradient_direction: Specify gradient direction feature. Type is CV_32FC2. Values are expected to be normalized: x^2 + y^2 == 1
• gradient_magnitude: Specify cost of gradient magnitude function: Type is CV_32FC1. Values should be in range [0, 1].
• image: Optional parameter. Must be specified if subset of features is specified (non-specified features are calculated internally)

Note

This alternative version of IntelligentScissorsMBTrait::apply_image_features function uses the following default values for its arguments:

• image: noArray()

fn build_map(&mut self, source_pt: Point) -> Result<()>

Prepares a map of optimal paths for the given source point on the image

Note: applyImage() / applyImageFeatures() must be called before this call

Parameters

• sourcePt: The source point used to find the paths

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 IntelligentScissorsMBTrait for BoxedRefMut<'_, IntelligentScissorsMB>

impl IntelligentScissorsMBTrait for IntelligentScissorsMB