Trait opencv::hub_prelude::IntelligentScissorsMBTrait

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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<()> { ... }
}
Expand description

Mutable methods for crate::imgproc::IntelligentScissorsMB

Required Methods§

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

Provided Methods§

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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)
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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
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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 [set_gradient_magnitude_max_limit] function uses the following default values for its arguments:

  • gradient_magnitude_threshold_max: 0.0f
source

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
source

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 [set_edge_feature_zero_crossing_parameters] function uses the following default values for its arguments:

  • gradient_magnitude_min_value: 0.0f
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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
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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 [set_edge_feature_canny_parameters] function uses the following default values for its arguments:

  • aperture_size: 3
  • l2gradient: false
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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
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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()
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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 [apply_image_features] function uses the following default values for its arguments:

  • image: noArray()
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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

Object Safety§

This trait is not object safe.

Implementors§