[−][src]Trait opencv::prelude::TextDetectionModelTrait

pub trait TextDetectionModelTrait: ModelTrait {
    pub fn as_raw_TextDetectionModel(&self) -> *const c_void;

    pub fn as_raw_mut_TextDetectionModel(&mut self) -> *mut c_void;

    pub fn detect(
        &self, 
        frame: &dyn ToInputArray, 
        detections: &mut Vector<Vector<Point>>, 
        confidences: &mut Vector<f32>
    ) -> Result<()> { ... }

    pub fn detect_1(
        &self, 
        frame: &dyn ToInputArray, 
        detections: &mut Vector<Vector<Point>>
    ) -> Result<()> { ... }

    pub fn detect_text_rectangles(
        &self, 
        frame: &dyn ToInputArray, 
        detections: &mut Vector<RotatedRect>, 
        confidences: &mut Vector<f32>
    ) -> Result<()> { ... }

    pub fn detect_text_rectangles_1(
        &self, 
        frame: &dyn ToInputArray, 
        detections: &mut Vector<RotatedRect>
    ) -> Result<()> { ... }
}

Base class for text detection networks

Required methods

pub fn as_raw_TextDetectionModel(&self) -> *const c_void[src]

pub fn as_raw_mut_TextDetectionModel(&mut self) -> *mut c_void[src]

Loading content...

Provided methods

pub fn detect(
    &self,
    frame: &dyn ToInputArray,
    detections: &mut Vector<Vector<Point>>,
    confidences: &mut Vector<f32>
) -> Result<()>[src]

Performs detection

Given the input @p frame, prepare network input, run network inference, post-process network output and return result detections.

Each result is quadrangle's 4 points in this order:

  • bottom-left
  • top-left
  • top-right
  • bottom-right

Use cv::getPerspectiveTransform function to retrive image region without perspective transformations.

Note: If DL model doesn't support that kind of output then result may be derived from detectTextRectangles() output.

Parameters

  • frame: The input image
  • detections:[out] array with detections' quadrangles (4 points per result)
  • confidences:[out] array with detection confidences

pub fn detect_1(
    &self,
    frame: &dyn ToInputArray,
    detections: &mut Vector<Vector<Point>>
) -> Result<()>[src]

Performs detection

Given the input @p frame, prepare network input, run network inference, post-process network output and return result detections.

Each result is quadrangle's 4 points in this order:

  • bottom-left
  • top-left
  • top-right
  • bottom-right

Use cv::getPerspectiveTransform function to retrive image region without perspective transformations.

Note: If DL model doesn't support that kind of output then result may be derived from detectTextRectangles() output.

Parameters

  • frame: The input image
  • detections:[out] array with detections' quadrangles (4 points per result)
  • confidences:[out] array with detection confidences

Overloaded parameters

pub fn detect_text_rectangles(
    &self,
    frame: &dyn ToInputArray,
    detections: &mut Vector<RotatedRect>,
    confidences: &mut Vector<f32>
) -> Result<()>[src]

Performs detection

Given the input @p frame, prepare network input, run network inference, post-process network output and return result detections.

Each result is rotated rectangle.

Note: Result may be inaccurate in case of strong perspective transformations.

Parameters

  • frame: the input image
  • detections:[out] array with detections' RotationRect results
  • confidences:[out] array with detection confidences

pub fn detect_text_rectangles_1(
    &self,
    frame: &dyn ToInputArray,
    detections: &mut Vector<RotatedRect>
) -> Result<()>[src]

Performs detection

Given the input @p frame, prepare network input, run network inference, post-process network output and return result detections.

Each result is rotated rectangle.

Note: Result may be inaccurate in case of strong perspective transformations.

Parameters

  • frame: the input image
  • detections:[out] array with detections' RotationRect results
  • confidences:[out] array with detection confidences

Overloaded parameters

Loading content...

Implementors

Loading content...