Struct opencv::dnn::TextDetectionModel

#[repr(C)]
pub struct TextDetectionModel { /* fields omitted */ }

Base class for text detection networks

Implementations

impl TextDetectionModel

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

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(
    self,
    frame: &dyn ToInputArray,
    detections: &mut Vector<Vector<Point>>
) -> Result<()>

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<()>

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<()>

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

Trait Implementations

impl Clone for TextDetectionModel

fn clone(&self) -> TextDetectionModel

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for TextDetectionModel

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq<TextDetectionModel> for TextDetectionModel

fn eq(&self, other: &TextDetectionModel) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &TextDetectionModel) -> bool

This method tests for !=.

impl Copy for TextDetectionModel

impl StructuralPartialEq for TextDetectionModel