[−][src]Trait opencv::dnn::TextDetectionModelTrait
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]
Provided methods
pub fn detect(
&self,
frame: &dyn ToInputArray,
detections: &mut Vector<Vector<Point>>,
confidences: &mut Vector<f32>
) -> Result<()>
[src]
&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_1(
&self,
frame: &dyn ToInputArray,
detections: &mut Vector<Vector<Point>>
) -> Result<()>
[src]
&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<()>
[src]
&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<()>
[src]
&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