Struct opencv::dnn::TextRecognitionModel

pub struct TextRecognitionModel { /* fields omitted */ }

This class represents high-level API for text recognition networks.

TextRecognitionModel allows to set params for preprocessing input image. TextRecognitionModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and return recognition result. For TextRecognitionModel, CRNN-CTC is supported.

Implementations

impl TextRecognitionModel

pub fn as_raw_TextRecognitionModel(&self) -> *const c_void

pub fn as_raw_mut_TextRecognitionModel(&mut self) -> *mut c_void

impl TextRecognitionModel

pub fn default() -> Result<TextRecognitionModel>

pub fn new(network: &Net) -> Result<TextRecognitionModel>

Create Text Recognition model from deep learning network Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method

Parameters

• network: Net object

pub fn new_1(model: &str, config: &str) -> Result<TextRecognitionModel>

Create text recognition model from network represented in one of the supported formats Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method

Parameters

• model: Binary file contains trained weights
• config: Text file contains network configuration

C++ default parameters

• config: ""

Trait Implementations

impl Boxed for TextRecognitionModel

pub unsafe fn from_raw(ptr: *mut c_void) -> Self

Wrap the specified raw pointer

pub fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

pub fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

pub fn as_raw_mut(&mut self) -> *mut c_void

Return the underlying mutable raw pointer

impl Drop for TextRecognitionModel

pub fn drop(&mut self)

Executes the destructor for this type.

impl ModelTrait for TextRecognitionModel

pub fn as_raw_Model(&self) -> *const c_void

pub fn as_raw_mut_Model(&mut self) -> *mut c_void

pub fn set_input_size(&mut self, size: Size) -> Result<Model>

Set input size for frame.

pub fn set_input_size_1(&mut self, width: i32, height: i32) -> Result<Model>

Set input size for frame.

pub fn set_input_mean(&mut self, mean: Scalar) -> Result<Model>

Set mean value for frame.

pub fn set_input_scale(&mut self, scale: f64) -> Result<Model>

Set scalefactor value for frame.

pub fn set_input_crop(&mut self, crop: bool) -> Result<Model>

Set flag crop for frame.

pub fn set_input_swap_rb(&mut self, swap_rb: bool) -> Result<Model>

Set flag swapRB for frame.

pub fn set_input_params(
    &mut self,
    scale: f64,
    size: Size,
    mean: Scalar,
    swap_rb: bool,
    crop: bool
) -> Result<()>

Set preprocessing parameters for frame.

pub fn predict(
    &self,
    frame: &dyn ToInputArray,
    outs: &mut dyn ToOutputArray
) -> Result<()>

Given the @p input frame, create input blob, run net and return the output @p blobs.

pub fn set_preferable_backend(&mut self, backend_id: Backend) -> Result<Model>

See also

pub fn set_preferable_target(&mut self, target_id: Target) -> Result<Model>

See also

pub fn get_network_(&self) -> Result<Net>

pub fn get_network__1(&mut self) -> Result<Net>

impl Send for TextRecognitionModel

impl TextRecognitionModelTrait for TextRecognitionModel

pub fn as_raw_TextRecognitionModel(&self) -> *const c_void

pub fn as_raw_mut_TextRecognitionModel(&mut self) -> *mut c_void

pub fn set_decode_type(
    &mut self,
    decode_type: &str
) -> Result<TextRecognitionModel>

Set the decoding method of translating the network output into string

pub fn get_decode_type(&self) -> Result<String>

Get the decoding method

pub fn set_vocabulary(
    &mut self,
    vocabulary: &Vector<String>
) -> Result<TextRecognitionModel>

Set the vocabulary for recognition.

pub fn get_vocabulary(&self) -> Result<Vector<String>>

Get the vocabulary for recognition.

pub fn recognize(&self, frame: &dyn ToInputArray) -> Result<String>

Given the @p input frame, create input blob, run net and return recognition result

pub fn recognize_1(
    &self,
    frame: &dyn ToInputArray,
    roi_rects: &dyn ToInputArray,
    results: &mut Vector<String>
) -> Result<()>

Given the @p input frame, create input blob, run net and return recognition result