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 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 from_file(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

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

Wrap the specified raw pointer

fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

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

Return the underlying mutable raw pointer

impl Drop for TextRecognitionModel

fn drop(&mut self)

Executes the destructor for this type.

impl From<TextRecognitionModel> for Model

fn from(s: TextRecognitionModel) -> Self

Performs the conversion.

impl ModelTrait for TextRecognitionModel

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

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

Set input size for frame.

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

Set input size for frame.

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

Set mean value for frame.

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

Set scalefactor value for frame.

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

Set flag crop for frame.

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

Set flag swapRB for frame.

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

Set preprocessing parameters for frame.

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

See also

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

See also

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

impl ModelTraitConst for TextRecognitionModel

fn as_raw_Model(&self) -> *const c_void

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.

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

impl TextRecognitionModelTrait for TextRecognitionModel

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

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

Set the decoding method of translating the network output into string

fn set_decode_opts_ctc_prefix_beam_search(
    &mut self,
    beam_size: i32,
    voc_prune_size: i32
) -> Result<TextRecognitionModel>

Set the decoding method options for "CTC-prefix-beam-search" decode usage

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

Set the vocabulary for recognition.

impl TextRecognitionModelTraitConst for TextRecognitionModel

fn as_raw_TextRecognitionModel(&self) -> *const c_void

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

Get the decoding method

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

Get the vocabulary for recognition.

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

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

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

impl Send for TextRecognitionModel