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Reference

ONNX inference module.

ONNXPlateRecognizer

ONNX inference class for performing license plates OCR.

Source code in fast_plate_ocr/inference/onnx_inference.py 
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class ONNXPlateRecognizer:
    """
    ONNX inference class for performing license plates OCR.
    """

    def __init__(
        self,
        hub_ocr_model: OcrModel | None = None,
        device: Literal["cuda", "cpu", "auto"] = "auto",
        providers: Sequence[str | tuple[str, dict]] | None = None,
        sess_options: ort.SessionOptions | None = None,
        model_path: str | os.PathLike[str] | None = None,
        config_path: str | os.PathLike[str] | None = None,
        force_download: bool = False,
    ) -> None:
        """
        Initializes the ONNXPlateRecognizer with the specified OCR model and inference device.

        The current OCR models available from the HUB are:

        - `argentinian-plates-cnn-model`: OCR for Argentinian license plates. Uses fully conv
            architecture.
        - `argentinian-plates-cnn-synth-model`: OCR for Argentinian license plates trained with
            synthetic and real data. Uses fully conv architecture.
        - `european-plates-mobile-vit-v2-model`: OCR for European license plates. Uses MobileVIT-2
            for the backbone.

        Args:
            hub_ocr_model: Name of the OCR model to use from the HUB.
            device: Device type for inference. Should be one of ('cpu', 'cuda', 'auto'). If
                'auto' mode, the device will be deduced from
                `onnxruntime.get_available_providers()`.
            providers: Optional sequence of providers in order of decreasing precedence. If not
                specified, all available providers are used based on the device argument.
            sess_options: Advanced session options for ONNX Runtime.
            model_path: Path to ONNX model file to use (In case you want to use a custom one).
            config_path: Path to config file to use (In case you want to use a custom one).
            force_download: Force and download the model, even if it already exists.
        Returns:
            None.
        """
        self.logger = logging.getLogger(__name__)

        if providers is not None:
            self.providers = providers
            self.logger.info("Using custom providers: %s", providers)
        else:
            if device == "cuda":
                self.providers = ["CUDAExecutionProvider"]
            elif device == "cpu":
                self.providers = ["CPUExecutionProvider"]
            elif device == "auto":
                self.providers = ort.get_available_providers()
            else:
                raise ValueError(
                    f"Device should be one of ('cpu', 'cuda', 'auto'). Got '{device}'."
                )

            self.logger.info("Using device '%s' with providers: %s", device, self.providers)

        if model_path and config_path:
            model_path = pathlib.Path(model_path)
            config_path = pathlib.Path(config_path)
            if not model_path.exists() or not config_path.exists():
                raise FileNotFoundError("Missing model/config file!")
            self.model_name = model_path.stem
        elif hub_ocr_model:
            self.model_name = hub_ocr_model
            model_path, config_path = hub.download_model(
                model_name=hub_ocr_model, force_download=force_download
            )
        else:
            raise ValueError(
                "Either provide a model from the HUB or a custom model_path and config_path"
            )

        self.config = load_config_from_yaml(config_path)
        self.model = ort.InferenceSession(
            model_path, providers=self.providers, sess_options=sess_options
        )
        self.logger.info("Using ONNX Runtime with %s.", self.providers)

    def benchmark(self, n_iter: int = 10_000, include_processing: bool = False) -> None:
        """
        Benchmark time taken to run the OCR model. This reports the average inference time and the
        throughput in plates per second.

        Args:
            n_iter: The number of iterations to run the benchmark. This determines how many times
                the inference will be executed to compute the average performance metrics.
            include_processing: Indicates whether the benchmark should include preprocessing and
                postprocessing times in the measurement.
        """
        cum_time = 0.0
        x = np.random.randint(
            0, 256, size=(1, self.config["img_height"], self.config["img_width"], 1), dtype=np.uint8
        )
        for _ in range(n_iter):
            with measure_time() as time_taken:
                if include_processing:
                    self.run(x)
                else:
                    self.model.run(None, {"input": x})
            cum_time += time_taken()

        avg_time = (cum_time / n_iter) if n_iter > 0 else 0.0
        avg_pps = (1_000 / avg_time) if n_iter > 0 else 0.0

        console = Console()
        model_info = Panel(
            Text(f"Model: {self.model_name}\nProviders: {self.providers}", style="bold green"),
            title="Model Information",
            border_style="bright_blue",
            expand=False,
        )
        console.print(model_info)
        table = Table(title=f"Benchmark for '{self.model_name}' Model", border_style="bright_blue")
        table.add_column("Metric", justify="center", style="cyan", no_wrap=True)
        table.add_column("Value", justify="center", style="magenta")
        table.add_row("Number of Iterations", str(n_iter))
        table.add_row("Average Time (ms)", f"{avg_time:.4f}")
        table.add_row("Plates Per Second (PPS)", f"{avg_pps:.4f}")
        console.print(table)

    def run(
        self,
        source: str | list[str] | npt.NDArray | list[npt.NDArray],
        return_confidence: bool = False,
    ) -> tuple[list[str], npt.NDArray] | list[str]:
        """
        Performs OCR to recognize license plate characters from an image or a list of images.

        Args:
            source: The path(s) to the image(s), a numpy array representing an image or a list
                of NumPy arrays. If a numpy array is provided, it is expected to already be in
                grayscale format, with shape `(H, W) `or `(H, W, 1)`. A list of numpy arrays with
                different image sizes may also be provided.
            return_confidence: Whether to return confidence scores along with plate predictions.

        Returns:
            A list of plates for each input image. If `return_confidence` is True, a numpy
                array is returned with the shape `(N, plate_slots)`, where N is the batch size and
                each plate slot is the confidence for the recognized license plate character.
        """
        x = _load_image_from_source(source)
        # Preprocess
        x = preprocess_image(x, self.config["img_height"], self.config["img_width"])
        # Run model
        y: list[npt.NDArray] = self.model.run(None, {"input": x})
        # Postprocess model output
        return postprocess_output(
            y[0],
            self.config["max_plate_slots"],
            self.config["alphabet"],
            return_confidence=return_confidence,
        )

__init__(hub_ocr_model=None, device='auto', providers=None, sess_options=None, model_path=None, config_path=None, force_download=False)

Initializes the ONNXPlateRecognizer with the specified OCR model and inference device.

The current OCR models available from the HUB are:

  • argentinian-plates-cnn-model: OCR for Argentinian license plates. Uses fully conv architecture.
  • argentinian-plates-cnn-synth-model: OCR for Argentinian license plates trained with synthetic and real data. Uses fully conv architecture.
  • european-plates-mobile-vit-v2-model: OCR for European license plates. Uses MobileVIT-2 for the backbone.

Parameters:

Name Type Description Default
hub_ocr_model OcrModel | None

Name of the OCR model to use from the HUB.

 None
device Literal['cuda', 'cpu', 'auto']

Device type for inference. Should be one of ('cpu', 'cuda', 'auto'). If 'auto' mode, the device will be deduced from onnxruntime.get_available_providers().

 'auto'
providers Sequence[str | tuple[str, dict]] | None

Optional sequence of providers in order of decreasing precedence. If not specified, all available providers are used based on the device argument.

 None
sess_options SessionOptions | None

Advanced session options for ONNX Runtime.

 None
model_path str | PathLike[str] | None

Path to ONNX model file to use (In case you want to use a custom one).

 None
config_path str | PathLike[str] | None

Path to config file to use (In case you want to use a custom one).

 None
force_download bool

Force and download the model, even if it already exists.

 False

Returns: None.

Source code in fast_plate_ocr/inference/onnx_inference.py 
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def __init__(
    self,
    hub_ocr_model: OcrModel | None = None,
    device: Literal["cuda", "cpu", "auto"] = "auto",
    providers: Sequence[str | tuple[str, dict]] | None = None,
    sess_options: ort.SessionOptions | None = None,
    model_path: str | os.PathLike[str] | None = None,
    config_path: str | os.PathLike[str] | None = None,
    force_download: bool = False,
) -> None:
    """
    Initializes the ONNXPlateRecognizer with the specified OCR model and inference device.

    The current OCR models available from the HUB are:

    - `argentinian-plates-cnn-model`: OCR for Argentinian license plates. Uses fully conv
        architecture.
    - `argentinian-plates-cnn-synth-model`: OCR for Argentinian license plates trained with
        synthetic and real data. Uses fully conv architecture.
    - `european-plates-mobile-vit-v2-model`: OCR for European license plates. Uses MobileVIT-2
        for the backbone.

    Args:
        hub_ocr_model: Name of the OCR model to use from the HUB.
        device: Device type for inference. Should be one of ('cpu', 'cuda', 'auto'). If
            'auto' mode, the device will be deduced from
            `onnxruntime.get_available_providers()`.
        providers: Optional sequence of providers in order of decreasing precedence. If not
            specified, all available providers are used based on the device argument.
        sess_options: Advanced session options for ONNX Runtime.
        model_path: Path to ONNX model file to use (In case you want to use a custom one).
        config_path: Path to config file to use (In case you want to use a custom one).
        force_download: Force and download the model, even if it already exists.
    Returns:
        None.
    """
    self.logger = logging.getLogger(__name__)

    if providers is not None:
        self.providers = providers
        self.logger.info("Using custom providers: %s", providers)
    else:
        if device == "cuda":
            self.providers = ["CUDAExecutionProvider"]
        elif device == "cpu":
            self.providers = ["CPUExecutionProvider"]
        elif device == "auto":
            self.providers = ort.get_available_providers()
        else:
            raise ValueError(
                f"Device should be one of ('cpu', 'cuda', 'auto'). Got '{device}'."
            )

        self.logger.info("Using device '%s' with providers: %s", device, self.providers)

    if model_path and config_path:
        model_path = pathlib.Path(model_path)
        config_path = pathlib.Path(config_path)
        if not model_path.exists() or not config_path.exists():
            raise FileNotFoundError("Missing model/config file!")
        self.model_name = model_path.stem
    elif hub_ocr_model:
        self.model_name = hub_ocr_model
        model_path, config_path = hub.download_model(
            model_name=hub_ocr_model, force_download=force_download
        )
    else:
        raise ValueError(
            "Either provide a model from the HUB or a custom model_path and config_path"
        )

    self.config = load_config_from_yaml(config_path)
    self.model = ort.InferenceSession(
        model_path, providers=self.providers, sess_options=sess_options
    )
    self.logger.info("Using ONNX Runtime with %s.", self.providers)

benchmark(n_iter=10000, include_processing=False)

Benchmark time taken to run the OCR model. This reports the average inference time and the throughput in plates per second.

Parameters:

Name Type Description Default
n_iter int

The number of iterations to run the benchmark. This determines how many times the inference will be executed to compute the average performance metrics.

 10000
include_processing bool

Indicates whether the benchmark should include preprocessing and postprocessing times in the measurement.

 False
Source code in fast_plate_ocr/inference/onnx_inference.py 
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def benchmark(self, n_iter: int = 10_000, include_processing: bool = False) -> None:
    """
    Benchmark time taken to run the OCR model. This reports the average inference time and the
    throughput in plates per second.

    Args:
        n_iter: The number of iterations to run the benchmark. This determines how many times
            the inference will be executed to compute the average performance metrics.
        include_processing: Indicates whether the benchmark should include preprocessing and
            postprocessing times in the measurement.
    """
    cum_time = 0.0
    x = np.random.randint(
        0, 256, size=(1, self.config["img_height"], self.config["img_width"], 1), dtype=np.uint8
    )
    for _ in range(n_iter):
        with measure_time() as time_taken:
            if include_processing:
                self.run(x)
            else:
                self.model.run(None, {"input": x})
        cum_time += time_taken()

    avg_time = (cum_time / n_iter) if n_iter > 0 else 0.0
    avg_pps = (1_000 / avg_time) if n_iter > 0 else 0.0

    console = Console()
    model_info = Panel(
        Text(f"Model: {self.model_name}\nProviders: {self.providers}", style="bold green"),
        title="Model Information",
        border_style="bright_blue",
        expand=False,
    )
    console.print(model_info)
    table = Table(title=f"Benchmark for '{self.model_name}' Model", border_style="bright_blue")
    table.add_column("Metric", justify="center", style="cyan", no_wrap=True)
    table.add_column("Value", justify="center", style="magenta")
    table.add_row("Number of Iterations", str(n_iter))
    table.add_row("Average Time (ms)", f"{avg_time:.4f}")
    table.add_row("Plates Per Second (PPS)", f"{avg_pps:.4f}")
    console.print(table)

run(source, return_confidence=False)

Performs OCR to recognize license plate characters from an image or a list of images.

Parameters:

Name Type Description Default
source str | list[str] | NDArray | list[NDArray]

The path(s) to the image(s), a numpy array representing an image or a list of NumPy arrays. If a numpy array is provided, it is expected to already be in grayscale format, with shape (H, W)or (H, W, 1). A list of numpy arrays with different image sizes may also be provided.

 required
return_confidence bool

Whether to return confidence scores along with plate predictions.

 False

Returns:

Type Description
tuple[list[str], NDArray] | list[str]

A list of plates for each input image. If return_confidence is True, a numpy array is returned with the shape (N, plate_slots), where N is the batch size and each plate slot is the confidence for the recognized license plate character.
Source code in fast_plate_ocr/inference/onnx_inference.py 
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def run(
    self,
    source: str | list[str] | npt.NDArray | list[npt.NDArray],
    return_confidence: bool = False,
) -> tuple[list[str], npt.NDArray] | list[str]:
    """
    Performs OCR to recognize license plate characters from an image or a list of images.

    Args:
        source: The path(s) to the image(s), a numpy array representing an image or a list
            of NumPy arrays. If a numpy array is provided, it is expected to already be in
            grayscale format, with shape `(H, W) `or `(H, W, 1)`. A list of numpy arrays with
            different image sizes may also be provided.
        return_confidence: Whether to return confidence scores along with plate predictions.

    Returns:
        A list of plates for each input image. If `return_confidence` is True, a numpy
            array is returned with the shape `(N, plate_slots)`, where N is the batch size and
            each plate slot is the confidence for the recognized license plate character.
    """
    x = _load_image_from_source(source)
    # Preprocess
    x = preprocess_image(x, self.config["img_height"], self.config["img_width"])
    # Run model
    y: list[npt.NDArray] = self.model.run(None, {"input": x})
    # Postprocess model output
    return postprocess_output(
        y[0],
        self.config["max_plate_slots"],
        self.config["alphabet"],
        return_confidence=return_confidence,
    )