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WaveletBuffer

Bases: impl.WaveletBuffer

Universal buffer for the wavelet decomposition

Source code in python/src/wavelet_buffer/wavelet_buffer.py 
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class WaveletBuffer(impl.WaveletBuffer):
    """Universal buffer for the wavelet decomposition"""

    def __init__(
        self,
        signal_shape: List[int],
        signal_number: int,
        decomposition_steps: int,
        wavelet_type: WaveletType,
        decompositions=None,
    ):
        """
        Args:
            signal_shape: for 1D signal with size N [N], for 2DN signal with size MXN, [M,N]
            signal_number: N for 2DN signal, e.g. RGB image would be 3 signals
            decomposition_steps: number of decomposition steps
            wavelet_type: type WaveletType.NONE, DB{1..5}, if it is NONE no wavelet composition
            decompositions: you can create a buffer with initialized subbands
        """
        if decompositions:
            super().__init__(
                signal_shape,
                signal_number,
                decomposition_steps,
                wavelet_type,
                decompositions,
            )
        else:
            super().__init__(
                signal_shape, signal_number, decomposition_steps, wavelet_type
            )

    @staticmethod
    def decomposition_size(
        signal_shape: List[int], decomposition_steps: int, **_kwargs
    ) -> int:
        """
        Estimate number of subbands for specific parameters
        Args:
            signal_shape: for 1D signal with size N [N], for 2DN signal with size MXN, [M,N]
            decomposition_steps: number of decomposition steps
        Returns:
            number of subbands for each signal
        """
        return impl.WaveletBuffer.decomposition_size(
            signal_shape, 0, decomposition_steps, WaveletType.NONE
        )

    @staticmethod
    def parse(blob: bytes) -> "WaveletBuffer":
        """
        Restore serialized buffer from string
        Args:
            blob: serialized buffer
        Returns:
            restored wavelet buffer
        """
        return impl.WaveletBuffer.parse(blob)

    def decompose(self, data: "np.ndarray[np.float32]", denoiser: BaseDenoiser) -> None:
        """
        Decompose signal with wavelet decomposition
        The signal should have a shape which was set in the constructor of the buffer

        Args:
            data: signal to decompose
            denoiser: denoiser algorithm
        Returns:
            RuntimeError if input data isn't valid
        """
        super(WaveletBuffer, self).decompose(data, denoiser)

    def compose(self, scale_factor: int = 0) -> "np.ndarray[np.float32]":
        """
        Compose signal from subbands with given scale factor
        Args:
            scale_factor: who many steps of wavelet decomposition should be re-compose
        Returns:
            restored signals
        """
        return super(WaveletBuffer, self).compose(scale_factor)

    def serialize(self, compression_level: int = 0) -> bytes:
        """
        Serialize and compress buffer to binary string
        Args:
            compression_level: 0- no compression, 31 max compression (2-bits for each value)
        Returns:
            serialized and compressed buffer
        """
        return super(WaveletBuffer, self).serialize(compression_level)

    @property
    def parameters(self) -> WaveletParameters:
        """
        Returns:
            current wavelet parameters
        """
        return super(WaveletBuffer, self).parameters

parameters: WaveletParameters property

Returns:

Type Description
WaveletParameters

current wavelet parameters

__init__(signal_shape, signal_number, decomposition_steps, wavelet_type, decompositions=None)

Parameters:

Name Type Description Default
signal_shape List[int]

for 1D signal with size N [N], for 2DN signal with size MXN, [M,N]

 required
signal_number int

N for 2DN signal, e.g. RGB image would be 3 signals

 required
decomposition_steps int

number of decomposition steps

 required
wavelet_type WaveletType

type WaveletType.NONE, DB{1..5}, if it is NONE no wavelet composition

 required
decompositions

you can create a buffer with initialized subbands

 None
Source code in python/src/wavelet_buffer/wavelet_buffer.py 
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def __init__(
    self,
    signal_shape: List[int],
    signal_number: int,
    decomposition_steps: int,
    wavelet_type: WaveletType,
    decompositions=None,
):
    """
    Args:
        signal_shape: for 1D signal with size N [N], for 2DN signal with size MXN, [M,N]
        signal_number: N for 2DN signal, e.g. RGB image would be 3 signals
        decomposition_steps: number of decomposition steps
        wavelet_type: type WaveletType.NONE, DB{1..5}, if it is NONE no wavelet composition
        decompositions: you can create a buffer with initialized subbands
    """
    if decompositions:
        super().__init__(
            signal_shape,
            signal_number,
            decomposition_steps,
            wavelet_type,
            decompositions,
        )
    else:
        super().__init__(
            signal_shape, signal_number, decomposition_steps, wavelet_type
        )

compose(scale_factor=0)

Compose signal from subbands with given scale factor

Parameters:

Name Type Description Default
scale_factor int

who many steps of wavelet decomposition should be re-compose

 0

Returns:

Type Description
np.ndarray[np.float32]

restored signals
Source code in python/src/wavelet_buffer/wavelet_buffer.py 
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def compose(self, scale_factor: int = 0) -> "np.ndarray[np.float32]":
    """
    Compose signal from subbands with given scale factor
    Args:
        scale_factor: who many steps of wavelet decomposition should be re-compose
    Returns:
        restored signals
    """
    return super(WaveletBuffer, self).compose(scale_factor)

decompose(data, denoiser)

Decompose signal with wavelet decomposition The signal should have a shape which was set in the constructor of the buffer

Parameters:

Name Type Description Default
data np.ndarray[np.float32]

signal to decompose

 required
denoiser BaseDenoiser

denoiser algorithm

 required

Returns:

Type Description
None

RuntimeError if input data isn't valid
Source code in python/src/wavelet_buffer/wavelet_buffer.py 
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def decompose(self, data: "np.ndarray[np.float32]", denoiser: BaseDenoiser) -> None:
    """
    Decompose signal with wavelet decomposition
    The signal should have a shape which was set in the constructor of the buffer

    Args:
        data: signal to decompose
        denoiser: denoiser algorithm
    Returns:
        RuntimeError if input data isn't valid
    """
    super(WaveletBuffer, self).decompose(data, denoiser)

decomposition_size(signal_shape, decomposition_steps, **_kwargs) staticmethod

Estimate number of subbands for specific parameters

Parameters:

Name Type Description Default
signal_shape List[int]

for 1D signal with size N [N], for 2DN signal with size MXN, [M,N]

 required
decomposition_steps int

number of decomposition steps

 required

Returns:

Type Description
int

number of subbands for each signal
Source code in python/src/wavelet_buffer/wavelet_buffer.py 
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@staticmethod
def decomposition_size(
    signal_shape: List[int], decomposition_steps: int, **_kwargs
) -> int:
    """
    Estimate number of subbands for specific parameters
    Args:
        signal_shape: for 1D signal with size N [N], for 2DN signal with size MXN, [M,N]
        decomposition_steps: number of decomposition steps
    Returns:
        number of subbands for each signal
    """
    return impl.WaveletBuffer.decomposition_size(
        signal_shape, 0, decomposition_steps, WaveletType.NONE
    )

parse(blob) staticmethod

Restore serialized buffer from string

Parameters:

Name Type Description Default
blob bytes

serialized buffer

 required

Returns:

Type Description
WaveletBuffer

restored wavelet buffer
Source code in python/src/wavelet_buffer/wavelet_buffer.py 
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@staticmethod
def parse(blob: bytes) -> "WaveletBuffer":
    """
    Restore serialized buffer from string
    Args:
        blob: serialized buffer
    Returns:
        restored wavelet buffer
    """
    return impl.WaveletBuffer.parse(blob)

serialize(compression_level=0)

Serialize and compress buffer to binary string

Parameters:

Name Type Description Default
compression_level int

0- no compression, 31 max compression (2-bits for each value)

 0

Returns:

Type Description
bytes

serialized and compressed buffer
Source code in python/src/wavelet_buffer/wavelet_buffer.py 
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def serialize(self, compression_level: int = 0) -> bytes:
    """
    Serialize and compress buffer to binary string
    Args:
        compression_level: 0- no compression, 31 max compression (2-bits for each value)
    Returns:
        serialized and compressed buffer
    """
    return super(WaveletBuffer, self).serialize(compression_level)