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# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
Exposure and level adjustment module
------------------------------------
This module provides functions for adjusting image exposure, contrast, and intensity
levels.
Features include:
- Dynamic range scaling and adjustment
- Various normalization methods (maximum, amplitude, area, energy, RMS)
- Data type preserving transformations
These tools support image enhancement and preprocessing operations that adjust
the intensity distribution of images while preserving their essential characteristics.
"""
from __future__ import annotations
import numpy as np
from sigima.enums import NormalizationMethod
from sigima.tools.checks import check_2d_array
from sigima.tools.image.preprocessing import scale_data_to_min_max
@check_2d_array(non_constant=True)
def normalize(
data: np.ndarray,
parameter: NormalizationMethod = NormalizationMethod.MAXIMUM,
) -> np.ndarray:
"""Normalize input array to a given parameter.
Args:
data: Input data
parameter: Normalization parameter (default: MAXIMUM)
Returns:
Normalized array
"""
if parameter == NormalizationMethod.MAXIMUM:
return scale_data_to_min_max(data, np.nanmin(data) / np.nanmax(data), 1.0)
if parameter == NormalizationMethod.AMPLITUDE:
return scale_data_to_min_max(data, 0.0, 1.0)
fdata = np.array(data, dtype=float)
if parameter == NormalizationMethod.AREA:
return fdata / np.nansum(fdata)
if parameter == NormalizationMethod.ENERGY:
return fdata / np.sqrt(np.nansum(fdata * fdata.conjugate()))
if parameter == NormalizationMethod.RMS:
return fdata / np.sqrt(np.nanmean(fdata * fdata.conjugate()))
raise ValueError(f"Unsupported parameter {parameter}")
@check_2d_array
def flatfield(
rawdata: np.ndarray, flatdata: np.ndarray, threshold: float | None = None
) -> np.ndarray:
"""Compute flat-field correction
Args:
rawdata: Raw data
flatdata: Flat-field data
threshold: Threshold for flat-field correction (default: None)
Returns:
Flat-field corrected data
"""
dtemp = np.array(rawdata, dtype=float, copy=True) * np.nanmean(flatdata)
dunif = np.array(flatdata, dtype=float, copy=True)
dunif[dunif == 0] = 1.0
dcorr_all = np.array(dtemp / dunif, dtype=rawdata.dtype)
dcorr = np.array(rawdata, copy=True)
dcorr[rawdata > threshold] = dcorr_all[rawdata > threshold]
return dcorr
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