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generated by cgit v1.2.1 (git 2.18.0) at 2025-05-29 09:08:28 +0000
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# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi (Trading) Limited
#
# ctt_noise.py - camera tuning tool noise calibration

from ctt_image_load import *
import matplotlib.pyplot as plt


"""
Find noise standard deviation and fit to model:

    noise std = a + b*sqrt(pixel mean)
"""
def noise(Cam, Img, plot):
    Cam.log += '\nProcessing image: {}'.format(Img.name)
    stds = []
    means = []
    """
    iterate through macbeth square patches
    """
    for ch_patches in Img.patches:
        for patch in ch_patches:
            """
            renormalise patch
            """
            patch = np.array(patch)
            patch = (patch-Img.blacklevel_16)/Img.againQ8_norm
            std = np.std(patch)
            mean = np.mean(patch)
            stds.append(std)
            means.append(mean)

    """
    clean data and ensure all means are above 0
    """
    stds = np.array(stds)
    means = np.array(means)
    means = np.clip(np.array(means), 0, None)
    sq_means = np.sqrt(means)

    """
    least squares fit model
    """
    fit = np.polyfit(sq_means, stds, 1)
    Cam.log += '\nBlack level = {}'.format(Img.blacklevel_16)
    Cam.log += '\nNoise profile: offset = {}'.format(int(fit[1]))
    Cam.log += ' slope = {:.3f}'.format(fit[0])
    """
    remove any values further than std from the fit

    anomalies most likely caused by:
    > ucharacteristically noisy white patch
    > saturation in the white patch
    """
    fit_score = np.abs(stds - fit[0]*sq_means - fit[1])
    fit_std = np.std(stds)
    fit_score_norm = fit_score - fit_std
    anom_ind = np.where(fit_score_norm > 1)
    fit_score_norm.sort()