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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()
sq_means_clean = np.delete(sq_means, anom_ind)
stds_clean = np.delete(stds, anom_ind)
removed = len(stds) - len(stds_clean)
if removed != 0:
Cam.log += '\nIdentified and removed {} anomalies.'.format(removed)
Cam.log += '\nRecalculating fit'
"""
recalculate fit with outliers removed
"""
fit = np.polyfit(sq_means_clean, stds_clean, 1)
Cam.log += '\nNoise profile: offset = {}'.format(int(fit[1]))
Cam.log += ' slope = {:.3f}'.format(fit[0])
"""
if fit const is < 0 then force through 0 by
dividing by sq_means and fitting poly order 0
"""
corrected = 0
if fit[1] < 0:
corrected = 1
ones = np.ones(len(means))
y_data = stds/sq_means
fit2 = np.polyfit(ones, y_data, 0)
Cam.log += '\nOffset below zero. Fit recalculated with zero offset'
Cam.log += '\nNoise profile: offset = 0'
Cam.log += ' slope = {:.3f}'.format(fit2[0])
# print('new fit')
# print(fit2)
"""
plot fit for debug
"""
if plot:
x = np.arange(sq_means.max()//0.88)
fit_plot = x*fit[0] + fit[1]
plt.scatter(sq_means, stds, label='data', color='blue')
plt.scatter(sq_means[anom_ind], stds[anom_ind], color='orange', label='anomalies')
plt.plot(x, fit_plot, label='fit', color='red', ls=':')
if fit[1] < 0:
fit_plot_2 = x*fit2[0]
plt.plot(x, fit_plot_2, label='fit 0 intercept', color='green', ls='--')
plt.plot(0, 0)
plt.title('Noise Plot\nImg: {}'.format(Img.str))
plt.legend(loc = 'upper left')
plt.xlabel('Sqrt Pixel Value')
plt.ylabel('Noise Standard Deviation')
plt.grid()
plt.show()
"""
End of plotting code
"""
"""
format output to include forced 0 constant
"""
Cam.log += '\n'
if corrected:
fit = [fit2[0], 0]
return fit
else:
return fit
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