626172a16b
Source files in libcamera start by a comment block header, which
includes the file name and a one-line description of the file contents.
While the latter is useful to get a quick overview of the file contents
at a glance, the former is mostly a source of inconvenience. The name in
the comments can easily get out of sync with the file name when files
are renamed, and copy & paste during development have often lead to
incorrect names being used to start with.
Readers of the source code are expected to know which file they're
looking it. Drop the file name from the header comment block.
The change was generated with the following script:
----------------------------------------
dirs="include/libcamera src test utils"
declare -rA patterns=(
['c']=' \* '
['cpp']=' \* '
['h']=' \* '
['py']='# '
['sh']='# '
)
for ext in ${!patterns[@]} ; do
files=$(for dir in $dirs ; do find $dir -name "*.${ext}" ; done)
pattern=${patterns[${ext}]}
for file in $files ; do
name=$(basename ${file})
sed -i "s/^\(${pattern}\)${name} - /\1/" "$file"
done
done
----------------------------------------
This misses several files that are out of sync with the comment block
header. Those will be addressed separately and manually.
Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
Reviewed-by: Daniel Scally <dan.scally@ideasonboard.com>
123 lines
3.5 KiB
Python
123 lines
3.5 KiB
Python
# SPDX-License-Identifier: BSD-2-Clause
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#
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# Copyright (C) 2019, Raspberry Pi Ltd
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#
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# camera tuning tool noise calibration
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from ctt_image_load import *
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import matplotlib.pyplot as plt
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"""
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Find noise standard deviation and fit to model:
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noise std = a + b*sqrt(pixel mean)
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"""
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def noise(Cam, Img, plot):
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Cam.log += '\nProcessing image: {}'.format(Img.name)
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stds = []
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means = []
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"""
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iterate through macbeth square patches
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"""
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for ch_patches in Img.patches:
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for patch in ch_patches:
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"""
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renormalise patch
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"""
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patch = np.array(patch)
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patch = (patch-Img.blacklevel_16)/Img.againQ8_norm
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std = np.std(patch)
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mean = np.mean(patch)
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stds.append(std)
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means.append(mean)
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"""
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clean data and ensure all means are above 0
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"""
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stds = np.array(stds)
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means = np.array(means)
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means = np.clip(np.array(means), 0, None)
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sq_means = np.sqrt(means)
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"""
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least squares fit model
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"""
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fit = np.polyfit(sq_means, stds, 1)
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Cam.log += '\nBlack level = {}'.format(Img.blacklevel_16)
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Cam.log += '\nNoise profile: offset = {}'.format(int(fit[1]))
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Cam.log += ' slope = {:.3f}'.format(fit[0])
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"""
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remove any values further than std from the fit
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anomalies most likely caused by:
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> ucharacteristically noisy white patch
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> saturation in the white patch
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"""
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fit_score = np.abs(stds - fit[0]*sq_means - fit[1])
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fit_std = np.std(stds)
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fit_score_norm = fit_score - fit_std
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anom_ind = np.where(fit_score_norm > 1)
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fit_score_norm.sort()
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sq_means_clean = np.delete(sq_means, anom_ind)
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stds_clean = np.delete(stds, anom_ind)
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removed = len(stds) - len(stds_clean)
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if removed != 0:
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Cam.log += '\nIdentified and removed {} anomalies.'.format(removed)
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Cam.log += '\nRecalculating fit'
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"""
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recalculate fit with outliers removed
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"""
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fit = np.polyfit(sq_means_clean, stds_clean, 1)
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Cam.log += '\nNoise profile: offset = {}'.format(int(fit[1]))
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Cam.log += ' slope = {:.3f}'.format(fit[0])
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"""
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if fit const is < 0 then force through 0 by
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dividing by sq_means and fitting poly order 0
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"""
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corrected = 0
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if fit[1] < 0:
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corrected = 1
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ones = np.ones(len(means))
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y_data = stds/sq_means
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fit2 = np.polyfit(ones, y_data, 0)
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Cam.log += '\nOffset below zero. Fit recalculated with zero offset'
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Cam.log += '\nNoise profile: offset = 0'
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Cam.log += ' slope = {:.3f}'.format(fit2[0])
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# print('new fit')
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# print(fit2)
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"""
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plot fit for debug
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"""
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if plot:
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x = np.arange(sq_means.max()//0.88)
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fit_plot = x*fit[0] + fit[1]
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plt.scatter(sq_means, stds, label='data', color='blue')
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plt.scatter(sq_means[anom_ind], stds[anom_ind], color='orange', label='anomalies')
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plt.plot(x, fit_plot, label='fit', color='red', ls=':')
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if fit[1] < 0:
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fit_plot_2 = x*fit2[0]
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plt.plot(x, fit_plot_2, label='fit 0 intercept', color='green', ls='--')
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plt.plot(0, 0)
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plt.title('Noise Plot\nImg: {}'.format(Img.str))
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plt.legend(loc='upper left')
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plt.xlabel('Sqrt Pixel Value')
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plt.ylabel('Noise Standard Deviation')
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plt.grid()
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plt.show()
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"""
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End of plotting code
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"""
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"""
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format output to include forced 0 constant
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"""
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Cam.log += '\n'
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if corrected:
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fit = [fit2[0], 0]
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return fit
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else:
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return fit
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