[PATCH v1 4/6] libtuning: Modify ctt_awb.awb() so that it can run in our context
Kieran Bingham
kieran.bingham at ideasonboard.com
Tue Aug 6 10:55:39 CEST 2024
Quoting Stefan Klug (2024-08-06 07:41:52)
> Hi Kieran,
>
> Thanks for the review.
>
> On Mon, Aug 05, 2024 at 02:47:34PM +0100, Kieran Bingham wrote:
> > Quoting Stefan Klug (2024-08-05 13:05:05)
> > > Modify the awb function that was copied from the Raspberry Pi tuning
> > > scripts in a way that it can easily be called from the libtuning code.
> > > In essence the logging was replaced by calls to a python logger and the
> > > need for the Cam object was removed by providing a list of images to the
> > > function.
> > >
> > > Signed-off-by: Stefan Klug <stefan.klug at ideasonboard.com>
> > > ---
> > > utils/tuning/libtuning/ctt_awb.py | 55 ++++++++++++++++---------------
> > > 1 file changed, 28 insertions(+), 27 deletions(-)
> > >
> > > diff --git a/utils/tuning/libtuning/ctt_awb.py b/utils/tuning/libtuning/ctt_awb.py
> > > index abf22321a0ea..73a1bc1a840c 100644
> > > --- a/utils/tuning/libtuning/ctt_awb.py
> > > +++ b/utils/tuning/libtuning/ctt_awb.py
> > > @@ -4,6 +4,8 @@
> > > #
> > > # camera tuning tool for AWB
> > >
> > > +import logging
> > > +
> > > import matplotlib.pyplot as plt
> > > from bisect import bisect_left
> > > from scipy.optimize import fmin
> > > @@ -11,12 +13,12 @@ import numpy as np
> > >
> > > from .image import Image
> > >
> > > +logger = logging.getLogger(__name__)
> > >
> > > """
> > > obtain piecewise linear approximation for colour curve
> > > """
> > > -def awb(Cam, cal_cr_list, cal_cb_list, plot):
> > > - imgs = Cam.imgs
> >
> > Does this break any other code? Or was this just a module that had been
> > 'copied/imported' and not yet used?
>
> Yes the files were copied verbatim. Only the parts needed were adjusted
> to work. Might well be that this one is the last one missing.
Ok, so if it doesn't break bisectability: (which ... doesn't quite apply
in the same way for the tuning tool which doesn't get built, but could
be 'used'?)
Reviewed-by: Kieran Bingham <kieran.bingham at ideasonboard.com>
>
> >
> >
> >
> > > +def awb(imgs, cal_cr_list, cal_cb_list, plot):
> > > """
> > > condense alsc calibration tables into one dictionary
> > > """
> > > @@ -39,7 +41,7 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):
> > > rb_raw = []
> > > rbs_hat = []
> > > for Img in imgs:
> > > - Cam.log += '\nProcessing '+Img.name
> > > + logger.info(f'Processing {Img.name}')
> > > """
> > > get greyscale patches with alsc applied if alsc enabled.
> > > Note: if alsc is disabled then colour_cals will be set to None and the
> > > @@ -51,7 +53,7 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):
> > > """
> > > r_g = np.mean(r_patchs/g_patchs)
> > > b_g = np.mean(b_patchs/g_patchs)
> > > - Cam.log += '\n r : {:.4f} b : {:.4f}'.format(r_g, b_g)
> > > + logger.info(' r : {:.4f} b : {:.4f}'.format(r_g, b_g))
> > > """
> > > The curve tends to be better behaved in so-called hatspace.
> > > R, B, G represent the individual channels. The colour curve is plotted in
> > > @@ -74,12 +76,11 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):
> > > """
> > > r_g_hat = r_g/(1+r_g+b_g)
> > > b_g_hat = b_g/(1+r_g+b_g)
> > > - Cam.log += '\n r_hat : {:.4f} b_hat : {:.4f}'.format(r_g_hat, b_g_hat)
> > > - rbs_hat.append((r_g_hat, b_g_hat, Img.col))
> > > + logger.info('\n r_hat : {:.4f} b_hat : {:.4f}'.format(r_g_hat, b_g_hat))
> > > + rbs_hat.append((r_g_hat, b_g_hat, Img.color))
> > > rb_raw.append((r_g, b_g))
> > > - Cam.log += '\n'
> > >
> > > - Cam.log += '\nFinished processing images'
> > > + logger.info('Finished processing images')
> > > """
> > > sort all lits simultaneously by r_hat
> > > """
> > > @@ -95,7 +96,7 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):
> > > fit quadratic fit to r_g hat and b_g_hat
> > > """
> > > a, b, c = np.polyfit(rbs_hat[0], rbs_hat[1], 2)
> > > - Cam.log += '\nFit quadratic curve in hatspace'
> > > + logger.info('Fit quadratic curve in hatspace')
> > > """
> > > the algorithm now approximates the shortest distance from each point to the
> > > curve in dehatspace. Since the fit is done in hatspace, it is easier to
> > > @@ -151,14 +152,14 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):
> > > if (x+y) > (rr+bb):
> > > dist *= -1
> > > dists.append(dist)
> > > - Cam.log += '\nFound closest point on fit line to each point in dehatspace'
> > > + logger.info('Found closest point on fit line to each point in dehatspace')
> > > """
> > > calculate wiggle factors in awb. 10% added since this is an upper bound
> > > """
> > > transverse_neg = - np.min(dists) * 1.1
> > > transverse_pos = np.max(dists) * 1.1
> > > - Cam.log += '\nTransverse pos : {:.5f}'.format(transverse_pos)
> > > - Cam.log += '\nTransverse neg : {:.5f}'.format(transverse_neg)
> > > + logger.info('Transverse pos : {:.5f}'.format(transverse_pos))
> > > + logger.info('Transverse neg : {:.5f}'.format(transverse_neg))
> > > """
> > > set minimum transverse wiggles to 0.1 .
> > > Wiggle factors dictate how far off of the curve the algorithm searches. 0.1
> > > @@ -167,10 +168,10 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):
> > > """
> > > if transverse_pos < 0.01:
> > > transverse_pos = 0.01
> > > - Cam.log += '\nForced transverse pos to 0.01'
> > > + logger.info('Forced transverse pos to 0.01')
> > > if transverse_neg < 0.01:
> > > transverse_neg = 0.01
> > > - Cam.log += '\nForced transverse neg to 0.01'
> > > + logger.info('Forced transverse neg to 0.01')
> > >
> > > """
> > > generate new b_hat values at each r_hat according to fit
> > > @@ -202,25 +203,25 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):
> > > i = len(c_fit) - 1
> > > while i > 0:
> > > if c_fit[i] > c_fit[i-1]:
> > > - Cam.log += '\nColour temperature increase found\n'
> > > - Cam.log += '{} K at r = {} to '.format(c_fit[i-1], r_fit[i-1])
> > > - Cam.log += '{} K at r = {}'.format(c_fit[i], r_fit[i])
> > > + logger.info('Colour temperature increase found')
> > > + logger.info('{} K at r = {} to '.format(c_fit[i-1], r_fit[i-1]))
> > > + logger.info('{} K at r = {}'.format(c_fit[i], r_fit[i]))
> > > """
> > > if colour temperature increases then discard point furthest from
> > > the transformed fit (dehatspace)
> > > """
> > > error_1 = abs(dists[i-1])
> > > error_2 = abs(dists[i])
> > > - Cam.log += '\nDistances from fit:\n'
> > > - Cam.log += '{} K : {:.5f} , '.format(c_fit[i], error_1)
> > > - Cam.log += '{} K : {:.5f}'.format(c_fit[i-1], error_2)
> > > + logger.info('Distances from fit:')
> > > + logger.info('{} K : {:.5f} , '.format(c_fit[i], error_1))
> > > + logger.info('{} K : {:.5f}'.format(c_fit[i-1], error_2))
> > > """
> > > find bad index
> > > note that in python false = 0 and true = 1
> > > """
> > > bad = i - (error_1 < error_2)
> > > - Cam.log += '\nPoint at {} K deleted as '.format(c_fit[bad])
> > > - Cam.log += 'it is furthest from fit'
> > > + logger.info('Point at {} K deleted as '.format(c_fit[bad]))
> > > + logger.info('it is furthest from fit')
> > > """
> > > delete bad point
> > > """
> > > @@ -239,12 +240,12 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):
> > > return formatted ct curve, ordered by increasing colour temperature
> > > """
> > > ct_curve = list(np.array(list(zip(b_fit, r_fit, c_fit))).flatten())[::-1]
> > > - Cam.log += '\nFinal CT curve:'
> > > + logger.info('Final CT curve:')
> > > for i in range(len(ct_curve)//3):
> > > j = 3*i
> > > - Cam.log += '\n ct: {} '.format(ct_curve[j])
> > > - Cam.log += ' r: {} '.format(ct_curve[j+1])
> > > - Cam.log += ' b: {} '.format(ct_curve[j+2])
> > > + logger.info(' ct: {} '.format(ct_curve[j]))
> > > + logger.info(' r: {} '.format(ct_curve[j+1]))
> > > + logger.info(' b: {} '.format(ct_curve[j+2]))
> > >
> > > """
> > > plotting code for debug
> > > @@ -303,7 +304,7 @@ def get_alsc_patches(Img, colour_cals, grey=True):
> > > """
> > > if grey:
> > > cen_coords = Img.cen_coords[3::4]
> > > - col = Img.col
> > > + col = Img.color
> > > patches = [np.array(Img.patches[i]) for i in Img.order]
> > > r_patchs = patches[0][3::4] - Img.blacklevel_16
> > > b_patchs = patches[3][3::4] - Img.blacklevel_16
> > > --
> > > 2.43.0
> > >
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