[v4,4/6] libtuning: Modify ctt_awb.awb() so that it can run in our context

20897 diff mbox series

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@ideasonboard.com>
Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com>
---
 utils/tuning/libtuning/ctt_awb.py | 55 ++++++++++++++++---------------
 1 file changed, 28 insertions(+), 27 deletions(-)

On Tue, Aug 13, 2024 at 10:44:21AM +0200, Stefan Klug wrote:
> 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@ideasonboard.com>
> Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com>

Reviewed-by: Paul Elder <paul.elder@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
> +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
>

Hi Stefan,

Thank you for the patch.

On Tue, Aug 13, 2024 at 10:44:21AM +0200, Stefan Klug wrote:
> 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

s/was/is/

> need for the Cam object was removed by providing a list of images to the

s/was/is/

The commit message should use the imperative mood though.

> function.

That sounds like two changes, calling for two patches.

> Signed-off-by: Stefan Klug <stefan.klug@ideasonboard.com>
> Reviewed-by: Kieran Bingham <kieran.bingham@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
> +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))

As you use an f-string above, I think you can write this

        logger.info(f'       r : {r_g:.4f}       b : {b_g:.4f}'

Same below.

>          """
>          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