[v1,08/11] libtuning: module: awb: Add bayes AWB support
diff mbox series

Message ID 20250109115412.356768-9-stefan.klug@ideasonboard.com
State New
Headers show
Series
  • Add Bayesian AWB algorithm to libipa and rkisp1
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Commit Message

Stefan Klug Jan. 9, 2025, 11:53 a.m. UTC 
To support the bayesian AWB algorithm in libtuning, the necessary data
needs to be collected and written to the tuning file.

Prior probabilities and AwbModes are defined by the user and therefore
added to the example config file.

Extend the output to also contain the necessary data for the bayesian
AWB algorithm.

Signed-off-by: Stefan Klug <stefan.klug@ideasonboard.com>
---
 utils/tuning/config-example.yaml             | 34 +++++++++++++++++++-
 utils/tuning/libtuning/modules/awb/awb.py    | 16 +++++----
 utils/tuning/libtuning/modules/awb/rkisp1.py | 21 ++++++++----
 3 files changed, 58 insertions(+), 13 deletions(-)

Comments

Paul Elder Jan. 14, 2025, 12:14 a.m. UTC | #1 
On Thu, Jan 09, 2025 at 12:53:59PM +0100, Stefan Klug wrote:
> To support the bayesian AWB algorithm in libtuning, the necessary data
> needs to be collected and written to the tuning file.
> 
> Prior probabilities and AwbModes are defined by the user and therefore
> added to the example config file.
> 
> Extend the output to also contain the necessary data for the bayesian
> AWB algorithm.
> 
> Signed-off-by: Stefan Klug <stefan.klug@ideasonboard.com>
> ---
>  utils/tuning/config-example.yaml             | 34 +++++++++++++++++++-
>  utils/tuning/libtuning/modules/awb/awb.py    | 16 +++++----
>  utils/tuning/libtuning/modules/awb/rkisp1.py | 21 ++++++++----
>  3 files changed, 58 insertions(+), 13 deletions(-)
> 
> diff --git a/utils/tuning/config-example.yaml b/utils/tuning/config-example.yaml
> index 1b7f52cd2fff..30e88341df01 100644
> --- a/utils/tuning/config-example.yaml
> +++ b/utils/tuning/config-example.yaml
> @@ -5,7 +5,39 @@ general:
>      do_alsc_colour: 1
>      luminance_strength: 0.5
>    awb:
> -    greyworld: 0
> +    # Algorithm can be either 'grey' or 'bayes'
> +    algorithm: bayes
> +    # priors is only used for the bayes algorithm
> +    priors:
> +      - lux: 0
> +        ct: [ 2000, 13000]

I think s/]/ ]/ would be nicer.

> +        probability: [ 1.0, 1.0 ]
> +    AwbMode:
> +      AwbAuto:
> +        lo: 2500
> +        hi: 8000
> +      AwbIncandescent:
> +        lo: 2500
> +        hi: 3000
> +      AwbTungsten:
> +        lo: 3000
> +        hi: 3500
> +      AwbFluorescent:
> +        lo: 4000
> +        hi: 4700
> +      AwbIndoor:
> +        lo: 3000
> +        hi: 5000
> +      AwbDaylight:
> +        lo: 5500
> +        hi: 6500
> +      AwbCloudy:
> +        lo: 6500
> +        hi: 8000
> +      # One custom mode can be defined if needed
> +      #AwbCustom:
> +      #  lo: 2000
> +      #  hi: 1300
>    macbeth:
>      small: 1
>      show: 0
> diff --git a/utils/tuning/libtuning/modules/awb/awb.py b/utils/tuning/libtuning/modules/awb/awb.py
> index c154cf3b8609..0dc4f59dcb26 100644
> --- a/utils/tuning/libtuning/modules/awb/awb.py
> +++ b/utils/tuning/libtuning/modules/awb/awb.py
> @@ -27,10 +27,14 @@ class AWB(Module):
>  
>          imgs = [img for img in images if img.macbeth is not None]
>  
> -        gains, _, _ = awb(imgs, None, None, False)
> -        gains = np.reshape(gains, (-1, 3))
> +        ct_curve, transverse_pos, transverse_neg = awb(imgs, None, None, False)
> +        ct_curve = np.reshape(ct_curve, (-1, 3))
> +        gains = [{
> +            'ct': int(v[0]),
> +            'gains': [float(1.0 / v[1]), float(1.0 / v[2])]
> +        } for v in ct_curve]
> +
> +        return {'colourGains': gains,
> +                'transversePos': transverse_pos,
> +                'transverseNeg': transverse_neg}

I don't think we use transversePos and transverseNeg?


Paul

>  
> -        return [{
> -                    'ct': int(v[0]),
> -                    'gains': [float(1.0 / v[1]), float(1.0 / v[2])]
> -                } for v in gains]
> diff --git a/utils/tuning/libtuning/modules/awb/rkisp1.py b/utils/tuning/libtuning/modules/awb/rkisp1.py
> index 0c95843b83d3..d562d26eb8cc 100644
> --- a/utils/tuning/libtuning/modules/awb/rkisp1.py
> +++ b/utils/tuning/libtuning/modules/awb/rkisp1.py
> @@ -6,9 +6,6 @@
>  
>  from .awb import AWB
>  
> -import libtuning as lt
> -
> -
>  class AWBRkISP1(AWB):
>      hr_name = 'AWB (RkISP1)'
>      out_name = 'Awb'
> @@ -20,8 +17,20 @@ class AWBRkISP1(AWB):
>          return True
>  
>      def process(self, config: dict, images: list, outputs: dict) -> dict:
> -        output = {}
> -
> -        output['colourGains'] = self.do_calculation(images)
> +        if not 'awb' in config['general']:
> +            raise ValueError('AWB configuration missing')
> +        awb_config = config['general']['awb']
> +        algorithm = awb_config['algorithm']
> +
> +        output = {'algorithm': algorithm}
> +        data = self.do_calculation(images)
> +        if algorithm == 'grey':
> +            output['colourGains'] = data['colourGains']
> +        elif algorithm == 'bayes':
> +            output['AwbMode'] = awb_config['AwbMode']
> +            output['priors'] = awb_config['priors']
> +            output.update(data)
> +        else:
> +            raise ValueError(f"Unknown AWB algorithm {output['algorithm']}")
>  
>          return output
> -- 
> 2.43.0
>
Paul Elder Jan. 14, 2025, 12:22 a.m. UTC | #2 
On Mon, Jan 13, 2025 at 06:14:09PM -0600, Paul Elder wrote:
> On Thu, Jan 09, 2025 at 12:53:59PM +0100, Stefan Klug wrote:
> > To support the bayesian AWB algorithm in libtuning, the necessary data
> > needs to be collected and written to the tuning file.
> > 
> > Prior probabilities and AwbModes are defined by the user and therefore
> > added to the example config file.
> > 
> > Extend the output to also contain the necessary data for the bayesian
> > AWB algorithm.
> > 
> > Signed-off-by: Stefan Klug <stefan.klug@ideasonboard.com>
> > ---
> >  utils/tuning/config-example.yaml             | 34 +++++++++++++++++++-
> >  utils/tuning/libtuning/modules/awb/awb.py    | 16 +++++----
> >  utils/tuning/libtuning/modules/awb/rkisp1.py | 21 ++++++++----
> >  3 files changed, 58 insertions(+), 13 deletions(-)
> > 
> > diff --git a/utils/tuning/config-example.yaml b/utils/tuning/config-example.yaml
> > index 1b7f52cd2fff..30e88341df01 100644
> > --- a/utils/tuning/config-example.yaml
> > +++ b/utils/tuning/config-example.yaml
> > @@ -5,7 +5,39 @@ general:
> >      do_alsc_colour: 1
> >      luminance_strength: 0.5
> >    awb:
> > -    greyworld: 0
> > +    # Algorithm can be either 'grey' or 'bayes'
> > +    algorithm: bayes
> > +    # priors is only used for the bayes algorithm
> > +    priors:
> > +      - lux: 0
> > +        ct: [ 2000, 13000]
> 
> I think s/]/ ]/ would be nicer.
> 
> > +        probability: [ 1.0, 1.0 ]
> > +    AwbMode:
> > +      AwbAuto:
> > +        lo: 2500
> > +        hi: 8000
> > +      AwbIncandescent:
> > +        lo: 2500
> > +        hi: 3000
> > +      AwbTungsten:
> > +        lo: 3000
> > +        hi: 3500
> > +      AwbFluorescent:
> > +        lo: 4000
> > +        hi: 4700
> > +      AwbIndoor:
> > +        lo: 3000
> > +        hi: 5000
> > +      AwbDaylight:
> > +        lo: 5500
> > +        hi: 6500
> > +      AwbCloudy:
> > +        lo: 6500
> > +        hi: 8000
> > +      # One custom mode can be defined if needed
> > +      #AwbCustom:
> > +      #  lo: 2000
> > +      #  hi: 1300
> >    macbeth:
> >      small: 1
> >      show: 0
> > diff --git a/utils/tuning/libtuning/modules/awb/awb.py b/utils/tuning/libtuning/modules/awb/awb.py
> > index c154cf3b8609..0dc4f59dcb26 100644
> > --- a/utils/tuning/libtuning/modules/awb/awb.py
> > +++ b/utils/tuning/libtuning/modules/awb/awb.py
> > @@ -27,10 +27,14 @@ class AWB(Module):
> >  
> >          imgs = [img for img in images if img.macbeth is not None]
> >  
> > -        gains, _, _ = awb(imgs, None, None, False)
> > -        gains = np.reshape(gains, (-1, 3))
> > +        ct_curve, transverse_pos, transverse_neg = awb(imgs, None, None, False)
> > +        ct_curve = np.reshape(ct_curve, (-1, 3))
> > +        gains = [{
> > +            'ct': int(v[0]),
> > +            'gains': [float(1.0 / v[1]), float(1.0 / v[2])]
> > +        } for v in ct_curve]
> > +
> > +        return {'colourGains': gains,
> > +                'transversePos': transverse_pos,
> > +                'transverseNeg': transverse_neg}
> 
> I don't think we use transversePos and transverseNeg?
> 
> 
> Paul
> 
> >  
> > -        return [{
> > -                    'ct': int(v[0]),
> > -                    'gains': [float(1.0 / v[1]), float(1.0 / v[2])]
> > -                } for v in gains]
> > diff --git a/utils/tuning/libtuning/modules/awb/rkisp1.py b/utils/tuning/libtuning/modules/awb/rkisp1.py
> > index 0c95843b83d3..d562d26eb8cc 100644
> > --- a/utils/tuning/libtuning/modules/awb/rkisp1.py
> > +++ b/utils/tuning/libtuning/modules/awb/rkisp1.py
> > @@ -6,9 +6,6 @@
> >  
> >  from .awb import AWB
> >  
> > -import libtuning as lt
> > -
> > -
> >  class AWBRkISP1(AWB):
> >      hr_name = 'AWB (RkISP1)'
> >      out_name = 'Awb'
> > @@ -20,8 +17,20 @@ class AWBRkISP1(AWB):
> >          return True
> >  
> >      def process(self, config: dict, images: list, outputs: dict) -> dict:
> > -        output = {}
> > -
> > -        output['colourGains'] = self.do_calculation(images)
> > +        if not 'awb' in config['general']:
> > +            raise ValueError('AWB configuration missing')
> > +        awb_config = config['general']['awb']
> > +        algorithm = awb_config['algorithm']
> > +
> > +        output = {'algorithm': algorithm}
> > +        data = self.do_calculation(images)
> > +        if algorithm == 'grey':
> > +            output['colourGains'] = data['colourGains']
> > +        elif algorithm == 'bayes':
> > +            output['AwbMode'] = awb_config['AwbMode']
> > +            output['priors'] = awb_config['priors']
> > +            output.update(data)

Oh nvm I found it :)

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

> > +        else:
> > +            raise ValueError(f"Unknown AWB algorithm {output['algorithm']}")
> >  
> >          return output
> > -- 
> > 2.43.0
> >

Patch
diff mbox series 

diff --git a/utils/tuning/config-example.yaml b/utils/tuning/config-example.yaml
index 1b7f52cd2fff..30e88341df01 100644
--- a/utils/tuning/config-example.yaml
+++ b/utils/tuning/config-example.yaml
@@ -5,7 +5,39 @@  general:
     do_alsc_colour: 1
     luminance_strength: 0.5
   awb:
-    greyworld: 0
+    # Algorithm can be either 'grey' or 'bayes'
+    algorithm: bayes
+    # priors is only used for the bayes algorithm
+    priors:
+      - lux: 0
+        ct: [ 2000, 13000]
+        probability: [ 1.0, 1.0 ]
+    AwbMode:
+      AwbAuto:
+        lo: 2500
+        hi: 8000
+      AwbIncandescent:
+        lo: 2500
+        hi: 3000
+      AwbTungsten:
+        lo: 3000
+        hi: 3500
+      AwbFluorescent:
+        lo: 4000
+        hi: 4700
+      AwbIndoor:
+        lo: 3000
+        hi: 5000
+      AwbDaylight:
+        lo: 5500
+        hi: 6500
+      AwbCloudy:
+        lo: 6500
+        hi: 8000
+      # One custom mode can be defined if needed
+      #AwbCustom:
+      #  lo: 2000
+      #  hi: 1300
   macbeth:
     small: 1
     show: 0
diff --git a/utils/tuning/libtuning/modules/awb/awb.py b/utils/tuning/libtuning/modules/awb/awb.py
index c154cf3b8609..0dc4f59dcb26 100644
--- a/utils/tuning/libtuning/modules/awb/awb.py
+++ b/utils/tuning/libtuning/modules/awb/awb.py
@@ -27,10 +27,14 @@  class AWB(Module):
 
         imgs = [img for img in images if img.macbeth is not None]
 
-        gains, _, _ = awb(imgs, None, None, False)
-        gains = np.reshape(gains, (-1, 3))
+        ct_curve, transverse_pos, transverse_neg = awb(imgs, None, None, False)
+        ct_curve = np.reshape(ct_curve, (-1, 3))
+        gains = [{
+            'ct': int(v[0]),
+            'gains': [float(1.0 / v[1]), float(1.0 / v[2])]
+        } for v in ct_curve]
+
+        return {'colourGains': gains,
+                'transversePos': transverse_pos,
+                'transverseNeg': transverse_neg}
 
-        return [{
-                    'ct': int(v[0]),
-                    'gains': [float(1.0 / v[1]), float(1.0 / v[2])]
-                } for v in gains]
diff --git a/utils/tuning/libtuning/modules/awb/rkisp1.py b/utils/tuning/libtuning/modules/awb/rkisp1.py
index 0c95843b83d3..d562d26eb8cc 100644
--- a/utils/tuning/libtuning/modules/awb/rkisp1.py
+++ b/utils/tuning/libtuning/modules/awb/rkisp1.py
@@ -6,9 +6,6 @@ 
 
 from .awb import AWB
 
-import libtuning as lt
-
-
 class AWBRkISP1(AWB):
     hr_name = 'AWB (RkISP1)'
     out_name = 'Awb'
@@ -20,8 +17,20 @@  class AWBRkISP1(AWB):
         return True
 
     def process(self, config: dict, images: list, outputs: dict) -> dict:
-        output = {}
-
-        output['colourGains'] = self.do_calculation(images)
+        if not 'awb' in config['general']:
+            raise ValueError('AWB configuration missing')
+        awb_config = config['general']['awb']
+        algorithm = awb_config['algorithm']
+
+        output = {'algorithm': algorithm}
+        data = self.do_calculation(images)
+        if algorithm == 'grey':
+            output['colourGains'] = data['colourGains']
+        elif algorithm == 'bayes':
+            output['AwbMode'] = awb_config['AwbMode']
+            output['priors'] = awb_config['priors']
+            output.update(data)
+        else:
+            raise ValueError(f"Unknown AWB algorithm {output['algorithm']}")
 
         return output