[PATCH v1 08/11] libtuning: module: awb: Add bayes AWB support

[Stefan Klug]

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 at 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]
+        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
-- 
2.43.0