libtuning: module: awb: Add bayes AWB support

To support the bayesian AWB algorithm in libtuning, the necessary data
needs to be collected and written to the tuning file.

Extend libtuning to calculate and output that additional data.

Prior probabilities and AwbModes are manually specified and not
calculated in the tuning process. Add sample values from the RaspberryPi
tuning files to the example config file.

Signed-off-by: Stefan Klug <stefan.klug@ideasonboard.com>
Reviewed-by: Paul Elder <paul.elder@ideasonboard.com>
Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com>

utils/tuning/config-example.yaml

@@ -5,7 +5,49 @@ general:
     do_alsc_colour: 1
     luminance_strength: 0.5
   awb:
-    greyworld: 0
+    # Algorithm can either be 'grey' or 'bayes'
+    algorithm: bayes
+    # Priors is only used for the bayes algorithm. They are defined in
+    # logarithmic space. A good staring point is:
+    # - lux: 0
+    #   ct: [ 2000, 3000, 13000 ]
+    #   probability: [ 1.0, 0.0, 0.0 ]
+    # - lux: 800
+    #   ct: [ 2000, 6000, 13000 ]
+    #   probability: [ 0.0, 2.0, 2.0 ]
+    # - lux: 1500
+    #   ct: [ 2000, 4000, 6000, 6500, 7000, 13000 ]
+    #   probability: [ 0.0, 1.0, 6.0, 7.0, 1.0, 1.0 ]
+    priors:
+      - lux: 0
+        ct: [ 2000, 13000 ]
+        probability: [ 0.0, 0.0 ]
+    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

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]

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 = {}
+        if not 'awb' in config['general']:
+            raise ValueError('AWB configuration missing')
+        awb_config = config['general']['awb']
+        algorithm = awb_config['algorithm']
 
-        output['colourGains'] = self.do_calculation(images)
+        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