diff options
Diffstat (limited to 'utils')
| -rw-r--r-- | utils/tuning/config-example.yaml | 44 | ||||
| -rw-r--r-- | utils/tuning/libtuning/modules/awb/awb.py | 16 | ||||
| -rw-r--r-- | utils/tuning/libtuning/modules/awb/rkisp1.py | 21 |
3 files changed, 68 insertions, 13 deletions
diff --git a/utils/tuning/config-example.yaml b/utils/tuning/config-example.yaml index 1b7f52cd..1bbb2757 100644 --- a/utils/tuning/config-example.yaml +++ b/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 diff --git a/utils/tuning/libtuning/modules/awb/awb.py b/utils/tuning/libtuning/modules/awb/awb.py index c154cf3b..0dc4f59d 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 0c95843b..d562d26e 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 |