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|
# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi (Trading) Limited
#
# ctt_awb.py - camera tuning tool for AWB
from ctt_image_load import *
import matplotlib.pyplot as plt
from bisect import bisect_left
from scipy.optimize import fmin
"""
obtain piecewise linear approximation for colour curve
"""
def awb(Cam, cal_cr_list, cal_cb_list, plot):
imgs = Cam.imgs
"""
condense alsc calibration tables into one dictionary
"""
if cal_cr_list is None:
colour_cals = None
else:
colour_cals = {}
for cr, cb in zip(cal_cr_list, cal_cb_list):
cr_tab = cr['table']
cb_tab = cb['table']
"""
normalise tables so min value is 1
"""
cr_tab = cr_tab/np.min(cr_tab)
cb_tab = cb_tab/np.min(cb_tab)
colour_cals[cr['ct']] = [cr_tab, cb_tab]
"""
obtain data from greyscale macbeth patches
"""
rb_raw = []
rbs_hat = []
for Img in imgs:
Cam.log += '\nProcessing '+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
function will just return the greyscale patches
"""
r_patchs, b_patchs, g_patchs = get_alsc_patches(Img, colour_cals)
"""
calculate ratio of r, b to g
"""
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)
"""
The curve tends to be better behaved in so-called hatspace.
R, B, G represent the individual channels. The colour curve is plotted in
r, b space, where:
r = R/G
b = B/G
This will be referred to as dehatspace... (sorry)
Hatspace is defined as:
r_hat = R/(R+B+G)
b_hat = B/(R+B+G)
To convert from dehatspace to hastpace (hat operation):
r_hat = r/(1+r+b)
b_hat = b/(1+r+b)
To convert from hatspace to dehatspace (dehat operation):
r = r_hat/(1-r_hat-b_hat)
b = b_hat/(1-r_hat-b_hat)
Proof is left as an excercise to the reader...
Throughout the code, r and b are sometimes referred to as r_g and b_g
as a reminder that they are ratios
"""
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))
rb_raw.append((r_g, b_g))
Cam.log += '\n'
Cam.log += '\nFinished processing images'
"""
sort all lits simultaneously by r_hat
"""
rbs_zip = list(zip(rbs_hat, rb_raw))
rbs_zip.sort(key=lambda x: x[0][0])
rbs_hat, rb_raw = list(zip(*rbs_zip))
"""
unzip tuples ready for processing
"""
rbs_hat = list(zip(*rbs_hat))
rb_raw = list(zip(*rb_raw))
"""
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'
"""
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
find the actual shortest distance in hatspace and use the projection back
into dehatspace as an overestimate.
The distance will be used for two things:
1) In the case that colour temperature does not strictly decrease with
increasing r/g, the closest point to the line will be chosen out of an
increasing pair of colours.
2) To calculate transverse negative an dpositive, the maximum positive
and negative distance from the line are chosen. This benefits from the
overestimate as the transverse pos/neg are upper bound values.
"""
"""
define fit function
"""
def f(x):
return a*x**2 + b*x + c
"""
iterate over points (R, B are x and y coordinates of points) and calculate
distance to line in dehatspace
"""
dists = []
for i, (R, B) in enumerate(zip(rbs_hat[0], rbs_hat[1])):
"""
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