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+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi Ltd
+#
+# camera tuning tool for AWB
+
+import matplotlib.pyplot as plt
+from bisect import bisect_left
+from scipy.optimize import fmin
+import numpy as np
+
+from .image import Image
+
+
+"""
+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])):
+ """
+ define function to minimise as square distance between datapoint and
+ point on curve. Squaring is monotonic so minimising radius squared is
+ equivalent to minimising radius
+ """
+ def f_min(x):
+ y = f(x)
+ return((x-R)**2+(y-B)**2)
+ """
+ perform optimisation with scipy.optmisie.fmin
+ """
+ x_hat = fmin(f_min, R, disp=0)[0]
+ y_hat = f(x_hat)
+ """
+ dehat
+ """
+ x = x_hat/(1-x_hat-y_hat)
+ y = y_hat/(1-x_hat-y_hat)
+ rr = R/(1-R-B)
+ bb = B/(1-R-B)
+ """
+ calculate euclidean distance in dehatspace
+ """
+ dist = ((x-rr)**2+(y-bb)**2)**0.5
+ """
+ return negative if point is below the fit curve
+ """
+ if (x+y) > (rr+bb):
+ dist *= -1
+ dists.append(dist)
+ Cam.log += '\nFound closest point on fit line to each point in dehatspace'
+ """
+ calculate wiggle factors in awb. 10% added since this is an upper bound
+ """
+ transverse_neg = - np.min(dists) * 1.1
+ transverse_pos = np.max(dists) * 1.1
+ Cam.log += '\nTransverse pos : {:.5f}'.format(transverse_pos)
+ Cam.log += '\nTransverse neg : {:.5f}'.format(transverse_neg)
+ """
+ set minimum transverse wiggles to 0.1 .
+ Wiggle factors dictate how far off of the curve the algorithm searches. 0.1
+ is a suitable minimum that gives better results for lighting conditions not
+ within calibration dataset. Anything less will generalise poorly.
+ """
+ if transverse_pos < 0.01:
+ transverse_pos = 0.01
+ Cam.log += '\nForced transverse pos to 0.01'
+ if transverse_neg < 0.01:
+ transverse_neg = 0.01
+ Cam.log += '\nForced transverse neg to 0.01'
+
+ """
+ generate new b_hat values at each r_hat according to fit
+ """
+ r_hat_fit = np.array(rbs_hat[0])
+ b_hat_fit = a*r_hat_fit**2 + b*r_hat_fit + c
+ """
+ transform from hatspace to dehatspace
+ """
+ r_fit = r_hat_fit/(1-r_hat_fit-b_hat_fit)
+ b_fit = b_hat_fit/(1-r_hat_fit-b_hat_fit)
+ c_fit = np.round(rbs_hat[2], 0)
+ """
+ round to 4dp
+ """
+ r_fit = np.where((1000*r_fit) % 1 <= 0.05, r_fit+0.0001, r_fit)
+ r_fit = np.where((1000*r_fit) % 1 >= 0.95, r_fit-0.0001, r_fit)
+ b_fit = np.where((1000*b_fit) % 1 <= 0.05, b_fit+0.0001, b_fit)
+ b_fit = np.where((1000*b_fit) % 1 >= 0.95, b_fit-0.0001, b_fit)
+ r_fit = np.round(r_fit, 4)
+ b_fit = np.round(b_fit, 4)
+ """
+ The following code ensures that colour temperature decreases with
+ increasing r/g
+ """
+ """
+ iterate backwards over list for easier indexing
+ """
+ i = len(c_fit) - 1
+ while i > 0:
+ if c_fit[i] > c_fit[i-1]:
+ Cam.log += '\nColour temperature increase found\n'
+ Cam.log += '{} K at r = {} to '.format(c_fit[i-1], r_fit[i-1])
+ Cam.log += '{} K at r = {}'.format(c_fit[i], r_fit[i])
+ """
+ if colour temperature increases then discard point furthest from
+ the transformed fit (dehatspace)
+ """
+ error_1 = abs(dists[i-1])
+ error_2 = abs(dists[i])
+ Cam.log += '\nDistances from fit:\n'
+ Cam.log += '{} K : {:.5f} , '.format(c_fit[i], error_1)
+ Cam.log += '{} K : {:.5f}'.format(c_fit[i-1], error_2)
+ """
+ find bad index
+ note that in python false = 0 and true = 1
+ """
+ bad = i - (error_1 < error_2)
+ Cam.log += '\nPoint at {} K deleted as '.format(c_fit[bad])
+ Cam.log += 'it is furthest from fit'
+ """
+ delete bad point
+ """
+ r_fit = np.delete(r_fit, bad)
+ b_fit = np.delete(b_fit, bad)
+ c_fit = np.delete(c_fit, bad).astype(np.uint16)
+ """
+ note that if a point has been discarded then the length has decreased
+ by one, meaning that decreasing the index by one will reassess the kept
+ point against the next point. It is therefore possible, in theory, for
+ two adjacent points to be discarded, although probably rare
+ """
+ i -= 1
+
+ """
+ return formatted ct curve, ordered by increasing colour temperature
+ """
+ ct_curve = list(np.array(list(zip(b_fit, r_fit, c_fit))).flatten())[::-1]
+ Cam.log += '\nFinal CT curve:'
+ for i in range(len(ct_curve)//3):
+ j = 3*i
+ Cam.log += '\n ct: {} '.format(ct_curve[j])
+ Cam.log += ' r: {} '.format(ct_curve[j+1])
+ Cam.log += ' b: {} '.format(ct_curve[j+2])
+
+ """
+ plotting code for debug
+ """
+ if plot:
+ x = np.linspace(np.min(rbs_hat[0]), np.max(rbs_hat[0]), 100)
+ y = a*x**2 + b*x + c
+ plt.subplot(2, 1, 1)
+ plt.title('hatspace')
+ plt.plot(rbs_hat[0], rbs_hat[1], ls='--', color='blue')
+ plt.plot(x, y, color='green', ls='-')
+ plt.scatter(rbs_hat[0], rbs_hat[1], color='red')
+ for i, ct in enumerate(rbs_hat[2]):
+ plt.annotate(str(ct), (rbs_hat[0][i], rbs_hat[1][i]))
+ plt.xlabel('$\\hat{r}$')
+ plt.ylabel('$\\hat{b}$')
+ """
+ optional set axes equal to shortest distance so line really does
+ looks perpendicular and everybody is happy
+ """
+ # ax = plt.gca()
+ # ax.set_aspect('equal')
+ plt.grid()
+ plt.subplot(2, 1, 2)
+ plt.title('dehatspace - indoors?')
+ plt.plot(r_fit, b_fit, color='blue')
+ plt.scatter(rb_raw[0], rb_raw[1], color='green')
+ plt.scatter(r_fit, b_fit, color='red')
+ for i, ct in enumerate(c_fit):
+ plt.annotate(str(ct), (r_fit[i], b_fit[i]))
+ plt.xlabel('$r$')
+ plt.ylabel('$b$')
+ """
+ optional set axes equal to shortest distance so line really does
+ looks perpendicular and everybody is happy
+ """
+ # ax = plt.gca()
+ # ax.set_aspect('equal')
+ plt.subplots_adjust(hspace=0.5)
+ plt.grid()
+ plt.show()
+ """
+ end of plotting code
+ """
+ return(ct_curve, np.round(transverse_pos, 5), np.round(transverse_neg, 5))
+
+
+"""
+obtain greyscale patches and perform alsc colour correction
+"""
+def get_alsc_patches(Img, colour_cals, grey=True):
+ """
+ get patch centre coordinates, image colour and the actual
+ patches for each channel, remembering to subtract blacklevel
+ If grey then only greyscale patches considered
+ """
+ if grey:
+ cen_coords = Img.cen_coords[3::4]
+ col = Img.col
+ patches = [np.array(Img.patches[i]) for i in Img.order]
+ r_patchs = patches[0][3::4] - Img.blacklevel_16
+ b_patchs = patches[3][3::4] - Img.blacklevel_16
+ """
+ note two green channels are averages
+ """
+ g_patchs = (patches[1][3::4]+patches[2][3::4])/2 - Img.blacklevel_16
+ else:
+ cen_coords = Img.cen_coords
+ col = Img.color
+ patches = [np.array(Img.patches[i]) for i in Img.order]
+ r_patchs = patches[0] - Img.blacklevel_16
+ b_patchs = patches[3] - Img.blacklevel_16
+ g_patchs = (patches[1]+patches[2])/2 - Img.blacklevel_16
+
+ if colour_cals is None:
+ return r_patchs, b_patchs, g_patchs
+ """
+ find where image colour fits in alsc colour calibration tables
+ """
+ cts = list(colour_cals.keys())
+ pos = bisect_left(cts, col)
+ """
+ if img colour is below minimum or above maximum alsc calibration colour, simply
+ pick extreme closest to img colour
+ """
+ if pos % len(cts) == 0:
+ """
+ this works because -0 = 0 = first and -1 = last index
+ """
+ col_tabs = np.array(colour_cals[cts[-pos//len(cts)]])
+ """
+ else, perform linear interpolation between existing alsc colour
+ calibration tables
+ """
+ else:
+ bef = cts[pos-1]
+ aft = cts[pos]
+ da = col-bef
+ db = aft-col
+ bef_tabs = np.array(colour_cals[bef])
+ aft_tabs = np.array(colour_cals[aft])
+ col_tabs = (bef_tabs*db + aft_tabs*da)/(da+db)
+ col_tabs = np.reshape(col_tabs, (2, 12, 16))
+ """
+ calculate dx, dy used to calculate alsc table
+ """
+ w, h = Img.w/2, Img.h/2
+ dx, dy = int(-(-(w-1)//16)), int(-(-(h-1)//12))
+ """
+ make list of pairs of gains for each patch by selecting the correct value
+ in alsc colour calibration table
+ """
+ patch_gains = []
+ for cen in cen_coords:
+ x, y = cen[0]//dx, cen[1]//dy
+ # We could probably do with some better spatial interpolation here?
+ col_gains = (col_tabs[0][y][x], col_tabs[1][y][x])
+ patch_gains.append(col_gains)
+
+ """
+ multiply the r and b channels in each patch by the respective gain, finally
+ performing the alsc colour correction
+ """
+ for i, gains in enumerate(patch_gains):
+ r_patchs[i] = r_patchs[i] * gains[0]
+ b_patchs[i] = b_patchs[i] * gains[1]
+
+ """
+ return greyscale patches, g channel and correct r, b channels
+ """
+ return r_patchs, b_patchs, g_patchs