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utils: raspberrypi: ctt: Fix pycodestyle E231

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E231 missing whitespace after ','
E231 missing whitespace after ':'

Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com>
Reviewed-by: David Plowman <david.plowman@raspberrypi.com>
This commit is contained in:
Laurent Pinchart 2020-05-02 03:32:00 +03:00
parent 7a653369cb
commit 93a133fb17
11 changed files with 493 additions and 493 deletions
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118
utils/raspberrypi/ctt/ctt_awb.py
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@ -12,7 +12,7 @@ from scipy.optimize import fmin
"""
obtain piecewise linear approximation for colour curve
"""
def awb(Cam,cal_cr_list,cal_cb_list,plot):
def awb(Cam, cal_cr_list, cal_cb_list, plot):
imgs = Cam.imgs
"""
condense alsc calibration tables into one dictionary
@ -21,7 +21,7 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
colour_cals = None
else:
colour_cals = {}
for cr,cb in zip(cal_cr_list,cal_cb_list):
for cr, cb in zip(cal_cr_list, cal_cb_list):
cr_tab = cr['table']
cb_tab = cb['table']
"""
@ -29,7 +29,7 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
"""
cr_tab= cr_tab/np.min(cr_tab)
cb_tab= cb_tab/np.min(cb_tab)
colour_cals[cr['ct']] = [cr_tab,cb_tab]
colour_cals[cr['ct']] = [cr_tab, cb_tab]
"""
obtain data from greyscale macbeth patches
"""
@ -42,17 +42,17 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
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)
r_patchs, b_patchs, g_patchs = get_alsc_patches(Img, colour_cals)
"""
calculate ratio of r,b to g
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)
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, 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)
@ -71,18 +71,18 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
"""
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 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))
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
"""
@ -91,7 +91,7 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
"""
fit quadratic fit to r_g hat and b_g_hat
"""
a,b,c = np.polyfit(rbs_hat[0],rbs_hat[1],2)
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
@ -113,11 +113,11 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
def f(x):
return a*x**2 + b*x + c
"""
iterate over points (R,B are x and y coordinates of points) and calculate
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])):
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
@ -129,7 +129,7 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
"""
perform optimisation with scipy.optmisie.fmin
"""
x_hat = fmin(f_min,R,disp=0)[0]
x_hat = fmin(f_min, R, disp=0)[0]
y_hat = f(x_hat)
"""
dehat
@ -179,16 +179,16 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
"""
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)
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)
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
@ -200,8 +200,8 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
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])
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)
@ -209,8 +209,8 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
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)
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
@ -221,9 +221,9 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
"""
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)
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
@ -235,7 +235,7 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
"""
return formatted ct curve, ordered by increasing colour temperature
"""
ct_curve = list(np.array(list(zip(b_fit,r_fit,c_fit))).flatten())[::-1]
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
@ -247,15 +247,15 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
plotting code for debug
"""
if plot:
x = np.linspace(np.min(rbs_hat[0]),np.max(rbs_hat[0]),100)
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.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')
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.annotate(str(ct), (rbs_hat[0][i], rbs_hat[1][i]))
plt.xlabel('$\hat{r}$')
plt.ylabel('$\hat{b}$')
"""
@ -265,13 +265,13 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
# ax = plt.gca()
# ax.set_aspect('equal')
plt.grid()
plt.subplot(2,1,2)
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.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('$b$')
"""
@ -286,15 +286,15 @@ def awb(Cam,cal_cr_list,cal_cb_list,plot):
"""
end of plotting code
"""
return(ct_curve,np.round(transverse_pos,5),np.round(transverse_neg,5))
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):
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
patches for each channel, remembering to subtract blacklevel
If grey then only greyscale patches considered
"""
if grey:
@ -316,12 +316,12 @@ def get_alsc_patches(Img,colour_cals,grey=True):
g_patchs = (patches[1]+patches[2])/2 - Img.blacklevel_16
if colour_cals == None:
return r_patchs,b_patchs,g_patchs
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)
pos = bisect_left(cts, col)
"""
if img colour is below minimum or above maximum alsc calibration colour, simply
pick extreme closest to img colour
@ -343,32 +343,32 @@ def get_alsc_patches(Img,colour_cals,grey=True):
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))
col_tabs = np.reshape(col_tabs, (2, 12, 16))
"""
calculate dx,dy used to calculate alsc table
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))
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
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])
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):
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 greyscale patches, g channel and correct r, b channels
"""
return r_patchs,b_patchs,g_patchs
return r_patchs, b_patchs, g_patchs