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-rw-r--r--utils/raspberrypi/ctt/ctt_alsc.py83
1 files changed, 46 insertions, 37 deletions
diff --git a/utils/raspberrypi/ctt/ctt_alsc.py b/utils/raspberrypi/ctt/ctt_alsc.py
index e51d6931..1d94dfa5 100644
--- a/utils/raspberrypi/ctt/ctt_alsc.py
+++ b/utils/raspberrypi/ctt/ctt_alsc.py
@@ -2,7 +2,7 @@
#
# Copyright (C) 2019, Raspberry Pi Ltd
#
-# ctt_alsc.py - camera tuning tool for ALSC (auto lens shading correction)
+# camera tuning tool for ALSC (auto lens shading correction)
from ctt_image_load import *
import matplotlib.pyplot as plt
@@ -13,8 +13,9 @@ from mpl_toolkits.mplot3d import Axes3D
"""
preform alsc calibration on a set of images
"""
-def alsc_all(Cam, do_alsc_colour, plot):
+def alsc_all(Cam, do_alsc_colour, plot, grid_size=(16, 12), max_gain=8.0):
imgs_alsc = Cam.imgs_alsc
+ grid_w, grid_h = grid_size
"""
create list of colour temperatures and associated calibration tables
"""
@@ -23,7 +24,7 @@ def alsc_all(Cam, do_alsc_colour, plot):
list_cb = []
list_cg = []
for Img in imgs_alsc:
- col, cr, cb, cg, size = alsc(Cam, Img, do_alsc_colour, plot)
+ col, cr, cb, cg, size = alsc(Cam, Img, do_alsc_colour, plot, grid_size=grid_size, max_gain=max_gain)
list_col.append(col)
list_cr.append(cr)
list_cb.append(cb)
@@ -68,11 +69,12 @@ def alsc_all(Cam, do_alsc_colour, plot):
t_b = np.where((100*t_b) % 1 >= 0.95, t_b-0.001, t_b)
t_r = np.round(t_r, 3)
t_b = np.round(t_b, 3)
- r_corners = (t_r[0], t_r[15], t_r[-1], t_r[-16])
- b_corners = (t_b[0], t_b[15], t_b[-1], t_b[-16])
- r_cen = t_r[5*16+7]+t_r[5*16+8]+t_r[6*16+7]+t_r[6*16+8]
+ r_corners = (t_r[0], t_r[grid_w - 1], t_r[-1], t_r[-grid_w])
+ b_corners = (t_b[0], t_b[grid_w - 1], t_b[-1], t_b[-grid_w])
+ middle_pos = (grid_h // 2 - 1) * grid_w + grid_w - 1
+ r_cen = t_r[middle_pos]+t_r[middle_pos + 1]+t_r[middle_pos + grid_w]+t_r[middle_pos + grid_w + 1]
r_cen = round(r_cen/4, 3)
- b_cen = t_b[5*16+7]+t_b[5*16+8]+t_b[6*16+7]+t_b[6*16+8]
+ b_cen = t_b[middle_pos]+t_b[middle_pos + 1]+t_b[middle_pos + grid_w]+t_b[middle_pos + grid_w + 1]
b_cen = round(b_cen/4, 3)
Cam.log += '\nRed table corners: {}'.format(r_corners)
Cam.log += '\nRed table centre: {}'.format(r_cen)
@@ -116,8 +118,9 @@ def alsc_all(Cam, do_alsc_colour, plot):
"""
calculate g/r and g/b for 32x32 points arranged in a grid for a single image
"""
-def alsc(Cam, Img, do_alsc_colour, plot=False):
+def alsc(Cam, Img, do_alsc_colour, plot=False, grid_size=(16, 12), max_gain=8.0):
Cam.log += '\nProcessing image: ' + Img.name
+ grid_w, grid_h = grid_size
"""
get channel in correct order
"""
@@ -128,31 +131,34 @@ def alsc(Cam, Img, do_alsc_colour, plot=False):
where w is a multiple of 32.
"""
w, h = Img.w/2, Img.h/2
- dx, dy = int(-(-(w-1)//16)), int(-(-(h-1)//12))
+ dx, dy = int(-(-(w-1)//grid_w)), int(-(-(h-1)//grid_h))
"""
average the green channels into one
"""
av_ch_g = np.mean((channels[1:3]), axis=0)
if do_alsc_colour:
"""
- obtain 16x12 grid of intensities for each channel and subtract black level
+ obtain grid_w x grid_h grid of intensities for each channel and subtract black level
"""
- g = get_16x12_grid(av_ch_g, dx, dy) - Img.blacklevel_16
- r = get_16x12_grid(channels[0], dx, dy) - Img.blacklevel_16
- b = get_16x12_grid(channels[3], dx, dy) - Img.blacklevel_16
+ g = get_grid(av_ch_g, dx, dy, grid_size) - Img.blacklevel_16
+ r = get_grid(channels[0], dx, dy, grid_size) - Img.blacklevel_16
+ b = get_grid(channels[3], dx, dy, grid_size) - Img.blacklevel_16
"""
calculate ratios as 32 bit in order to be supported by medianBlur function
"""
- cr = np.reshape(g/r, (12, 16)).astype('float32')
- cb = np.reshape(g/b, (12, 16)).astype('float32')
- cg = np.reshape(1/g, (12, 16)).astype('float32')
+ cr = np.reshape(g/r, (grid_h, grid_w)).astype('float32')
+ cb = np.reshape(g/b, (grid_h, grid_w)).astype('float32')
+ cg = np.reshape(1/g, (grid_h, grid_w)).astype('float32')
"""
median blur to remove peaks and save as float 64
"""
cr = cv2.medianBlur(cr, 3).astype('float64')
+ cr = cr/np.min(cr) # gain tables are easier for humans to read if the minimum is 1.0
cb = cv2.medianBlur(cb, 3).astype('float64')
+ cb = cb/np.min(cb)
cg = cv2.medianBlur(cg, 3).astype('float64')
cg = cg/np.min(cg)
+ cg = [min(v, max_gain) for v in cg.flatten()] # never exceed the max luminance gain
"""
debugging code showing 2D surface plot of vignetting. Quite useful for
@@ -164,7 +170,7 @@ def alsc(Cam, Img, do_alsc_colour, plot=False):
"""
note Y is plotted as -Y so plot has same axes as image
"""
- X, Y = np.meshgrid(range(16), range(12))
+ X, Y = np.meshgrid(range(grid_w), range(grid_h))
ha.plot_surface(X, -Y, cr, cmap=cm.coolwarm, linewidth=0)
ha.set_title('ALSC Plot\nImg: {}\n\ncr'.format(Img.str))
hb = hf.add_subplot(312, projection='3d')
@@ -176,21 +182,22 @@ def alsc(Cam, Img, do_alsc_colour, plot=False):
# print(Img.str)
plt.show()
- return Img.col, cr.flatten(), cb.flatten(), cg.flatten(), (w, h, dx, dy)
+ return Img.col, cr.flatten(), cb.flatten(), cg, (w, h, dx, dy)
else:
"""
only perform calculations for luminance shading
"""
- g = get_16x12_grid(av_ch_g, dx, dy) - Img.blacklevel_16
- cg = np.reshape(1/g, (12, 16)).astype('float32')
+ g = get_grid(av_ch_g, dx, dy, grid_size) - Img.blacklevel_16
+ cg = np.reshape(1/g, (grid_h, grid_w)).astype('float32')
cg = cv2.medianBlur(cg, 3).astype('float64')
cg = cg/np.min(cg)
+ cg = [min(v, max_gain) for v in cg.flatten()] # never exceed the max luminance gain
if plot:
hf = plt.figure(figssize=(8, 8))
ha = hf.add_subplot(1, 1, 1, projection='3d')
- X, Y = np.meashgrid(range(16), range(12))
+ X, Y = np.meashgrid(range(grid_w), range(grid_h))
ha.plot_surface(X, -Y, cg, cmap=cm.coolwarm, linewidth=0)
ha.set_title('ALSC Plot (Luminance only!)\nImg: {}\n\ncg').format(Img.str)
plt.show()
@@ -199,21 +206,22 @@ def alsc(Cam, Img, do_alsc_colour, plot=False):
"""
-Compresses channel down to a 16x12 grid
+Compresses channel down to a grid of the requested size
"""
-def get_16x12_grid(chan, dx, dy):
+def get_grid(chan, dx, dy, grid_size):
+ grid_w, grid_h = grid_size
grid = []
"""
since left and bottom border will not necessarily have rectangles of
dimension dx x dy, the 32nd iteration has to be handled separately.
"""
- for i in range(11):
- for j in range(15):
+ for i in range(grid_h - 1):
+ for j in range(grid_w - 1):
grid.append(np.mean(chan[dy*i:dy*(1+i), dx*j:dx*(1+j)]))
- grid.append(np.mean(chan[dy*i:dy*(1+i), 15*dx:]))
- for j in range(15):
- grid.append(np.mean(chan[11*dy:, dx*j:dx*(1+j)]))
- grid.append(np.mean(chan[11*dy:, 15*dx:]))
+ grid.append(np.mean(chan[dy*i:dy*(1+i), (grid_w - 1)*dx:]))
+ for j in range(grid_w - 1):
+ grid.append(np.mean(chan[(grid_h - 1)*dy:, dx*j:dx*(1+j)]))
+ grid.append(np.mean(chan[(grid_h - 1)*dy:, (grid_w - 1)*dx:]))
"""
return as np.array, ready for further manipulation
"""
@@ -223,7 +231,7 @@ def get_16x12_grid(chan, dx, dy):
"""
obtains sigmas for red and blue, effectively a measure of the 'error'
"""
-def get_sigma(Cam, cal_cr_list, cal_cb_list):
+def get_sigma(Cam, cal_cr_list, cal_cb_list, grid_size):
Cam.log += '\nCalculating sigmas'
"""
provided colour alsc tables were generated for two different colour
@@ -241,8 +249,8 @@ def get_sigma(Cam, cal_cr_list, cal_cb_list):
sigma_rs = []
sigma_bs = []
for i in range(len(cts)-1):
- sigma_rs.append(calc_sigma(cal_cr_list[i]['table'], cal_cr_list[i+1]['table']))
- sigma_bs.append(calc_sigma(cal_cb_list[i]['table'], cal_cb_list[i+1]['table']))
+ sigma_rs.append(calc_sigma(cal_cr_list[i]['table'], cal_cr_list[i+1]['table'], grid_size))
+ sigma_bs.append(calc_sigma(cal_cb_list[i]['table'], cal_cb_list[i+1]['table'], grid_size))
Cam.log += '\nColour temperature interval {} - {} K'.format(cts[i], cts[i+1])
Cam.log += '\nSigma red: {}'.format(sigma_rs[-1])
Cam.log += '\nSigma blue: {}'.format(sigma_bs[-1])
@@ -263,12 +271,13 @@ def get_sigma(Cam, cal_cr_list, cal_cb_list):
"""
calculate sigma from two adjacent gain tables
"""
-def calc_sigma(g1, g2):
+def calc_sigma(g1, g2, grid_size):
+ grid_w, grid_h = grid_size
"""
reshape into 16x12 matrix
"""
- g1 = np.reshape(g1, (12, 16))
- g2 = np.reshape(g2, (12, 16))
+ g1 = np.reshape(g1, (grid_h, grid_w))
+ g2 = np.reshape(g2, (grid_h, grid_w))
"""
apply gains to gain table
"""
@@ -280,8 +289,8 @@ def calc_sigma(g1, g2):
neighbours, then append to list
"""
diffs = []
- for i in range(10):
- for j in range(14):
+ for i in range(grid_h - 2):
+ for j in range(grid_w - 2):
"""
note indexing is incremented by 1 since all patches on borders are
not counted
generated by cgit v1.2.1 (git 2.18.0) at 2024-11-01 01:32:43 +0000