[libcamera-devel] [PATCH 3/3] utils: raspberrypi: ctt: Code tidying
Ben Benson
ben.benson at raspberrypi.com
Thu Jul 6 03:39:26 CEST 2023
Altered the way that some lines are laid out, made functions
more attractive to look at, and tidied up messy areas.
Signed-off-by Ben Benson <ben.benson at raspberrypi.com>
---
utils/raspberrypi/ctt/ctt_ccm.py | 61 +++++++++++++++-----------------
1 file changed, 29 insertions(+), 32 deletions(-)
diff --git a/utils/raspberrypi/ctt/ctt_ccm.py b/utils/raspberrypi/ctt/ctt_ccm.py
index bd44b4d8..85ca6827 100644
--- a/utils/raspberrypi/ctt/ctt_ccm.py
+++ b/utils/raspberrypi/ctt/ctt_ccm.py
@@ -47,11 +47,8 @@ def degamma(x):
def gamma(x):
- # return (x * * (1 / 2.4) * 1.055 - 0.055)
- e = []
- for i in range(len(x)):
- e.append(((x[i] / 255) ** (1 / 2.4) * 1.055 - 0.055) * 255)
- return e
+ # Take 3 long array of color values and gamma them
+ return [((colour / 255) ** (1 / 2.4) * 1.055 - 0.055) * 255 for colour in x]
"""
@@ -96,10 +93,8 @@ def ccm(Cam, cal_cr_list, cal_cb_list):
"""
m_srgb = degamma(m_rgb) # now in 16 bit color.
- m_lab = []
- for col in m_srgb:
- m_lab.append(colors.RGB_to_LAB(col / 256))
- # This produces matrix of LAB values for ideal color chart)
+ m_lab = [colors.RGB_to_LAB(color / 256) for color in m_srgb]
+ # This produces array of LAB values for ideal color chart)
"""
reorder reference values to match how patches are ordered
@@ -168,7 +163,7 @@ def ccm(Cam, cal_cr_list, cal_cb_list):
sumde = 0
ccm = do_ccm(r, g, b, m_srgb)
# This is the initial guess that our optimisation code works with.
-
+ original_ccm = ccm
r1 = ccm[0]
r2 = ccm[1]
g1 = ccm[3]
@@ -199,12 +194,13 @@ def ccm(Cam, cal_cr_list, cal_cb_list):
[r1, r2, g1, g2, b1, b2] = result.x
# The new, optimised color correction matrix values
optimised_ccm = [r1, r2, (1 - r1 - r2), g1, g2, (1 - g1 - g2), b1, b2, (1 - b1 - b2)]
+
# This is the optimised Color Matrix (preserving greys by summing rows up to 1)
Cam.log += str(optimised_ccm)
Cam.log += "\n Old Color Correction Matrix Below \n"
Cam.log += str(ccm)
- formatted_ccm = np.array(ccm).reshape((3, 3))
+ formatted_ccm = np.array(original_ccm).reshape((3, 3))
'''
below is a whole load of code that then applies the latest color
@@ -213,22 +209,23 @@ def ccm(Cam, cal_cr_list, cal_cb_list):
'''
optimised_ccm_rgb = [] # Original Color Corrected Matrix RGB / LAB
optimised_ccm_lab = []
- for w in range(24):
- RGB = np.array([r[w], g[w], b[w]])
- ccm_applied_rgb = np.dot(formatted_ccm, (RGB / 256))
- optimised_ccm_rgb.append(gamma(ccm_applied_rgb))
- optimised_ccm_lab.append(colors.RGB_to_LAB(ccm_applied_rgb))
formatted_optimised_ccm = np.array(ccm).reshape((3, 3))
after_gamma_rgb = []
after_gamma_lab = []
- for w in range(24):
- RGB = np.array([r[w], g[w], b[w]])
+
+ for red, green, blue in zip(r, g, b):
+ RGB = np.array([red, green, blue])
+ ccm_applied_rgb = np.dot(formatted_ccm, (RGB / 256))
+ optimised_ccm_rgb.append(gamma(ccm_applied_rgb))
+ optimised_ccm_lab.append(colors.RGB_to_LAB(ccm_applied_rgb))
+
optimised_ccm_applied_rgb = np.dot(formatted_optimised_ccm, RGB / 256)
after_gamma_rgb.append(gamma(optimised_ccm_applied_rgb))
after_gamma_lab.append(colors.RGB_to_LAB(optimised_ccm_applied_rgb))
+
'''
- Gamma After RGB / LAB
+ Gamma After RGB / LAB - not used in calculations, only used for visualisation
We now want to spit out some data that shows
how the optimisation has improved the color matricies
'''
@@ -303,8 +300,8 @@ def guess(x0, r, g, b, m_lab): # provides a method of numerical feedback f
def transform_and_evaluate(ccm, r, g, b, m_lab): # Transforms colors to LAB and applies the correction matrix
# create list of matrix changed colors
realrgb = []
- for i in range(len(r)):
- RGB = np.array([r[i], g[i], b[i]])
+ for red, green, blue in zip(r, g, b):
+ RGB = np.array([red, green, blue])
rgb_post_ccm = np.dot(ccm, RGB) # This is RGB values after the color correction matrix has been applied
realrgb.append(colors.RGB_to_LAB(rgb_post_ccm))
# now compare that with m_lab and return numeric result, averaged for each patch
@@ -315,12 +312,12 @@ def sumde(listA, listB):
global typenum, test_patches
sumde = 0
maxde = 0
- patchde = []
- for i in range(len(listA)):
- if maxde < (deltae(listA[i], listB[i])):
- maxde = deltae(listA[i], listB[i])
- patchde.append(deltae(listA[i], listB[i]))
- sumde += deltae(listA[i], listB[i])
+ patchde = [] # Create array of the delta E values for each patch. useful for optimisation of certain patches
+ for listA_item, listB_item in zip(listA, listB):
+ if maxde < (deltae(listA_item, listB_item)):
+ maxde = deltae(listA_item, listB_item)
+ patchde.append(deltae(listA_item, listB_item))
+ sumde += deltae(listA_item, listB_item)
'''
The different options specified at the start allow for
the maximum to be returned, average or specific patches
@@ -330,9 +327,8 @@ def sumde(listA, listB):
if typenum == 1:
return maxde
if typenum == 2:
- output = 0
- for y in range(len(test_patches)):
- output += patchde[test_patches[y]] # grabs the specific patches (no need for averaging here)
+ output = sum([patchde[test_patch] for test_patch in test_patches])
+ # Selects only certain patches and returns the output for them
return output
@@ -341,8 +337,9 @@ calculates the ccm for an individual image.
ccms are calculate in rgb space, and are fit by hand. Although it is a 3x3
matrix, each row must add up to 1 in order to conserve greyness, simplifying
calculation.
-Should you want to fit them in another space (e.g. LAB) we wish you the best of
-luck and send us the code when you are done! :-)
+The initial CCM is calculated in RGB, and then optimised in LAB color space
+This simplifies the initial calculation but then gets us the accuracy of
+using LAB color space.
"""
--
2.34.1
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