diff options
| -rw-r--r-- | utils/raspberrypi/ctt/ctt_ccm.py | 61 |
1 files changed, 27 insertions, 34 deletions
diff --git a/utils/raspberrypi/ctt/ctt_ccm.py b/utils/raspberrypi/ctt/ctt_ccm.py index 49159535..a09bfd09 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) + # Produce array of LAB values for ideal color chart + m_lab = [colors.RGB_to_LAB(color / 256) for color in m_srgb] """ 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] @@ -188,7 +183,7 @@ def ccm(Cam, cal_cr_list, cal_cb_list): We use our old CCM as the initial guess for the program to find the optimised matrix ''' - result = minimize(guess, x0, args=(r, g, b, m_lab), tol=0.0000000001) + result = minimize(guess, x0, args=(r, g, b, m_lab), tol=0.01) ''' This produces a color matrix which has the lowest delta E possible, based off the input data. Note it is impossible for this to reach @@ -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 +209,21 @@ 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)) + formatted_optimised_ccm = np.array(optimised_ccm).reshape((3, 3)) after_gamma_rgb = [] after_gamma_lab = [] - for w in range(24): - RGB = np.array([r[w], g[w], b[w]]) - optimised_ccm_applied_rgb = np.dot(formatted_optimised_ccm, RGB / 256) + + for RGB in zip(r, g, b): + ccm_applied_rgb = np.dot(formatted_ccm, (np.array(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, np.array(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 matrices ''' @@ -303,9 +298,8 @@ 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]]) - rgb_post_ccm = np.dot(ccm, RGB) # This is RGB values after the color correction matrix has been applied + for RGB in zip(r, g, b): + rgb_post_ccm = np.dot(ccm, np.array(RGB) / 256) # 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 return (sumde(realrgb, m_lab) / 24) # returns an average result of delta E @@ -315,12 +309,12 @@ 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 +324,8 @@ 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 |