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+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi (Trading) Limited
+#
+# ctt_alsc.py - camera tuning tool for ALSC (auto lens shading correction)
+
+from ctt_image_load import *
+import matplotlib.pyplot as plt
+from matplotlib import cm
+from mpl_toolkits.mplot3d import Axes3D
+
+"""
+preform alsc calibration on a set of images
+"""
+def alsc_all(Cam,do_alsc_colour,plot):
+ imgs_alsc = Cam.imgs_alsc
+ """
+ create list of colour temperatures and associated calibration tables
+ """
+ list_col = []
+ list_cr = []
+ list_cb = []
+ list_cg = []
+ for Img in imgs_alsc:
+ col,cr,cb,cg,size = alsc(Cam,Img,do_alsc_colour,plot)
+ list_col.append(col)
+ list_cr.append(cr)
+ list_cb.append(cb)
+ list_cg.append(cg)
+ Cam.log += '\n'
+ Cam.log += '\nFinished processing images'
+ w,h,dx,dy = size
+ Cam.log += '\nChannel dimensions: w = {} h = {}'.format(int(w),int(h))
+ Cam.log += '\n16x12 grid rectangle size: w = {} h = {}'.format(dx,dy)
+
+ """
+ convert to numpy array for data manipulation
+ """
+ list_col = np.array(list_col)
+ list_cr = np.array(list_cr)
+ list_cb = np.array(list_cb)
+ list_cg = np.array(list_cg)
+
+ cal_cr_list = []
+ cal_cb_list = []
+
+ """
+ only do colour calculations if required
+ """
+ if do_alsc_colour:
+ Cam.log += '\nALSC colour tables'
+ for ct in sorted(set(list_col)):
+ Cam.log += '\nColour temperature: {} K'.format(ct)
+ """
+ average tables for the same colour temperature
+ """
+ indices = np.where(list_col == ct)
+ ct = int(ct)
+ t_r = np.mean(list_cr[indices],axis=0)
+ t_b = np.mean(list_cb[indices],axis=0)
+ """
+ force numbers to be stored to 3dp.... :(
+ """
+ t_r = np.where((100*t_r)%1<=0.05, t_r+0.001,t_r)
+ t_b = np.where((100*t_b)%1<=0.05, t_b+0.001,t_b)
+ t_r = np.where((100*t_r)%1>=0.95, t_r-0.001,t_r)
+ 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_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 = round(b_cen/4,3)
+ Cam.log += '\nRed table corners: {}'.format(r_corners)
+ Cam.log += '\nRed table centre: {}'.format(r_cen)
+ Cam.log += '\nBlue table corners: {}'.format(b_corners)
+ Cam.log += '\nBlue table centre: {}'.format(b_cen)
+ cr_dict = {
+ 'ct':ct,
+ 'table':list(t_r)
+ }
+ cb_dict = {
+ 'ct':ct,
+ 'table':list(t_b)
+ }
+ cal_cr_list.append(cr_dict)
+ cal_cb_list.append(cb_dict)
+ Cam.log += '\n'
+ else:
+ cal_cr_list,cal_cb_list = None,None
+
+ """
+ average all values for luminance shading and return one table for all temperatures
+ """
+ lum_lut = np.mean(list_cg,axis=0)
+ lum_lut = np.where((100*lum_lut)%1<=0.05,lum_lut+0.001,lum_lut)
+ lum_lut = np.where((100*lum_lut)%1>=0.95,lum_lut-0.001,lum_lut)
+ lum_lut = list(np.round(lum_lut,3))
+
+ """
+ calculate average corner for lsc gain calculation further on
+ """
+ corners = (lum_lut[0],lum_lut[15],lum_lut[-1],lum_lut[-16])
+ Cam.log += '\nLuminance table corners: {}'.format(corners)
+ l_cen = lum_lut[5*16+7]+lum_lut[5*16+8]+lum_lut[6*16+7]+lum_lut[6*16+8]
+ l_cen = round(l_cen/4,3)
+ Cam.log += '\nLuminance table centre: {}'.format(l_cen)
+ av_corn = np.sum(corners)/4
+
+ return cal_cr_list,cal_cb_list,lum_lut,av_corn
+
+
+"""
+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):
+ Cam.log += '\nProcessing image: ' + Img.name
+ """
+ get channel in correct order
+ """
+ channels = [Img.channels[i] for i in Img.order]
+ """
+ calculate size of single rectangle.
+ -(-(w-1)//32) is a ceiling division. w-1 is to deal robustly with the case
+ 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))
+ """
+ average the green channels into one
+ """
+ av_ch_g = np.mean((channels[1:2]),axis = 0)
+ if do_alsc_colour:
+ """
+ obtain 16x12 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
+ """
+ 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')
+ """
+ median blur to remove peaks and save as float 64
+ """
+ cr = cv2.medianBlur(cr,3).astype('float64')
+ cb = cv2.medianBlur(cb,3).astype('float64')
+ cg = cv2.medianBlur(cg,3).astype('float64')
+ cg = cg/np.min(cg)
+
+ """
+ debugging code showing 2D surface plot of vignetting. Quite useful for
+ for sanity check
+ """
+ if plot:
+ hf = plt.figure(figsize=(8,8))
+ ha = hf.add_subplot(311, projection='3d')
+ """
+ note Y is plotted as -Y so plot has same axes as image
+ """
+ X,Y = np.meshgrid(range(16),range(12))
+ 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')
+ hb.plot_surface(X,-Y,cb,cmap=cm.coolwarm,linewidth=0)
+ hb.set_title('cb')
+ hc = hf.add_subplot(313, projection='3d')
+ hc.plot_surface(X,-Y,cg,cmap=cm.coolwarm,linewidth=0)
+ hc.set_title('g')
+ # print(Img.str)
+ plt.show()
+
+ return Img.col,cr.flatten(),cb.flatten(),cg.flatten(),(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')
+ cg = cv2.medianBlur(cg,3).astype('float64')
+ cg = cg/np.min(cg)
+
+ 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))
+ 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()
+
+ return Img.col,None,None,cg.flatten(),(w,h,dx,dy)
+
+
+"""
+Compresses channel down to a 16x12 grid
+"""
+def get_16x12_grid(chan,dx,dy):
+ 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):
+ 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:]))
+ """
+ return as np.array, ready for further manipulation
+ """
+ return np.array(grid)
+
+"""
+obtains sigmas for red and blue, effectively a measure of the 'error'
+"""
+def get_sigma(Cam,cal_cr_list,cal_cb_list):
+ Cam.log += '\nCalculating sigmas'
+ """
+ provided colour alsc tables were generated for two different colour
+ temperatures sigma is calculated by comparing two calibration temperatures
+ adjacent in colour space
+ """
+ """
+ create list of colour temperatures
+ """
+ cts = [cal['ct'] for cal in cal_cr_list]
+ # print(cts)
+ """
+ calculate sigmas for each adjacent cts and return worst one
+ """
+ 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']))
+ 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])
+
+ """
+ return maximum sigmas, not necessarily from the same colour temperature
+ interval
+ """
+ sigma_r = max(sigma_rs) if sigma_rs else 0.005
+ sigma_b = max(sigma_bs) if sigma_bs else 0.005
+ Cam.log += '\nMaximum sigmas: Red = {} Blue = {}'.format(sigma_r, sigma_b)
+
+
+ # print(sigma_rs,sigma_bs)
+ # print(sigma_r,sigma_b)
+ return sigma_r,sigma_b
+
+"""
+calculate sigma from two adjacent gain tables
+"""
+def calc_sigma(g1,g2):
+ """
+ reshape into 16x12 matrix
+ """
+ g1 = np.reshape(g1,(12,16))
+ g2 = np.reshape(g2,(12,16))
+ """
+ apply gains to gain table
+ """
+ gg = g1/g2
+ if np.mean(gg) < 1:
+ gg = 1/gg
+ """
+ for each internal patch, compute average difference between it and its 4
+ neighbours, then append to list
+ """
+ diffs = []
+ for i in range(10):
+ for j in range(14):
+ """
+ note indexing is incremented by 1 since all patches on borders are
+ not counted
+ """
+ diff = np.abs(gg[i+1][j+1]-gg[i][j+1])
+ diff += np.abs(gg[i+1][j+1]-gg[i+2][j+1])
+ diff += np.abs(gg[i+1][j+1]-gg[i+1][j])
+ diff += np.abs(gg[i+1][j+1]-gg[i+1][j+2])
+ diffs.append(diff/4)
+
+ """
+ return mean difference
+ """
+ mean_diff = np.mean(diffs)
+ return(np.round(mean_diff,5))