diff options
Diffstat (limited to 'utils/raspberrypi/ctt/ctt_alsc.py')
| -rw-r--r-- | utils/raspberrypi/ctt/ctt_alsc.py | 144 |
1 files changed, 72 insertions, 72 deletions
diff --git a/utils/raspberrypi/ctt/ctt_alsc.py b/utils/raspberrypi/ctt/ctt_alsc.py index fe1cff65..cd99495f 100644 --- a/utils/raspberrypi/ctt/ctt_alsc.py +++ b/utils/raspberrypi/ctt/ctt_alsc.py @@ -12,7 +12,7 @@ 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): imgs_alsc = Cam.imgs_alsc """ create list of colour temperatures and associated calibration tables @@ -22,16 +22,16 @@ 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) 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) + 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 @@ -56,66 +56,66 @@ def alsc_all(Cam,do_alsc_colour,plot): """ 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) + 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]) + 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) + 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) + 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) + 'ct': ct, + 'table': list(t_r) } cb_dict = { - 'ct':ct, - 'table':list(t_b) + '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 + 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)) + 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]) + 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) + 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 + 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): +def alsc(Cam, Img, do_alsc_colour, plot=False): Cam.log += '\nProcessing image: ' + Img.name """ get channel in correct order @@ -126,31 +126,31 @@ def alsc(Cam,Img,do_alsc_colour,plot=False): -(-(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)) + 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) + 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 + 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') + 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') + 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) """ @@ -158,49 +158,49 @@ def alsc(Cam,Img,do_alsc_colour,plot=False): for sanity check """ if plot: - hf = plt.figure(figsize=(8,8)) + 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) + 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.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.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) + 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') + 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) + 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) + 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): +def get_16x12_grid(chan, dx, dy): grid = [] """ since left and bottom border will not necessarily have rectangles of @@ -208,11 +208,11 @@ def get_16x12_grid(chan,dx,dy): """ 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:])) + 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[11*dy:, dx*j:dx*(1+j)])) + grid.append(np.mean(chan[11*dy:, 15*dx:])) """ return as np.array, ready for further manipulation """ @@ -221,7 +221,7 @@ 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): Cam.log += '\nCalculating sigmas' """ provided colour alsc tables were generated for two different colour @@ -239,9 +239,9 @@ 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'])) - Cam.log += '\nColour temperature interval {} - {} K'.format(cts[i],cts[i+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]) @@ -254,19 +254,19 @@ def get_sigma(Cam,cal_cr_list,cal_cb_list): 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 + # 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): +def calc_sigma(g1, g2): """ reshape into 16x12 matrix """ - g1 = np.reshape(g1,(12,16)) - g2 = np.reshape(g2,(12,16)) + g1 = np.reshape(g1, (12, 16)) + g2 = np.reshape(g2, (12, 16)) """ apply gains to gain table """ @@ -294,4 +294,4 @@ def calc_sigma(g1,g2): return mean difference """ mean_diff = np.mean(diffs) - return(np.round(mean_diff,5)) + return(np.round(mean_diff, 5)) |