diff options
Diffstat (limited to 'utils/raspberrypi/ctt/ctt_alsc.py')
| -rw-r--r-- | utils/raspberrypi/ctt/ctt_alsc.py | 75 |
1 files changed, 40 insertions, 35 deletions
diff --git a/utils/raspberrypi/ctt/ctt_alsc.py b/utils/raspberrypi/ctt/ctt_alsc.py index b0201ac4..66ce8c14 100644 --- a/utils/raspberrypi/ctt/ctt_alsc.py +++ b/utils/raspberrypi/ctt/ctt_alsc.py @@ -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)): 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) 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)): Cam.log += '\nProcessing image: ' + Img.name + grid_w, grid_h = grid_size """ get channel in correct order """ @@ -128,24 +131,24 @@ 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 """ @@ -164,7 +167,7 @@ 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') @@ -182,15 +185,15 @@ def alsc(Cam, Img, do_alsc_colour, plot=False): """ 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) 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 +202,22 @@ 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 +227,7 @@ 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 +245,8 @@ 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 +267,13 @@ 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 +285,8 @@ 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 |