src/qcam/assets/feathericons/key.svg


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# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi Ltd
#
# 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[1:3]), 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.
    """