libcamera/vivid.git - libcamera pipeline handler for VIVID

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author	Laurent Pinchart <laurent.pinchart@ideasonboard.com>	2022-10-19 02:34:13 +0300
committer	Laurent Pinchart <laurent.pinchart@ideasonboard.com>	2022-10-24 16:40:24 +0300
commit	11f5c3ad0561a4e4b1e06f477ab2022c69803ad2 (patch)
tree	15038a08c2da9c7f25c547f8a5376c1af8f50713 /LICENSES/MIT.txt
parent	32d5f0d2a41a3e973afa4318b37a6db34fe82983 (diff)

ipa: ipu3: Fill AGC and AWB metadata in algorithms

Fill the frame metadata in the AGC and AWB algorithm's prepare() function. This removes the need to fill metadata manually in the IPA module's processStatsBuffer() function. Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Jacopo Mondi <jacopo@jmondi.org> Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com>

Diffstat (limited to 'LICENSES/MIT.txt')

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# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi Ltd
#
# ctt_awb.py - camera tuning tool for AWB

from ctt_image_load import *
import matplotlib.pyplot as plt
from bisect import bisect_left
from scipy.optimize import fmin


"""
obtain piecewise linear approximation for colour curve
"""
def awb(Cam, cal_cr_list, cal_cb_list, plot):
    imgs = Cam.imgs
    """
    condense alsc calibration tables into one dictionary
    """
    if cal_cr_list is None:
        colour_cals = None
    else:
        colour_cals = {}
        for cr, cb in zip(cal_cr_list, cal_cb_list):
            cr_tab = cr['table']
            cb_tab = cb['table']
            """
            normalise tables so min value is 1
            """
            cr_tab = cr_tab/np.min(cr_tab)
            cb_tab = cb_tab/np.min(cb_tab)
            colour_cals[cr['ct']] = [cr_tab, cb_tab]
    """
    obtain data from greyscale macbeth patches
    """
    rb_raw = []
    rbs_hat = []
    for Img in imgs:
        Cam.log += '\nProcessing '+Img.name
        """
        get greyscale patches with alsc applied if alsc enabled.
        Note: if alsc is disabled then colour_cals will be set to None and the
        function will just return the greyscale patches
        """
        r_patchs, b_patchs, g_patchs = get_alsc_patches(Img, colour_cals)
        """
        calculate ratio of r, b to g
        """
        r_g = np.mean(r_patchs/g_patchs)
        b_g = np.mean(b_patchs/g_patchs)
        Cam.log += '\n       r : {:.4f}       b : {:.4f}'.format(r_g, b_g)
        """
        The curve tends to be better behaved in so-called hatspace.
        R, B, G represent the individual channels. The colour curve is plotted in
        r, b space, where:
            r = R/G
            b = B/G
        This will be referred to as dehatspace... (sorry)
        Hatspace is defined as:
            r_hat = R/(R+B+G)
            b_hat = B/(R+B+G)
        To convert from dehatspace to hastpace (hat operation):
            r_hat = r/(1+r+b)
            b_hat = b/(1+r+b)
        To convert from hatspace to dehatspace (dehat operation):
            r = r_hat/(1-r_hat-b_hat)
            b = b_hat/(1-r_hat-b_hat)
        Proof is left as an excercise to the reader...
        Throughout the code, r and b are sometimes referred to as r_g and b_g
        as a reminder that they are ratios
        """
        r_g_hat = r_g/(1+r_g+b_g)
        b_g_hat = b_g/(1+r_g+b_g)
        Cam.log += '\n   r_hat : {:.4f}   b_hat : {:.4f}'.format(r_g_hat, b_g_hat)
        rbs_hat.append((r_g_hat, b_g_hat, Img.col))
        rb_raw.append((r_g, b_g))
        Cam.log += '\n'

    Cam.log += '\nFinished processing images'
    """
    sort all lits simultaneously by r_hat
    """
    rbs_zip = list(zip(rbs_hat, rb_raw))
    rbs_zip.sort(key=lambda x: x[0][0])
    rbs_hat, rb_raw = list(zip(*rbs_zip))
    """
    unzip tuples ready for processing
    """
    rbs_hat = list(zip(*rbs_hat))
    rb_raw = list(zip(*rb_raw))
    """
    fit quadratic fit to r_g hat and b_g_hat
    """
    a, b, c = np.polyfit(rbs_hat[0], rbs_hat[1], 2)
    Cam.log += '\nFit quadratic curve in hatspace'
    """
    the algorithm now approximates the shortest distance from each point to the
    curve in dehatspace. Since the fit is done in hatspace, it is easier to
    find the actual shortest distance in hatspace and use the projection back
    into dehatspace as an overestimate.
    The distance will be used for two things:
        1) In the case that colour temperature does not strictly decrease with
        increasing r/g, the closest point to the line will be chosen out of an
        increasing pair of colours.

        2) To calculate transverse negative an dpositive, the maximum positive
        and negative distance from the line are chosen. This benefits from the
        overestimate as the transverse pos/neg are upper bound values.
    """
    """
    define fit function
    """
    def f(x):
        return a*x**2 + b*x + c
    """
    iterate over points (R, B are x and y coordinates of points) and calculate
    distance to line in dehatspace
    """
    dists = []
    for i, (R, B) in enumerate(zip(rbs_hat[0], rbs_hat[1])):
        """
        define function to minimise as square distance between datapoint and
        point on curve. Squaring is monotonic so minimising radius squared is
        equivalent to minimising radius
        """
        def f_min(x):
            y = f(x)
            return((x-R)**2+(y-B)**2)
        """
        perform optimisation with scipy.optmisie.fmin
        """
        x_hat = fmin(f_min, R, disp=0)[0]
        y_hat = f(x_hat)
        """
        dehat
        """
        x = x_hat/(1-x_hat-y_hat)
        y = y_hat/(1-x_hat-y_hat)
        rr = R/(1-R-B)
        bb = B/(1-R-B)
        """
        calculate euclidean distance in dehatspace
        """
        dist = ((x-rr)**2+(y-bb)**2)**0.5
        """
        return negative if point is below the fit curve
        """
        if (x+y) > (rr+bb):
            dist *= -1
        dists.append(dist)
    Cam.log += '\nFound closest point on fit line to each point in dehatspace'
    """
    calculate wiggle factors in awb. 10% added since this is an upper bound
    """
    transverse_neg = - np.min(dists) * 1.1
    transverse_pos = np.max(dists) * 1.1
    Cam.log += '\nTransverse pos : {:.5f}'.format(transverse_pos)
    Cam.log += '\nTransverse neg : {:.5f}'.format(transverse_neg)
    """
    set minimum transverse wiggles to 0.1 .
    Wiggle factors dictate how far off of the curve the algorithm searches. 0.1
    is a suitable minimum that gives better results for lighting conditions not
    within calibration dataset. Anything less will generalise poorly.
    """
    if transverse_pos < 0.01:
        transverse_pos = 0.01
        Cam.log += '\nForced transverse pos to 0.01'
    if transverse_neg < 0.01:
        transverse_neg = 0.01
        Cam.log += '\nForced transverse neg to 0.01'

    """
    generate new b_hat values at each r_hat according to fit
    """
    r_hat_fit = np.array(rbs_hat[0])
    b_hat_fit = a*r_hat_fit**2 + b*r_hat_fit + c
    """
    transform from hatspace to dehatspace
    """
    r_fit = r_hat_fit/(1-r_hat_fit-b_hat_fit)
    b_fit = b_hat_fit/(1-r_hat_fit-b_hat_fit)


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