/* SPDX-License-Identifier: LGPL-2.1-or-later */ /* * Copyright (C) 2019, Google Inc. * * event_notifier.cpp - File descriptor event notifier */ #include #include #include #include "message.h" #include "thread.h" /** * \file event_notifier.h * \brief File descriptor event notifier */ namespace libcamera { /** * \class EventNotifier * \brief Notify of activity on a file descriptor * * The EventNotifier models a file descriptor event source that can be * monitored. It is created with the file descriptor to be monitored and the * type of event, and is enabled by default. It will emit the \ref activated * signal whenever an event of the monitored type occurs on the file descriptor. * * Supported type of events are EventNotifier::Read, EventNotifier::Write and * EventNotifier::Exception. The type is specified when constructing the * notifier, and can be retrieved using the type() function. To listen to * multiple event types on the same file descriptor multiple notifiers must be * created. * * The notifier can be disabled with the setEnable() function. When the notifier * is disabled it ignores events and does not emit the \ref activated signal. * The notifier can then be re-enabled with the setEnable() function. * * Creating multiple notifiers of the same type for the same file descriptor is * not allowed and results in undefined behaviour. * * Notifier events are detected and dispatched from the * EventDispatcher::processEvents() function. */ /** * \enum EventNotifier::Type * Type of file descriptor event to listen for. * \var EventNotifier::Read * Data is available to be read from the file descriptor * \var EventNotifier::Write * Data can be written to the file descriptor * \var EventNotifier::Exception * An exception has occurred on the file descriptor */ /** * \brief Construct an event notifier with a file descriptor and event type * \param[in] fd The file descriptor to monitor * \param[in] type The event type to monitor * \param[in] parent The parent Object */ EventNotifier::EventNotifier(int fd, Type type, Object *parent) : Object(parent), fd_(fd), type_(type), enabled_(false) { setEnabled(true); } EventNotifier::~EventNotifier() { setEnabled(false); } /** * \fn EventNotifier::type() * \brief Retrieve the type of the event being monitored * \return The type of the event */ /** * \fn EventNotifier::fd() * \brief Retrieve the file descriptor being monitored * \return The file descriptor */ /** * \fn EventNotifier::enabled() * \brief Retrieve the notifier state * \return True if the notifier is enabled, or false otherwise * \sa setEnable() */ /** * \brief Enable or disable the notifier * \param[in] enable True to enable the notifier, false to disable it * * This function enables or disables the notifier. A disabled notifier ignores * events and does not emit the \ref activated signal. * * \context This function is \threadbound. */ void EventNotifier::setEnabled(bool enable) { if (enabled_ == enable) return; enabled_ = enable; EventDispatcher *dispatcher = thread()->eventDispatcher(); if (enable) dispatcher->registerEventNotifier(this); else dispatcher->unregisterEventNotifier(this); } /** * \var EventNotifier::activated * \brief Signal emitted when the event occurs * * This signal is emitted when the event \ref type() occurs on the file * descriptor monitored by the notifier. The notifier pointer is passed as a * parameter. */ void EventNotifier::message(Message *msg) { if (msg->type() == Message::ThreadMoveMessage) { if (enabled_) { setEnabled(false); invokeMethod(&EventNotifier::setEnabled, ConnectionTypeQueued, true); } } Object::message(msg); } } /* namespace libcamera */ 'h' value='imx8mp/extensible-format'/>
blob: cebecfc248a6a143f9612edaaa955ceab7fbdf06 (plain)
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# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi (Trading) Limited
#
# ctt_ccm.py - camera tuning tool for CCM (colour correction matrix)

from ctt_image_load import *
from ctt_awb import get_alsc_patches


"""
takes 8-bit macbeth chart values, degammas and returns 16 bit
"""
def degamma(x):
    x = x / ((2**8)-1)
    x = np.where(x < 0.04045, x/12.92, ((x+0.055)/1.055)**2.4)
    x = x * ((2**16)-1)
    return x


"""
FInds colour correction matrices for list of images
"""
def ccm(Cam, cal_cr_list, cal_cb_list):
    imgs = Cam.imgs
    """
    standard macbeth chart colour values
    """
    m_rgb = np.array([  # these are in sRGB
        [116, 81, 67],    # dark skin
        [199, 147, 129],  # light skin
        [91, 122, 156],   # blue sky
        [90, 108, 64],    # foliage
        [130, 128, 176],  # blue flower
        [92, 190, 172],   # bluish green
        [224, 124, 47],   # orange
        [68, 91, 170],     # purplish blue
        [198, 82, 97],    # moderate red
        [94, 58, 106],    # purple
        [159, 189, 63],   # yellow green
        [230, 162, 39],   # orange yellow
        [35, 63, 147],    # blue
        [67, 149, 74],    # green
        [180, 49, 57],    # red
        [238, 198, 20],   # yellow
        [193, 84, 151],   # magenta
        [0, 136, 170],    # cyan (goes out of gamut)
        [245, 245, 243],  # white 9.5
        [200, 202, 202],  # neutral 8
        [161, 163, 163],  # neutral 6.5
        [121, 121, 122],  # neutral 5
        [82, 84, 86],     # neutral 3.5
        [49, 49, 51]      # black 2
    ])

    """
    convert reference colours from srgb to rgb
    """
    m_srgb = degamma(m_rgb)
    """
    reorder reference values to match how patches are ordered
    """
    m_srgb = np.array([m_srgb[i::6] for i in range(6)]).reshape((24, 3))

    """
    reformat alsc correction tables or set colour_cals to None if alsc is
    deactivated
    """
    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]

    """
    for each image, perform awb and alsc corrections.
    Then calculate the colour correction matrix for that image, recording the
    ccm and the colour tempertaure.
    """
    ccm_tab = {}
    for Img in imgs:
        Cam.log += '\nProcessing image: ' + Img.name
        """
        get macbeth patches with alsc applied if alsc enabled.
        Note: if alsc is disabled then colour_cals will be set to None and no
        the function will simply return the macbeth patches
        """
        r, b, g = get_alsc_patches(Img, colour_cals, grey=False)
        """
        do awb
        Note: awb is done by measuring the macbeth chart in the image, rather
        than from the awb calibration. This is done so the awb will be perfect
        and the ccm matrices will be more accurate.
        """
        r_greys, b_greys, g_greys = r[3::4], b[3::4], g[3::4]
        r_g = np.mean(r_greys/g_greys)
        b_g = np.mean(b_greys/g_greys)
        r = r / r_g
        b = b / b_g

        """
        normalise brightness wrt reference macbeth colours and then average
        each channel for each patch
        """
        gain = np.mean(m_srgb)/np.mean((r, g, b))
        Cam.log += '\nGain with respect to standard colours: {:.3f}'.format(gain)
        r = np.mean(gain*r, axis=1)
        b = np.mean(gain*b, axis=1)
        g = np.mean(gain*g, axis=1)

        """
        calculate ccm matrix
        """
        ccm = do_ccm(r, g, b, m_srgb)

        """
        if a ccm has already been calculated for that temperature then don't
        overwrite but save both. They will then be averaged later on
        """
        if Img.col in ccm_tab.keys():
            ccm_tab[Img.col].append(ccm)
        else:
            ccm_tab[Img.col] = [ccm]
        Cam.log += '\n'

    Cam.log += '\nFinished processing images'
    """
    average any ccms that share a colour temperature
    """
    for k, v in ccm_tab.items():
        tab = np.mean(v, axis=0)
        tab = np.where((10000*tab) % 1 <= 0.05, tab+0.00001, tab)
        tab = np.where((10000*tab) % 1 >= 0.95, tab-0.00001, tab)
        ccm_tab[k] = list(np.round(tab, 5))
        Cam.log += '\nMatrix calculated for colour temperature of {} K'.format(k)

    """
    return all ccms with respective colour temperature in the correct format,
    sorted by their colour temperature
    """
    sorted_ccms = sorted(ccm_tab.items(), key=lambda kv: kv[0])
    ccms = []
    for i in sorted_ccms:
        ccms.append({
            'ct': i[0],
            'ccm': i[1]
        })
    return ccms


"""
calculates the ccm for an individual image.
ccms are calculate in rgb space, and are fit by hand. Although it is a 3x3
matrix, each row must add up to 1 in order to conserve greyness, simplifying
calculation.
Should you want to fit them in another space (e.g. LAB) we wish you the best of
luck and send us the code when you are done! :-)
"""
def do_ccm(r, g, b, m_srgb):
    rb = r-b
    gb = g-b
    rb_2s = (rb*rb)
    rb_gbs = (rb*gb)
    gb_2s = (gb*gb)

    r_rbs = rb * (m_srgb[..., 0] - b)
    r_gbs = gb * (m_srgb[..., 0] - b)
    g_rbs = rb * (m_srgb[..., 1] - b)
    g_gbs = gb * (m_srgb[..., 1] - b)
    b_rbs = rb * (m_srgb[..., 2] - b)
    b_gbs = gb * (m_srgb[..., 2] - b)

    """
    Obtain least squares fit
    """
    rb_2 = np.sum(rb_2s)
    gb_2 = np.sum(gb_2s)
    rb_gb = np.sum(rb_gbs)
    r_rb = np.sum(r_rbs)
    r_gb = np.sum(r_gbs)
    g_rb = np.sum(g_rbs)
    g_gb = np.sum(g_gbs)
    b_rb = np.sum(b_rbs)
    b_gb = np.sum(b_gbs)

    det = rb_2*gb_2 - rb_gb*rb_gb

    """
    Raise error if matrix is singular...
    This shouldn't really happen with real data but if it does just take new
    pictures and try again, not much else to be done unfortunately...
    """
    if det < 0.001:
        raise ArithmeticError

    r_a = (gb_2*r_rb - rb_gb*r_gb)/det
    r_b = (rb_2*r_gb - rb_gb*r_rb)/det
    """
    Last row can be calculated by knowing the sum must be 1
    """
    r_c = 1 - r_a - r_b

    g_a = (gb_2*g_rb - rb_gb*g_gb)/det
    g_b = (rb_2*g_gb - rb_gb*g_rb)/det
    g_c = 1 - g_a - g_b

    b_a = (gb_2*b_rb - rb_gb*b_gb)/det
    b_b = (rb_2*b_gb - rb_gb*b_rb)/det
    b_c = 1 - b_a - b_b

    """
    format ccm
    """
    ccm = [r_a, r_b, r_c, g_a, g_b, g_c, b_a, b_b, b_c]

    return ccm