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path: root/test/serialization/control_serialization.cpp

Age	Commit message (Collapse)	Author
2021-09-27	libcamera: control_serializer: Separate the handles space	Jacopo Mondi
	Two independent instances of the ControlSerializer class are in use at the IPC boundaries, one in the Proxy class that serializes data from the pipeline handler to the IPA, and one in the ProxyWorker which serializes data in the opposite direction. Each instance operates autonomously, without any centralized point of control, and each one assigns a numerical handle to each ControlInfoMap it serializes. This creates a risk of potential collision on the handle values, as both instances will use the same numerical space and are not aware of what handles has been already used by the instance "on the other side". To fix that, partition the handles numerical space by initializing the control serializer with a seed according to the role of the component that creates the serializer and increment the handle number by 2, to avoid any collision risk. While this is temporary and rather hacky solution, it solves an issue with isolated IPA modules without too much complexity added. Signed-off-by: Jacopo Mondi <jacopo@jmondi.org> Reviewed-by: Paul Elder <paul.elder@ideasonboard.com> Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
2021-08-12	test: control serialization: Test lookup by ControlId	Jacopo Mondi
	Test that lookup by ControlId reference works in the control serialization test making sure that the control limits are not changed by de-serialization. The test currently fails and demonstates that lookup by ControlId is currently not supported until the introduction of the next patch. Signed-off-by: Jacopo Mondi <jacopo@jmondi.org> Reviewed-by: Paul Elder <paul.elder@ideasonboard.com> Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
2020-05-16	libcamera: Move internal headers to include/libcamera/internal/	Laurent Pinchart
	The libcamera internal headers are located in src/libcamera/include/. The directory is added to the compiler headers search path with a meson include_directories() directive, and internal headers are included with (e.g. for the internal semaphore.h header) #include "semaphore.h" All was well, until libcxx decided to implement the C++20 synchronization library. The __threading_support header gained a #include <semaphore.h> to include the pthread's semaphore support. As include_directories() adds src/libcamera/include/ to the compiler search path with -I, the internal semaphore.h is included instead of the pthread version. Needless to say, the compiler isn't happy. Three options have been considered to fix this issue: - Use -iquote instead of -I. The -iquote option instructs gcc to only consider the header search path for headers included with the "" version. Meson unfortunately doesn't support this option. - Rename the internal semaphore.h header. This was deemed to be the beginning of a long whack-a-mole game, where namespace clashes with system libraries would appear over time (possibly dependent on particular system configurations) and would need to be constantly fixed. - Move the internal headers to another directory to create a unique namespace through path components. This causes lots of churn in all the existing source files through the all project. The first option would be best, but isn't available to us due to missing support in meson. Even if -iquote support was added, we would need to fix the problem before a new version of meson containing the required support would be released. The third option is thus the only practical solution available. Bite the bullet, and do it, moving headers to include/libcamera/internal/. Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Acked-by: Jacopo Mondi <jacopo@jmondi.org>
2020-04-27	test: Use float values for brightness, contrast and saturation	Laurent Pinchart
	Two tests use the brightness, contrast and saturation controls with integer failures. They were not updated by commit eff4b1aa01c1 which turned those controls into floats. This doesn't cause test failures as the control API converts the value types. For correctness, update the tests to use float values. Fixes: eff4b1aa01c1 ("libcamera: controls: Reorder and update description of existing controls") Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Jacopo Mondi <jacopo@jmondi.org>
2019-11-20	test: Add control serialization test	Jacopo Mondi
	Add a test that exercises the ControlSerializer to serialize and deserialize ControlInfoMap and ControlList. Signed-off-by: Jacopo Mondi <jacopo@jmondi.org> Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Niklas Söderlund <niklas.soderlund@ragnatech.se>

generated by cgit v1.2.1 (git 2.18.0) at 2025-06-01 16:57:46 +0000

# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi Ltd
#
# 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