libcamera/vivid.git - libcamera pipeline handler for VIVID

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author	Laurent Pinchart <laurent.pinchart@ideasonboard.com>	2019-06-19 17:41:06 +0300
committer	Laurent Pinchart <laurent.pinchart@ideasonboard.com>	2019-06-19 17:51:12 +0300
commit	329b38d2541c84e16b2cf2d8f6ad7af87eec2ecd (patch)
tree	78a81521a0dd475d6f11fb433b7f177f1bce4150 /package/gentoo
parent	e32d82e65510fdc7d70093ca03e1feddb32e494c (diff)

libcamera: stream: Include missing array header

The file uses the std::array class but doesn't include the corresponding header. This breaks compilation with clang and libc++. Fix it. Fixes: 63c578ed9993 ("libcamera: stream: Add StreamFormats") Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Jacopo Mondi <jacopo@jmondi.org> Reviewed-by: Niklas Söderlund <niklas.soderlund@ragnatech.se>

Diffstat (limited to 'package/gentoo')

0 files changed, 0 insertions, 0 deletions

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# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi (Trading) Limited
#
# ctt_macbeth_locator.py - camera tuning tool Macbeth chart locator

from ctt_ransac import *
from ctt_tools import *
import warnings

"""
NOTE: some custom functions have been used here to make the code more readable.
These are defined in tools.py if they are needed for reference.
"""


"""
Some inconsistencies between packages cause runtime warnings when running
the clustering algorithm. This catches these warnings so they don't flood the
output to the console
"""
def fxn():
    warnings.warn("runtime", RuntimeWarning)


"""
Define the success message
"""
success_msg = 'Macbeth chart located successfully'

def find_macbeth(Cam, img, mac_config=(0, 0)):
    small_chart, show = mac_config
    print('Locating macbeth chart')
    Cam.log += '\nLocating macbeth chart'
    """
    catch the warnings
    """
    warnings.simplefilter("ignore")
    fxn()

    """
    Reference macbeth chart is created that will be correlated with the located
    macbeth chart guess to produce a confidence value for the match.
    """
    ref = cv2.imread(Cam.path + 'ctt_ref.pgm', flags=cv2.IMREAD_GRAYSCALE)
    ref_w = 120
    ref_h = 80
    rc1 = (0, 0)
    rc2 = (0, ref_h)
    rc3 = (ref_w, ref_h)
    rc4 = (ref_w, 0)
    ref_corns = np.array((rc1, rc2, rc3, rc4), np.float32)
    ref_data = (ref, ref_w, ref_h, ref_corns)

    """
    locate macbeth chart
    """
    cor, mac, coords, msg = get_macbeth_chart(img, ref_data)

    """
    following bits of code tries to fix common problems with simple
    techniques.
    If now or at any point the best correlation is of above 0.75, then
    nothing more is tried as this is a high enough confidence to ensure
    reliable macbeth square centre placement.
    """

    """
    brighten image 2x
    """
    if cor < 0.75:
        a = 2
        img_br = cv2.convertScaleAbs(img, alpha=a, beta=0)
        cor_b, mac_b, coords_b, msg_b = get_macbeth_chart(img_br, ref_data)
        if cor_b > cor:
            cor, mac, coords, msg = cor_b, mac_b, coords_b, msg_b

    """
    brighten image 4x
    """
    if cor < 0.75:
        a = 4
        img_br = cv2.convertScaleAbs(img, alpha=a, beta=0)
        cor_b, mac_b, coords_b, msg_b = get_macbeth_chart(img_br, ref_data)
        if cor_b > cor:
            cor, mac, coords, msg = cor_b, mac_b, coords_b, msg_b

    """
    In case macbeth chart is too small, take a selection of the image and
    attempt to locate macbeth chart within that. The scale increment is
    root 2
    """
    """
    These variables will be used to transform the found coordinates at smaller
    scales back into the original. If ii is still -1 after this section that
    means it was not successful
    """
    ii = -1
    w_best = 0
    h_best = 0
    d_best = 100
    """
    d_best records the scale of the best match. Macbeth charts are only looked
    for at one scale increment smaller than the current best match in order to avoid
    unecessarily searching for macbeth charts at small scales.
    If a macbeth chart ha already been found then set d_best to 0
    """
    if cor != 0:
        d_best = 0

    """
    scale 3/2 (approx root2)
    """
    if cor < 0.75:
        imgs = []
        """
        get size of image
        """
        shape = list(img.shape[:2])
        w, h = shape
        """
        set dimensions of the subselection and the step along each axis between
        selections
        """
        w_sel = int(2*w/3)
        h_sel = int(2*h/3)
        w_inc = int(w/6)
        h_inc = int(h/6)
        """
        for each subselection, look for a macbeth chart
        """
        for i in range(3):
            for j in range(3):
                w_s, h_s = i*w_inc, j*h_inc
                img_sel = img[w_s:w_s+w_sel, h_s:h_s+h_sel]
                cor_ij, mac_ij, coords_ij, msg_ij = get_macbeth_chart(img_sel, ref_data)
                """
                if the correlation is better than the best then record the
                scale and current subselection at which macbeth chart was
                found. Also record the coordinates, macbeth chart and message.
                """
                if cor_ij > cor:
                    cor = cor_ij
                    mac, coords, msg = mac_ij, coords_ij, msg_ij
                    ii, jj = i, j
                    w_best, h_best = w_inc, h_inc
                    d_best = 1

    """
    scale 2
    """
    if cor < 0.75:
        imgs = []
        shape = list(img.shape[:2])
        w, h = shape
        w_sel = int(w/2)
        h_sel = int(h/2)
        w_inc = int(w/8)
        h_inc = int(h/8)
        for i in range(5):
            for j in range(5):
                w_s, h_s = i*w_inc, j*h_inc
                img_sel = img[w_s:w_s+w_sel, h_s:h_s+h_sel]
                cor_ij, mac_ij, coords_ij, msg_ij = get_macbeth_chart(img_sel, ref_data)
                if cor_ij > cor:
                    cor = cor_ij
                    mac, coords, msg = mac_ij, coords_ij, msg_ij
                    ii, jj = i, j
                    w_best, h_best = w_inc, h_inc
                    d_best = 2

    """
    The following code checks for macbeth charts at even smaller scales. This
    slows the code down significantly and has therefore been omitted by default,
    however it is not unusably slow so might be useful if the macbeth chart
    is too small to be picked up to by the current subselections.
    Use this for macbeth charts with side lengths around 1/5 image dimensions
    (and smaller...?) it is, however, recommended that macbeth charts take up as
    large as possible a proportion of the image.
    """

    if small_chart:

        if cor < 0.75 and d_best > 1:
            imgs = []
            shape = list(img.shape[:2])
            w, h = shape
            w_sel = int(w/3)
            h_sel = int(h/3)
            w_inc = int(w/12)
            h_inc = int(h/12)
            for i in range(9):
                for j in range(9):
                    w_s, h_s = i*w_inc, j*h_inc
                    img_sel = img[w_s:w_s+w_sel, h_s:h_s+h_sel]
                    cor_ij, mac_ij, coords_ij, msg_ij = get_macbeth_chart(img_sel, ref_data)
                    if cor_ij > cor:
                        cor = cor_ij
                        mac, coords, msg = mac_ij, coords_ij, msg_ij
                        ii, jj = i, j
                        w_best, h_best = w_inc, h_inc
                        d_best = 3

        if cor < 0.75 and d_best > 2:
            imgs = []
            shape = list(img.shape[:2])
            w, h = shape
            w_sel = int(w/4)
            h_sel = int(h/4)
            w_inc = int(w/16)
            h_inc = int(h/16)
            for i in range(13):
                for j in range(13):
                    w_s, h_s = i*w_inc, j*h_inc
                    img_sel = img[w_s:w_s+w_sel, h_s:h_s+h_sel]
                    cor_ij, mac_ij, coords_ij, msg_ij = get_macbeth_chart(img_sel, ref_data)
                    if cor_ij > cor:
                        cor = cor_ij
                        mac, coords, msg = mac_ij, coords_ij, msg_ij
                        ii, jj = i, j
                        w_best, h_best = w_inc, h_inc

    """
    Transform coordinates from subselection to original image
    """
    if ii != -1:
        for a in range(len(coords)):
            for b in range(len(coords[a][0])):
                coords[a][0][b][1] += ii*w_best
                coords[a][0][b][0] += jj*h_best

    """
    initialise coords_fit variable
    """
    coords_fit = None
    # print('correlation: {}'.format(cor))
    """
    print error or success message
    """
    print(msg)
    Cam.log += '\n' + msg
    if msg == success_msg:
        coords_fit = coords
        Cam.log += '\nMacbeth chart vertices:\n'
        Cam.log += '{}'.format(2*np.round(coords_fit[0][0]), 0)
        """
        if correlation is lower than 0.75 there may be a risk of macbeth chart
        corners not having been located properly. It might be worth running
        with show set to true to check where the macbeth chart centres have
        been located.
        """
        print('Confidence: {:.3f}'.format(cor))
        Cam.log += '\nConfidence: {:.3f}'.format(cor)
        if cor < 0.75:
            print('Caution: Low confidence guess!')
            Cam.log += 'WARNING: Low confidence guess!'
        # cv2.imshow('MacBeth', mac)
        # represent(mac, 'MacBeth chart')

    """
    extract data from coords_fit and plot on original image
    """
    if show and coords_fit is not None:
        copy = img.copy()
        verts = coords_fit[0][0]
        cents = coords_fit[1][0]

        """
        draw circles at vertices of macbeth chart
        """
        for vert in verts:
            p = tuple(np.round(vert).astype(np.int32))
            cv2.circle(copy, p, 10, 1, -1)
        """
        draw circles at centres of squares
        """
        for i in range(len(cents)):
            cent = cents[i]
            p = tuple(np.round(cent).astype(np.int32))
            """
            draw black circle on white square, white circle on black square an
            grey circle everywhere else.
            """
            if i == 3:
                cv2.circle(copy, p, 8, 0, -1)
            elif i == 23:
                cv2.circle(copy, p, 8, 1, -1)
            else:
                cv2.circle(copy, p, 8, 0.5, -1)
        copy, _ = reshape(copy, 400)
        represent(copy)

    return(coords_fit)


def get_macbeth_chart(img, ref_data):
    """
    function returns coordinates of macbeth chart vertices and square centres,
    along with an error/success message for debugging purposes. Additionally,
    it scores the match with a confidence value.

    Brief explanation of the macbeth chart locating algorithm:


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