/* SPDX-License-Identifier: GPL-2.0-or-later */ /* * Copyright (C) 2020, Google Inc. * * span.cpp - Span tests */ /* * Include first to ensure the header is self-contained, as there's no span.cpp * in libcamera. */ #include #include #include #include #include "test.h" using namespace std; using namespace libcamera; class SpanTest : public Test { protected: int run() { int i[4]{ 1, 2, 3, 4 }; std::array a{ 1, 2, 3, 4 }; const std::array ca{ 1, 2, 3, 4 }; std::vector v{ 1, 2, 3, 4 }; const std::vector cv{ 1, 2, 3, 4 }; /* * Compile-test construction and usage of spans with static * extent. Commented-out tests are expected not to compile, or * to generate undefined behaviour. */ Span{}; /* Span{}; */ Span{ &i[0], 4 }; Span{ &i[0], &i[3] }; Span{ i }; /* Span{ i }; */ /* Span{ i }; */ Span{ a }; Span{ a }; /* Span{ a }; */ /* Span{ a }; */ Span{ ca }; /* Span{ ca }; */ /* Span{ ca }; */ /* Span{ ca }; */ Span{ v }; Span{ v }; /* Span{ v }; */ Span{ v }; /* Span{ v }; */ /* Span{ v }; */ Span staticSpan{ i }; Span{ staticSpan }; Span{ staticSpan }; /* Span{ staticSpan }; */ staticSpan = Span{ v }; if (*staticSpan.begin() != 1) { std::cout << "Span::begin() failed" << std::endl; return TestFail; } if (*staticSpan.cbegin() != 1) { std::cout << "Span::cbegin() failed" << std::endl; return TestFail; } staticSpan.end(); staticSpan.cend(); if (*staticSpan.rbegin() != 4) { std::cout << "Span::rbegin() failed" << std::endl; return TestFail; } if (*staticSpan.crbegin() != 4) { std::cout << "Span::crbegin() failed" << std::endl; return TestFail; } staticSpan.rend(); staticSpan.crend(); staticSpan.front(); staticSpan.back(); staticSpan[0]; staticSpan.data(); staticSpan.size(); staticSpan.size_bytes(); staticSpan.empty(); staticSpan.first<2>(); staticSpan.first(2); /* staticSpan.first<6>(); */ /* staticSpan.first(6); */ staticSpan.last<2>(); staticSpan.last(2); /* staticSpan.last<6>(); */ /* staticSpan.last(6); */ staticSpan.subspan<1>(); staticSpan.subspan<1, 2>(); staticSpan.subspan(1); staticSpan.subspan(1, 2); /* staticSpan.subspan(2, 4); */ /* * Compile-test construction and usage of spans with dynamic * extent. Commented-out tests are expected not to compile, or * to generate undefined behaviour. */ Span{}; Span{ &i[0], 4 }; Span{ &i[0], &i[3] }; Span{ i }; /* Span{ i }; */ Span{ a }; Span{ a }; /* Span{ a }; */ Span{ ca }; /* Span{ca}; */ /* Span{ca}; */ Span{ v }; Span{ v }; /* Span{ v }; */ Span{ v }; /* Span{ v }; */ /* Span{ v }; */ Span dynamicSpan{ i }; Span{ dynamicSpan }; Span{ dynamicSpan }; dynamicSpan = Span{ a }; if (*dynamicSpan.begin() != 1) { std::cout << "Span::begin() failed" << std::endl; return TestFail; } if (*dynamicSpan.cbegin() != 1) { std::cout << "Span::cbegin() failed" << std::endl; return TestFail; } dynamicSpan.end(); dynamicSpan.cend(); if (*dynamicSpan.rbegin() != 4) { std::cout << "Span::rbegin() failed" << std::endl; return TestFail; } if (*dynamicSpan.crbegin() != 4) { std::cout << "Span::crbegin() failed" << std::endl; return TestFail; } dynamicSpan.rend(); dynamicSpan.crend(); dynamicSpan.front(); dynamicSpan.back(); dynamicSpan[0]; dynamicSpan.data(); dynamicSpan.size(); dynamicSpan.size_bytes(); dynamicSpan.empty(); dynamicSpan.first<2>(); dynamicSpan.first(2); /* dynamicSpan.first<6>(); */ /* dynamicSpan.first(6); */ dynamicSpan.last<2>(); dynamicSpan.last(2); /* dynamicSpan.last<6>(); */ /* dynamicSpan.last(6); */ dynamicSpan.subspan<1>(); dynamicSpan.subspan<1, 2>(); dynamicSpan.subspan(1); dynamicSpan.subspan(1, 2); /* dynamicSpan.subspan(2, 4); */ return TestPass; } }; TEST_REGISTER(SpanTest) a> 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 
# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi Ltd
# Copyright (C) 2024, Ideas on Board Oy
#
# Locate and extract Macbeth charts from images
# (Copied from: ctt_macbeth_locator.py)

# \todo Add debugging

import cv2
import os
from pathlib import Path
import numpy as np
import warnings
import logging
from sklearn import cluster as cluster

from .ctt_ransac import get_square_verts, get_square_centres
from .image import Image

logger = logging.getLogger(__name__)


class MacbethError(Exception):
    pass


# Reshape image to fixed width without distorting returns image and scale
# factor
def reshape(img, width):
    factor = width / img.shape[0]
    return cv2.resize(img, None, fx=factor, fy=factor), factor


# Correlation function to quantify match
def correlate(im1, im2):
    f1 = im1.flatten()
    f2 = im2.flatten()
    cor = np.corrcoef(f1, f2)
    return cor[0][1]


# @brief Compute coordinates of macbeth chart vertices and square centres
# @return (max_cor, best_map_col_norm, fit_coords, success)
#
# Also returns an error/success message for debugging purposes. Additionally,
# it scores the match with a confidence value.
#
#    Brief explanation of the macbeth chart locating algorithm:
#    - Find rectangles within image
#    - Take rectangles within percentage offset of median perimeter. The
#        assumption is that these will be the macbeth squares
#    - For each potential square, find the 24 possible macbeth centre locations
#        that would produce a square in that location
#    - Find clusters of potential macbeth chart centres to find the potential
#        macbeth centres with the most votes, i.e. the most likely ones
#    - For each potential macbeth centre, use the centres of the squares that
#        voted for it to find macbeth chart corners
#    - For each set of corners, transform the possible match into normalised
#        space and correlate with a reference chart to evaluate the match
#    - Select the highest correlation as the macbeth chart match, returning the
#        correlation as the confidence score
#
# \todo Clean this up
def get_macbeth_chart(img, ref_data):
    ref, ref_w, ref_h, ref_corns = ref_data

    # The code will raise and catch a MacbethError in case of a problem, trying
    # to give some likely reasons why the problem occured, hence the try/except
    try:
        # Obtain image, convert to grayscale and normalise
        src = img
        src, factor = reshape(src, 200)
        original = src.copy()
        a = 125 / np.average(src)
        src_norm = cv2.convertScaleAbs(src, alpha=a, beta=0)

        # This code checks if there are seperate colour channels. In the past the
        # macbeth locator ran on jpgs and this makes it robust to different
        # filetypes. Note that running it on a jpg has 4x the pixels of the
        # average bayer channel so coordinates must be doubled.

        # This is best done in img_load.py in the get_patches method. The
        # coordinates and image width, height must be divided by two if the
        # macbeth locator has been run on a demosaicked image.
        if len(src_norm.shape) == 3:
            src_bw = cv2.cvtColor(src_norm, cv2.COLOR_BGR2GRAY)
        else:
            src_bw = src_norm
        original_bw = src_bw.copy()

        # Obtain image edges
        sigma = 2
        src_bw = cv2.GaussianBlur(src_bw, (0, 0), sigma)
        t1, t2 = 50, 100
        edges = cv2.Canny(src_bw, t1, t2)

        # Dilate edges to prevent self-intersections in contours
        k_size = 2
        kernel = np.ones((k_size, k_size))
        its = 1
        edges = cv2.dilate(edges, kernel, iterations=its)

        # Find contours in image
        conts, _ = cv2.findContours(edges, cv2.RETR_TREE,
                                    cv2.CHAIN_APPROX_NONE)
        if len(conts) == 0:
            raise MacbethError(
                '\nWARNING: No macbeth chart found!'
                '\nNo contours found in image\n'
                'Possible problems:\n'
                '- Macbeth chart is too dark or bright\n'
                '- Macbeth chart is occluded\n'
            )

        # Find quadrilateral contours
        epsilon = 0.07
        conts_per = []
        for i in range(len(conts)):
            per = cv2.arcLength(conts[i], True)
            poly = cv2.approxPolyDP(conts[i], epsilon * per, True)
            if len(poly) == 4 and cv2.isContourConvex(poly):
                conts_per.append((poly, per))

        if len(conts_per) == 0:
            raise MacbethError(
                '\nWARNING: No macbeth chart found!'
                '\nNo quadrilateral contours found'
                '\nPossible problems:\n'
                '- Macbeth chart is too dark or bright\n'
                '- Macbeth chart is occluded\n'
                '- Macbeth chart is out of camera plane\n'
            )

        # Sort contours by perimeter and get perimeters within percent of median
        conts_per = sorted(conts_per, key=lambda x: x[1])
        med_per = conts_per[int(len(conts_per) / 2)][1]
        side = med_per / 4
        perc = 0.1
        med_low, med_high = med_per * (1 - perc), med_per * (1 + perc)
        squares = []
        for i in conts_per:
            if med_low <= i[1] and med_high >= i[1]:
                squares.append(i[0])

        # Obtain coordinates of nomralised macbeth and squares
        square_verts, mac_norm = get_square_verts(0.06)
        # For each square guess, find 24 possible macbeth chart centres
        mac_mids = []
        squares_raw = []
        for i in range(len(squares)):
            square = squares[i]
            squares_raw.append(square)

            # Convert quads to rotated rectangles. This is required as the
            # 'squares' are usually quite irregular quadrilaterls, so
            # performing a transform would result in exaggerated warping and
            # inaccurate macbeth chart centre placement
            rect = cv2.minAreaRect(square)
            square = cv2.boxPoints(rect).astype(np.float32)

            # Reorder vertices to prevent 'hourglass shape'
            square = sorted(square, key=lambda x: x[0])
            square_1 = sorted(square[:2], key=lambda x: x[1])
            square_2 = sorted(square[2:], key=lambda x: -x[1])
            square = np.array(np.concatenate((square_1, square_2)), np.float32)
            square = np.reshape(square, (4, 2)).astype(np.float32)
            squares[i] = square

            # Find 24 possible macbeth chart centres by trasnforming normalised
            # macbeth square vertices onto candidate square vertices found in image
            for j in range(len(square_verts)):
                verts = square_verts[j]
                p_mat = cv2.getPerspectiveTransform(verts, square)
                mac_guess = cv2.perspectiveTransform(mac_norm, p_mat)
                mac_guess = np.round(mac_guess).astype(np.int32)

                mac_mid = np.mean(mac_guess, axis=1)
                mac_mids.append([mac_mid, (i, j)])

        if len(mac_mids) == 0:
            raise MacbethError(
                '\nWARNING: No macbeth chart found!'
                '\nNo possible macbeth charts found within image'
                '\nPossible problems:\n'
                '- Part of the macbeth chart is outside the image\n'
                '- Quadrilaterals in image background\n'
            )

        # Reshape data
        for i in range(len(mac_mids)):
            mac_mids[i][0] = mac_mids[i][0][0]

        # Find where midpoints cluster to identify most likely macbeth centres
        clustering = cluster.AgglomerativeClustering(
            n_clusters=None,
            compute_full_tree=True,
            distance_threshold=side * 2
        )
        mac_mids_list = [x[0] for x in mac_mids]

        if len(mac_mids_list) == 1:
            # Special case of only one valid centre found (probably not needed)
            clus_list = []
            clus_list.append([mac_mids, len(mac_mids)])

        else:
            clustering.fit(mac_mids_list)

            # Create list of all clusters
            clus_list = []
            if clustering.n_clusters_ > 1:
                for i in range(clustering.labels_.max() + 1):
                    indices = [j for j, x in enumerate(clustering.labels_) if x == i]
                    clus = []
                    for index in indices:
                        clus.append(mac_mids[index])
                    clus_list.append([clus, len(clus)])
                clus_list.sort(key=lambda x: -x[1])

            elif clustering.n_clusters_ == 1:
                # Special case of only one cluster found
                clus_list.append([mac_mids, len(mac_mids)])
            else:
                raise MacbethError(
                    '\nWARNING: No macebth chart found!'
                    '\nNo clusters found'
                    '\nPossible problems:\n'
                    '- NA\n'
                )

        # Keep only clusters with enough votes
        clus_len_max = clus_list[0][1]
        clus_tol = 0.7
        for i in range(len(clus_list)):
            if clus_list[i][1] < clus_len_max * clus_tol:
                clus_list = clus_list[:i]
                break
            cent = np.mean(clus_list[i][0], axis=0)[0]
            clus_list[i].append(cent)

        # Get centres of each normalised square
        reference = get_square_centres(0.06)

        # For each possible macbeth chart, transform image into
        # normalised space and find correlation with reference
        max_cor = 0
        best_map = None
        best_fit = None
        best_cen_fit = None
        best_ref_mat = None

        for clus in clus_list:
            clus = clus[0]
            sq_cents = []
            ref_cents = []
            i_list = [p[1][0] for p in clus]
            for point in clus:
                i, j = point[1]

                # Remove any square that voted for two different points within
                # the same cluster. This causes the same point in the image to be
                # mapped to two different reference square centres, resulting in
                # a very distorted perspective transform since cv2.findHomography
                # simply minimises error.
                # This phenomenon is not particularly likely to occur due to the
                # enforced distance threshold in the clustering fit but it is
                # best to keep this in just in case.
                if i_list.count(i) == 1:
                    square = squares_raw[i]
                    sq_cent = np.mean(square, axis=0)
                    ref_cent = reference[j]
                    sq_cents.append(sq_cent)
                    ref_cents.append(ref_cent)

                    # At least four squares need to have voted for a centre in
                    # order for a transform to be found
            if len(sq_cents) < 4:
                raise MacbethError(
                    '\nWARNING: No macbeth chart found!'
                    '\nNot enough squares found'
                    '\nPossible problems:\n'
                    '- Macbeth chart is occluded\n'
                    '- Macbeth chart is too dark of bright\n'
                )

            ref_cents = np.array(ref_cents)
            sq_cents = np.array(sq_cents)

            # Find best fit transform from normalised centres to image
            h_mat, mask = cv2.findHomography(ref_cents, sq_cents)
            if 'None' in str(type(h_mat)):
                raise MacbethError(
                    '\nERROR\n'
                )

            # Transform normalised corners and centres into image space
            mac_fit = cv2.perspectiveTransform(mac_norm, h_mat)
            mac_cen_fit = cv2.perspectiveTransform(np.array([reference]), h_mat)

            # Transform located corners into reference space
            ref_mat = cv2.getPerspectiveTransform(
                mac_fit,
                np.array([ref_corns])
            )
            map_to_ref = cv2.warpPerspective(
                original_bw, ref_mat,
                (ref_w, ref_h)
            )

            # Normalise brigthness
            a = 125 / np.average(map_to_ref)
            map_to_ref = cv2.convertScaleAbs(map_to_ref, alpha=a, beta=0)

            # Find correlation with bw reference macbeth
            cor = correlate(map_to_ref, ref)

            # Keep only if best correlation
            if cor > max_cor:
                max_cor = cor
                best_map = map_to_ref
                best_fit = mac_fit
                best_cen_fit = mac_cen_fit
                best_ref_mat = ref_mat

            # Rotate macbeth by pi and recorrelate in case macbeth chart is
            # upside-down
            mac_fit_inv = np.array(
                ([[mac_fit[0][2], mac_fit[0][3],
                  mac_fit[0][0], mac_fit[0][1]]])
            )
            mac_cen_fit_inv = np.flip(mac_cen_fit, axis=1)
            ref_mat = cv2.getPerspectiveTransform(
                mac_fit_inv,
                np.array([ref_corns])
            )
            map_to_ref = cv2.warpPerspective(
                original_bw, ref_mat,
                (ref_w, ref_h)
            )
            a = 125 / np.average(map_to_ref)
            map_to_ref = cv2.convertScaleAbs(map_to_ref, alpha=a, beta=0)
            cor = correlate(map_to_ref, ref)
            if cor > max_cor:
                max_cor = cor
                best_map = map_to_ref
                best_fit = mac_fit_inv
                best_cen_fit = mac_cen_fit_inv
                best_ref_mat = ref_mat

        # Check best match is above threshold
        cor_thresh = 0.6
        if max_cor < cor_thresh:
            raise MacbethError(
                '\nWARNING: Correlation too low'
                '\nPossible problems:\n'
                '- Bad lighting conditions\n'
                '- Macbeth chart is occluded\n'
                '- Background is too noisy\n'
                '- Macbeth chart is out of camera plane\n'
            )

        # Represent coloured macbeth in reference space
        best_map_col = cv2.warpPerspective(
            original, best_ref_mat, (ref_w, ref_h)
        )
        best_map_col = cv2.resize(
            best_map_col, None, fx=4, fy=4
        )
        a = 125 / np.average(best_map_col)
        best_map_col_norm = cv2.convertScaleAbs(
            best_map_col, alpha=a, beta=0
        )

        # Rescale coordinates to original image size
        fit_coords = (best_fit / factor, best_cen_fit / factor)

        return (max_cor, best_map_col_norm, fit_coords, True)

    # Catch macbeth errors and continue with code
    except MacbethError as error:
        # \todo: This happens so many times in a normal run, that it shadows
        # all the relevant output
        # logger.warning(error)
        return (0, None, None, False)


def find_macbeth(img, mac_config):
    small_chart = mac_config['small']
    show = mac_config['show']

    # Catch the warnings
    warnings.simplefilter("ignore")
    warnings.warn("runtime", RuntimeWarning)

    # Reference macbeth chart is created that will be correlated with the
    # located macbeth chart guess to produce a confidence value for the match.
    script_dir = Path(os.path.realpath(os.path.dirname(__file__)))
    macbeth_ref_path = script_dir.joinpath('macbeth_ref.pgm')
    ref = cv2.imread(str(macbeth_ref_path), 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, ret = get_macbeth_chart(img, ref_data)

    # Following bits of code try 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.

    # Keep a list that will include this and any brightened up versions of
    # the image for reuse.
    all_images = [img]

    for brightness in [2, 4]:
        if cor >= 0.75:
            break
        img_br = cv2.convertScaleAbs(img, alpha=brightness, beta=0)
        all_images.append(img_br)
        cor_b, mac_b, coords_b, ret_b = get_macbeth_chart(img_br, ref_data)
        if cor_b > cor:
            cor, mac, coords, ret = cor_b, mac_b, coords_b, ret_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

    for index, pair in enumerate([{'sel': 2 / 3, 'inc': 1 / 6},
                                  {'sel': 1 / 2, 'inc': 1 / 8},
                                  {'sel': 1 / 3, 'inc': 1 / 12},
                                  {'sel': 1 / 4, 'inc': 1 / 16}]):
        if cor >= 0.75:
            break

        # Check if we need to check 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 index >= 2 and (not small_chart or d_best <= index - 1):
            break

        w, h = list(img.shape[:2])
        # Set dimensions of the subselection and the step along each axis
        # between selections
        w_sel = int(w * pair['sel'])
        h_sel = int(h * pair['sel'])
        w_inc = int(w * pair['inc'])
        h_inc = int(h * pair['inc'])

        loop = int(((1 - pair['sel']) / pair['inc']) + 1)
        # For each subselection, look for a macbeth chart
        for img_br in all_images:
            for i in range(loop):
                for j in range(loop):
                    w_s, h_s = i * w_inc, j * h_inc
                    img_sel = img_br[w_s:w_s + w_sel, h_s:h_s + h_sel]
                    cor_ij, mac_ij, coords_ij, ret_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, ret = mac_ij, coords_ij, ret_ij
                        ii, jj = i, j
                        w_best, h_best = w_inc, h_inc
                        d_best = index + 1

    # 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

    if not ret:
        return None

    coords_fit = coords
    if cor < 0.75:
        logger.warning(f'Low confidence {cor:.3f} for macbeth chart')

    if show:
        draw_macbeth_results(img, coords_fit)

    return coords_fit


def locate_macbeth(image: Image, config: dict):
    # Find macbeth centres
    av_chan = (np.mean(np.array(image.channels), axis=0) / (2**16))
    av_val = np.mean(av_chan)
    if av_val < image.blacklevel_16 / (2**16) + 1 / 64:
        logger.warning(f'Image {image.path.name} too dark')
        return None

    macbeth = find_macbeth(av_chan, config['general']['macbeth'])

    if macbeth is None:
        logger.warning(f'No macbeth chart found in {image.path.name}')
        return None

    mac_cen_coords = macbeth[1]
    if not image.get_patches(mac_cen_coords):
        logger.warning(f'Macbeth patches have saturated in {image.path.name}')
        return None

    image.macbeth = macbeth

    return macbeth
generated by cgit v1.2.1 (git 2.18.0) at 2024-12-22 14:42:40 +0000