# SPDX-License-Identifier: BSD-2-Clause # # Copyright (C) 2023, Raspberry Pi Ltd # # ctt_cac.py - CAC (Chromatic Aberration Correction) tuning tool from PIL import Image import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from ctt_dots_locator import find_dots_locations # This is the wrapper file that creates a JSON entry for you to append # to your camera tuning file. # It calculates the chromatic aberration at different points throughout # the image and uses that to produce a martix that can then be used # in the camera tuning files to correct this aberration. def pprint_array(array): # Function to print the array in a tidier format array = array output = "" for i in range(len(array)): for j in range(len(array[0])): output += str(round(array[i, j], 2)) + ", " # Add the necessary indentation to the array output += "\n " # Cut off the end of the array (nicely formats it) return output[:-22] def plot_shifts(red_shifts, blue_shifts): # If users want, they can pass a command line option to show the shifts on a graph # Can be useful to check that the functions are all working, and that the sample # images are doing the right thing Xs = np.array(red_shifts)[:, 0] Ys = np.array(red_shifts)[:, 1] Zs = np.array(red_shifts)[:, 2] Zs2 = np.array(red_shifts)[:, 3] Zs3 = np.array(blue_shifts)[:, 2] Zs4 = np.array(blue_shifts)[:, 3] fig, axs = plt.subplots(2, 2) ax = fig.add_subplot(2, 2, 1, projection='3d') ax.scatter(Xs, Ys, Zs, cmap=cm.jet, linewidth=0) ax.set_title('Red X Shift') ax = fig.add_subplot(2, 2, 2, projection='3d') ax.scatter(Xs, Ys, Zs2, cmap=cm.jet, linewidth=0) ax.set_title('Red Y Shift') ax = fig.add_subplot(2, 2, 3, projection='3d') ax.scatter(Xs, Ys, Zs3, cmap=cm.jet, linewidth=0) ax.set_title('Blue X Shift') ax = fig.add_subplot(2, 2, 4, projection='3d') ax.scatter(Xs, Ys, Zs4, cmap=cm.jet, linewidth=0) ax.set_title('Blue Y Shift') fig.tight_layout() plt.show() def shifts_to_yaml(red_shift, blue_shift, image_dimensions, output_grid_size=9): # Convert the shifts to a numpy array for easier handling and initialise other variables red_shifts = np.array(red_shift) blue_shifts = np.array(blue_shift) # create a grid that's smaller than the output grid, which we then interpolate from to get the output values xrgrid = np.zeros((output_grid_size - 1, output_grid_size - 1)) xbgrid = np.zeros((output_grid_size - 1, output_grid_size - 1)) yrgrid = np.zeros((output_grid_size - 1, output_grid_size - 1)) ybgrid = np.zeros((output_grid_size - 1, output_grid_size - 1)) xrsgrid = [] xbsgrid = [] yrsgrid = [] ybsgrid = [] xg = np.zeros((output_grid_size - 1, output_grid_size - 1)) yg = np.zeros((output_grid_size - 1, output_grid_size - 1)) # Format the grids - numpy doesn't work for this, it wants a # nice uniformly spaced grid, which we don't know if we have yet, hence the rather mundane setup for x in range(output_grid_size - 1): xrsgrid.append([]) yrsgrid.append([]) xbsgrid.append([]) ybsgrid.append([]) for y in range(output_grid_size - 1): xrsgrid[x].append([]) yrsgrid[x].append([]) xbsgrid[x].append([]) ybsgrid[x].append([]) image_size = (image_dimensions[0], image_dimensions[1]) gridxsize = image_size[0] / (output_grid_size - 1) gridysize = image_size[1] / (output_grid_size - 1) # Iterate through each dot, and it's shift values and put these into the correct grid location for red_shift in red_shifts: xgridloc = int(red_shift[0] / gridxsize) ygridloc = int(red_shift[1] / gridysize) xrsgrid[xgridloc][ygridloc].append(red_shift[2]) yrsgrid[xgridloc][ygridloc].append(red_shift[3]) for blue_shift in blue_shifts: xgridloc = int(blue_shift[0] / gridxsize) ygridloc = int(blue_shift[1] / gridysize) xbsgrid[xgridloc][ygridloc].append(blue_shift[2]) ybsgrid[xgridloc][ygridloc].append(blue_shift[3]) # Now calculate the average pixel shift for each square in the grid for x in range(output_grid_size - 1): for y in range(output_grid_size - 1): xrgrid[x, y] = np.mean(xrsgrid[x][y]) yrgrid[x, y] = np.mean(yrsgrid[x][y]) xbgrid[x, y] = np.mean(xbsgrid[x][y]) ybgrid[x, y] = np.mean(ybsgrid[x][y]) # Next, we start to interpolate the central points of the grid that gets passed to the tuning file input_grids = np.array([xrgrid, yrgrid, xbgrid, ybgrid]) output_grids = np.zeros((4, output_grid_size, output_grid_size)) # Interpolate the centre of the grid output_grids[:, 1:-1, 1:-1] = (input_grids[:, 1:, :-1] + input_grids[:, 1:, 1:] + input_grids[:, :-1, 1:] + input_grids[:, :-1, :-1]) / 4 # Edge cases: output_grids[:, 1:-1, 0] = ((input_grids[:, :-1, 0] + input_grids[:, 1:, 0]) / 2 - output_grids[:, 1:-1, 1]) * 2 + output_grids[:, 1:-1, 1] output_grids[:, 1:-1, -1] = ((input_grids[:, :-1, 7] + input_grids[:, 1:, 7]) / 2 - output_grids[:, 1:-1, -2]) * 2 + output_grids[:, 1:-1, -2] output_grids[:, 0, 1:-1] = ((input_grids[:, 0, :-1] + input_grids[:, 0, 1:]) / 2 - output_grids[:, 1, 1:-1]) * 2 + output_grids[:, 1, 1:-1] output_grids[:, -1, 1:-1] = ((input_grids[:, 7, :-1] + input_grids[:, 7, 1:]) / 2 - output_grids[:, -2, 1:-1]) * 2 + output_grids[:, -2, 1:-1] # Corner Cases: output_grids[:, 0, 0] = (output_grids[:, 0, 1] - output_grids[:, 1, 1]) + (output_grids[:, 1, 0] - output_grids[:, 1, 1]) + output_grids[:, 1, 1] output_grids[:, 0, -1] = (output_grids[:, 0, -2] - output_grids[:, 1, -2]) + (output_grids[:, 1, -1] - output_grids[:, 1, -2]) + output_grids[:, 1, -2] output_grids[:, -1, 0] = (output_grids[:, -1, 1] - output_grids[:, -2, 1]) + (output_grids[:, -2, 0] - output_grids[:, -2, 1]) + output_grids[:, -2, 1] output_grids[:, -1, -1] = (output_grids[:, -2, -1] - output_grids[:, -2, -2]) + (output_grids[:, -1, -2] - output_grids[:, -2, -2]) + output_grids[:, -2, -2] # Below, we swap the x and the y coordinates, and also multiply by a factor of -1 # This is due to the PiSP (standard) dimensions being flipped in comparison to # PIL image coordinate directions, hence why xr -> yr. Also, the shifts calculated are colour shifts, # and the PiSP block asks for the values it should shift by (hence the * -1, to convert from colour shift to a pixel shift) output_grid_yr, output_grid_xr, output_grid_yb, output_grid_xb = output_grids * -1 return output_grid_xr, output_grid_yr, output_grid_xb, output_grid_yb def analyse_dot(dot, dot_location=[0, 0]): # Scan through the dot, calculate the centroid of each colour channel by doing: # pixel channel brightness * distance from top left corner # Sum these, and divide by the sum of each channel's brightnesses to get a centroid for each channel red_channel = np.array(dot)[:, :, 0] y_num_pixels = len(red_channel[0]) x_num_pixels = len(red_channel) yred_weight = np.sum(np.dot(red_channel, np.arange(y_num_pixels))) xred_weight = np.sum(np.dot(np.arange(x_num_pixels), red_channel)) red_sum = np.sum(red_channel) green_channel = np.array(dot)[:, :, 1] ygreen_weight = np.sum(np.dot(green_channel, np.arange(y_num_pixels))) xgreen_weight = np.sum(np.dot(np.arange(x_num_pixels), green_channel)) green_sum = np.sum(green_channel) blue_channel = np.array(dot)[:, :, 2] yblue_weight = np.sum(np.dot(blue_channel, np.arange(y_num_pixels))) xblue_weight = np.sum(np.dot(np.arange(x_num_pixels), blue_channel)) blue_sum = np.sum(blue_channel) # We return this structure. It contains 2 arrays that contain: # the locations of the dot center, along with the channel shifts in the x and y direction: # [ [red_center_x, red_center_y, red_x_shift, red_y_shift], [blue_center_x, blue_center_y, blue_x_shift, blue_y_shift] ] return [[int(dot_location[0]) + int(len(dot) / 2), int(dot_location[1]) + int(len(dot[0]) / 2), xred_weight / red_sum - xgreen_weight / green_sum, yred_weight / red_sum - ygreen_weight / green_sum], [dot_location[0] + int(len(dot) / 2), dot_location[1] + int(len(dot[0]) / 2), xblue_weight / blue_sum - xgreen_weight / green_sum, yblue_weight / blue_sum - ygreen_weight / green_sum]] def cac(Cam): filelist = Cam.imgs_cac Cam.log += '\nCAC analysing files: {}'.format(str(filelist)) np.set_printoptions(precision=3) np.set_printoptions(suppress=True) # Create arrays to hold all the dots data and their colour offsets red_shift = [] # Format is: [[Dot Center X, Dot Center Y, x shift, y shift]] blue_shift = [] # Iterate through the files # Multiple files is reccomended to average out the lens aberration through rotations for file in filelist: Cam.log += '\nCAC processing file' print("\n Processing file") # Read the raw RGB values rgb = file.rgb image_size = [file.h, file.w] # Image size, X, Y # Create a colour copy of the RGB values to use later in the calibration imout = Image.new(mode="RGB", size=image_size) rgb_image = np.array(imout) # The rgb values need reshaping from a 1d array to a 3d array to be worked with easily rgb.reshape((image_size[0], image_size[1], 3)) rgb_image = rgb # Pass the RGB image through to the dots locating program # Returns an array of the dots (colour rectangles around the dots), and an array of their locations print("Finding dots") Cam.log += '\nFinding dots' dots, dots_locations = find_dots_locations(rgb_image) # Now, analyse each dot. Work out the centroid of each colour channel, and use that to work out # by how far the chromatic aberration has shifted each channel Cam.log += '\nDots found: {}'.format(str(len(dots))) print('Dots found: ' + str(len(dots))) for dot, dot_location in zip(dots, dots_locations): if len(dot) > 0: if (dot_location[0] > 0) and (dot_location[1] > 0): ret = analyse_dot(dot, dot_location) red_shift.append(ret[0]) blue_shift.append(ret[1]) # Take our arrays of red shifts and locations, push them through to be interpolated into a 9x9 matrix # for the CAC block to handle and then store these as a .json file to be added to the camera # tuning file print("\nCreating output grid") Cam.log += '\nCreating output grid' rx, ry, bx, by = shifts_to_yaml(red_shift, blue_shift, image_size) print("CAC correction complete!") Cam.log += '\nCAC correction complete!' # Give the JSON dict back to the main ctt program return {"strength": 1.0, "lut_rx": list(rx.round(2).reshape(81)), "lut_ry": list(ry.round(2).reshape(81)), "lut_bx": list(bx.round(2).reshape(81)), "lut_by": list(by.round(2).reshape(81))} 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 
# 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