# SPDX-License-Identifier: BSD-2-Clause # # Copyright (C) 2019-2020, Raspberry Pi Ltd # # camera tuning tool image loading from ctt_tools import * from ctt_macbeth_locator import * import json import pyexiv2 as pyexif import rawpy as raw """ Image class load image from raw data and extracts metadata. Once image is extracted from data, it finds 24 16x16 patches for each channel, centred at the macbeth chart squares """ class Image: def __init__(self, buf): self.buf = buf self.patches = None self.saturated = False ''' obtain metadata from buffer ''' def get_meta(self): self.ver = ba_to_b(self.buf[4:5]) self.w = ba_to_b(self.buf[0xd0:0xd2]) self.h = ba_to_b(self.buf[0xd2:0xd4]) self.pad = ba_to_b(self.buf[0xd4:0xd6]) self.fmt = self.buf[0xf5] self.sigbits = 2*self.fmt + 4 self.pattern = self.buf[0xf4] self.exposure = ba_to_b(self.buf[0x90:0x94]) self.againQ8 = ba_to_b(self.buf[0x94:0x96]) self.againQ8_norm = self.againQ8/256 camName = self.buf[0x10:0x10+128] camName_end = camName.find(0x00) self.camName = self.buf[0x10:0x10+128][:camName_end].decode() """ Channel order depending on bayer pattern """ bayer_case = { 0: (0, 1, 2, 3), # red 1: (2, 0, 3, 1), # green next to red 2: (3, 2, 1, 0), # green next to blue 3: (1, 0, 3, 2), # blue 128: (0, 1, 2, 3) # arbitrary order for greyscale casw } self.order = bayer_case[self.pattern] ''' manual blacklevel - not robust ''' if 'ov5647' in self.camName: self.blacklevel = 16 else: self.blacklevel = 64 self.blacklevel_16 = self.blacklevel << (6) return 1 ''' print metadata for debug ''' def print_meta(self): print('\nData:') print(' ver = {}'.format(self.ver)) print(' w = {}'.format(self.w)) print(' h = {}'.format(self.h)) print(' pad = {}'.format(self.pad)) print(' fmt = {}'.format(self.fmt)) print(' sigbits = {}'.format(self.sigbits)) print(' pattern = {}'.format(self.pattern)) print(' exposure = {}'.format(self.exposure)) print(' againQ8 = {}'.format(self.againQ8)) print(' againQ8_norm = {}'.format(self.againQ8_norm)) print(' camName = {}'.format(self.camName)) print(' blacklevel = {}'.format(self.blacklevel)) print(' blacklevel_16 = {}'.format(self.blacklevel_16)) return 1 """ get image from raw scanline data """ def get_image(self, raw): self.dptr = [] """ check if data is 10 or 12 bits """ if self.sigbits == 10: """ calc length of scanline """ lin_len = ((((((self.w+self.pad+3)>>2)) * 5)+31)>>5) * 32 """ stack scan lines into matrix """ raw = np.array(raw).reshape(-1, lin_len).astype(np.int64)[:self.h, ...] """ separate 5 bits in each package, stopping when w is satisfied """ ba0 = raw[..., 0:5*((self.w+3)>>2):5] ba1 = raw[..., 1:5*((self.w+3)>>2):5] ba2 = raw[..., 2:5*((self.w+3)>>2):5] ba3 = raw[..., 3:5*((self.w+3)>>2):5] ba4 = raw[..., 4:5*((self.w+3)>>2):5] """ assemble 10 bit numbers """ ch0 = np.left_shift((np.left_shift(ba0, 2) + (ba4 % 4)), 6) ch1 = np.left_shift((np.left_shift(ba1, 2) + (np.right_shift(ba4, 2) % 4)), 6) ch2 = np.left_shift((np.left_shift(ba2, 2) + (np.right_shift(ba4, 4) % 4)), 6) ch3 = np.left_shift((np.left_shift(ba3, 2) + (np.right_shift(ba4, 6) % 4)), 6) """ interleave bits """ mat = np.empty((self.h, self.w), dtype=ch0.dtype) mat[..., 0::4] = ch0 mat[..., 1::4] = ch1 mat[..., 2::4] = ch2 mat[..., 3::4] = ch3 """ There is som eleaking memory somewhere in the code. This code here seemed to make things good enough that the code would run for reasonable numbers of images, however this is techincally just a workaround. (sorry) """ ba0, ba1, ba2, ba3, ba4 = None, None, None, None, None del ba0, ba1, ba2, ba3, ba4 ch0, ch1, ch2, ch3 = None, None, None, None del ch0, ch1, ch2, ch3 """ same as before but 12 bit case """ elif self.sigbits == 12: lin_len = ((((((self.w+self.pad+1)>>1)) * 3)+31)>>5) * 32 raw = np.array(raw).reshape(-1, lin_len).astype(np.int64)[:self.h, ...] ba0 = raw[..., 0:3*((self.w+1)>>1):3] ba1 = raw[..., 1:3*((self.w+1)>>1):3] ba2 = raw[..., 2:3*((self.w+1)>>1):3] ch0 = np.left_shift((np.left_shift(ba0, 4) + ba2 % 16), 4) ch1 = np.left_shift((np.left_shift(ba1, 4) + (np.right_shift(ba2, 4)) % 16), 4) mat = np.empty((self.h, self.w), dtype=ch0.dtype) mat[..., 0::2] = ch0 mat[..., 1::2] = ch1 else: """ data is neither 10 nor 12 or incorrect data """ print('ERROR: wrong bit format, only 10 or 12 bit supported') return 0 """ separate bayer channels """ c0 = mat[0::2, 0::2] c1 = mat[0::2, 1::2] c2 = mat[1::2, 0::2] c3 = mat[1::2, 1::2] self.channels = [c0, c1, c2, c3] return 1 """ obtain 16x16 patch centred at macbeth square centre for each channel """ def get_patches(self, cen_coords, size=16): """ obtain channel widths and heights """ ch_w, ch_h = self.w, self.h cen_coords = list(np.array((cen_coords[0])).astype(np.int32)) self.cen_coords = cen_coords """ squares are ordered by stacking macbeth chart columns from left to right. Some useful patch indices: white = 3 black = 23 'reds' = 9, 10 'blues' = 2, 5, 8, 20, 22 'greens' = 6, 12, 17 greyscale = 3, 7, 11, 15, 19, 23 """ all_patches = [] for ch in self.channels: ch_patches = [] for cen in cen_coords: ''' macbeth centre is placed at top left of central 2x2 patch to account for rounding Patch pixels are sorted by pixel brightness so spatial information is lost. ''' patch = ch[cen[1]-7:cen[1]+9, cen[0]-7:cen[0]+9].flatten() patch.sort() if patch[-5] == (2**self.sigbits-1)*2**(16-self.sigbits): self.saturated = True ch_patches.append(patch) # print('\nNew Patch\n') all_patches.append(ch_patches) # print('\n\nNew Channel\n\n') self.patches = all_patches return 1 def brcm_load_image(Cam, im_str): """ Load image where raw data and metadata is in the BRCM format """ try: """ create byte array """ with open(im_str, 'rb') as image: f = image.read() b = bytearray(f) """ return error if incorrect image address """ except FileNotFoundError: print('\nERROR:\nInvalid image address') Cam.log += '\nWARNING: Invalid image address' return 0 """ return error if problem reading file """ if f is None: print('\nERROR:\nProblem reading file') Cam.log += '\nWARNING: Problem readin file' return 0 # print('\nLooking for EOI and BRCM header') """ find end of image followed by BRCM header by turning bytearray into hex string and string matching with regexp """ start = -1 match = bytearray(b'\xff\xd9@BRCM') match_str = binascii.hexlify(match) b_str = binascii.hexlify(b) """ note index is divided by two to go from string to hex """ indices = [m.start()//2 for m in re.finditer(match_str, b_str)] # print(indices) try: start = indices[0] + 3 except IndexError: print('\nERROR:\nNo Broadcom header found') Cam.log += '\nWARNING: No Broadcom header found!' return 0 """ extract data after header """ # print('\nExtracting data after header') buf = b[start:start+32768] Img = Image(buf) Img.str = im_str # print('Data found successfully') """ obtain metadata """ # print('\nReading metadata') Img.get_meta() Cam.log += '\nExposure : {} us'.format(Img.exposure) Cam.log += '\nNormalised gain : {}'.format(Img.againQ8_norm) # print('Metadata read successfully') """ obtain raw image data """ # print('\nObtaining raw image data') raw = b[start+32768:] Img.get_image(raw) """ delete raw to stop memory errors """ raw = None del raw # print('Raw image data obtained successfully') return Img def dng_load_image(Cam, im_str): try: Img = Image(None) # RawPy doesn't load all the image tags that we need, so we use py3exiv2 metadata = pyexif.ImageMetadata(im_str) metadata.read() Img.ver = 100 # random value """ The DNG and TIFF/EP specifications use different IFDs to store the raw image data and the Exif tags. DNG stores them in a SubIFD and in an Exif IFD respectively (named "SubImage1" and "Photo" by pyexiv2), while TIFF/EP stores them both in IFD0 (name "Image"). Both are used in "DNG" files, with libcamera-apps following the DNG recommendation and applications based on picamera2 following TIFF/EP. This code detects which tags are being used, and therefore extracts the correct values. """ try: Img.w = metadata['Exif.SubImage1.ImageWidth'].value subimage = "SubImage1" photo = "Photo" except KeyError: Img.w = metadata['Exif.Image.ImageWidth'].value subimage = "Image" photo = "Image" Img.pad = 0 Img.h = metadata[f'Exif.{subimage}.ImageLength'].value white = metadata[f'Exif.{subimage}.WhiteLevel'].value Img.sigbits = int(white).bit_length() Img.fmt = (Img.sigbits - 4) // 2 Img.exposure = int(metadata[f'Exif.{photo}.ExposureTime'].value * 1000000) Img.againQ8 = metadata[f'Exif.{photo}.ISOSpeedRatings'].value * 256 / 100 Img.againQ8_norm = Img.againQ8 / 256 Img.camName = metadata['Exif.Image.Model'].value Img.blacklevel = int(metadata[f'Exif.{subimage}.BlackLevel'].value[0]) Img.blacklevel_16 = Img.blacklevel << (16 - Img.sigbits) bayer_case = { '0 1 1 2': (0, (0, 1, 2, 3)), '1 2 0 1': (1, (2, 0, 3, 1)), '2 1 1 0': (2, (3, 2, 1, 0)), '1 0 2 1': (3, (1, 0, 3, 2)) } cfa_pattern = metadata[f'Exif.{subimage}.CFAPattern'].value Img.pattern = bayer_case[cfa_pattern][0] Img.order = bayer_case[cfa_pattern][1] # Now use RawPy tp get the raw Bayer pixels raw_im = raw.imread(im_str) raw_data = raw_im.raw_image shift = 16 - Img.sigbits c0 = np.left_shift(raw_data[0::2, 0::2].astype(np.int64), shift) c1 = np.left_shift(raw_data[0::2, 1::2].astype(np.int64), shift) c2 = np.left_shift(raw_data[1::2, 0::2].astype(np.int64), shift) c3 = np.left_shift(raw_data[1::2, 1::2].astype(np.int64), shift) Img.channels = [c0, c1, c2, c3] Img.rgb = raw_im.postprocess() except Exception: print("\nERROR: failed to load DNG file", im_str) print("Either file does not exist or is incompatible") Cam.log += '\nERROR: DNG file does not exist or is incompatible' raise return Img ''' load image from file location and perform calibration check correct filetype mac boolean is true if image is expected to contain macbeth chart and false if not (alsc images don't have macbeth charts) ''' def load_image(Cam, im_str, mac_config=None, show=False, mac=True, show_meta=False): """ check image is correct filetype """ if '.jpg' in im_str or '.jpeg' in im_str or '.brcm' in im_str or '.dng' in im_str: if '.dng' in im_str: Img = dng_load_image(Cam, im_str) else: Img = brcm_load_image(Cam, im_str) """ handle errors smoothly if loading image failed """ if Img == 0: return 0 if show_meta: Img.print_meta() if mac: """ find macbeth centres, discarding images that are too dark or light """ av_chan = (np.mean(np.array(Img.channels), axis=0)/(2**16)) av_val = np.mean(av_chan) # print(av_val) if av_val < Img.blacklevel_16/(2**16)+1/64: macbeth = None print('\nError: Image too dark!') Cam.log += '\nWARNING: Image too dark!' else: macbeth = find_macbeth(Cam, av_chan, mac_config) """ if no macbeth found return error """ if macbeth is None: print('\nERROR: No macbeth chart found') return 0 mac_cen_coords = macbeth[1] # print('\nMacbeth centres located successfully') """ obtain image patches """ # print('\nObtaining image patches') Img.get_patches(mac_cen_coords) if Img.saturated: print('\nERROR: Macbeth patches have saturated') Cam.log += '\nWARNING: Macbeth patches have saturated!' return 0 """ clear memory """ Img.buf = None del Img.buf # print('Image patches obtained successfully') """ optional debug """ if show and __name__ == '__main__': copy = sum(Img.channels)/2**18 copy = np.reshape(copy, (Img.h//2, Img.w//2)).astype(np.float64) copy, _ = reshape(copy, 800) represent(copy) return Img """ return error if incorrect filetype """ else: # print('\nERROR:\nInvalid file extension') return 0 """ bytearray splice to number little endian """ def ba_to_b(b): total = 0 for i in range(len(b)): total += 256**i * b[i] return total n339'>339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 
/* SPDX-License-Identifier: LGPL-2.1-or-later */
/*
 * Copyright (C) 2019, Google Inc.
 *
 * rkisp1.cpp - Pipeline handler for Rockchip ISP1
 */

#include <algorithm>
#include <array>
#include <iomanip>
#include <memory>
#include <numeric>
#include <queue>

#include <linux/media-bus-format.h>

#include <libcamera/buffer.h>
#include <libcamera/camera.h>
#include <libcamera/control_ids.h>
#include <libcamera/formats.h>
#include <libcamera/ipa/core_ipa_interface.h>
#include <libcamera/ipa/rkisp1_ipa_interface.h>
#include <libcamera/ipa/rkisp1_ipa_proxy.h>
#include <libcamera/request.h>
#include <libcamera/stream.h>

#include "libcamera/internal/camera_sensor.h"
#include "libcamera/internal/delayed_controls.h"
#include "libcamera/internal/device_enumerator.h"
#include "libcamera/internal/ipa_manager.h"
#include "libcamera/internal/log.h"
#include "libcamera/internal/media_device.h"
#include "libcamera/internal/pipeline_handler.h"
#include "libcamera/internal/utils.h"
#include "libcamera/internal/v4l2_subdevice.h"
#include "libcamera/internal/v4l2_videodevice.h"

#include "rkisp1_path.h"

namespace libcamera {

LOG_DEFINE_CATEGORY(RkISP1)

class PipelineHandlerRkISP1;
class RkISP1CameraData;

struct RkISP1FrameInfo {
	unsigned int frame;
	Request *request;

	FrameBuffer *paramBuffer;
	FrameBuffer *statBuffer;
	FrameBuffer *mainPathBuffer;
	FrameBuffer *selfPathBuffer;