/* SPDX-License-Identifier: LGPL-2.1-or-later */ /* * Copyright (C) 2020, Raspberry Pi Ltd * * dng_writer.cpp - DNG writer */ #include "dng_writer.h" #include #include #include #include #include #include #include using namespace libcamera; enum CFAPatternColour : uint8_t { CFAPatternRed = 0, CFAPatternGreen = 1, CFAPatternBlue = 2, }; struct FormatInfo { uint8_t bitsPerSample; CFAPatternColour pattern[4]; void (*packScanline)(void *output, const void *input, unsigned int width); void (*thumbScanline)(const FormatInfo &info, void *output, const void *input, unsigned int width, unsigned int stride); }; struct Matrix3d { Matrix3d() { } Matrix3d(float m0, float m1, float m2, float m3, float m4, float m5, float m6, float m7, float m8) { m[0] = m0, m[1] = m1, m[2] = m2; m[3] = m3, m[4] = m4, m[5] = m5; m[6] = m6, m[7] = m7, m[8] = m8; } Matrix3d(const Span &span) : Matrix3d(span[0], span[1], span[2], span[3], span[4], span[5], span[6], span[7], span[8]) { } static Matrix3d diag(float diag0, float diag1, float diag2) { return Matrix3d(diag0, 0, 0, 0, diag1, 0, 0, 0, diag2); } static Matrix3d identity() { return Matrix3d(1, 0, 0, 0, 1, 0, 0, 0, 1); } Matrix3d transpose() const { return { m[0], m[3], m[6], m[1], m[4], m[7], m[2], m[5], m[8] }; } Matrix3d cofactors() const { return { m[4] * m[8] - m[5] * m[7], -(m[3] * m[8] - m[5] * m[6]), m[3] * m[7] - m[4] * m[6], -(m[1] * m[8] - m[2] * m[7]), m[0] * m[8] - m[2] * m[6], -(m[0] * m[7] - m[1] * m[6]), m[1] * m[5] - m[2] * m[4], -(m[0] * m[5] - m[2] * m[3]), m[0] * m[4] - m[1] * m[3] }; } Matrix3d adjugate() const { return cofactors().transpose(); } float determinant() const { return m[0] * (m[4] * m[8] - m[5] * m[7]) - m[1] * (m[3] * m[8] - m[5] * m[6]) + m[2] * (m[3] * m[7] - m[4] * m[6]); } Matrix3d inverse() const { return adjugate() * (1.0 / determinant()); } Matrix3d operator*(const Matrix3d &other) const { Matrix3d result; for (unsigned int i = 0; i < 3; i++) { for (unsigned int j = 0; j < 3; j++) { result.m[i * 3 + j] = m[i * 3 + 0] * other.m[0 + j] + m[i * 3 + 1] * other.m[3 + j] + m[i * 3 + 2] * other.m[6 + j]; } } return result; } Matrix3d operator*(float f) const { Matrix3d result; for (unsigned int i = 0; i < 9; i++) result.m[i] = m[i] * f; return result; } float m[9]; }; void packScanlineSBGGR8(void *output, const void *input, unsigned int width) { const uint8_t *in = static_cast(input); uint8_t *out = static_cast(output); std::copy(in, in + width, out); } void packScanlineSBGGR10P(void *output, const void *input, unsigned int width) { const uint8_t *in = static_cast(input); uint8_t *out = static_cast(output); /* \todo Can this be made more efficient? */ for (unsigned int x = 0; x < width; x += 4) { *out++ = in[0]; *out++ = (in[4] & 0x03) << 6 | in[1] >> 2; *out++ = (in[1] & 0x03) << 6 | (in[4] & 0x0c) << 2 | in[2] >> 4; *out++ = (in[2] & 0x0f) << 4 | (in[4] & 0x30) >> 2 | in[3] >> 6; *out++ = (in[3] & 0x3f) << 2 | (in[4] & 0xc0) >> 6; in += 5; } } void packScanlineSBGGR12P(void *output, const void *input, unsigned int width) { const uint8_t *in = static_cast(input); uint8_t *out = static_cast(output); /* \todo Can this be made more efficient? */ for (unsigned int i = 0; i < width; i += 2) { *out++ = in[0]; *out++ = (in[2] & 0x0f) << 4 | in[1] >> 4; *out++ = (in[1] & 0x0f) << 4 | in[2] >> 4; in += 3; } } void thumbScanlineSBGGRxxP(const FormatInfo &info, void *output, const void *input, unsigned int width, unsigned int stride) { const uint8_t *in = static_cast(input); uint8_t *out = static_cast(output); /* Number of bytes corresponding to 16 pixels. */ unsigned int skip = info.bitsPerSample * 16 / 8; for (unsigned int x = 0; x < width; x++) { uint8_t value = (in[0] + in[1] + in[stride] + in[stride + 1]) >> 2; *out++ = value; *out++ = value; *out++ = value; in += skip; } } void packScanlineIPU3(void *output, const void *input, unsigned int width) { const uint8_t *in = static_cast(input); uint16_t *out = static_cast(output); /* * Upscale the 10-bit format to 16-bit as it's not trivial to pack it * as 10-bit without gaps. * * \todo Improve packing to keep the 10-bit sample size. */ unsigned int x = 0; while (true) { for (unsigned int i = 0; i < 6; i++) { *out++ = (in[1] & 0x03) << 14 | (in[0] & 0xff) << 6; if (++x >= width) return; *out++ = (in[2] & 0x0f) << 12 | (in[1] & 0xfc) << 4; if (++x >= width) return; *out++ = (in[3] & 0x3f) << 10 | (in[2] & 0xf0) << 2; if (++x >= width) return; *out++ = (in[4] & 0xff) << 8 | (in[3] & 0xc0) << 0; if (++x >= width) return; in += 5; } *out++ = (in[1] & 0x03) << 14 | (in[0] & 0xff) << 6; if (++x >= width) return; in += 2; } } void thumbScanlineIPU3([[maybe_unused]] const FormatInfo &info, void *output, const void *input, unsigned int width, unsigned int stride) { uint8_t *out = static_cast(output); for (unsigned int x = 0; x < width; x++) { unsigned int pixel = x * 16; unsigned int block = pixel / 25; unsigned int pixelInBlock = pixel - block * 25; /* * If the pixel is the last in the block cheat a little and * move one pixel backward to avoid reading between two blocks * and having to deal with the padding bits. */ if (pixelInBlock == 24) pixelInBlock--; const uint8_t *in = static_cast(input) + block * 32 + (pixelInBlock / 4) * 5; uint16_t val1, val2, val3, val4; switch (pixelInBlock % 4) { case 0: val1 = (in[1] & 0x03) << 14 | (in[0] & 0xff) << 6; val2 = (in[2] & 0x0f) << 12 | (in[1] & 0xfc) << 4; val3 = (in[stride + 1] & 0x03) << 14 | (in[stride + 0] & 0xff) << 6; val4 = (in[stride + 2] & 0x0f) << 12 | (in[stride + 1] & 0xfc) << 4; break; case 1: val1 = (in[2] & 0x0f) << 12 | (in[1] & 0xfc) << 4; val2 = (in[3] & 0x3f) << 10 | (in[2] & 0xf0) << 2; val3 = (in[stride + 2] & 0x0f) << 12 | (in[stride + 1] & 0xfc) << 4; val4 = (in[stride + 3] & 0x3f) << 10 | (in[stride + 2] & 0xf0) << 2; break; case 2: val1 = (in[3] & 0x3f) << 10 | (in[2] & 0xf0) << 2; val2 = (in[4] & 0xff) << 8 | (in[3] & 0xc0) << 0; val3 = (in[stride + 3] & 0x3f) << 10 | (in[stride + 2] & 0xf0) << 2; val4 = (in[stride + 4] & 0xff) << 8 | (in[stride + 3] & 0xc0) << 0; break; case 3: val1 = (in[4] & 0xff) << 8 | (in[3] & 0xc0) << 0; val2 = (in[6] & 0x03) << 14 | (in[5] & 0xff) << 6; val3 = (in[stride + 4] & 0xff) << 8 | (in[stride + 3] & 0xc0) << 0; val4 = (in[stride + 6] & 0x03) << 14 | (in[stride + 5] & 0xff) << 6; break; } uint8_t value = (val1 + val2 + val3 + val4) >> 10; *out++ = value; *out++ = value; *out++ = value; } } static const std::map formatInfo = { { formats::SBGGR8, { .bitsPerSample = 8, .pattern = { CFAPatternBlue, CFAPatternGreen, CFAPatternGreen, CFAPatternRed }, .packScanline = packScanlineSBGGR8, .thumbScanline = thumbScanlineSBGGRxxP, } }, { formats::SGBRG8, { .bitsPerSample = 8, .pattern = { CFAPatternGreen, CFAPatternBlue, CFAPatternRed, CFAPatternGreen }, .packScanline = packScanlineSBGGR8, .thumbScanline = thumbScanlineSBGGRxxP, } }, { formats::SGRBG8, { .bitsPerSample = 8, .pattern = { CFAPatternGreen, CFAPatternRed, CFAPatternBlue, CFAPatternGreen }, .packScanline = packScanlineSBGGR8, .thumbScanline = thumbScanlineSBGGRxxP, } }, { formats::SRGGB8, { .bitsPerSample = 8, .pattern = { CFAPatternRed, CFAPatternGreen, CFAPatternGreen, CFAPatternBlue }, .packScanline = packScanlineSBGGR8, .thumbScanline = thumbScanlineSBGGRxxP, } }, { formats::SBGGR10_CSI2P, { .bitsPerSample = 10, .pattern = { CFAPatternBlue, CFAPatternGreen, CFAPatternGreen, CFAPatternRed }, .packScanline = packScanlineSBGGR10P, .thumbScanline = thumbScanlineSBGGRxxP, } }, { formats::SGBRG10_CSI2P, { .bitsPerSample = 10, .pattern = { CFAPatternGreen, CFAPatternBlue, CFAPatternRed, CFAPatternGreen }, .packScanline = packScanlineSBGGR10P, .thumbScanline = thumbScanlineSBGGRxxP, } }, { formats::SGRBG10_CSI2P, { .bitsPerSample = 10, .pattern = { CFAPatternGreen, CFAPatternRed, CFAPatternBlue, CFAPatternGreen }, .packScanline = packScanlineSBGGR10P, .thumbScanline = thumbScanlineSBGGRxxP, } }, { formats::SRGGB10_CSI2P, { .bitsPerSample = 10, .pattern = { CFAPatternRed, CFAPatternGreen, CFAPatternGreen, CFAPatternBlue }, .packScanline = packScanlineSBGGR10P, .thumbScanline = thumbScanlineSBGGRxxP, } }, { formats::SBGGR12_CSI2P, { .bitsPerSample = 12, .pattern = { CFAPatternBlue, CFAPatternGreen, CFAPatternGreen, CFAPatternRed }, .packScanline = packScanlineSBGGR12P, .thumbScanline = thumbScanlineSBGGRxxP, } }, { formats::SGBRG12_CSI2P, { .bitsPerSample = 12, .pattern = { CFAPatternGreen, CFAPatternBlue, CFAPatternRed, CFAPatternGreen }, .packScanline = packScanlineSBGGR12P, .thumbScanline = thumbScanlineSBGGRxxP, } }, { formats::SGRBG12_CSI2P, { .bitsPerSample = 12, .pattern = { CFAPatternGreen, CFAPatternRed, CFAPatternBlue, CFAPatternGreen }, .packScanline = packScanlineSBGGR12P, .thumbScanline = thumbScanlineSBGGRxxP, } }, { formats::SRGGB12_CSI2P, { .bitsPerSample = 12, .pattern = { CFAPatternRed, CFAPatternGreen, CFAPatternGreen, CFAPatternBlue }, .packScanline = packScanlineSBGGR12P, .thumbScanline = thumbScanlineSBGGRxxP, } }, { formats::SBGGR10_IPU3, { .bitsPerSample = 16, .pattern = { CFAPatternBlue, CFAPatternGreen, CFAPatternGreen, CFAPatternRed }, .packScanline = packScanlineIPU3, .thumbScanline = thumbScanlineIPU3, } }, { formats::SGBRG10_IPU3, { .bitsPerSample = 16, .pattern = { CFAPatternGreen, CFAPatternBlue, CFAPatternRed, CFAPatternGreen }, .packScanline = packScanlineIPU3, .thumbScanline = thumbScanlineIPU3, } }, { formats::SGRBG10_IPU3, { .bitsPerSample = 16, .pattern = { CFAPatternGreen, CFAPatternRed, CFAPatternBlue, CFAPatternGreen }, .packScanline = packScanlineIPU3, .thumbScanline = thumbScanlineIPU3, } }, { formats::SRGGB10_IPU3, { .bitsPerSample = 16, .pattern = { CFAPatternRed, CFAPatternGreen, CFAPatternGreen, CFAPatternBlue }, .packScanline = packScanlineIPU3, .thumbScanline = thumbScanlineIPU3, } }, }; int DNGWriter::write(const char *filename, const Camera *camera, const StreamConfiguration &config, const ControlList &metadata, [[maybe_unused]] const FrameBuffer *buffer, const void *data) { const ControlList &cameraProperties = camera->properties(); const auto it = formatInfo.find(config.pixelFormat); if (it == formatInfo.cend()) { std::cerr << "Unsupported pixel format" << std::endl; return -EINVAL; } const FormatInfo *info = &it->second; TIFF *tif = TIFFOpen(filename, "w"); if (!tif) { std::cerr << "Failed to open tiff file" << std::endl; return -EINVAL; } /* * Scanline buffer, has to be large enough to store both a RAW scanline * or a thumbnail scanline. The latter will always be much smaller than * the former as we downscale by 16 in both directions. */ uint8_t scanline[(config.size.width * info->bitsPerSample + 7) / 8]; toff_t rawIFDOffset = 0; toff_t exifIFDOffset = 0; /* * Start with a thumbnail in IFD 0 for compatibility with TIFF baseline * readers, as required by the TIFF/EP specification. Tags that apply to * the whole file are stored here. */ const uint8_t version[] = { 1, 2, 0, 0 }; TIFFSetField(tif, TIFFTAG_DNGVERSION, version); TIFFSetField(tif, TIFFTAG_DNGBACKWARDVERSION, version); TIFFSetField(tif, TIFFTAG_FILLORDER, FILLORDER_MSB2LSB); TIFFSetField(tif, TIFFTAG_MAKE, "libcamera"); const auto &model = cameraProperties.get(properties::Model); if (model) { TIFFSetField(tif, TIFFTAG_MODEL, model->c_str()); /* \todo set TIFFTAG_UNIQUECAMERAMODEL. */ } TIFFSetField(tif, TIFFTAG_SOFTWARE, "qcam"); TIFFSetField(tif, TIFFTAG_ORIENTATION, ORIENTATION_TOPLEFT); /* * Thumbnail-specific tags. The thumbnail is stored as an RGB image * with 1/16 of the raw image resolution. Greyscale would save space, * but doesn't seem well supported by RawTherapee. */ TIFFSetField(tif, TIFFTAG_SUBFILETYPE, FILETYPE_REDUCEDIMAGE); TIFFSetField(tif, TIFFTAG_IMAGEWIDTH, config.size.width / 16); TIFFSetField(tif, TIFFTAG_IMAGELENGTH, config.size.height / 16); TIFFSetField(tif, TIFFTAG_BITSPERSAMPLE, 8); TIFFSetField(tif, TIFFTAG_COMPRESSION, COMPRESSION_NONE); TIFFSetField(tif, TIFFTAG_PHOTOMETRIC, PHOTOMETRIC_RGB); TIFFSetField(tif, TIFFTAG_SAMPLESPERPIXEL, 3); TIFFSetField(tif, TIFFTAG_PLANARCONFIG, PLANARCONFIG_CONTIG); TIFFSetField(tif, TIFFTAG_SAMPLEFORMAT, SAMPLEFORMAT_UINT); /* * Fill in some reasonable colour information in the DNG. We supply * the "neutral" colour values which determine the white balance, and the * "ColorMatrix1" which converts XYZ to (un-white-balanced) camera RGB. * Note that this is not a "proper" colour calibration for the DNG, * nonetheless, many tools should be able to render the colours better. */ float neutral[3] = { 1, 1, 1 }; Matrix3d wbGain = Matrix3d::identity(); /* From http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html */ const Matrix3d rgb2xyz(0.4124564, 0.3575761, 0.1804375, 0.2126729, 0.7151522, 0.0721750, 0.0193339, 0.1191920, 0.9503041); Matrix3d ccm = Matrix3d::identity(); /* * Pick a reasonable number eps to protect against singularities. It * should be comfortably larger than the point at which we run into * numerical trouble, yet smaller than any plausible gain that we might * apply to a colour, either explicitly or as part of the colour matrix. */ const double eps = 1e-2; const auto &colourGains = metadata.get(controls::ColourGains); if (colourGains) { if ((*colourGains)[0] > eps && (*colourGains)[1] > eps) { wbGain = Matrix3d::diag((*colourGains)[0], 1, (*colourGains)[1]); neutral[0] = 1.0 / (*colourGains)[0]; /* red */ neutral[2] = 1.0 / (*colourGains)[1]; /* blue */ } } const auto &ccmControl = metadata.get(controls::ColourCorrectionMatrix); if (ccmControl) { Matrix3d ccmSupplied(*ccmControl); if (ccmSupplied.determinant() > eps) ccm = ccmSupplied; } /* * rgb2xyz is known to be invertible, and we've ensured above that both * the ccm and wbGain matrices are non-singular, so the product of all * three is guaranteed to be invertible too. */ Matrix3d colorMatrix1 = (rgb2xyz * ccm * wbGain).inverse(); TIFFSetField(tif, TIFFTAG_COLORMATRIX1, 9, colorMatrix1.m); TIFFSetField(tif, TIFFTAG_ASSHOTNEUTRAL, 3, neutral); /* * Reserve space for the SubIFD and ExifIFD tags, pointing to the IFD * for the raw image and EXIF data respectively. The real offsets will * be set later. */ TIFFSetField(tif, TIFFTAG_SUBIFD, 1, &rawIFDOffset); TIFFSetField(tif, TIFFTAG_EXIFIFD, exifIFDOffset); /* Write the thumbnail. */ const uint8_t *row = static_cast(data); for (unsigned int y = 0; y < config.size.height / 16; y++) { info->thumbScanline(*info, &scanline, row, config.size.width / 16, config.stride); if (TIFFWriteScanline(tif, &scanline, y, 0) != 1) { std::cerr << "Failed to write thumbnail scanline" << std::endl; TIFFClose(tif); return -EINVAL; } row += config.stride * 16; } TIFFWriteDirectory(tif); /* Create a new IFD for the RAW image. */ const uint16_t cfaRepeatPatternDim[] = { 2, 2 }; const uint8_t cfaPlaneColor[] = { CFAPatternRed, CFAPatternGreen, CFAPatternBlue }; TIFFSetField(tif, TIFFTAG_SUBFILETYPE, 0); TIFFSetField(tif, TIFFTAG_IMAGEWIDTH, config.size.width); TIFFSetField(tif, TIFFTAG_IMAGELENGTH, config.size.height); TIFFSetField(tif, TIFFTAG_BITSPERSAMPLE, info->bitsPerSample); TIFFSetField(tif, TIFFTAG_COMPRESSION, COMPRESSION_NONE); TIFFSetField(tif, TIFFTAG_PHOTOMETRIC, PHOTOMETRIC_CFA); TIFFSetField(tif, TIFFTAG_SAMPLESPERPIXEL, 1); TIFFSetField(tif, TIFFTAG_PLANARCONFIG, PLANARCONFIG_CONTIG); TIFFSetField(tif, TIFFTAG_SAMPLEFORMAT, SAMPLEFORMAT_UINT); TIFFSetField(tif, TIFFTAG_CFAREPEATPATTERNDIM, cfaRepeatPatternDim); if (TIFFLIB_VERSION < 20201219) TIFFSetField(tif, TIFFTAG_CFAPATTERN, info->pattern); else TIFFSetField(tif, TIFFTAG_CFAPATTERN, 4, info->pattern); TIFFSetField(tif, TIFFTAG_CFAPLANECOLOR, 3, cfaPlaneColor); TIFFSetField(tif, TIFFTAG_CFALAYOUT, 1); const uint16_t blackLevelRepeatDim[] = { 2, 2 }; float blackLevel[] = { 0.0f, 0.0f, 0.0f, 0.0f }; uint32_t whiteLevel = (1 << info->bitsPerSample) - 1; const auto &blackLevels = metadata.get(controls::SensorBlackLevels); if (blackLevels) { Span levels = *blackLevels; /* * The black levels control is specified in R, Gr, Gb, B order. * Map it to the TIFF tag that is specified in CFA pattern * order. */ unsigned int green = (info->pattern[0] == CFAPatternRed || info->pattern[1] == CFAPatternRed) ? 0 : 1; for (unsigned int i = 0; i < 4; ++i) { unsigned int level; switch (info->pattern[i]) { case CFAPatternRed: level = levels[0]; break; case CFAPatternGreen: level = levels[green + 1]; green = (green + 1) % 2; break; case CFAPatternBlue: default: level = levels[3]; break; } /* Map the 16-bit value to the bits per sample range. */ blackLevel[i] = level >> (16 - info->bitsPerSample); } } TIFFSetField(tif, TIFFTAG_BLACKLEVELREPEATDIM, &blackLevelRepeatDim); TIFFSetField(tif, TIFFTAG_BLACKLEVEL, 4, &blackLevel); TIFFSetField(tif, TIFFTAG_WHITELEVEL, 1, &whiteLevel); /* Write RAW content. */ row = static_cast(data); for (unsigned int y = 0; y < config.size.height; y++) { info->packScanline(&scanline, row, config.size.width); if (TIFFWriteScanline(tif, &scanline, y, 0) != 1) { std::cerr << "Failed to write RAW scanline" << std::endl; TIFFClose(tif); return -EINVAL; } row += config.stride; } /* Checkpoint the IFD to retrieve its offset, and write it out. */ TIFFCheckpointDirectory(tif); rawIFDOffset = TIFFCurrentDirOffset(tif); TIFFWriteDirectory(tif); /* Create a new IFD for the EXIF data and fill it. */ TIFFCreateEXIFDirectory(tif); /* Store creation time. */ time_t rawtime; struct tm *timeinfo; char strTime[20]; time(&rawtime); timeinfo = localtime(&rawtime); strftime(strTime, 20, "%Y:%m:%d %H:%M:%S", timeinfo); /* * \todo Handle timezone information by setting OffsetTimeOriginal and * OffsetTimeDigitized once libtiff catches up to the specification and * has EXIFTAG_ defines to handle them. */ TIFFSetField(tif, EXIFTAG_DATETIMEORIGINAL, strTime); TIFFSetField(tif, EXIFTAG_DATETIMEDIGITIZED, strTime); const auto &analogGain = metadata.get(controls::AnalogueGain); if (analogGain) { uint16_t iso = std::min(std::max(*analogGain * 100, 0.0f), 65535.0f); TIFFSetField(tif, EXIFTAG_ISOSPEEDRATINGS, 1, &iso); } const auto &exposureTime = metadata.get(controls::ExposureTime); if (exposureTime) TIFFSetField(tif, EXIFTAG_EXPOSURETIME, *exposureTime / 1e6); TIFFWriteCustomDirectory(tif, &exifIFDOffset); /* Update the IFD offsets and close the file. */ TIFFSetDirectory(tif, 0); TIFFSetField(tif, TIFFTAG_SUBIFD, 1, &rawIFDOffset); TIFFSetField(tif, TIFFTAG_EXIFIFD, exifIFDOffset); TIFFWriteDirectory(tif); TIFFClose(tif); return 0; } href='#n497'>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

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

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

    """
    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)
        all_images.append(img_br)
        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)
        all_images.append(img_br)
        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
        loop over this and any brightened up images that we made to increase the
        likelihood of success
        """
        for img_br in all_images:
            for i in range(3):
                for j in range(3):
                    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, 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)
        # Again, loop over any brightened up images as well
        for img_br in all_images:
            for i in range(5):
                for j in range(5):
                    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, 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' + str(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:
    - 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
    """

    """
    get reference macbeth chart 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 occred, 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)
                """
                keep only if candidate macbeth is within image border
                (deprecated)
                """
                in_border = True
                # for p in mac_guess[0]:
                #     pptest = cv2.pointPolygonTest(
                #         img_con,
                #         tuple(p),
                #         False
                #     )
                #     if pptest == -1:
                #         in_border = False
                #         break

                if in_border:
                    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)
            # try:
            #     clustering.fit(mac_mids_list)
            # except RuntimeWarning as error:
            #     return(0, None, None, error)

            """
            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
                """
                # print('only 1 cluster')
                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)

        """
        represent most popular cluster centroids
        """
        # copy = original_bw.copy()
        # copy = cv2.cvtColor(copy, cv2.COLOR_GRAY2RGB)
        # copy = cv2.resize(copy, None, fx=2, fy=2)
        # for clus in clus_list:
        #     centroid = tuple(2*np.round(clus[2]).astype(np.int32))
        #     cv2.circle(copy, centroid, 7, (255, 0, 0), -1)
        #     cv2.circle(copy, centroid, 2, (0, 0, 255), -1)
        # represent(copy)

        """
        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 or 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'
            )
            """
            Following code is mostly representation for debugging purposes
            """

        """
        draw macbeth corners and centres on image
        """
        copy = original.copy()
        copy = cv2.resize(original, None, fx=2, fy=2)
        # print('correlation = {}'.format(round(max_cor, 2)))
        for point in best_fit[0]:
            point = np.array(point, np.float32)
            point = tuple(2*np.round(point).astype(np.int32))
            cv2.circle(copy, point, 4, (255, 0, 0), -1)
        for point in best_cen_fit[0]:
            point = np.array(point, np.float32)
            point = tuple(2*np.round(point).astype(np.int32))
            cv2.circle(copy, point, 4, (0, 0, 255), -1)
            copy = copy.copy()
            cv2.circle(copy, point, 4, (0, 0, 255), -1)

        """
        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
        )
        # cv2.imshow('Macbeth', best_map_col)
        # represent(copy)

        """
        rescale coordinates to original image size
        """
        fit_coords = (best_fit/factor, best_cen_fit/factor)

        return(max_cor, best_map_col_norm, fit_coords, success_msg)

        """
    catch macbeth errors and continue with code
    """
    except MacbethError as error:
        return(0, None, None, error)