utils/raspberrypi/ctt/ctt_image_load.py


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.. SPDX-License-Identifier: CC-BY-SA-4.0

==========
 Licenses
==========

TL;DR summary: The libcamera core is covered by the LGPL-2.1-or-later license.
IPA modules included in libcamera are covered by a free software license.
Third-parties may develop IPA modules outside of libcamera and distribute them
under a closed-source license, provided they do not include source code from
the libcamera project.

The libcamera project contains multiple libraries, applications and utilities.
Licenses are expressed through SPDX tags in text-based files that support
comments, and through the .reuse/dep5 file otherwise. A copy of all licenses is
stored in the LICENSES directory.

The following text summarizes the licenses covering the different components of
the project to offer a quick overview for developers. The SPDX and DEP5
information are however authoritative and shall prevail in case of
inconsistencies with the text below.

The libcamera core source code, located under the include/libcamera/ and
src/libcamera/ directories, is fully covered by the LGPL-2.1-or-later license,
which thus covers distribution of the libcamera.so binary. Other files located
in those directories, most notably the meson build files, and various related
build scripts, may be covered by different licenses. None of their source code
is incorporated in the in the libcamera.so binary, they thus don't affect the
distribution terms of the binary.

The IPA modules, located in src/ipa/, are covered by free software licenses
chosen by the module authors. The LGPL-2.1-or-later license is recommended.
Those modules are compiled as separate binaries and dynamically loaded by the
libcamera core at runtime.

The IPA module API is defined in headers located in include/libcamera/ipa/ and
covered by the LGPL-2.1-or-later license. Using the data types (including
classes, structures and enumerations) and macros defined in the IPA module and
libcamera core API headers in IPA modules doesn't extend the LGPL license to
the IPA modules. Third-party closed-source IPA modules are thus permitted,
provided they comply with the licensing requirements of any software they
include or link to.

The libcamera Android camera HAL component is located in src/android/. The
libcamera-specific source code is covered by the LGPL-2.1-or-later license. The
component additionally contains header files and source code, located
respectively in include/android/ and src/android/metadata/, copied verbatim
from Android and covered by the Apache-2.0 license.

The libcamera GStreamer and V4L2 adaptation source code, located respectively
in src/gstreamer/ and src/v4l2/, is fully covered by the LGPL-2.1-or-later
license. Those components are compiled to separate binaries and do not
influence the license of the libcamera core.

The cam and qcam sample applications, as well as the unit tests, located
respectively in src/cam/, src/qcam/ and test/, are covered by the
GPL-2.0-or-later license. qcam additionally includes an icon set covered by the
MIT license. Those applications are compiled to separate binaries and do not
influence the license of the libcamera core.

Additional utilities are located in the utils/ directory and are covered by
various licenses. They are not part of the libcamera core and do not influence
its license.

Finally, copies of various Linux kernel headers are included in include/linux/
to avoid depending on particular versions of those headers being installed in
the system. The Linux kernel headers are covered by their respective license,
including the Linux kernel license syscall exception. Using a copy of those
headers doesn't affect libcamera licensing terms in any way compared to using
the same headers installed in the system from kernel headers packages provided
by Linux distributions.
# 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