23b6965a93
The DNG specification is based on the TIFF file format and recommends storing the raw image data in a SubIFD and the Exif tags in an Exif IFD. Other options are allowed, even if not recommended, such as storing both the raw image data and the Exif data in IFD0, as done by the TIFF/EP specification. libcamera-apps use pyexiv2 to produce DNG files, following the DNG recommendation, while applications based on picamera2 use PiDNG, which adopts the TIFF/EP structure. Why it does so is not currently clear (see https://github.com/schoolpost/PiDNG/issues/65 for discussions on this topic), but as files based on the DNG and TIFF/EP variants exist in the wild, both need to be supported by ctt. Add code to identify which tags are being used, and then load the metadata from the correct tags. Signed-off-by: William Vinnicombe <william.vinnicombe@raspberrypi.com> Reviewed-by: David Plowman <david.plowman@raspberrypi.com> Reviewed-by: Naushir Patuck <naush@raspberrypi.com> Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
454 lines
15 KiB
Python
454 lines
15 KiB
Python
# SPDX-License-Identifier: BSD-2-Clause
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#
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# Copyright (C) 2019-2020, Raspberry Pi Ltd
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#
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# ctt_image_load.py - camera tuning tool image loading
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from ctt_tools import *
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from ctt_macbeth_locator import *
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import json
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import pyexiv2 as pyexif
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import rawpy as raw
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"""
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Image class load image from raw data and extracts metadata.
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Once image is extracted from data, it finds 24 16x16 patches for each
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channel, centred at the macbeth chart squares
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"""
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class Image:
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def __init__(self, buf):
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self.buf = buf
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self.patches = None
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self.saturated = False
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'''
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obtain metadata from buffer
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'''
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def get_meta(self):
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self.ver = ba_to_b(self.buf[4:5])
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self.w = ba_to_b(self.buf[0xd0:0xd2])
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self.h = ba_to_b(self.buf[0xd2:0xd4])
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self.pad = ba_to_b(self.buf[0xd4:0xd6])
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self.fmt = self.buf[0xf5]
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self.sigbits = 2*self.fmt + 4
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self.pattern = self.buf[0xf4]
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self.exposure = ba_to_b(self.buf[0x90:0x94])
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self.againQ8 = ba_to_b(self.buf[0x94:0x96])
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self.againQ8_norm = self.againQ8/256
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camName = self.buf[0x10:0x10+128]
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camName_end = camName.find(0x00)
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self.camName = self.buf[0x10:0x10+128][:camName_end].decode()
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"""
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Channel order depending on bayer pattern
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"""
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bayer_case = {
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0: (0, 1, 2, 3), # red
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1: (2, 0, 3, 1), # green next to red
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2: (3, 2, 1, 0), # green next to blue
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3: (1, 0, 3, 2), # blue
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128: (0, 1, 2, 3) # arbitrary order for greyscale casw
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}
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self.order = bayer_case[self.pattern]
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'''
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manual blacklevel - not robust
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'''
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if 'ov5647' in self.camName:
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self.blacklevel = 16
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else:
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self.blacklevel = 64
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self.blacklevel_16 = self.blacklevel << (6)
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return 1
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'''
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print metadata for debug
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'''
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def print_meta(self):
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print('\nData:')
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print(' ver = {}'.format(self.ver))
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print(' w = {}'.format(self.w))
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print(' h = {}'.format(self.h))
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print(' pad = {}'.format(self.pad))
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print(' fmt = {}'.format(self.fmt))
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print(' sigbits = {}'.format(self.sigbits))
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print(' pattern = {}'.format(self.pattern))
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print(' exposure = {}'.format(self.exposure))
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print(' againQ8 = {}'.format(self.againQ8))
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print(' againQ8_norm = {}'.format(self.againQ8_norm))
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print(' camName = {}'.format(self.camName))
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print(' blacklevel = {}'.format(self.blacklevel))
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print(' blacklevel_16 = {}'.format(self.blacklevel_16))
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return 1
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"""
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get image from raw scanline data
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"""
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def get_image(self, raw):
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self.dptr = []
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"""
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check if data is 10 or 12 bits
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"""
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if self.sigbits == 10:
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"""
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calc length of scanline
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"""
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lin_len = ((((((self.w+self.pad+3)>>2)) * 5)+31)>>5) * 32
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"""
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stack scan lines into matrix
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"""
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raw = np.array(raw).reshape(-1, lin_len).astype(np.int64)[:self.h, ...]
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"""
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separate 5 bits in each package, stopping when w is satisfied
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"""
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ba0 = raw[..., 0:5*((self.w+3)>>2):5]
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ba1 = raw[..., 1:5*((self.w+3)>>2):5]
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ba2 = raw[..., 2:5*((self.w+3)>>2):5]
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ba3 = raw[..., 3:5*((self.w+3)>>2):5]
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ba4 = raw[..., 4:5*((self.w+3)>>2):5]
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"""
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assemble 10 bit numbers
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"""
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ch0 = np.left_shift((np.left_shift(ba0, 2) + (ba4 % 4)), 6)
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ch1 = np.left_shift((np.left_shift(ba1, 2) + (np.right_shift(ba4, 2) % 4)), 6)
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ch2 = np.left_shift((np.left_shift(ba2, 2) + (np.right_shift(ba4, 4) % 4)), 6)
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ch3 = np.left_shift((np.left_shift(ba3, 2) + (np.right_shift(ba4, 6) % 4)), 6)
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"""
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interleave bits
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"""
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mat = np.empty((self.h, self.w), dtype=ch0.dtype)
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mat[..., 0::4] = ch0
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mat[..., 1::4] = ch1
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mat[..., 2::4] = ch2
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mat[..., 3::4] = ch3
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"""
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There is som eleaking memory somewhere in the code. This code here
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seemed to make things good enough that the code would run for
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reasonable numbers of images, however this is techincally just a
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workaround. (sorry)
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"""
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ba0, ba1, ba2, ba3, ba4 = None, None, None, None, None
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del ba0, ba1, ba2, ba3, ba4
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ch0, ch1, ch2, ch3 = None, None, None, None
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del ch0, ch1, ch2, ch3
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"""
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same as before but 12 bit case
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"""
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elif self.sigbits == 12:
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lin_len = ((((((self.w+self.pad+1)>>1)) * 3)+31)>>5) * 32
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raw = np.array(raw).reshape(-1, lin_len).astype(np.int64)[:self.h, ...]
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ba0 = raw[..., 0:3*((self.w+1)>>1):3]
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ba1 = raw[..., 1:3*((self.w+1)>>1):3]
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ba2 = raw[..., 2:3*((self.w+1)>>1):3]
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ch0 = np.left_shift((np.left_shift(ba0, 4) + ba2 % 16), 4)
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ch1 = np.left_shift((np.left_shift(ba1, 4) + (np.right_shift(ba2, 4)) % 16), 4)
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mat = np.empty((self.h, self.w), dtype=ch0.dtype)
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mat[..., 0::2] = ch0
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mat[..., 1::2] = ch1
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else:
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"""
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data is neither 10 nor 12 or incorrect data
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"""
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print('ERROR: wrong bit format, only 10 or 12 bit supported')
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return 0
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"""
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separate bayer channels
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"""
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c0 = mat[0::2, 0::2]
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c1 = mat[0::2, 1::2]
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c2 = mat[1::2, 0::2]
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c3 = mat[1::2, 1::2]
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self.channels = [c0, c1, c2, c3]
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return 1
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"""
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obtain 16x16 patch centred at macbeth square centre for each channel
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"""
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def get_patches(self, cen_coords, size=16):
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"""
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obtain channel widths and heights
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"""
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ch_w, ch_h = self.w, self.h
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cen_coords = list(np.array((cen_coords[0])).astype(np.int32))
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self.cen_coords = cen_coords
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"""
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squares are ordered by stacking macbeth chart columns from
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left to right. Some useful patch indices:
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white = 3
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black = 23
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'reds' = 9, 10
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'blues' = 2, 5, 8, 20, 22
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'greens' = 6, 12, 17
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greyscale = 3, 7, 11, 15, 19, 23
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"""
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all_patches = []
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for ch in self.channels:
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ch_patches = []
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for cen in cen_coords:
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'''
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macbeth centre is placed at top left of central 2x2 patch
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to account for rounding
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Patch pixels are sorted by pixel brightness so spatial
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information is lost.
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'''
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patch = ch[cen[1]-7:cen[1]+9, cen[0]-7:cen[0]+9].flatten()
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patch.sort()
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if patch[-5] == (2**self.sigbits-1)*2**(16-self.sigbits):
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self.saturated = True
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ch_patches.append(patch)
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# print('\nNew Patch\n')
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all_patches.append(ch_patches)
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# print('\n\nNew Channel\n\n')
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self.patches = all_patches
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return 1
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def brcm_load_image(Cam, im_str):
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"""
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Load image where raw data and metadata is in the BRCM format
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"""
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try:
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"""
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create byte array
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"""
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with open(im_str, 'rb') as image:
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f = image.read()
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b = bytearray(f)
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"""
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return error if incorrect image address
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"""
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except FileNotFoundError:
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print('\nERROR:\nInvalid image address')
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Cam.log += '\nWARNING: Invalid image address'
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return 0
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"""
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return error if problem reading file
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"""
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if f is None:
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print('\nERROR:\nProblem reading file')
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Cam.log += '\nWARNING: Problem readin file'
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return 0
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# print('\nLooking for EOI and BRCM header')
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"""
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find end of image followed by BRCM header by turning
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bytearray into hex string and string matching with regexp
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"""
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start = -1
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match = bytearray(b'\xff\xd9@BRCM')
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match_str = binascii.hexlify(match)
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b_str = binascii.hexlify(b)
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"""
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note index is divided by two to go from string to hex
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"""
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indices = [m.start()//2 for m in re.finditer(match_str, b_str)]
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# print(indices)
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try:
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start = indices[0] + 3
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except IndexError:
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print('\nERROR:\nNo Broadcom header found')
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Cam.log += '\nWARNING: No Broadcom header found!'
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return 0
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"""
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extract data after header
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"""
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# print('\nExtracting data after header')
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buf = b[start:start+32768]
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Img = Image(buf)
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Img.str = im_str
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# print('Data found successfully')
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"""
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obtain metadata
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"""
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# print('\nReading metadata')
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Img.get_meta()
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Cam.log += '\nExposure : {} us'.format(Img.exposure)
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Cam.log += '\nNormalised gain : {}'.format(Img.againQ8_norm)
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# print('Metadata read successfully')
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"""
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obtain raw image data
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"""
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# print('\nObtaining raw image data')
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raw = b[start+32768:]
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Img.get_image(raw)
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"""
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delete raw to stop memory errors
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"""
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raw = None
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del raw
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# print('Raw image data obtained successfully')
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return Img
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def dng_load_image(Cam, im_str):
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try:
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Img = Image(None)
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# RawPy doesn't load all the image tags that we need, so we use py3exiv2
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metadata = pyexif.ImageMetadata(im_str)
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metadata.read()
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Img.ver = 100 # random value
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"""
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The DNG and TIFF/EP specifications use different IFDs to store the raw
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image data and the Exif tags. DNG stores them in a SubIFD and in an Exif
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IFD respectively (named "SubImage1" and "Photo" by pyexiv2), while
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TIFF/EP stores them both in IFD0 (name "Image"). Both are used in "DNG"
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files, with libcamera-apps following the DNG recommendation and
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applications based on picamera2 following TIFF/EP.
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This code detects which tags are being used, and therefore extracts the
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correct values.
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"""
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try:
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Img.w = metadata['Exif.SubImage1.ImageWidth'].value
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subimage = "SubImage1"
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photo = "Photo"
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except KeyError:
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Img.w = metadata['Exif.Image.ImageWidth'].value
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subimage = "Image"
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photo = "Image"
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Img.pad = 0
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Img.h = metadata[f'Exif.{subimage}.ImageLength'].value
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white = metadata[f'Exif.{subimage}.WhiteLevel'].value
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Img.sigbits = int(white).bit_length()
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Img.fmt = (Img.sigbits - 4) // 2
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Img.exposure = int(metadata[f'Exif.{photo}.ExposureTime'].value * 1000000)
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Img.againQ8 = metadata[f'Exif.{photo}.ISOSpeedRatings'].value * 256 / 100
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Img.againQ8_norm = Img.againQ8 / 256
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Img.camName = metadata['Exif.Image.Model'].value
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Img.blacklevel = int(metadata[f'Exif.{subimage}.BlackLevel'].value[0])
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Img.blacklevel_16 = Img.blacklevel << (16 - Img.sigbits)
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bayer_case = {
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'0 1 1 2': (0, (0, 1, 2, 3)),
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'1 2 0 1': (1, (2, 0, 3, 1)),
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'2 1 1 0': (2, (3, 2, 1, 0)),
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'1 0 2 1': (3, (1, 0, 3, 2))
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}
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cfa_pattern = metadata[f'Exif.{subimage}.CFAPattern'].value
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Img.pattern = bayer_case[cfa_pattern][0]
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Img.order = bayer_case[cfa_pattern][1]
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# Now use RawPy tp get the raw Bayer pixels
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raw_im = raw.imread(im_str)
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raw_data = raw_im.raw_image
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shift = 16 - Img.sigbits
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c0 = np.left_shift(raw_data[0::2, 0::2].astype(np.int64), shift)
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c1 = np.left_shift(raw_data[0::2, 1::2].astype(np.int64), shift)
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c2 = np.left_shift(raw_data[1::2, 0::2].astype(np.int64), shift)
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c3 = np.left_shift(raw_data[1::2, 1::2].astype(np.int64), shift)
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Img.channels = [c0, c1, c2, c3]
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except Exception:
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print("\nERROR: failed to load DNG file", im_str)
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print("Either file does not exist or is incompatible")
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Cam.log += '\nERROR: DNG file does not exist or is incompatible'
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raise
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return Img
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'''
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load image from file location and perform calibration
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check correct filetype
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mac boolean is true if image is expected to contain macbeth chart and false
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if not (alsc images don't have macbeth charts)
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'''
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def load_image(Cam, im_str, mac_config=None, show=False, mac=True, show_meta=False):
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"""
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check image is correct filetype
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"""
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if '.jpg' in im_str or '.jpeg' in im_str or '.brcm' in im_str or '.dng' in im_str:
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if '.dng' in im_str:
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Img = dng_load_image(Cam, im_str)
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else:
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Img = brcm_load_image(Cam, im_str)
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"""
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handle errors smoothly if loading image failed
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"""
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if Img == 0:
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return 0
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if show_meta:
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Img.print_meta()
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if mac:
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"""
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find macbeth centres, discarding images that are too dark or light
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"""
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av_chan = (np.mean(np.array(Img.channels), axis=0)/(2**16))
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av_val = np.mean(av_chan)
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# print(av_val)
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if av_val < Img.blacklevel_16/(2**16)+1/64:
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macbeth = None
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print('\nError: Image too dark!')
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Cam.log += '\nWARNING: Image too dark!'
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else:
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macbeth = find_macbeth(Cam, av_chan, mac_config)
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"""
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if no macbeth found return error
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"""
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if macbeth is None:
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print('\nERROR: No macbeth chart found')
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return 0
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mac_cen_coords = macbeth[1]
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# print('\nMacbeth centres located successfully')
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"""
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obtain image patches
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"""
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# print('\nObtaining image patches')
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Img.get_patches(mac_cen_coords)
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if Img.saturated:
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print('\nERROR: Macbeth patches have saturated')
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Cam.log += '\nWARNING: Macbeth patches have saturated!'
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return 0
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"""
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clear memory
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"""
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Img.buf = None
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del Img.buf
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# print('Image patches obtained successfully')
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"""
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optional debug
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"""
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if show and __name__ == '__main__':
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copy = sum(Img.channels)/2**18
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copy = np.reshape(copy, (Img.h//2, Img.w//2)).astype(np.float64)
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copy, _ = reshape(copy, 800)
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represent(copy)
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return Img
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"""
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return error if incorrect filetype
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"""
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else:
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# print('\nERROR:\nInvalid file extension')
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return 0
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"""
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bytearray splice to number little endian
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"""
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def ba_to_b(b):
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total = 0
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for i in range(len(b)):
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total += 256**i * b[i]
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return total
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