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# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi Ltd
#
# Container for an image and associated metadata
import binascii
import numpy as np
from pathlib import Path
import pyexiv2 as pyexif
import rawpy as raw
import re
import libtuning as lt
import libtuning.utils as utils
import logging
logger = logging.getLogger(__name__)
class Image:
def __init__(self, path: Path):
self.path = path
self.lsc_only = False
self.color = -1
self.lux = -1
self.macbeth = None
try:
self._load_metadata_exif()
except Exception as e:
logger.error(f'Failed to load metadata from {self.path}: {e}')
raise e
try:
self._read_image_dng()
except Exception as e:
logger.error(f'Failed to load image data from {self.path}: {e}')
raise e
@property
def name(self):
return self.path.name
# May raise KeyError as there are too many to check
def _load_metadata_exif(self):
# RawPy doesn't load all the image tags that we need, so we use py3exiv2
metadata = pyexif.ImageMetadata(str(self.path))
metadata.read()
# 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:
self.w = metadata['Exif.SubImage1.ImageWidth'].value
subimage = 'SubImage1'
photo = 'Photo'
except KeyError:
self.w = metadata['Exif.Image.ImageWidth'].value
subimage = 'Image'
photo = 'Image'
self.pad = 0
self.h = metadata[f'Exif.{subimage}.ImageLength'].value
white = metadata[f'Exif.{subimage}.WhiteLevel'].value
self.sigbits = int(white).bit_length()
self.fmt = (self.sigbits - 4) // 2
self.exposure = int(metadata[f'Exif.{photo}.ExposureTime'].value * 1000000)
self.againQ8 = metadata[f'Exif.{photo}.ISOSpeedRatings'].value * 256 / 100
self.againQ8_norm = self.againQ8 / 256
self.camName = metadata['Exif.Image.Model'].value
self.blacklevel = int(metadata[f'Exif.{subimage}.BlackLevel'].value[0])
self.blacklevel_16 = self.blacklevel << (16 - self.sigbits)
# Channel order depending on bayer pattern
# The key is the order given by exif, where 0 is R, 1 is G, and 2 is B
# The value is the index where the color can be found, where the first
# is R, then G, then G, then B.
bayer_case = {
'0 1 1 2': (lt.Color.R, lt.Color.GR, lt.Color.GB, lt.Color.B),
'1 2 0 1': (lt.Color.GB, lt.Color.B, lt.Color.R, lt.Color.GR),
'2 1 1 0': (lt.Color.B, lt.Color.GB, lt.Color.GR, lt.Color.R),
'1 0 2 1': (lt.Color.GR, lt.Color.R, lt.Color.B, lt.Color.GB)
}
# Note: This needs to be in IFD0
cfa_pattern = metadata[f'Exif.{subimage}.CFAPattern'].value
self.order = bayer_case[cfa_pattern]
def _read_image_dng(self):
raw_im = raw.imread(str(self.path))
raw_data = raw_im.raw_image
shift = 16 - self.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)
self.channels = [c0, c1, c2, c3]
# Reorder the channels into R, GR, GB, B
self.channels = [self.channels[i] for i in self.order]
# \todo Move this to macbeth.py
def get_patches(self, cen_coords, size=16):
saturated = False
# 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):
saturated = True
ch_patches.append(patch)
all_patches.append(ch_patches)
self.patches = np.array(all_patches)
return not saturated
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