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-rw-r--r--utils/tuning/README.rst11
-rw-r--r--utils/tuning/libtuning/__init__.py13
-rw-r--r--utils/tuning/libtuning/image.py136
-rw-r--r--utils/tuning/libtuning/libtuning.py208
-rw-r--r--utils/tuning/libtuning/macbeth.py516
-rw-r--r--utils/tuning/libtuning/macbeth_ref.pgm6
-rw-r--r--utils/tuning/libtuning/utils.py125
7 files changed, 1015 insertions, 0 deletions
diff --git a/utils/tuning/README.rst b/utils/tuning/README.rst
new file mode 100644
index 00000000..ce533b2c
--- /dev/null
+++ b/utils/tuning/README.rst
@@ -0,0 +1,11 @@
+.. SPDX-License-Identifier: CC-BY-SA-4.0
+
+.. TODO: Write an overview of libtuning
+
+Dependencies
+------------
+
+- cv2
+- numpy
+- pyexiv2
+- rawpy
diff --git a/utils/tuning/libtuning/__init__.py b/utils/tuning/libtuning/__init__.py
new file mode 100644
index 00000000..93049976
--- /dev/null
+++ b/utils/tuning/libtuning/__init__.py
@@ -0,0 +1,13 @@
+# SPDX-License-Identifier: GPL-2.0-or-later
+#
+# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
+
+from libtuning.utils import *
+from libtuning.libtuning import *
+
+from libtuning.image import *
+from libtuning.macbeth import *
+
+from libtuning.average import *
+from libtuning.gradient import *
+from libtuning.smoothing import *
diff --git a/utils/tuning/libtuning/image.py b/utils/tuning/libtuning/image.py
new file mode 100644
index 00000000..aa9d20b5
--- /dev/null
+++ b/utils/tuning/libtuning/image.py
@@ -0,0 +1,136 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi Ltd
+#
+# image.py - 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
+
+
+class Image:
+ def __init__(self, path: Path):
+ self.path = path
+ self.lsc_only = False
+ self.color = -1
+ self.lux = -1
+
+ try:
+ self._load_metadata_exif()
+ except Exception as e:
+ utils.eprint(f'Failed to load metadata from {self.path}: {e}')
+ raise e
+
+ try:
+ self._read_image_dng()
+ except Exception as e:
+ utils.eprint(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.R, lt.Color.B, 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 = all_patches
+
+ return not saturated
diff --git a/utils/tuning/libtuning/libtuning.py b/utils/tuning/libtuning/libtuning.py
new file mode 100644
index 00000000..d84c148f
--- /dev/null
+++ b/utils/tuning/libtuning/libtuning.py
@@ -0,0 +1,208 @@
+# SPDX-License-Identifier: GPL-2.0-or-later
+#
+# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
+#
+# libtuning.py - An infrastructure for camera tuning tools
+
+import argparse
+
+import libtuning as lt
+import libtuning.utils as utils
+from libtuning.utils import eprint
+
+from enum import Enum, IntEnum
+
+
+class Color(IntEnum):
+ R = 0
+ GR = 1
+ GB = 2
+ B = 3
+
+
+class Debug(Enum):
+ Plot = 1
+
+
+# @brief What to do with the leftover pixels after dividing them into ALSC
+# sectors, when the division gradient is uniform
+# @var Float Force floating point division so all sectors divide equally
+# @var DistributeFront Divide the remainder equally (until running out,
+# obviously) into the existing sectors, starting from the front
+# @var DistributeBack Same as DistributeFront but starting from the back
+class Remainder(Enum):
+ Float = 0
+ DistributeFront = 1
+ DistributeBack = 2
+
+
+# @brief A helper class to contain a default value for a module configuration
+# parameter
+class Param(object):
+ # @var Required The value contained in this instance is irrelevant, and the
+ # value must be provided by the tuning configuration file.
+ # @var Optional If the value is not provided by the tuning configuration
+ # file, then the value contained in this instance will be used instead.
+ # @var Hardcode The value contained in this instance will be used
+ class Mode(Enum):
+ Required = 0
+ Optional = 1
+ Hardcode = 2
+
+ # @param name Name of the parameter. Shall match the name used in the
+ # configuration file for the parameter
+ # @param required Whether or not a value is required in the config
+ # parameter of get_value()
+ # @param val Default value (only relevant if mode is Optional)
+ def __init__(self, name: str, required: Mode, val=None):
+ self.name = name
+ self.__required = required
+ self.val = val
+
+ def get_value(self, config: dict):
+ if self.__required is self.Mode.Hardcode:
+ return self.val
+
+ if self.__required is self.Mode.Required and self.name not in config:
+ raise ValueError(f'Parameter {self.name} is required but not provided in the configuration')
+
+ return config[self.name] if self.required else self.val
+
+ @property
+ def required(self):
+ return self.__required is self.Mode.Required
+
+ # @brief Used by libtuning to auto-generate help information for the tuning
+ # script on the available parameters for the configuration file
+ # \todo Implement this
+ @property
+ def info(self):
+ raise NotImplementedError
+
+
+class Tuner(object):
+
+ # External functions
+
+ def __init__(self, platform_name):
+ self.name = platform_name
+ self.modules = []
+ self.parser = None
+ self.generator = None
+ self.output_order = []
+ self.config = {}
+ self.output = {}
+
+ def add(self, module):
+ self.modules.append(module)
+
+ def set_input_parser(self, parser):
+ self.parser = parser
+
+ def set_output_formatter(self, output):
+ self.generator = output
+
+ def set_output_order(self, modules):
+ self.output_order = modules
+
+ # @brief Convert classes in self.output_order to the instances in self.modules
+ def _prepare_output_order(self):
+ output_order = self.output_order
+ self.output_order = []
+ for module_type in output_order:
+ modules = [module for module in self.modules if module.type == module_type.type]
+ if len(modules) > 1:
+ eprint(f'Multiple modules found for module type "{module_type.type}"')
+ return False
+ if len(modules) < 1:
+ eprint(f'No module found for module type "{module_type.type}"')
+ return False
+ self.output_order.append(modules[0])
+
+ return True
+
+ # \todo Validate parser and generator at Tuner construction time?
+ def _validate_settings(self):
+ if self.parser is None:
+ eprint('Missing parser')
+ return False
+
+ if self.generator is None:
+ eprint('Missing generator')
+ return False
+
+ if len(self.modules) == 0:
+ eprint('No modules added')
+ return False
+
+ if len(self.output_order) != len(self.modules):
+ eprint('Number of outputs does not match number of modules')
+ return False
+
+ return True
+
+ def _process_args(self, argv, platform_name):
+ parser = argparse.ArgumentParser(description=f'Camera Tuning for {platform_name}')
+ parser.add_argument('-i', '--input', type=str, required=True,
+ help='''Directory containing calibration images (required).
+ Images for ALSC must be named "alsc_{Color Temperature}k_1[u].dng",
+ and all other images must be named "{Color Temperature}k_{Lux Level}l.dng"''')
+ parser.add_argument('-o', '--output', type=str, required=True,
+ help='Output file (required)')
+ # It is not our duty to scan all modules to figure out their default
+ # options, so simply return an empty configuration if none is provided.
+ parser.add_argument('-c', '--config', type=str, default='',
+ help='Config file (optional)')
+ # \todo Check if we really need this or if stderr is good enough, or if
+ # we want a better logging infrastructure with log levels
+ parser.add_argument('-l', '--log', type=str, default=None,
+ help='Output log file (optional)')
+ return parser.parse_args(argv[1:])
+
+ def run(self, argv):
+ args = self._process_args(argv, self.name)
+ if args is None:
+ return -1
+
+ if not self._validate_settings():
+ return -1
+
+ if not self._prepare_output_order():
+ return -1
+
+ if len(args.config) > 0:
+ self.config, disable = self.parser.parse(args.config, self.modules)
+ else:
+ self.config = {'general': {}}
+ disable = []
+
+ # Remove disabled modules
+ for module in disable:
+ if module in self.modules:
+ self.modules.remove(module)
+
+ for module in self.modules:
+ if not module.validate_config(self.config):
+ eprint(f'Config is invalid for module {module.type}')
+ return -1
+
+ has_lsc = any(isinstance(m, lt.modules.lsc.LSC) for m in self.modules)
+ # Only one LSC module allowed
+ has_only_lsc = has_lsc and len(self.modules) == 1
+
+ images = utils.load_images(args.input, self.config, not has_only_lsc, has_lsc)
+ if images is None or len(images) == 0:
+ eprint(f'No images were found, or able to load')
+ return -1
+
+ # Do the tuning
+ for module in self.modules:
+ out = module.process(self.config, images, self.output)
+ if out is None:
+ eprint(f'Module {module.name} failed to process, aborting')
+ break
+ self.output[module] = out
+
+ self.generator.write(args.output, self.output, self.output_order)
+
+ return 0
diff --git a/utils/tuning/libtuning/macbeth.py b/utils/tuning/libtuning/macbeth.py
new file mode 100644
index 00000000..5faddf66
--- /dev/null
+++ b/utils/tuning/libtuning/macbeth.py
@@ -0,0 +1,516 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi Ltd
+#
+# macbeth.py - Locate and extract Macbeth charts from images
+# (Copied from: ctt_macbeth_locator.py)
+
+# \todo Add debugging
+
+import cv2
+import os
+from pathlib import Path
+import numpy as np
+
+from libtuning.image import Image
+
+
+# Reshape image to fixed width without distorting returns image and scale
+# factor
+def reshape(img, width):
+ factor = width / img.shape[0]
+ return cv2.resize(img, None, fx=factor, fy=factor), factor
+
+
+# Correlation function to quantify match
+def correlate(im1, im2):
+ f1 = im1.flatten()
+ f2 = im2.flatten()
+ cor = np.corrcoef(f1, f2)
+ return cor[0][1]
+
+
+# @brief Compute coordinates of macbeth chart vertices and square centres
+# @return (max_cor, best_map_col_norm, fit_coords, success)
+#
+# Also returns 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
+#
+# \todo Clean this up
+def get_macbeth_chart(img, ref_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 occured, 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)
+
+ 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)
+
+ # 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
+ 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)
+
+ # 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 of 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'
+ )
+
+ # 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
+ )
+
+ # Rescale coordinates to original image size
+ fit_coords = (best_fit / factor, best_cen_fit / factor)
+
+ return (max_cor, best_map_col_norm, fit_coords, True)
+
+ # Catch macbeth errors and continue with code
+ except MacbethError as error:
+ eprint(error)
+ return (0, None, None, False)
+
+
+def find_macbeth(img, mac_config):
+ small_chart = mac_config['small']
+ show = mac_config['show']
+
+ # Catch the warnings
+ warnings.simplefilter("ignore")
+ warnings.warn("runtime", RuntimeWarning)
+
+ # Reference macbeth chart is created that will be correlated with the
+ # located macbeth chart guess to produce a confidence value for the match.
+ script_dir = Path(os.path.realpath(os.path.dirname(__file__)))
+ macbeth_ref_path = script_dir.joinpath('macbeth_ref.pgm')
+ ref = cv2.imread(str(macbeth_ref_path), 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, ret = get_macbeth_chart(img, ref_data)
+
+ # Following bits of code try 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.
+
+ for brightness in [2, 4]:
+ if cor >= 0.75:
+ break
+ img_br = cv2.convertScaleAbs(img, alpha=brightness, beta=0)
+ cor_b, mac_b, coords_b, ret_b = get_macbeth_chart(img_br, ref_data)
+ if cor_b > cor:
+ cor, mac, coords, ret = cor_b, mac_b, coords_b, ret_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
+
+ for index, pair in enumerate([{'sel': 2 / 3, 'inc': 1 / 6},
+ {'sel': 1 / 2, 'inc': 1 / 8},
+ {'sel': 1 / 3, 'inc': 1 / 12},
+ {'sel': 1 / 4, 'inc': 1 / 16}]):
+ if cor >= 0.75:
+ break
+
+ # Check if we need to check 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 index >= 2 and (not small_chart or d_best <= index - 1):
+ break
+
+ w, h = list(img.shape[:2])
+ # Set dimensions of the subselection and the step along each axis
+ # between selections
+ w_sel = int(w * pair['sel'])
+ h_sel = int(h * pair['sel'])
+ w_inc = int(w * pair['inc'])
+ h_inc = int(h * pair['inc'])
+
+ loop = ((1 - pair['sel']) / pair['inc']) + 1
+ # For each subselection, look for a macbeth chart
+ for i in range(loop):
+ for j in range(loop):
+ 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, ret_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, ret = mac_ij, coords_ij, ret_ij
+ ii, jj = i, j
+ w_best, h_best = w_inc, h_inc
+ d_best = index + 1
+
+ # 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
+
+ if not ret:
+ return None
+
+ coords_fit = coords
+ if cor < 0.75:
+ eprint(f'Warning: Low confidence {cor:.3f} for macbeth chart in {img.path.name}')
+
+ if show:
+ draw_macbeth_results(img, coords_fit)
+
+ return coords_fit
+
+
+def locate_macbeth(image: Image, config: dict):
+ # Find macbeth centres
+ av_chan = (np.mean(np.array(image.channels), axis=0) / (2**16))
+ av_val = np.mean(av_chan)
+ if av_val < image.blacklevel_16 / (2**16) + 1 / 64:
+ eprint(f'Image {image.path.name} too dark')
+ return None
+
+ macbeth = find_macbeth(av_chan, config['general']['macbeth'])
+
+ if macbeth is None:
+ eprint(f'No macbeth chart found in {image.path.name}')
+ return None
+
+ mac_cen_coords = macbeth[1]
+ if not image.get_patches(mac_cen_coords):
+ eprint(f'Macbeth patches have saturated in {image.path.name}')
+ return None
+
+ return macbeth
diff --git a/utils/tuning/libtuning/macbeth_ref.pgm b/utils/tuning/libtuning/macbeth_ref.pgm
new file mode 100644
index 00000000..37897140
--- /dev/null
+++ b/utils/tuning/libtuning/macbeth_ref.pgm
@@ -0,0 +1,6 @@
+# SPDX-License-Identifier: BSD-2-Clause
+P5
+# Reference macbeth chart
+120 80
+255
+  !#!" #!"&&$#$#'"%&#+2///..../.........-()))))))))))))))))))(((-,*)'(&)#($%(%"###""!%""&"&&!$" #!$ !"! $&**" !#5.,%+,-5"0<HBAA54" %##((()*+,---.........+*)))))))))))))))-.,,--+))('((''('%'%##"!""!"!""""#!   ! %�/v��z:����L�����c�,!#""%%''')**+)-../..../.-*)))))))))))))**,,)**'(''&'((&&%%##$! !!!! ! !  !  5*"-)&7(1.75Rnge`\`$ ""!"%%%'')())++--/---,-..,-.,++**))))())*)*)''%'%&%&'&%%"""""        !  !!$&$$&##(+*,,/10122126545./66402006486869650*.1.***)*+)()&((('('##)('&%%&%$$$#$%$%$ (((*))('((('('(&%V0;>>;@@>@AAAACBCB=&<�����������������<5x���������������|64RYVTSRRRMMNLKJJLH+&0gijgdeffmmnpnkji`#3����������������bY! 3FHHIIIHIJIIJHIII@#?�����������������=7}����������������:5Wcbcbdcb`^^`^^_^Y,'6����������������r'<����������������l%2FHHIIHJJJJJJIIJI?%;�����������������>7|����������������;8Xfeeegeccb`^aba]Z+)<����������������r)>����������������q#3GHIIIIJIIJJIHIJI@&5�����������������=8~����������������;8Zgghggedbdcbda^\Z+(;����������������y)9����������������z"3GIIJJJJJKJJJJJJJ@'4�����������������>9|����������������=8Zhighgeeeedeca__[/)B����������������v&:����������������|#3GJJIIJKKKJJJKKJK@&6�����������������>9~����������������<8Yghegggffihccab^\/*C����������������z'9�����������������$  6IKJJMMMKMKKMKKMLC&2�����������������@9�����������������<9Yghhhhijiegdcebc^0)G����������������(7�����������������% 6JLMMNMMKMMNMMMMMD&2�����������������@:~����������������=9Xfghhjiigdgddedc`1)M����������������}(:��������������¾�& "8LNOONNOMONNMMNOND'3�����������������@;����������������=:Ziiigheegegegggdc1,Q����������������~)8�����������������%# 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diff --git a/utils/tuning/libtuning/utils.py b/utils/tuning/libtuning/utils.py
new file mode 100644
index 00000000..b60f2c9b
--- /dev/null
+++ b/utils/tuning/libtuning/utils.py
@@ -0,0 +1,125 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi Ltd
+# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
+#
+# utils.py - Utilities for libtuning
+
+import decimal
+import math
+import numpy as np
+import os
+from pathlib import Path
+import re
+import sys
+
+import libtuning as lt
+from libtuning.image import Image
+from libtuning.macbeth import locate_macbeth
+
+# Utility functions
+
+
+def eprint(*args, **kwargs):
+ print(*args, file=sys.stderr, **kwargs)
+
+
+def get_module_by_type_name(modules, name):
+ for module in modules:
+ if module.type == name:
+ return module
+ return None
+
+
+# Private utility functions
+
+
+def _list_image_files(directory):
+ d = Path(directory)
+ files = [d.joinpath(f) for f in os.listdir(d)
+ if re.search(r'\.(jp[e]g$)|(dng$)', f)]
+ files.sort()
+ return files
+
+
+def _parse_image_filename(fn: Path):
+ result = re.search(r'^(alsc_)?(\d+)[kK]_(\d+)?[lLuU]?.\w{3,4}$', fn.name)
+ if result is None:
+ eprint(f'The file name of {fn.name} is incorrectly formatted')
+ return None, None, None
+
+ color = int(result.group(2))
+ lsc_only = result.group(1) is not None
+ lux = None if lsc_only else int(result.group(3))
+
+ return color, lux, lsc_only
+
+
+# \todo Implement this from check_imgs() in ctt.py
+def _validate_images(images):
+ return True
+
+
+# Public utility functions
+
+
+# @brief Load images into a single list of Image instances
+# @param input_dir Directory from which to load image files
+# @param config Configuration dictionary
+# @param load_nonlsc Whether or not to load non-lsc images
+# @param load_lsc Whether or not to load lsc-only images
+# @return A list of Image instances
+def load_images(input_dir: str, config: dict, load_nonlsc: bool, load_lsc: bool) -> list:
+ files = _list_image_files(input_dir)
+ if len(files) == 0:
+ eprint(f'No images found in {input_dir}')
+ return None
+
+ images = []
+ for f in files:
+ color, lux, lsc_only = _parse_image_filename(f)
+ if color is None:
+ continue
+
+ # Skip lsc image if we don't need it
+ if lsc_only and not load_lsc:
+ eprint(f'Skipping {f.name} as this tuner has no LSC module')
+ continue
+
+ # Skip non-lsc image if we don't need it
+ if not lsc_only and not load_nonlsc:
+ eprint(f'Skipping {f.name} as this tuner only has an LSC module')
+ continue
+
+ # Load image
+ try:
+ image = Image(f)
+ except Exception as e:
+ eprint(f'Failed to load image {f.name}: {e}')
+ continue
+
+ # Populate simple fields
+ image.lsc_only = lsc_only
+ image.color = color
+ image.lux = lux
+
+ # Black level comes from the TIFF tags, but they are overridable by the
+ # config file.
+ if 'blacklevel' in config['general']:
+ image.blacklevel_16 = config['general']['blacklevel']
+
+ if lsc_only:
+ images.append(image)
+ continue
+
+ # Handle macbeth
+ macbeth = locate_macbeth(config)
+ if macbeth is None:
+ continue
+
+ images.append(image)
+
+ if not _validate_images(images):
+ return None
+
+ return images