| Message ID | 20221124113550.2182342-1-paul.elder@ideasonboard.com |
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Hi Paul, Thank you for the patch. On Thu, Nov 24, 2022 at 08:35:39PM +0900, Paul Elder via libcamera-devel wrote: > Implement the core of libtuning, our new tuning tool infrastructure. It > leverages components from raspberrypi's ctt that could be reused for > tuning tools for other platforms. > > The core components include: > - The Image class > - libtuning (entry point and other core functions) > - macbeth-related tools, including the macbeth reference image > - utils > > Signed-off-by: Paul Elder <paul.elder@ideasonboard.com> > > --- > Changes in v4: > - change Image.name to property > - fix saturated logic in get_patches > - move lsc vs non-lsc image loading decision from utils to Tuner > - this means we have to import libtuning in libtuning.py... is this > fine? > - remove raspberry pi's special rounding function, as it has worse > rounding accuracy than simple rounding > - remove cli args from module.process parameters > > Changes in v3: > - *Split into separate patches* > - The following changes apply to the next two patches as well > - fix style > - rename Camera to Tuner > - remove indirection from fake polymorphism > - remove unused options property from Module > - remove unimplemented gradients > - convert readme to rst > - fix readme license > - reorder dependencies list > - add file descriptions > - remove indirection from Image loading > - remove Image member variables that are unused due to dropping BRCM > support > - remove G from Color enum > - Color was /not/ renamed to BayerComponent because it was much too > long for use in code > - add @property getters to Param > - fix undefined functions/variables > > Changes in v2: > - fix all python errors > - fix style > - add SPDX and copyright > - remove validateConfig() from the base/abstract Module class > - actually append the image after loading, even if it's alsc_only > - s/average_functions/average/ > - remove separate params field for Average and Smoothing > - move remainder parameter in Gradient to Linear, as it only applies to > that > - from gradient.Linear, remove the remainders that I thought don't make > sense > - add Float to gradient.Linear's remainder types, to divide everything > in as a float; useful for rkisp1's sector sizes (the x-size and y-size > tuning options) > - add a map function to Gradient, for mapping values onto a curve > - in Smoothing, move ksize to a constructor parameter > - remove brcm image loading > - move process_args from utils to libtuning > - move Module's type string and human-readble module name to class > variable > - move locate_macbeth from utils to macbeth > - add out_name to Module, for the output to know what name to write for > the key in the tuning output (eg. rkisp1 uses "LensShadingCorrection" > while raspberrypi uses "rpi.alsc") > --- > utils/tuning/README.rst | 11 + > utils/tuning/libtuning/__init__.py | 13 + > utils/tuning/libtuning/image.py | 136 +++++++ > utils/tuning/libtuning/libtuning.py | 208 ++++++++++ > utils/tuning/libtuning/macbeth.py | 516 +++++++++++++++++++++++++ > utils/tuning/libtuning/macbeth_ref.pgm | 6 + > utils/tuning/libtuning/utils.py | 125 ++++++ > 7 files changed, 1015 insertions(+) > create mode 100644 utils/tuning/README.rst > create mode 100644 utils/tuning/libtuning/__init__.py > create mode 100644 utils/tuning/libtuning/image.py > create mode 100644 utils/tuning/libtuning/libtuning.py > create mode 100644 utils/tuning/libtuning/macbeth.py > create mode 100644 utils/tuning/libtuning/macbeth_ref.pgm > create mode 100644 utils/tuning/libtuning/utils.py > > 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..469e6940 > --- /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 getVal() > + # @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 is self.Mode.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 Nothing to address now, but I can see this evolving into checking that there's only one module of each type. Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> > + > + 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///..../.........-()))))))))))))))))))(((-,*)'(&)#($%(%"###""!%""&"&&!$" #!$ !"! 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()*q????????????????o39v|}wwwwwwrqtuspn=9^gadcfgce`dbUY[\^>;DIJDB?FEGE=7>8634.(&&(%&*&%%'+*)+*#%( )''03364443233222243/-+133423333423766645789:><<<;<;<?=?;<<:78673/001113--.-+*)&&#"&$#%&""$!! ))+rbPpAD9-*******+*++)++--.//./.0/21453469:=;98<;<>=;><7766666741012.-13/-+-/(''&&&%%&$.%0()-%-#-#' # & ( % )))h?n?YQg?7(*))))*)**,--....../0/0001357666::;;>?>AA866666666656565300/20/.-*)(('((&&%)d=yoP?<???F?QFx;?2?1?0 ))*RQ.0*,,5*(*))))*,**,+/.../...02/22224456468;:>BB;>;:76666666666755303033/,.-*(())('&')#)"##(+$+ * # ) ) & > 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
On Fri, Nov 25, 2022 at 02:56:43AM +0200, Laurent Pinchart wrote: > Hi Paul, > > Thank you for the patch. > > On Thu, Nov 24, 2022 at 08:35:39PM +0900, Paul Elder via libcamera-devel wrote: > > Implement the core of libtuning, our new tuning tool infrastructure. It > > leverages components from raspberrypi's ctt that could be reused for > > tuning tools for other platforms. > > > > The core components include: > > - The Image class > > - libtuning (entry point and other core functions) > > - macbeth-related tools, including the macbeth reference image > > - utils > > > > Signed-off-by: Paul Elder <paul.elder@ideasonboard.com> > > > > --- > > Changes in v4: > > - change Image.name to property > > - fix saturated logic in get_patches > > - move lsc vs non-lsc image loading decision from utils to Tuner > > - this means we have to import libtuning in libtuning.py... is this > > fine? > > - remove raspberry pi's special rounding function, as it has worse > > rounding accuracy than simple rounding > > - remove cli args from module.process parameters > > > > Changes in v3: > > - *Split into separate patches* > > - The following changes apply to the next two patches as well > > - fix style > > - rename Camera to Tuner > > - remove indirection from fake polymorphism > > - remove unused options property from Module > > - remove unimplemented gradients > > - convert readme to rst > > - fix readme license > > - reorder dependencies list > > - add file descriptions > > - remove indirection from Image loading > > - remove Image member variables that are unused due to dropping BRCM > > support > > - remove G from Color enum > > - Color was /not/ renamed to BayerComponent because it was much too > > long for use in code > > - add @property getters to Param > > - fix undefined functions/variables > > > > Changes in v2: > > - fix all python errors > > - fix style > > - add SPDX and copyright > > - remove validateConfig() from the base/abstract Module class > > - actually append the image after loading, even if it's alsc_only > > - s/average_functions/average/ > > - remove separate params field for Average and Smoothing > > - move remainder parameter in Gradient to Linear, as it only applies to > > that > > - from gradient.Linear, remove the remainders that I thought don't make > > sense > > - add Float to gradient.Linear's remainder types, to divide everything > > in as a float; useful for rkisp1's sector sizes (the x-size and y-size > > tuning options) > > - add a map function to Gradient, for mapping values onto a curve > > - in Smoothing, move ksize to a constructor parameter > > - remove brcm image loading > > - move process_args from utils to libtuning > > - move Module's type string and human-readble module name to class > > variable > > - move locate_macbeth from utils to macbeth > > - add out_name to Module, for the output to know what name to write for > > the key in the tuning output (eg. rkisp1 uses "LensShadingCorrection" > > while raspberrypi uses "rpi.alsc") > > --- > > utils/tuning/README.rst | 11 + > > utils/tuning/libtuning/__init__.py | 13 + > > utils/tuning/libtuning/image.py | 136 +++++++ > > utils/tuning/libtuning/libtuning.py | 208 ++++++++++ > > utils/tuning/libtuning/macbeth.py | 516 +++++++++++++++++++++++++ > > utils/tuning/libtuning/macbeth_ref.pgm | 6 + > > utils/tuning/libtuning/utils.py | 125 ++++++ > > 7 files changed, 1015 insertions(+) > > create mode 100644 utils/tuning/README.rst > > create mode 100644 utils/tuning/libtuning/__init__.py > > create mode 100644 utils/tuning/libtuning/image.py > > create mode 100644 utils/tuning/libtuning/libtuning.py > > create mode 100644 utils/tuning/libtuning/macbeth.py > > create mode 100644 utils/tuning/libtuning/macbeth_ref.pgm > > create mode 100644 utils/tuning/libtuning/utils.py > > > > 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..469e6940 > > --- /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 getVal() > > + # @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 is self.Mode.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 > > Nothing to address now, but I can see this evolving into checking that > there's only one module of each type. The evolution that I saw was having more than two different types of images, but yeah that's another possible evolution too. I decided to deal with it when the time came, and that the extra complexity wasn't worth it for now. > > Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Thanks, Paul > > > + > > + 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 > > + !#!" #!"&&$#$#'"%&#+2///..../.........-()))))))))))))))))))(((-,*)'(&)#($%(%"###""!%""&"&&!$" #!$ !"! 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()*q????????????????o39v|}wwwwwwrqtuspn=9^gadcfgce`dbUY[\^>;DIJDB?FEGE=7>8634.(&&(%&*&%%'+*)+*#%( )''03364443233222243/-+133423333423766645789:><<<;<;<?=?;<<:78673/001113--.-+*)&&#"&$#%&""$!! ))+rbPpAD9-*******+*++)++--.//./.0/21453469:=;98<;<>=;><7766666741012.-13/-+-/(''&&&%%&$.%0()-%-#-#' # & ( % )))h?n?YQg?7(*))))*)**,--....../0/0001357666::;;>?>AA866666666656565300/20/.-*)(('((&&%)d=yoP?<???F?QFx;?2?1?0 ))*RQ.0*,,5*(*))))*,**,+/.../...02/22224456468;:>BB;>;:76666666666755303033/,.-*(())('&')#)"##(+$+ * # ) ) & > > 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