aa02706a34
In ctt_ccm.py the logging functionality of the Cam object was used. As we don't want to port over that class, it needs to be replaced anyways. While at it, also replace the eprint function as it doesn't add any value over the logging framework and misses the ability for easy log formatting. For nice output formatting add the coloredlogs library. Signed-off-by: Stefan Klug <stefan.klug@ideasonboard.com> Reviewed-by: Paul Elder <paul.elder@ideasonboard.com> Reviewed-by: Daniel Scally <dan.scally@ideasonboard.com>
530 lines
20 KiB
Python
530 lines
20 KiB
Python
# SPDX-License-Identifier: BSD-2-Clause
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#
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# Copyright (C) 2019, Raspberry Pi Ltd
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# Copyright (C) 2024, Ideas on Board Oy
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#
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# Locate and extract Macbeth charts from images
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# (Copied from: ctt_macbeth_locator.py)
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# \todo Add debugging
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import cv2
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import os
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from pathlib import Path
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import numpy as np
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import warnings
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import logging
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from sklearn import cluster as cluster
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from .ctt_ransac import get_square_verts, get_square_centres
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from libtuning.image import Image
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logger = logging.getLogger(__name__)
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# Reshape image to fixed width without distorting returns image and scale
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# factor
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def reshape(img, width):
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factor = width / img.shape[0]
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return cv2.resize(img, None, fx=factor, fy=factor), factor
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# Correlation function to quantify match
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def correlate(im1, im2):
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f1 = im1.flatten()
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f2 = im2.flatten()
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cor = np.corrcoef(f1, f2)
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return cor[0][1]
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# @brief Compute coordinates of macbeth chart vertices and square centres
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# @return (max_cor, best_map_col_norm, fit_coords, success)
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#
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# Also returns an error/success message for debugging purposes. Additionally,
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# it scores the match with a confidence value.
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#
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# Brief explanation of the macbeth chart locating algorithm:
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# - Find rectangles within image
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# - Take rectangles within percentage offset of median perimeter. The
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# assumption is that these will be the macbeth squares
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# - For each potential square, find the 24 possible macbeth centre locations
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# that would produce a square in that location
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# - Find clusters of potential macbeth chart centres to find the potential
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# macbeth centres with the most votes, i.e. the most likely ones
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# - For each potential macbeth centre, use the centres of the squares that
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# voted for it to find macbeth chart corners
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# - For each set of corners, transform the possible match into normalised
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# space and correlate with a reference chart to evaluate the match
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# - Select the highest correlation as the macbeth chart match, returning the
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# correlation as the confidence score
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#
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# \todo Clean this up
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def get_macbeth_chart(img, ref_data):
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ref, ref_w, ref_h, ref_corns = ref_data
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# The code will raise and catch a MacbethError in case of a problem, trying
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# to give some likely reasons why the problem occured, hence the try/except
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try:
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# Obtain image, convert to grayscale and normalise
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src = img
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src, factor = reshape(src, 200)
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original = src.copy()
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a = 125 / np.average(src)
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src_norm = cv2.convertScaleAbs(src, alpha=a, beta=0)
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# This code checks if there are seperate colour channels. In the past the
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# macbeth locator ran on jpgs and this makes it robust to different
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# filetypes. Note that running it on a jpg has 4x the pixels of the
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# average bayer channel so coordinates must be doubled.
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# This is best done in img_load.py in the get_patches method. The
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# coordinates and image width, height must be divided by two if the
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# macbeth locator has been run on a demosaicked image.
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if len(src_norm.shape) == 3:
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src_bw = cv2.cvtColor(src_norm, cv2.COLOR_BGR2GRAY)
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else:
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src_bw = src_norm
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original_bw = src_bw.copy()
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# Obtain image edges
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sigma = 2
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src_bw = cv2.GaussianBlur(src_bw, (0, 0), sigma)
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t1, t2 = 50, 100
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edges = cv2.Canny(src_bw, t1, t2)
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# Dilate edges to prevent self-intersections in contours
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k_size = 2
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kernel = np.ones((k_size, k_size))
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its = 1
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edges = cv2.dilate(edges, kernel, iterations=its)
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# Find contours in image
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conts, _ = cv2.findContours(edges, cv2.RETR_TREE,
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cv2.CHAIN_APPROX_NONE)
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if len(conts) == 0:
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raise MacbethError(
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'\nWARNING: No macbeth chart found!'
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'\nNo contours found in image\n'
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'Possible problems:\n'
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'- Macbeth chart is too dark or bright\n'
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'- Macbeth chart is occluded\n'
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)
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# Find quadrilateral contours
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epsilon = 0.07
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conts_per = []
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for i in range(len(conts)):
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per = cv2.arcLength(conts[i], True)
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poly = cv2.approxPolyDP(conts[i], epsilon * per, True)
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if len(poly) == 4 and cv2.isContourConvex(poly):
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conts_per.append((poly, per))
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if len(conts_per) == 0:
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raise MacbethError(
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'\nWARNING: No macbeth chart found!'
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'\nNo quadrilateral contours found'
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'\nPossible problems:\n'
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'- Macbeth chart is too dark or bright\n'
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'- Macbeth chart is occluded\n'
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'- Macbeth chart is out of camera plane\n'
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)
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# Sort contours by perimeter and get perimeters within percent of median
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conts_per = sorted(conts_per, key=lambda x: x[1])
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med_per = conts_per[int(len(conts_per) / 2)][1]
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side = med_per / 4
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perc = 0.1
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med_low, med_high = med_per * (1 - perc), med_per * (1 + perc)
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squares = []
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for i in conts_per:
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if med_low <= i[1] and med_high >= i[1]:
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squares.append(i[0])
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# Obtain coordinates of nomralised macbeth and squares
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square_verts, mac_norm = get_square_verts(0.06)
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# For each square guess, find 24 possible macbeth chart centres
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mac_mids = []
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squares_raw = []
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for i in range(len(squares)):
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square = squares[i]
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squares_raw.append(square)
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# Convert quads to rotated rectangles. This is required as the
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# 'squares' are usually quite irregular quadrilaterls, so
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# performing a transform would result in exaggerated warping and
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# inaccurate macbeth chart centre placement
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rect = cv2.minAreaRect(square)
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square = cv2.boxPoints(rect).astype(np.float32)
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# Reorder vertices to prevent 'hourglass shape'
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square = sorted(square, key=lambda x: x[0])
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square_1 = sorted(square[:2], key=lambda x: x[1])
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square_2 = sorted(square[2:], key=lambda x: -x[1])
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square = np.array(np.concatenate((square_1, square_2)), np.float32)
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square = np.reshape(square, (4, 2)).astype(np.float32)
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squares[i] = square
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# Find 24 possible macbeth chart centres by trasnforming normalised
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# macbeth square vertices onto candidate square vertices found in image
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for j in range(len(square_verts)):
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verts = square_verts[j]
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p_mat = cv2.getPerspectiveTransform(verts, square)
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mac_guess = cv2.perspectiveTransform(mac_norm, p_mat)
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mac_guess = np.round(mac_guess).astype(np.int32)
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mac_mid = np.mean(mac_guess, axis=1)
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mac_mids.append([mac_mid, (i, j)])
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if len(mac_mids) == 0:
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raise MacbethError(
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'\nWARNING: No macbeth chart found!'
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'\nNo possible macbeth charts found within image'
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'\nPossible problems:\n'
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'- Part of the macbeth chart is outside the image\n'
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'- Quadrilaterals in image background\n'
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)
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# Reshape data
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for i in range(len(mac_mids)):
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mac_mids[i][0] = mac_mids[i][0][0]
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# Find where midpoints cluster to identify most likely macbeth centres
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clustering = cluster.AgglomerativeClustering(
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n_clusters=None,
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compute_full_tree=True,
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distance_threshold=side * 2
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)
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mac_mids_list = [x[0] for x in mac_mids]
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if len(mac_mids_list) == 1:
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# Special case of only one valid centre found (probably not needed)
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clus_list = []
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clus_list.append([mac_mids, len(mac_mids)])
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else:
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clustering.fit(mac_mids_list)
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# Create list of all clusters
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clus_list = []
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if clustering.n_clusters_ > 1:
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for i in range(clustering.labels_.max() + 1):
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indices = [j for j, x in enumerate(clustering.labels_) if x == i]
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clus = []
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for index in indices:
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clus.append(mac_mids[index])
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clus_list.append([clus, len(clus)])
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clus_list.sort(key=lambda x: -x[1])
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elif clustering.n_clusters_ == 1:
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# Special case of only one cluster found
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clus_list.append([mac_mids, len(mac_mids)])
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else:
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raise MacbethError(
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'\nWARNING: No macebth chart found!'
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'\nNo clusters found'
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'\nPossible problems:\n'
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'- NA\n'
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)
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# Keep only clusters with enough votes
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clus_len_max = clus_list[0][1]
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clus_tol = 0.7
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for i in range(len(clus_list)):
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if clus_list[i][1] < clus_len_max * clus_tol:
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clus_list = clus_list[:i]
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break
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cent = np.mean(clus_list[i][0], axis=0)[0]
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clus_list[i].append(cent)
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# Get centres of each normalised square
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reference = get_square_centres(0.06)
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# For each possible macbeth chart, transform image into
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# normalised space and find correlation with reference
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max_cor = 0
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best_map = None
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best_fit = None
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best_cen_fit = None
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best_ref_mat = None
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for clus in clus_list:
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clus = clus[0]
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sq_cents = []
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ref_cents = []
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i_list = [p[1][0] for p in clus]
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for point in clus:
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i, j = point[1]
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# Remove any square that voted for two different points within
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# the same cluster. This causes the same point in the image to be
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# mapped to two different reference square centres, resulting in
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# a very distorted perspective transform since cv2.findHomography
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# simply minimises error.
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# This phenomenon is not particularly likely to occur due to the
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# enforced distance threshold in the clustering fit but it is
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# best to keep this in just in case.
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if i_list.count(i) == 1:
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square = squares_raw[i]
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sq_cent = np.mean(square, axis=0)
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ref_cent = reference[j]
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sq_cents.append(sq_cent)
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ref_cents.append(ref_cent)
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# At least four squares need to have voted for a centre in
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# order for a transform to be found
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if len(sq_cents) < 4:
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raise MacbethError(
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'\nWARNING: No macbeth chart found!'
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'\nNot enough squares found'
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'\nPossible problems:\n'
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'- Macbeth chart is occluded\n'
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'- Macbeth chart is too dark of bright\n'
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)
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ref_cents = np.array(ref_cents)
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sq_cents = np.array(sq_cents)
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# Find best fit transform from normalised centres to image
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h_mat, mask = cv2.findHomography(ref_cents, sq_cents)
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if 'None' in str(type(h_mat)):
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raise MacbethError(
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'\nERROR\n'
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)
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# Transform normalised corners and centres into image space
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mac_fit = cv2.perspectiveTransform(mac_norm, h_mat)
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mac_cen_fit = cv2.perspectiveTransform(np.array([reference]), h_mat)
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# Transform located corners into reference space
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ref_mat = cv2.getPerspectiveTransform(
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mac_fit,
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np.array([ref_corns])
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)
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map_to_ref = cv2.warpPerspective(
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original_bw, ref_mat,
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(ref_w, ref_h)
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)
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# Normalise brigthness
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a = 125 / np.average(map_to_ref)
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map_to_ref = cv2.convertScaleAbs(map_to_ref, alpha=a, beta=0)
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# Find correlation with bw reference macbeth
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cor = correlate(map_to_ref, ref)
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# Keep only if best correlation
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if cor > max_cor:
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max_cor = cor
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best_map = map_to_ref
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best_fit = mac_fit
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best_cen_fit = mac_cen_fit
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best_ref_mat = ref_mat
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# Rotate macbeth by pi and recorrelate in case macbeth chart is
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# upside-down
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mac_fit_inv = np.array(
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([[mac_fit[0][2], mac_fit[0][3],
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mac_fit[0][0], mac_fit[0][1]]])
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)
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mac_cen_fit_inv = np.flip(mac_cen_fit, axis=1)
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ref_mat = cv2.getPerspectiveTransform(
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mac_fit_inv,
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np.array([ref_corns])
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)
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map_to_ref = cv2.warpPerspective(
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original_bw, ref_mat,
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(ref_w, ref_h)
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)
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a = 125 / np.average(map_to_ref)
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map_to_ref = cv2.convertScaleAbs(map_to_ref, alpha=a, beta=0)
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cor = correlate(map_to_ref, ref)
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if cor > max_cor:
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max_cor = cor
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best_map = map_to_ref
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best_fit = mac_fit_inv
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best_cen_fit = mac_cen_fit_inv
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best_ref_mat = ref_mat
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# Check best match is above threshold
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cor_thresh = 0.6
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if max_cor < cor_thresh:
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raise MacbethError(
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'\nWARNING: Correlation too low'
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'\nPossible problems:\n'
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'- Bad lighting conditions\n'
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'- Macbeth chart is occluded\n'
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'- Background is too noisy\n'
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'- Macbeth chart is out of camera plane\n'
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)
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# Represent coloured macbeth in reference space
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best_map_col = cv2.warpPerspective(
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original, best_ref_mat, (ref_w, ref_h)
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)
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best_map_col = cv2.resize(
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best_map_col, None, fx=4, fy=4
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)
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a = 125 / np.average(best_map_col)
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best_map_col_norm = cv2.convertScaleAbs(
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best_map_col, alpha=a, beta=0
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)
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# Rescale coordinates to original image size
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fit_coords = (best_fit / factor, best_cen_fit / factor)
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return (max_cor, best_map_col_norm, fit_coords, True)
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# Catch macbeth errors and continue with code
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except MacbethError as error:
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logger.warning(error)
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return (0, None, None, False)
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def find_macbeth(img, mac_config):
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small_chart = mac_config['small']
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show = mac_config['show']
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# Catch the warnings
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warnings.simplefilter("ignore")
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warnings.warn("runtime", RuntimeWarning)
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# Reference macbeth chart is created that will be correlated with the
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# located macbeth chart guess to produce a confidence value for the match.
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script_dir = Path(os.path.realpath(os.path.dirname(__file__)))
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macbeth_ref_path = script_dir.joinpath('macbeth_ref.pgm')
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ref = cv2.imread(str(macbeth_ref_path), flags=cv2.IMREAD_GRAYSCALE)
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ref_w = 120
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ref_h = 80
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rc1 = (0, 0)
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rc2 = (0, ref_h)
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rc3 = (ref_w, ref_h)
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rc4 = (ref_w, 0)
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ref_corns = np.array((rc1, rc2, rc3, rc4), np.float32)
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ref_data = (ref, ref_w, ref_h, ref_corns)
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# Locate macbeth chart
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cor, mac, coords, ret = get_macbeth_chart(img, ref_data)
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# Following bits of code try to fix common problems with simple techniques.
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# If now or at any point the best correlation is of above 0.75, then
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# nothing more is tried as this is a high enough confidence to ensure
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# reliable macbeth square centre placement.
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# Keep a list that will include this and any brightened up versions of
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# the image for reuse.
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all_images = [img]
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for brightness in [2, 4]:
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if cor >= 0.75:
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break
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img_br = cv2.convertScaleAbs(img, alpha=brightness, beta=0)
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all_images.append(img_br)
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cor_b, mac_b, coords_b, ret_b = get_macbeth_chart(img_br, ref_data)
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if cor_b > cor:
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cor, mac, coords, ret = cor_b, mac_b, coords_b, ret_b
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# In case macbeth chart is too small, take a selection of the image and
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# attempt to locate macbeth chart within that. The scale increment is
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# root 2
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# These variables will be used to transform the found coordinates at
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# smaller scales back into the original. If ii is still -1 after this
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# section that means it was not successful
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ii = -1
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w_best = 0
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h_best = 0
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d_best = 100
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# d_best records the scale of the best match. Macbeth charts are only looked
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# for at one scale increment smaller than the current best match in order to avoid
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# unecessarily searching for macbeth charts at small scales.
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# If a macbeth chart ha already been found then set d_best to 0
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if cor != 0:
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d_best = 0
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for index, pair in enumerate([{'sel': 2 / 3, 'inc': 1 / 6},
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{'sel': 1 / 2, 'inc': 1 / 8},
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{'sel': 1 / 3, 'inc': 1 / 12},
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{'sel': 1 / 4, 'inc': 1 / 16}]):
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if cor >= 0.75:
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break
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# Check if we need to check macbeth charts at even smaller scales. This
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# slows the code down significantly and has therefore been omitted by
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# default, however it is not unusably slow so might be useful if the
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# macbeth chart is too small to be picked up to by the current
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# subselections. Use this for macbeth charts with side lengths around
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# 1/5 image dimensions (and smaller...?) it is, however, recommended
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# that macbeth charts take up as large as possible a proportion of the
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# image.
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if index >= 2 and (not small_chart or d_best <= index - 1):
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break
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w, h = list(img.shape[:2])
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# Set dimensions of the subselection and the step along each axis
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# between selections
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w_sel = int(w * pair['sel'])
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h_sel = int(h * pair['sel'])
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w_inc = int(w * pair['inc'])
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h_inc = int(h * pair['inc'])
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loop = int(((1 - pair['sel']) / pair['inc']) + 1)
|
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# For each subselection, look for a macbeth chart
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for img_br in all_images:
|
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for i in range(loop):
|
|
for j in range(loop):
|
|
w_s, h_s = i * w_inc, j * h_inc
|
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img_sel = img_br[w_s:w_s + w_sel, h_s:h_s + h_sel]
|
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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:
|
|
logger.warning(f'Low confidence {cor:.3f} for macbeth chart')
|
|
|
|
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:
|
|
logger.warning(f'Image {image.path.name} too dark')
|
|
return None
|
|
|
|
macbeth = find_macbeth(av_chan, config['general']['macbeth'])
|
|
|
|
if macbeth is None:
|
|
logger.warning(f'No macbeth chart found in {image.path.name}')
|
|
return None
|
|
|
|
mac_cen_coords = macbeth[1]
|
|
if not image.get_patches(mac_cen_coords):
|
|
logger.warning(f'Macbeth patches have saturated in {image.path.name}')
|
|
return None
|
|
|
|
return macbeth
|