diff options
| -rw-r--r-- | demo.py | 5 | ||||
| -rw-r--r-- | otsu2018.py | 31 |
2 files changed, 28 insertions, 8 deletions
@@ -1,5 +1,6 @@ import os import matplotlib.pyplot as plt + from colour import ( SpectralShape, COLOURCHECKER_SDS, ILLUMINANT_SDS, sd_to_XYZ) @@ -8,7 +9,7 @@ from otsu2018 import load_Otsu2018_spectra, Clustering if __name__ == '__main__': print('Loading spectral data...') - sds = load_Otsu2018_spectra('CommonData/spectrum_m.csv', every_nth=50) + sds = load_Otsu2018_spectra('CommonData/spectrum_m.csv', every_nth=1) shape = SpectralShape(380, 730, 10) print('Initializing the clustering...') @@ -16,7 +17,7 @@ if __name__ == '__main__': print('Clustering...') before = clustering.root.total_reconstruction_error() - clustering.optimise(2) + clustering.optimise(8) after = clustering.root.total_reconstruction_error() print('Error before: %g' % before) diff --git a/otsu2018.py b/otsu2018.py index 7d3c1eb..e188950 100644 --- a/otsu2018.py +++ b/otsu2018.py @@ -1,5 +1,7 @@ import numpy as np import matplotlib.pyplot as plt +import tqdm +import time from colour import (SpectralDistribution, STANDARD_OBSERVER_CMFS, ILLUMINANT_SDS, sd_to_XYZ, XYZ_to_xy) @@ -55,7 +57,7 @@ class PartitionAxis: self.direction = direction def __str__(self): - return '%s = %g' % ('yx'[self.direction], self.origin) + return '%s=%s' % ('yx'[self.direction], repr(self.origin)) class Colours: @@ -351,9 +353,11 @@ class Node: """ if self.best_partition is not None: + print('%s: already optimised' % self) return self.best_partition best_error = None + bar = tqdm.trange(2 * len(self.colours), leave=False) for direction in [0, 1]: for i in range(len(self.colours)): @@ -369,7 +373,9 @@ class Node: self.best_partition = (error, axis, partition) delta = error - self.reconstruction_error() - print('%10s %3d %10s %g' % (self, i, axis, delta)) + bar.update() + + bar.close() if self.best_partition is None: raise ClusteringError('no partitions are possible') @@ -511,15 +517,25 @@ class Clustering: Maximum number of splits. If the dataset is too small, this number might not be reached. """ + + t0 = time.time() + def elapsed(): + delta = time.time() - t0 + return '%dm%.3fs' % (delta // 60, delta % 60) + for repeat in range(repeats): + print('=== Iteration %d of %d ===' % (repeat + 1, repeats)) + best_total_error = None total_error = self.root.total_reconstruction_error() - for leaf in self.root.leaves: + for i, leaf in enumerate(self.root.leaves): + print('(%s) Optimising %s...' % (elapsed(), leaf)) + try: error, axis, partition = leaf.find_best_partition() except ClusteringError: - print('%s has no partitions' % leaf) + print('...no partitions are possible.') continue new_total_error = (total_error - leaf.reconstruction_error() @@ -532,12 +548,15 @@ class Clustering: best_partition = partition if best_total_error is None: - print('WARNING: only %d splits were possible' % repeat) + print('WARNING: only %d splits were possible.' % repeat) break - print('==== Splitting %s along %s ====' % (best_leaf, best_axis)) + print('\nSplit %s into %s and %s along %s.\n' + % (best_leaf, *best_partition, best_axis)) best_leaf.split(best_partition, best_axis) + print('Finished in %s.' % elapsed()) + def write_python_dataset(self, path): """ Write the clustering into a Python dataset compatible with Colour's |