diff options
| -rw-r--r-- | otsu2018.py | 16 |
1 files changed, 10 insertions, 6 deletions
diff --git a/otsu2018.py b/otsu2018.py index 6533822..713da1d 100644 --- a/otsu2018.py +++ b/otsu2018.py @@ -1,6 +1,5 @@ import numpy as np import matplotlib.pyplot as plt -import sklearn.decomposition from colour import (SpectralDistribution, STANDARD_OBSERVER_CMFS, ILLUMINANT_SDS, sd_to_XYZ, XYZ_to_xy) @@ -207,10 +206,14 @@ class Node: if not self.leaf: raise RuntimeError('Node.PCA called for a node that is not a leaf') - pca = sklearn.decomposition.PCA(3) - pca.fit(self.colours.reflectances) - self.basis_functions = pca.components_ - self.mean = pca.mean_ + # https://dev.to/akaame/implementing-simple-pca-using-numpy-3k0a + self.mean = np.mean(self.colours.reflectances, axis=0) + data = self.colours.reflectances - self.mean + cov = np.cov(data.T) / data.shape[0] + v, w = np.linalg.eig(cov) + idx = v.argsort()[::-1] + w = w[:,idx] + self.basis_functions = np.real(w[:, :3].T) # TODO: better names M = np.empty((3, 3)) @@ -241,6 +244,7 @@ class Node: weights = np.dot(self.M_inverse, XYZ - self.XYZ_mu) reflectance = np.dot(weights, self.basis_functions) + self.mean + reflectance = np.clip(reflectance, 0, 1) return SpectralDistribution(reflectance, self.clustering.wl) def reconstruction_error(self): @@ -313,7 +317,7 @@ class Node: axis = self.colours.xy[i, x_or_y] partition = self.colours.partition(x_or_y, axis) - if len(partition[0]) <= 5 or len(partition[1]) <= 5: + if len(partition[0]) < 3 or len(partition[1]) < 3: raise ClusteringError('partition created parts that are too small') lesser = Node(self.clustering, partition[0]) |