class Classifier(object):
"""docstring for Classifier."""
FACELET_ORDER = [
42,
39,
36,
43,
40,
37,
44,
41,
38, # U
9,
10,
11,
12,
13,
14,
15,
16,
17, # R
8,
7,
6,
5,
4,
3,
2,
1,
0, # F
45,
46,
47,
48,
49,
50,
51,
52,
53, # D
27,
28,
29,
30,
31,
32,
33,
34,
35, # L
26,
25,
24,
23,
22,
21,
20,
19,
18, # B
]
def __init__(self, face_order='URFDLB'):
self._fig = pl.figure()
self._axis = self._fig.add_subplot(111, projection='3d')
self._face_order = face_order
def fit(self, raw_colors):
self._X = [raw_colors[i] for i in Classifier.FACELET_ORDER]
np.save('X', self._X)
for label, color in enumerate(self._X):
color[0], color[2] = color[2], color[0]
self._axis.scatter(*color, color=color / 384)
pl.show()
self._X_pca = PCA(n_components=2).fit_transform(self._X)
print(self._X_pca)
np.save('pca', self._X_pca)
for i, point in enumerate(self._X_pca):
pl.scatter(*point, color=self._X[i] / 384)
pl.show()
centroids = np.array([self._X_pca[i] for i in range(4, 54, 9)])
for i, point in enumerate(centroids):
pl.scatter(*point, color=self._X[9 * i + 4] / 384)
pl.show()
print(centroids)
self._model = KMeans(n_clusters=6, init=centroids)
# self._model = GaussianMixture(n_components=6, means_init=centroids)
self._model.fit(self._X_pca)
def fit2(self, raw_colors):
from sklearn.mixture import GaussianMixture
self._X = [raw_colors[i] for i in Classifier.FACELET_ORDER]
for label, color in enumerate(self._X):
color[0], color[2] = color[2], color[0]
self._axis.scatter(*color, color=color / 384)
pl.show()
centroids = np.array([self._X[i] for i in range(4, 54, 9)])
print(centroids)
self._model = GaussianMixture(n_components=6, means_init=centroids)
self._model.fit(self._X)
self._X_pca = self._X
def fit3(self, raw_colors):
self._X = [raw_colors[i] for i in Classifier.FACELET_ORDER]
np.save('X', self._X)
X = np.load('X.npy').astype('uint8')
for i in range(len(X)):
X[i][0], X[i][2] = X[i][2], X[i][0]
X_hsv = X[np.newaxis, ...]
hsv = cv2.cvtColor(X_hsv, cv2.COLOR_RGB2HSV)[0]
centroid_id = list(range(4, 54, 9))
feats = hsv[:, ::2]
ax = pl.subplot(111)
for i, c in enumerate(feats):
if i % 9 == 4:
# print(type(raw_colors[i]))
pl.scatter(*feats[i], color=X[i] / 255.0, marker='x')
else:
pl.scatter(*feats[i], color=X[i] / 255.0, marker='o')
pl.show()
alphas = np.empty((6, 1))
means = np.empty((6, feats.shape[1]))
covs = np.empty((6, feats.shape[1], feats.shape[1]))
for i in range(6):
mean = feats[9 * i + 4]
cov = np.array([[0.5 * 10, 0.0], [0.0, 2.5 * 10]])
alphas[i] = 1 / 6.0
means[i] = mean
covs[i] = cov
print('alpha:', alphas[i])
print('mean:', mean)
print('cov:', cov)
self._gmm = GMM(6, feats, mu=means, sigma=covs, alpha=alphas)
self._gmm.execute()
print(self._gmm.alpha)
print(self._gmm.mu)
print(self._gmm.sigma)
self._ans = []
for xi in feats:
probas = np.array([
self._gmm.Normal(xi, self._gmm.mu[k], self._gmm.sigma[k],
len(xi)) for k in range(6)
])
self._ans.append(probas.argmax())
print(np.array(self._ans).reshape(6, 3, 3))
def eigsorted(cov):
vals, vecs = np.linalg.eigh(cov)
order = vals.argsort()[::-1]
return vals[order], vecs[:, order]
nstd = 2
ax = pl.subplot(111)
for i, c in enumerate(self._ans):
if i % 9 == 4:
pl.scatter(*feats[i],
color=X[centroid_id[c]] / 255.0,
marker='x')
else:
pl.scatter(*feats[i],
color=X[centroid_id[c]] / 255.0,
marker='o')
for k in range(6):
cov = self._gmm.sigma[k]
vals, vecs = eigsorted(cov)
theta = np.degrees(np.arctan2(*vecs[:, 0][::-1]))
w, h = 2 * nstd * np.sqrt(vals)
ell = mpl.patches.Ellipse(xy=self._gmm.mu[k],
width=w,
height=h,
angle=theta,
color=X[centroid_id[k]] / 255.0,
alpha=0.5)
ell.set_facecolor(X[centroid_id[k]] / 255.0)
ax.add_artist(ell)
for k in range(6):
cov = covs[k]
vals, vecs = eigsorted(cov)
theta = np.degrees(np.arctan2(*vecs[:, 0][::-1]))
w, h = 2 * nstd * np.sqrt(vals)
ell = mpl.patches.Ellipse(xy=self._gmm.mu[k],
width=w,
height=h,
angle=theta,
color=X[centroid_id[k]] / 255.0,
alpha=0.5)
# ell.set_facecolor(X[centroid_id[k]]/255.0)
ax.add_artist(ell)
pl.show()
def get_state(self):
# if self._X is None:
# raise Error('Se debe hacer fit primero.')
# pred2 = self._model.predict(self._X_pca)
# state = ''.join([self._face_order[i] for i in pred2])
# print(state)
# pred2 = pred2.reshape(-1, 3, 3)
# print(pred2)
# return state
FACES = 'URFDLB'
state = ''.join([FACES[i] for i in np.array(self._ans).reshape(-1)])
print(state)
return state
