def std_features(img, pcloud):
points = pcloud.get_numpy()
feat = np.std(points, axis=0)
feat[0] /= float(img.shape[0])
feat[1] /= float(img.shape[0])
# feat[2]/=250.0
return list(feat)
def pca_features(img, pcloud):
feats = []
pca = PCA(n_components=3)
pca.fit(pcloud.get_numpy())
feats += list(pca.explained_variance_ratio_)
for comp_i in pca.components_:
feats += list(comp_i)
return feats
def corl_features(img, pcloud):
feats = []
points = pcloud.get_numpy()
dim = pcloud.dims
for x_i in range(dim):
for y_i in range(dim):
if x_i != y_i:
corr_xy = scipy.stats.pearsonr(points[:, x_i], points[:, y_i])
feats.append(corr_xy[0])
return feats
def skewness_features(img, pcloud):
feats = []
points = pcloud.get_numpy()
dim = pcloud.dims
for x_i in range(dim):
print(points[:, x_i].shape)
corr_xy = st.skew(points[:, x_i])
feats.append(corr_xy)
# print(feats)
return feats
