def calc_features(f1, f2):
res = []
feats = [f1, f2]
quads = [(0, 0, 4, 4), (0, 4, 4, 8), (4, 4, 8, 8), (0, 8, 8, 16), (8, 8, 16, 16)]
features = []
for gin in [0, 1]:
g = feats[gin]
for quad in quads:
m = glcm.quadrant(g, quad).flatten()
features.append(m)
features = np.array(features)
res.append(features.transpose((1,0)))
res = np.vstack(res)
return res
# Add padding
train1_pad = np.pad(mosaic1_train, 15, mode='reflect')
# Calculate GLCM matrices
g1 = glcm.fast_glcm(train1_pad, size=31, stride=1, levels=16, dx=1, dy=0)
g2 = glcm.fast_glcm(train1_pad, size=31, stride=1, levels=16, dx=0, dy=1)
# Calculate selected quadrant features
quads = [(0, 0, 4, 4), (0, 4, 4, 8), (4, 4, 8, 8), (0, 8, 8, 16), (8, 8, 16, 16)]
tit = ["Q_11", "Q_12", "Q_14", "Q_2", "Q_4"]
plt.figure(1, figsize=(20,20))
i = 0
for quad, t in zip(quads, tit):
plt.subplot(1, len(quads), i+1)
plt.imshow(glcm.quadrant(g1, quad))
plt.title(t)
i += 1
plt.show()
quads = [(0, 0, 4, 4), (0, 4, 4, 8), (4, 4, 8, 8), (0, 8, 8, 16), (8, 8, 16, 16)]
tit = ["Q_11", "Q_12", "Q_14", "Q_2", "Q_4"]
plt.figure(1, figsize=(20,20))
i = 0
for quad, t in zip(quads, tit):
plt.subplot(1, len(quads), i+1)
plt.imshow(glcm.quadrant(g2, quad))
plt.title(t)
i += 1
