Ejemplo n.º 1
0
import os

import numpy as np
from matplotlib import pyplot as pp

import texture as t
import brainstem as b

RESULTS = os.path.expanduser("~/devel/results/filtered")

NAMES = b.get_filenames()
N_NAMES = len(NAMES)
ANGLE = 20

for i, name in enumerate(NAMES):
    print 'processing {} of {}: {}'.format(i, N_NAMES, name)
    img = b.get_cutout(name, rlevel=3)
    img = b.make_grey(img)
    filtered, _ = t.filter_img(img, angle=ANGLE)

    filter_file = os.path.splitext(name)[0] + "_filtered.npy"
    filter_path = os.path.join(RESULTS, filter_file)
    np.save(filter_path, filtered)
Ejemplo n.º 2
0
import texture as t
import brainstem as b
from sklearn.cluster import MiniBatchKMeans
import numpy as np
import matplotlib.pyplot as pp

names = b.get_filenames()
img = b.get_cutout(names[0])
img = b.make_grey(img)
freqs = t.get_freqs(img)
thetas = np.deg2rad([0, 45, 90, 135])
kernels, all_freqs = t.make_filter_bank(freqs[-5:], thetas)
kernels = list(np.real(k) for k in kernels)
filtered, all_freqs = t.filter_image(img, kernels, all_freqs, select=False)
features = t.compute_features(filtered, all_freqs)
feats_coords = t.add_coordinates(features, 1.5)
model = MiniBatchKMeans(6)
model.fit(feats_coords.reshape(-1, feats_coords.shape[-1]))
pp.imshow(model.labels_.reshape(img.shape))