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kmeans.py
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# -*- coding: utf-8 -*-
# pylint: disable=invalid-name
from __future__ import print_function
from spectral import *
import numpy as np
import pylab
import random
from scipy.spatial.distance import cdist
from math_utils import *
def kmeans_cosine(image, nclusters=10, max_iterations=20, **kwargs):
from spectral.utilities.errors import has_nan, NaNValueError
if has_nan(image):
raise NaNValueError('Image data contains NaN values.')
#orig_image = image
# defaults for kwargs
start_clusters = None
compare = None
iterations = None
for (key, val) in list(kwargs.items()):
if key == 'start_clusters':
start_clusters = val
elif key == 'compare':
compare = val
elif key == 'frames':
if not hasattr(val, 'append'):
raise TypeError('"frames" keyword argument must have "append"'
'attribute.')
iterations = val
else:
raise NameError('Unsupported keyword argument.')
(nrows, ncols, nbands) = image.shape
N = nrows * ncols
image = normalized(image.reshape((N, nbands)))
#image = image.reshape((N, nbands))
clusters = np.zeros((N,), int)
centers = None
if start_clusters is not None:
assert (start_clusters.shape[0] == nclusters), 'There must be \
nclusters clusters in the start_centers array.'
centers = np.array(normalized(start_clusters))
else:
# print('Initializing clusters along diagonal of N-dimensional bounding box.')
# boxMin = np.amin(image, 0)
# boxMax = np.amax(image, 0)
# delta = (boxMax - boxMin) / (nclusters - 1)
centers = np.empty((nclusters, nbands), float)
# for i in range(nclusters):
# centers[i] = boxMin + i * delta
random.seed(4)
for i in range(nclusters):
centers[i] = image[random.randrange(N)]
centers = centers.T
#distances = np.empty((N, nclusters), float)
old_centers = np.array(centers)
clusters = np.zeros((N,), int)
old_clusters = np.copy(clusters)
n_changed = 0
itnum = 1
while (itnum <= max_iterations):
try:
print('\rIteration %d (%d pixels reassinged previously)...' % (itnum, n_changed), end='')
# Assign all pixels
#distances[:] = np.matmul(image, centers)
clusters[:] = np.argmax(np.matmul(image, centers), 1)
# Update cluster centers
old_centers[:] = centers
for i in range(nclusters):
inds = np.nonzero(clusters == i)[0]
if len(inds) > 0:
centers[:, i] = np.mean(image[inds], 0, float)
centers[:, i] /= np.linalg.norm(centers[:, i])
if iterations is not None:
iterations.append(clusters.reshape(nrows, ncols))
if compare and compare(old_clusters, clusters):
print('done.')
break
else:
n_changed = np.sum(clusters != old_clusters)
# prin(np.abs(n_changed - old_n_changed)/(n_changed + old_n_changed))
# if np.abs(float(n_changed - old_n_changed))/(n_changed + old_n_changed) > 0.5 and n_changed != old_n_changed:
# print(centers)
# viewt = imshow(orig_image, classes=clusters.reshape(nrows, ncols))
# viewt.set_display_mode('overlay')
# viewt.class_alpha = 0.85
# old_n_changed = n_changed
# # raw_input("Press Enter to continue...")
if n_changed == 0:
break
sys.stdout.flush()
old_clusters[:] = clusters
old_centers[:] = centers
itnum += 1
except KeyboardInterrupt:
print("KeyboardInterrupt: Returning clusters from previous iteration.")
return (old_clusters.reshape(nrows, ncols), old_centers.T)
print('%d pixels reassigned in last iteration.' % (n_changed))
print('kmeans terminated with', len(set(old_clusters.ravel())), \
'clusters after', itnum - 1, 'iterations.')
return (old_clusters.reshape(nrows, ncols), centers.T)
def kmeans_L2(image, nclusters=10, max_iterations=20, **kwargs):
from spectral.utilities.errors import has_nan, NaNValueError
if has_nan(image):
raise NaNValueError('Image data contains NaN values.')
#orig_image = image
# defaults for kwargs
start_clusters = None
compare = None
iterations = None
for (key, val) in list(kwargs.items()):
if key == 'start_clusters':
start_clusters = val
elif key == 'compare':
compare = val
elif key == 'frames':
if not hasattr(val, 'append'):
raise TypeError('"frames" keyword argument must have "append"'
'attribute.')
iterations = val
else:
raise NameError('Unsupported keyword argument.')
(nrows, ncols, nbands) = image.shape
N = nrows * ncols
image = image.reshape((N, nbands))
#image = image.reshape((N, nbands))
clusters = np.zeros((N,), int)
centers = None
if start_clusters is not None:
assert (start_clusters.shape[0] == nclusters), 'There must be \
nclusters clusters in the start_centers array.'
centers = np.array(normalized(start_clusters))
else:
# print('Initializing clusters along diagonal of N-dimensional bounding box.')
# boxMin = np.amin(image, 0)
# boxMax = np.amax(image, 0)
# delta = (boxMax - boxMin) / (nclusters - 1)
centers = np.empty((nclusters, nbands), float)
# for i in range(nclusters):
# centers[i] = boxMin + i * delta
random.seed(4)
for i in range(nclusters):
centers[i] = image[random.randrange(N)]
centers = centers.T
#distances = np.empty((N, nclusters), float)
old_centers = np.array(centers)
clusters = np.zeros((N,), int)
old_clusters = np.copy(clusters)
#old_n_changed = 1
itnum = 1
while (itnum <= max_iterations):
try:
print('Iteration %d...' % itnum)
# Assign all pixels
#distances[:] = np.matmul(image, centers)
clusters[:] = np.argmin(
np.sum(image**2, axis=1)[:, np.newaxis]
- 2 * np.matmul(image, centers)
+ np.sum(centers**2, axis=0),
1)
# Update cluster centers
old_centers[:] = centers
for i in range(nclusters):
inds = np.nonzero(clusters == i)[0]
if len(inds) > 0:
centers[:, i] = np.mean(image[inds], 0, float)
if iterations is not None:
iterations.append(clusters.reshape(nrows, ncols))
if compare and compare(old_clusters, clusters):
print('done.')
break
else:
n_changed = np.sum(clusters != old_clusters)
print('%d pixels reassigned.' % (n_changed))
if n_changed == 0:
break
old_clusters[:] = clusters
old_centers[:] = centers
itnum += 1
except KeyboardInterrupt:
print("KeyboardInterrupt: Returning clusters from previous iteration.")
return (old_clusters.reshape(nrows, ncols), old_centers.T)
print('kmeans terminated with', len(set(old_clusters.ravel())), \
'clusters after', itnum - 1, 'iterations.')
return (old_clusters.reshape(nrows, ncols), centers.T)
def kmeans_cdist(image, nclusters=10, max_iterations=20, **kwargs):
from spectral.utilities.errors import has_nan, NaNValueError
if has_nan(image):
raise NaNValueError('Image data contains NaN values.')
#orig_image = image
# defaults for kwargs
start_clusters = None
compare = None
iterations = None
for (key, val) in list(kwargs.items()):
if key == 'start_clusters':
start_clusters = val
elif key == 'compare':
compare = val
elif key == 'frames':
if not hasattr(val, 'append'):
raise TypeError('"frames" keyword argument must have "append"'
'attribute.')
iterations = val
else:
raise NameError('Unsupported keyword argument.')
(nrows, ncols, nbands) = image.shape
N = nrows * ncols
image = image.reshape((N, nbands))
#image = image.reshape((N, nbands))
clusters = np.zeros((N,), int)
centers = None
if start_clusters is not None:
assert (start_clusters.shape[0] == nclusters), 'There must be \
nclusters clusters in the start_centers array.'
centers = np.array(start_clusters)
else:
# print('Initializing clusters along diagonal of N-dimensional bounding box.')
# boxMin = np.amin(image, 0)
# boxMax = np.amax(image, 0)
# delta = (boxMax - boxMin) / (nclusters - 1)
centers = np.empty((nclusters, nbands), float)
# for i in range(nclusters):
# centers[i] = boxMin + i * delta
random.seed(4)
for i in range(nclusters):
centers[i] = image[random.randrange(N)]
centers = centers.T
#distances = np.empty((N, nclusters), float)
old_centers = np.array(centers)
clusters = np.zeros((N,), int)
old_clusters = np.copy(clusters)
n_changed = 0
itnum = 1
while (itnum <= max_iterations):
try:
print('Iteration %d (%d pixels reassinged previously)...' % (itnum, n_changed))
# Assign all pixels
#distances[:] = np.matmul(image, centers)
clusters[:] = np.argmin(cdist(image, centers.T, metric='sqeuclidean'), axis=1)
# Update cluster centers
old_centers[:] = centers
for i in range(nclusters):
inds = np.nonzero(clusters == i)[0]
if len(inds) > 0:
centers[:, i] = np.mean(image[inds], 0, float)
if iterations is not None:
iterations.append(clusters.reshape(nrows, ncols))
if compare and compare(old_clusters, clusters):
print('done.')
break
else:
n_changed = np.sum(clusters != old_clusters)
if n_changed == 0:
break
old_clusters[:] = clusters
old_centers[:] = centers
itnum += 1
except KeyboardInterrupt:
print("KeyboardInterrupt: Returning clusters from previous iteration.")
return (old_clusters.reshape(nrows, ncols), old_centers.T)
print('%d pixels reassigned in last iteration.' % (n_changed))
print('kmeans terminated with', len(set(old_clusters.ravel())), \
'clusters after', itnum - 1, 'iterations.')
return (old_clusters.reshape(nrows, ncols), centers.T)