def test_SavitzkyGolay(n_clusters, data, result_path):
print('Testing SavitzkyGolay bands filter')
tdata = SavitzkyGolay_bands_filter(data, result_path)
km = skl.KMeans()
km.predict(tdata, n_clusters)
km.plot(result_path, colorMap='jet', suffix='SG_bands_filter')
tdata = SavitzkyGolay_spectra_filter(data, result_path)
km = skl.KMeans()
km.predict(tdata, n_clusters)
km.plot(result_path, colorMap='jet', suffix='SG_spectra_filter')
def test_MNF(n_clusters, n_components, data, result_path):
print('Testing MNF')
tdata = MNF(data, n_components, result_path)
km = skl.KMeans()
km.predict(tdata, n_clusters)
km.plot(result_path, colorMap='jet', suffix='MNF')
print('Testing MNF with component 2 noise reduction')
idata = MNF_reduce_component_2_noise_and_invert(data)
km = skl.KMeans()
km.predict(idata, n_clusters)
km.plot(result_path,
colorMap='jet',
suffix='MNF_with_component_2_noise_reduction')
def kmeans_pysp(img):
time1 = timer()
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=FutureWarning)
km = skl.KMeans()
m = km.predict(img, n_clusters=3, n_jobs=-1) # n_jobs = #CPUs
time2 = timer()
print('kmeans time: ', time2 - time1)
return m
def tests():
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
n_clusters = 5
km = skl.KMeans()
km.predict(data, n_clusters)
km.plot(result_path, colorMap='jet', suffix='data')
n_components = 40
test_MNF(n_clusters, n_components, data, result_path)
test_whiten(n_clusters, data, result_path)
test_SavitzkyGolay(n_clusters, data, result_path)
def test_whiten(n_clusters, data, result_path):
print('Testing whiten')
wdata = whiten(data)
km = skl.KMeans()
km.predict(wdata, n_clusters)
km.plot(result_path, colorMap='jet', suffix='whiten')