def test_mahalanobis_class_mean(self):
'''Test that a class's mean spectrum is classified as that class.
Note this assumes that class priors are equal.
'''
mdc = spy.MahalanobisDistanceClassifier(self.ts)
cl = mdc.classes[0]
assert (mdc.classify(cl.stats.mean) == cl.index)
def test_mahalanobis_classify_transformedimage_runs(self):
'''Mahalanobis classifier works with a TransformedImage object.'''
pc = spy.principal_components(self.data).reduce(num=3)
ximg = pc.transform(self.image)
ts = spy.create_training_classes(pc.transform(self.data),
self.gt,
calc_stats=True)
gmlc = spy.MahalanobisDistanceClassifier(ts)
ret = gmlc.classify_image(ximg)
def test_mahalanobis_spectrum_image_equal(self):
'''Tests that classification of spectrum is same as from image.'''
mdc = spy.MahalanobisDistanceClassifier(self.ts)
data = self.data[20:30, 30:40, :]
assert(mdc.classify_spectrum(data[2, 2]) == \
mdc.classify_image(data)[2, 2])
cm2 = 'classar = np.concatenate((' + allstr + '))'
exec cm1
exec cm2
#Adjust (either zero-pad or cut) classification array to work with source raster
(cx, cy) = maskar.shape
if src_raster_num_bands > classification_images_num_bands:
zerar = np.zeros((cx, cy))
diff = src_raster_num_bands - classification_images_num_bands
for i in range(diff):
classar = np.dstack((classar, zerar))
#Create training class and model
trcls = sp.create_training_classes(classar, maskar)
gmlc = sp.GaussianClassifier(trcls)
mldc = sp.MahalanobisDistanceClassifier(trcls)
#Classify image and display results
(kclmap, c) = sp.kmeans(barall, 12, 30) #unsupervised
gclmap = gmlc.classify_image(barall) #supervised
mclmap = mldc.classify_image(barall) #supervised
#Define classification colors for array conversion
def num2colR(arg):
switcher = {
1: 255,
2: 0,
3: 255,
4: 255,
5: 255,
def test_mahalanobis_classify_spyfile_runs(self):
'''Mahalanobis classifier works with a SpyFile object.'''
mdc = spy.MahalanobisDistanceClassifier(self.ts)
ret = mdc.classify_image(self.image)