def test_carina():
# Load the data.
carina_location = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'data/carina.tiff')
image_data = Data(location=carina_location,
radec=(10.7502222, -59.8677778),
meta={},
processing=[])
image_data.get_data()
# Add to the data collection
dc = DataCollection()
dc.add(image_data)
assert len(dc) == 1
#
# Create the cutouts with a processing step applied
#
sliding_window_cutouts = BasicCutoutGenerator(output_size=224,
step_size=550)
cc = CutoutCollection()
for cutout in sliding_window_cutouts.create_cutouts(image_data):
cc.add(cutout)
assert len(cc) == 35
cmp_arr = np.array(
[[[51, 66, 69], [50, 70, 78]], [[48, 66, 72], [49, 65, 72]]],
dtype=np.uint8)
assert np.allclose(cc[0].get_data()[:2, :2], cmp_arr)
#
# Compute the fingerprints for each cutout
#
fc = FingerprintCollection()
fc_save = FingerprintCalculatorResnet().save()
for fingerprint in fingerprint_calculate(cc, fc_save):
fc.add(fingerprint)
assert [x[1] for x in fc[0].predictions[:3]
] == ['hammerhead', 'stingray', 'binder']
#
# Compute the similarity metrics
#
similarity_tsne = similarity_calculate(fc, 'tsne')
assert True
def test_end2end():
# Load the data.
carina_location = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'data/carina.tiff')
image_data = Data(location=carina_location,
radec=(10.7502222, -59.8677778),
meta={},
processing=[])
image_data.get_data()
# Add to the data collection
dc = DataCollection()
dc.add(image_data)
#
# Create the cutouts with a processing step applied
#
sliding_window_cutouts = BasicCutoutGenerator(output_size=224,
step_size=550)
cc = CutoutCollection()
for cutout in sliding_window_cutouts.create_cutouts(image_data):
cc.add(cutout)
#
# Compute the fingerprints for each cutout
#
fc = FingerprintCollection()
fc_save = FingerprintCalculatorResnet().save()
for fingerprint in fingerprint_calculate(cc, fc_save):
fc.add(fingerprint)
#
# Compute the similarity metrics
#
similarity_tsne = similarity_calculate(fc, 'tsne')
new_similarity_tsne = Similarity.factory(similarity_tsne.save())
assert json.dumps(new_similarity_tsne.save(),
sort_keys=True) == json.dumps(similarity_tsne.save(),
sort_keys=True)
#
print('Going to calculate the sliding window cutouts')
sliding_window_cutouts = BasicCutoutGenerator(output_size=224, step_size=112)
#
# Create the cutouts
#
cutouts = sliding_window_cutouts.create_cutouts(data)
#
# Compute the fingerprints for each cutout
#
print('Calculate the fingerprint for each cutout')
fc_save = FingerprintCalculatorResnet().save()
fingerprints = fingerprint_calculate(cutouts, fc_save)
#
# Compute the similarity metrics
#
print('Calculating the tSNE similarity')
similarity_tsne = similarity_calculate(fingerprints, 'tsne')
with open('similarity_tsne.pck', 'wb') as fp:
pickle.dump(similarity_tsne.save(), fp)
print('Calculating the Jaccard similarity')
similarity_jaccard = similarity_calculate(fingerprints, 'jaccard')
with open('similarity_jaccard.pck', 'wb') as fp:
pickle.dump(similarity_jaccard.save(), fp)