def test_invalid_noise_types2(self, case):
kind, snr = case
img = Image()
arr = np.zeros([50, 50, 50])
arr[10:-10, 10:-10, 10:-10] = 255
img.image = arr
self.assertRaises(TypeError, img.add_noise, kind=kind, snr=snr)
def test_noise2(self):
img = Image()
arr = np.zeros([50, 50, 50])
arr[10:-10, 10:-10, 10:-10] = 255
img.image = arr
img.add_noise(kind='poisson', snr=2)
self.assertEqual(img.image.shape, arr.shape)
def test_resize_larger_than_3(self, case):
old_shape, zoom = case
img = Image()
img.image = np.ones(old_shape)
img.resize(zoom=zoom)
for i in range(len(img.image.shape)):
self.assertGreater(img.image.shape[i], 3)
def test_voxel_size_change(self, case):
old, zoom, new = case
img = Image()
img.image = np.ones([100, 100, 100])
img.metadata.set_voxel_size(old)
img.resize(zoom=zoom)
self.assertEqual(img.metadata['Voxel size'], new)
def test_valid_noise_metadata(self, case):
kind, snr = case
img = Image()
arr = np.zeros([50, 50, 50])
arr[10:-10, 10:-10, 10:-10] = 255
img.image = arr
img.add_noise(kind=kind, snr=snr)
self.assertEqual(img.metadata['SNR'], snr)
def test_valid_noise_types(self, kind):
img = Image()
arr = np.zeros([50, 50, 50])
arr[10:-10, 10:-10, 10:-10] = 255
img.image = arr
img.add_noise(kind=kind, snr=10)
self.assertIsNotNone(img.image)
self.assertGreater(np.sum(np.abs(img.image - arr)), 0)
def test_convolve(self):
img = Image()
arr = np.zeros([50, 50, 50])
arr[10:-10, 10:-10, 10:-10] = 255
img.image = arr
psf = PSF()
psf.generate(sigma=5, aspect_ratio=4)
img.convolve(psf)
self.assertEqual(len(img.image.shape), len(arr.shape))
self.assertEqual(img.metadata['Convolved'], True)
def test_filename(self, filename):
img = Image(filename)
for var in ['image', 'metadata', 'filename']:
self.assertIn(var, img.__dict__)
self.assertEqual(img.__dict__['image'], None)
self.assertEqual(img.metadata['Convolved'], False)
self.assertEqual(img.filename, filename)
def test_save_and_read(self, filename):
img = Image()
img.image = np.ones([5, 5, 5])
img.save(filename)
fn = img.filename
img.from_file(fn)
self.assertIsNotNone(img.image)
shutil.rmtree('data/')
def test_empty_arguments_from_file(self):
img = Image()
self.assertRaises(TypeError, img.from_file)
def test_convolve_None2(self):
img = Image()
arr = np.zeros([50, 50, 50])
arr[10:-10, 10:-10, 10:-10] = 255
psf = PSF()
self.assertRaises(ValueError, img.convolve, psf)
def test_resize(self, case):
old_shape, zoom, new_shape = case
img = Image()
img.image = np.ones(old_shape)
img.resize(zoom=zoom)
self.assertEqual(img.image.shape, tuple(new_shape))
def test_from_file_does_not_exist(self):
img = Image()
self.assertRaises(ValueError, img.from_file, filename='bla/image.tif')
def test_empty_arguments(self):
img = Image()
for var in ['image', 'filename']:
self.assertIn(var, img.__dict__)
self.assertEqual(img.__dict__[var], None)
def test_noise_None(self):
img = Image()
self.assertRaises(ValueError, img.add_noise, 'poisson', 2)
def test_convolve_None(self):
img = Image()
psf = PSF()
psf.generate(sigma=5, aspect_ratio=4)
self.assertRaises(ValueError, img.convolve, psf)
def test_invalid_noise_types(self, kind):
img = Image()
arr = np.zeros([50, 50, 50])
arr[10:-10, 10:-10, 10:-10] = 255
img.image = arr
self.assertRaises(AttributeError, img.add_noise, kind=kind, snr=10)
def test_noise_None2(self):
img = Image()
self.assertRaises(ValueError, img.add_noise, 'gaussian', 2)