def test_measure_psf(self, case):
sigma, aspect_ratio = case
psf = PSF()
psf.generate(sigma=sigma, aspect_ratio=aspect_ratio)
sigmas = psf.measure_psf_sigma()
self.assertEqual(len(sigmas), 3)
self.assertAlmostEqual(sigma/sigmas[1], 1, 1)
self.assertAlmostEqual(sigma/sigmas[2], 1, 1)
self.assertAlmostEqual(sigma*aspect_ratio/sigmas[0], 1, 1)
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_iterative(self):
cell = Cell(input_voxel_size=1, size=[10, 10, 10], phi=0, theta=0)
psf = PSF(sigma=0.1, aspect_ratio=2)
psf.save('data/psf.tif')
cell.convolve(psf)
cell.save('data/cell.tif')
iterative_deconvolve_3d(inputfile=os.getcwd() + '/data/cell.tif',
psffile=os.getcwd() + '/data/psf.tif',
outputfile=os.getcwd() + '/data/deconvolved.tif',
normalize=False, perform=True, detect=True, wiener=0,
low=5, terminate=0.001, iterations=2)
self.assertEqual(os.path.exists('data/deconvolved.tif'), True)
shutil.rmtree('data/')
def test_rltv(self):
cell = Cell(input_voxel_size=1, size=[10, 10, 10], phi=0, theta=0)
psf = PSF(sigma=0.1, aspect_ratio=2)
psf.save('data/psf.tif')
cell.convolve(psf)
cell.save('data/cell.tif')
imagej_path = get_fiji_path()
deconvolution_lab_rltv(imagej_path=imagej_path, inputfile=os.getcwd() + '/data/cell.tif',
psffile=os.getcwd() + '/data/psf.tif',
regularization_lambda=0.001, iterations=2,
outputfile=os.getcwd() + '/data/deconvolved.tif')
self.assertEqual(os.path.exists('data/deconvolved.tif'), True)
shutil.rmtree('data/')
def __convolve_batch_helper(item, inputfolder, psffolder, outputfolder,
**kwargs_to_ignore):
inputfile, psffile = item
stack = Stack(filename=inputfolder + inputfile)
psf = PSF(filename=psffolder + psffile)
stack.convolve(psf)
stack.save(outputfolder + psffile[:-4] + '/' + inputfile)
for c in ['PSF sigma xy um', 'PSF aspect ratio']:
stack.metadata[c] = psf.metadata[c]
stack.metadata.save(outputfolder + psffile[:-4] + '/' + inputfile[:-4] +
'.csv')
psf.save(outputfolder + psffile)
psf.metadata.save(outputfolder + psffile[:-4] + '.csv')
def __generate_psfs_batch_helper(item, outputfolder, input_voxel_size,
**kwargs_to_ignore):
metadata = Metadata()
metadata.set_voxel_size(input_voxel_size)
sigma, aspect_ratio = item
sigmaz = sigma * aspect_ratio
sigmax = sigma / metadata['Voxel size x']
sigmaz = sigmaz / metadata['Voxel size z']
psf = PSF(sigma=sigmax, aspect_ratio=sigmaz / sigmax)
psf.save(outputfolder + 'psf_sigma_' + str(sigma) + '_aspect_ratio_' +
str(aspect_ratio) + '.tif',
normalize_output=True)
metadata['PSF sigma xy um'] = sigma
metadata['PSF aspect ratio'] = aspect_ratio
metadata['isPSF'] = True
metadata.save(outputfolder + 'psf_sigma_' + str(sigma) + '_aspect_ratio_' +
str(aspect_ratio) + '.csv')
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_convolve_None(self):
img = Image()
psf = PSF()
psf.generate(sigma=5, aspect_ratio=4)
self.assertRaises(ValueError, img.convolve, psf)