def test_median_stack(self):
stack = Stack('median', 3)
self.assertEqual(stack.mode, 'median')
self.assertEqual(stack.num, 3)
self.assertEqual(stack._num, 0)
stack._update_stack(self.img1)
self.assertEqual(stack._num, 1)
stack._update_stack(self.img2)
self.assertEqual(stack._num, 2)
stack._update_stack(self.img3)
self.assertEqual(stack._num, 3)
self.assertItemsEqual(stack.calculate().img, np.ones((3, 3, 3),
dtype=np.uint8))
result = stack.calculate().img
self.assertItemsEqual(result, np.ones((3, 3, 3), dtype=np.uint16))
def setUp(self):
self.median = Stack('median', 3)
self.img1 = Image(img=np.zeros((3, 3, 3), dtype=np.float))
self.img2 = Image(img=np.zeros((3, 3, 3), dtype=np.float)+1)
self.img3 = Image(img=np.zeros((3, 3, 3), dtype=np.float)+2)
self.img4 = Image(img=np.zeros((3, 3, 3), dtype=np.float)+7)
def test_min_stack(self):
stack = Stack('min', 2)
self.assertEqual(stack.mode, 'min')
self.assertEqual(stack.num, 2)
self.assertEqual(stack._num, 0)
stack._update_stack(self.img1)
correct_result = np.zeros((3, 3, 3), dtype=np.uint8)
self.assertItemsEqual(stack.stack.img, correct_result)
stack._update_stack(self.img2)
self.assertItemsEqual(stack.calculate().img, correct_result)
def test_sigma_stack(self):
stack = Stack('sigma', 9, kwargs={'kappa': 2, 'max_iters': 1})
self.assertEqual(stack.mode, 'sigma')
self.assertEqual(stack.num, 9)
self.assertEqual(stack._num, 0)
for i in range(3):
stack._update_stack(self.img2)
stack._update_stack(self.img3)
stack._update_stack(self.img4)
result = stack.calculate().img
self.assertItemsEqual(result, 1./3. + 3 * np.ones((3, 3, 3),
dtype=np.uint16))
def test_mean_stack(self):
stack = Stack('mean', 3)
self.assertEqual(stack.mode, 'mean')
self.assertEqual(stack.num, 3)
self.assertEqual(stack._num, 0)
stack._update_stack(self.img1)
self.assertEqual(stack._num, 1)
stack._update_stack(self.img2)
self.assertEqual(stack._num, 2)
stack._update_stack(self.img3)
self.assertEqual(stack._num, 3)
self.assertItemsEqual(stack.stack.img, 3*np.ones((3, 3, 3),
dtype=np.float))
stack.stack.img[1, 1, 1] = 13
stack.stack.img[2, 2, 1] = 23
stack.stack.img = _scale(stack.stack.img, 16)
result = stack.stack.img
self.assertTrue(result.min() == 0)
self.assertTrue(result[1, 1, 1] == 32767)
self.assertTrue(result.max() == 65535)
def halostack_cli(args):
'''Commandline interface.'''
images = args['fname_in']
stacks = []
for stack in args['stacks']:
stacks.append(Stack(stack, len(images), nprocs=args['nprocs']))
base_img_fname = images[0]
base_img = Image(fname=base_img_fname, nprocs=args['nprocs'])
LOGGER.debug("Using %s as base image.", base_img.fname)
images.remove(images[0])
if not args['no_alignment'] and len(images) > 1:
view_img = base_img.luminance()
if isinstance(args['view_gamma'], float):
view_img.enhance({'gamma': args['view_gamma']})
print "\nClick tight area (two opposite corners) for "\
"reference location.\n"
args['focus_reference'] = get_two_points(view_img)
LOGGER.debug("Reference area: (%d, %d) with radius %d.",
args['focus_reference'][0], args['focus_reference'][1],
args['focus_reference'][2])
print "Click two corner points for the area where alignment "\
"reference will be in every image.\n"
args['focus_area'] = get_two_points(view_img)
LOGGER.debug("User-selected search area: (%d, %d) with radius %d.",
args['focus_area'][0], args['focus_area'][1],
args['focus_area'][2])
del view_img
if not args['no_alignment'] and len(images) > 1:
LOGGER.debug("Initializing alignment.")
aligner = Align(base_img,
cor_th=args['correlation_threshold'],
nprocs=args['nprocs'])
aligner.set_reference(args['focus_reference'])
aligner.set_search_area(args['focus_area'])
LOGGER.debug("Alignment initialized.")
if args['save_prefix'] is not None:
fname = intermediate_fname(args['save_prefix'], base_img_fname)
base_img.save(fname)
if len(args['enhance_images']) > 0:
LOGGER.info("Preprocessing image.")
base_img.enhance(args['enhance_images'])
for stack in stacks:
stack.add_image(base_img)
# memory management
del base_img
for img_fname in images:
# Read image
img = Image(fname=img_fname, nprocs=args['nprocs'])
if not args['no_alignment'] and len(images) > 1:
# align image
img = aligner.align(img)
if img is None:
LOGGER.warning("Skipping image.")
continue
if args['save_prefix'] is not None:
fname = intermediate_fname(args['save_prefix'], img_fname)
img.save(fname)
if len(args['enhance_images']) > 0:
LOGGER.info("Preprocessing image.")
img.enhance(args['enhance_images'])
for stack in stacks:
stack.add_image(img)
for i in range(len(stacks)):
img = stacks[i].calculate()
img.save(args['stack_fnames'][i], enhancements=args['enhance_stacks'])