def image_files_noise(request):
"""Three sham images; one has no signal, one has an intensity artifact."""
# for clarity we define images as integer arrays in [0, 11) and
# divide by 10 later
r = np.random.RandomState(0)
shape = (5, 5)
# no signal
i = conv(0.01 * np.ones(shape, dtype=float) + 0.005 * r.randn(*shape))
# normal image
j = conv(0.5 * r.rand(*shape))
# blown-out corner
k = 0.5 * r.rand(*shape)
k[3:, 3:] = 1.0
k = conv(k)
files = []
for im in [i, j, k]:
f, fn = tempfile.mkstemp(suffix='.png')
files.append(fn)
mio.imsave(fn, im)
def cleanup():
for fn in files:
os.remove(fn)
request.addfinalizer(cleanup)
illum = 0.01 * np.ones(shape, dtype=float)
return files, illum
def make_test_montage_files(missing_fields):
shape = (2, 2)
fields = list(range(0, 25))
for missing_field in missing_fields:
fields.remove(missing_field)
ims = [np.ones(shape, np.uint8) * i for i in fields]
files = []
for field, im in zip(fields, ims):
prefix = "MFGTMP_140206180002_A01f{0:02d}d0".format(field)
f, fn = tempfile.mkstemp(prefix=prefix, suffix=".tif")
files.append(fn)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
mio.imsave(fn, im)
def cleanup():
for file in files:
os.remove(file)
request.addfinalizer(cleanup)
return files
def image_files(request):
# for clarity we define images as integer arrays in [0, 11) and
# divide by 10 later
i = np.array([[ 7, 4, 1, 1, 0],
[ 2, 5, 9, 6, 7],
[ 2, 3, 3, 8, 5],
[ 3, 0, 1, 7, 5],
[ 6, 0, 10, 1, 6]], np.uint8)
j = np.array([[ 1, 10, 0, 9, 0],
[ 3, 10, 4, 1, 1],
[ 4, 10, 0, 7, 4],
[ 9, 3, 2, 0, 7],
[ 1, 3, 3, 9, 3]], np.uint8)
k = np.array([[ 9, 1, 7, 7, 3],
[ 9, 1, 6, 2, 2],
[ 2, 8, 2, 0, 3],
[ 4, 3, 8, 9, 10],
[ 6, 0, 2, 3, 10]], np.uint8)
files = []
for im in [i, j, k]:
f, fn = tempfile.mkstemp(suffix='.png')
files.append(fn)
mio.imsave(fn, im)
def cleanup():
for fn in files:
os.remove(fn)
request.addfinalizer(cleanup)
return files
def image_files_noise(request):
"""Three sham images; one has no signal, one has an intensity artifact."""
r = np.random.RandomState(0)
shape = (5, 5)
# no signal
i = conv(0.01 * np.ones(shape, dtype=float) + 0.005 * r.randn(*shape))
# normal image
j = conv(0.5 * r.rand(*shape))
# blown-out corner
k = 0.5 * r.rand(*shape)
k[3:, 3:] = 1.0
k = conv(k)
files = []
for im in [i, j, k]:
f, fn = tempfile.mkstemp(suffix='.png')
files.append(fn)
mio.imsave(fn, im)
def cleanup():
for fn in files:
os.remove(fn)
request.addfinalizer(cleanup)
illum = 0.01 * np.ones(shape, dtype=float)
return files, illum
def image_files():
# for clarity we define images as integer arrays in [0, 11) and
# divide by 10 later
i = np.array([[7, 4, 1, 1, 0],
[2, 5, 9, 6, 7],
[2, 3, 3, 8, 5],
[3, 0, 1, 7, 5],
[6, 0, 10, 1, 6]], np.uint8)
j = np.array([[1, 10, 0, 9, 0],
[3, 10, 4, 1, 1],
[4, 10, 0, 7, 4],
[9, 3, 2, 0, 7],
[1, 3, 3, 9, 3]], np.uint8)
k = np.array([[9, 1, 7, 7, 3],
[9, 1, 6, 2, 2],
[2, 8, 2, 0, 3],
[4, 3, 8, 9, 10],
[6, 0, 2, 3, 10]], np.uint8)
files = []
for im in [i, j, k]:
f, fn = tempfile.mkstemp(suffix='.png')
files.append(fn)
mio.imsave(fn, im)
yield files
for fn in files:
os.remove(fn)
def make_test_montage_files(missing_fields):
shape = (2, 2)
fields = list(range(0, 25))
for missing_field in missing_fields:
fields.remove(missing_field)
ims = [np.ones(shape, np.uint8) * i for i in fields]
files = []
for field, im in zip(fields, ims):
prefix = "MFGTMP_140206180002_A01f{0:02d}d0".format(field)
f, fn = tempfile.mkstemp(prefix=prefix, suffix=".tif")
files.append(fn)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
mio.imsave(fn, im)
def cleanup():
for file in files:
os.remove(file)
request.addfinalizer(cleanup)
return files
def test_imsave_tif_compress():
im = np.random.randint(0, 256, size=(1024, 1024, 3)).astype(np.uint8)
with NamedTemporaryFile(suffix='.tif') as fout:
fname = fout.name
fout.close()
mio.imsave(im, fname, compress=2)
imin = mio.imread(fname)
np.testing.assert_array_equal(im, imin)
def test_imsave_tif_compress():
im = np.random.randint(0, 256, size=(1024, 1024, 3)).astype(np.uint8)
with NamedTemporaryFile(suffix='.tif') as fout:
fname = fout.name
fout.close()
mio.imsave(im, fname, compress=2)
imin = mio.imread(fname)
np.testing.assert_array_equal(im, imin)
def image_files_n(request):
files = []
for i in range(0, num_files):
image = np.ones((2, 2), np.uint8) * i
file_prefix = '%02d' % (i,) + '_' # filenames must be sortable
f, fn = tempfile.mkstemp(prefix=file_prefix, suffix='.png')
files.append(fn)
mio.imsave(fn, image)
def cleanup():
for fn in files:
os.remove(fn)
request.addfinalizer(cleanup)
return files
def image_files_n(request):
files = []
for i in range(0, num_files):
image = np.ones((2, 2), np.uint8) * i
file_prefix = '%02d' % (i, ) + '_' # filenames must be sortable
f, fn = tempfile.mkstemp(prefix=file_prefix, suffix='.png')
files.append(fn)
mio.imsave(fn, image)
def cleanup():
for fn in files:
os.remove(fn)
request.addfinalizer(cleanup)
return files