def save_stack(name,
data,
luts=None,
display_ranges=None,
resolution=1.,
compress=0,
dimensions=None):
"""
Saves `data`, an array with 5, 4, 3, or 2 dimensions [TxZxCxYxX]. `resolution`
can be specified in microns per pixel. Setting `compress` to 1 saves a lot of
space for integer masks. The ImageJ lookup table for each channel can be set
with `luts` (e.g, ops.io.GRAY, ops.io.GREEN, etc). The display range can be set
with a list of (min, max) pairs for each channel.
>>> random_data = np.random.randint(0, 2**16, size=(3, 100, 100), dtype=np.uint16)
>>> luts = ops.io.GREEN, ops.io.RED, ops.io.BLUE
>>> display_ranges = (0, 60000), (0, 40000), (0, 20000)
>>> save('random_image', random_data, luts=luts, display_ranges=display_ranges)
Compatible array data types are:
bool (converted to uint8 0, 255)
uint8
uint16
float32
float64 (converted to float32)
"""
if name.split('.')[-1] != 'tif':
name += '.tif'
name = os.path.abspath(name)
if isinstance(data, list):
data = np.array(data)
if not (2 <= data.ndim <= 5):
error = 'Input has shape {}, but number of dimensions must be in range [2, 5]'
raise ValueError(error.format(data.shape))
if (data.dtype == np.int64):
if (data >= 0).all() and (data < 2**16).all():
data = data.astype(np.uint16)
else:
data = data.astype(np.float32)
print('Cast int64 to float32')
if data.dtype == np.float64:
data = data.astype(np.float32)
# print('Cast float64 to float32')
if data.dtype == np.bool:
data = 255 * data.astype(np.uint8)
if data.dtype == np.int32:
if data.min() >= 0 & data.max() < 2**16:
data = data.astype(np.uint16)
else:
raise ValueError('error casting from np.int32 to np.uint16, '
'data out of range')
if not data.dtype in (np.uint8, np.uint16, np.float32):
raise ValueError('Cannot save data of type %s' % data.dtype)
resolution = (1. / resolution, ) * 2
if not os.path.isdir(os.path.dirname(name)):
os.makedirs(os.path.dirname(name), exist_ok=True)
if data.ndim == 2:
# simple description
min, max = single_contrast(data, display_ranges)
description = imagej_description_2D(min, max)
imsave(name,
data,
photometric='minisblack',
description=description,
resolution=resolution,
compress=compress)
else:
# hyperstack description
nchannels = data.shape[-3]
luts, display_ranges = infer_luts_display_ranges(
data, luts, display_ranges)
leading_shape = data.shape[:-2]
if dimensions is None:
dimensions = 'TZC'[::-1][:len(leading_shape)][::-1]
if 'C' not in dimensions:
# TODO: support lut
contrast = single_contrast(data, display_ranges)
description = imagej_description(leading_shape,
dimensions,
contrast=contrast)
imsave(name,
data,
photometric='minisblack',
description=description,
resolution=resolution,
compress=compress)
else:
# the full monty
description = imagej_description(leading_shape, dimensions)
# metadata encoding LUTs and display ranges
# see http://rsb.info.nih.gov/ij/developer/source/ij/io/TiffEncoder.java.html
tag_50838 = ij_tag_50838(nchannels)
tag_50839 = ij_tag_50839(luts, display_ranges)
imsave(name,
data,
photometric='minisblack',
description=description,
resolution=resolution,
compress=compress,
extratags=[
(50838, 'I', len(tag_50838), tag_50838, True),
(50839, 'B', len(tag_50839), tag_50839, True),
])
def save_stack(name,
data,
luts=None,
display_ranges=None,
resolution=1.,
compress=0):
"""
Saves `data`, an array with 5, 4, 3, or 2 dimensions [TxZxCxYxX]. `resolution`
can be specified in microns per pixel. Setting `compress` to 1 saves a lot of
space for integer masks. The ImageJ lookup table for each channel can be set
with `luts` (e.g, ops.io.GRAY, ops.io.GREEN, etc). The display range can be set
with a list of (min, max) pairs for each channel.
>>> random_data = np.random.randint(0, 2**16, size=(3, 100, 100), dtype=np.uint16)
>>> luts = ops.io.GREEN, ops.io.RED, ops.io.BLUE
>>> display_ranges = (0, 60000), (0, 40000), (0, 20000)
>>> save('random_image', random_data, luts=luts, display_ranges=display_ranges)
Compatible array data types are:
bool (converted to uint8 0, 255)
uint8
uint16
float32
float64 (converted to float32)
"""
if name.split('.')[-1] != 'tif':
name += '.tif'
name = os.path.abspath(name)
if isinstance(data, list):
data = np.array(data)
if data.ndim == 2:
data = data[None]
if (data.dtype == np.int64):
if (data >= 0).all() and (data < 2**16).all():
data = data.astype(np.uint16)
else:
data = data.astype(np.float32)
print('Cast int64 to float32')
if data.dtype == np.float64:
data = data.astype(np.float32)
print('Cast float64 to float32')
if data.dtype == np.bool:
data = 255 * data.astype(np.uint8)
if not data.dtype in (np.uint8, np.uint16, np.float32):
raise ValueError('Cannot save data of type %s' % data.dtype)
nchannels = data.shape[-3]
resolution = (1. / resolution, ) * 2
if luts is None:
luts = DEFAULT_LUTS + (GRAY, ) * nchannels
if display_ranges is None:
display_ranges = [None] * data.shape[-3]
for i, dr in enumerate(display_ranges):
if dr is None:
x = data[..., i, :, :]
display_ranges[i] = x.min(), x.max()
try:
luts = luts[:nchannels]
display_ranges = display_ranges[:nchannels]
except IndexError:
raise IndexError('Must provide at least %d luts and display ranges' %
nchannels)
# metadata encoding LUTs and display ranges
# see http://rsb.info.nih.gov/ij/developer/source/ij/io/TiffEncoder.java.html
description = ij_description(data.shape)
tag_50838 = ij_tag_50838(nchannels)
tag_50839 = ij_tag_50839(luts, display_ranges)
if not os.path.isdir(os.path.dirname(name)):
os.makedirs(os.path.dirname(name))
imsave(name,
data,
photometric='minisblack',
description=description,
resolution=resolution,
compress=compress,
extratags=[
(50838, 'I', len(tag_50838), tag_50838, True),
(50839, 'B', len(tag_50839), tag_50839, True),
])