def testRename(self):
"""
Check it's at least possible to open one DataArray, when the files are
renamed
"""
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
self.no_of_images = 2
metadata = [{
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_EXP_TIME: 0.2, # s
},
{
model.MD_EXP_TIME: 1.2, # s
},
]
# Add metadata
for i in range(self.no_of_images):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i])
a[white[::-1]] = 124 + i
ldata.append(a)
# export
orig_name = "boo.0.tiff"
stiff.export(orig_name, ldata)
tokens = orig_name.split(".0.", 1)
ntokens = FILENAME.split(".0.", 1)
# Renaming the file
for i in range(self.no_of_images):
fname = tokens[0] + "." + str(i) + "." + tokens[1]
new_fname = ntokens[0] + "." + str(i) + "." + ntokens[1]
os.rename(fname, new_fname)
# Iterate through the new files
for i in range(self.no_of_images):
fname = ntokens[0] + "." + str(i) + "." + ntokens[1]
fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY)
self.assertEqual(fmt_mng.FORMAT, "TIFF",
"For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT))
rdata = fmt_mng.read_data(fname)
# Assert that at least one DA is there
# In practice, currently, we expected precisely 1
self.assertGreaterEqual(len(rdata), 1)
# Check the correct metadata is present
for j in range(self.no_of_images):
self.assertAlmostEqual(rdata[j].metadata[model.MD_EXP_TIME],
ldata[j].metadata[model.MD_EXP_TIME])
rthumbnail = fmt_mng.read_thumbnail(fname)
# No thumbnail handling for now, so assert that is empty
self.assertEqual(rthumbnail, [])
def testMissing(self):
"""
Check it's at least possible to open one DataArray, when the other parts
are missing.
"""
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
self.no_of_images = 2
metadata = [
{
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_EXP_TIME: 0.2, # s
},
{
model.MD_EXP_TIME: 1.2, # s
},
]
# Add metadata
for i in range(self.no_of_images):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i])
a[white[::-1]] = 124 + i
ldata.append(a)
# export
stiff.export(FILENAME, ldata)
# Ooops, the first file is gone => it should still be possible to open
# the other files
os.remove(FILENAME)
tokens = FILENAME.split(".0.", 1)
for i in range(1, self.no_of_images):
fname = tokens[0] + "." + str(i) + "." + tokens[1]
fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY)
self.assertEqual(
fmt_mng.FORMAT, "TIFF",
"For '%s', expected format TIFF but got %s" %
(fname, fmt_mng.FORMAT))
rdata = fmt_mng.read_data(fname)
# Assert that at least one DA is there
# In practice, currently, we expected precisely 1
self.assertGreaterEqual(len(rdata), 1)
# Check the correct metadata is present
self.assertAlmostEqual(rdata[0].metadata[model.MD_EXP_TIME],
ldata[i].metadata[model.MD_EXP_TIME])
rthumbnail = fmt_mng.read_thumbnail(fname)
# No thumbnail handling for now, so assert that is empty
self.assertEqual(rthumbnail, [])
def testMissing(self):
"""
Check it's at least possible to open one DataArray, when the other parts
are missing.
"""
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
self.no_of_images = 2
metadata = [{
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_EXP_TIME: 0.2, # s
},
{
model.MD_EXP_TIME: 1.2, # s
},
]
# Add metadata
for i in range(self.no_of_images):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i])
a[white[::-1]] = 124 + i
ldata.append(a)
# export
stiff.export(FILENAME, ldata)
# Ooops, the first file is gone => it should still be possible to open
# the other files
os.remove(FILENAME)
tokens = FILENAME.split(".0.", 1)
for i in range(1, self.no_of_images):
fname = tokens[0] + "." + str(i) + "." + tokens[1]
fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY)
self.assertEqual(fmt_mng.FORMAT, "TIFF",
"For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT))
rdata = fmt_mng.read_data(fname)
# Assert that at least one DA is there
# In practice, currently, we expected precisely 1
self.assertGreaterEqual(len(rdata), 1)
# Check the correct metadata is present
self.assertAlmostEqual(rdata[0].metadata[model.MD_EXP_TIME],
ldata[i].metadata[model.MD_EXP_TIME])
rthumbnail = fmt_mng.read_thumbnail(fname)
# No thumbnail handling for now, so assert that is empty
self.assertEqual(rthumbnail, [])
def testExportRead(self):
"""
Checks that we can read back an image
"""
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400)
] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint16")
white = (12, 52) # non symmetric position
ldata = []
num = 2
# TODO: check support for combining channels when same data shape
for i in range(num):
a = model.DataArray(numpy.zeros(sizes[i][-1:-3:-1], dtype))
a[white[-1:-3:-1]] = 1027
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 0] += 255 # red
blue = (12, 22) # non symmetric position
thumbnail[blue[-1:-3:-1]] = [0, 0, 255]
# export
stiff.export(FILENAME, ldata, thumbnail)
tokens = FILENAME.split(".0.", 1)
# Iterate through the files generated
for file_index in range(num):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(fname)
self.assertEqual(len(rdata), num)
for i, im in enumerate(rdata):
if len(im.shape) > 2:
subim = im[0, 0, 0] # remove C,T,Z dimensions
else:
subim = im # TODO: should it always be 5 dim?
self.assertEqual(subim.shape, sizes[i][-1::-1])
self.assertEqual(subim[white[-1:-3:-1]],
ldata[i][white[-1:-3:-1]])
def testExportReadPyramidal(self):
"""
Checks that we can read back a pyramidal image
"""
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400)] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint16")
white = (12, 52) # non symmetric position
ldata = []
num = 2
# TODO: check support for combining channels when same data shape
for i in range(num):
a = model.DataArray(numpy.zeros(sizes[i][-1:-3:-1], dtype))
a[white[-1:-3:-1]] = 1027
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 0] += 255 # red
blue = (12, 22) # non symmetric position
thumbnail[blue[-1:-3:-1]] = [0, 0, 255]
# export
stiff.export(FILENAME, ldata, thumbnail, pyramid=True)
tokens = FILENAME.split(".0.", 1)
# Iterate through the files generated
for file_index in range(num):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(fname)
self.assertEqual(len(rdata), num)
for i, im in enumerate(rdata):
if len(im.shape) > 2:
subim = im[0, 0, 0] # remove C,T,Z dimensions
else:
subim = im # TODO: should it always be 5 dim?
self.assertEqual(subim.shape, sizes[i][-1::-1])
self.assertEqual(subim[white[-1:-3:-1]], ldata[i][white[-1:-3:-1]])
def testExportOpener(self):
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
self.no_of_images = 2
metadata = [
{
model.MD_IN_WL: (500e-9, 520e-9), # m
},
{
model.MD_EXP_TIME: 1.2, # s
},
]
# Add wavelength metadata just to group them
for i in range(self.no_of_images):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i])
a[white[::-1]] = 124
ldata.append(a)
# export
stiff.export(FILENAME, ldata)
tokens = FILENAME.split(".0.", 1)
# Iterate through the files generated. Opening any of them should be
# returning _all_ the DAs
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY)
self.assertEqual(
fmt_mng.FORMAT, "TIFF",
"For '%s', expected format TIFF but got %s" %
(fname, fmt_mng.FORMAT))
rdata = fmt_mng.read_data(fname)
# Assert all the DAs are there
self.assertEqual(len(rdata), len(ldata))
for da in rdata:
self.assertEqual(da[white[::-1]], 124)
rthumbnail = fmt_mng.read_thumbnail(fname)
# No thumbnail handling for now, so assert that is empty
self.assertEqual(rthumbnail, [])
def testExportOpener(self):
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
self.no_of_images = 2
metadata = [{
model.MD_IN_WL: (500e-9, 520e-9), # m
},
{
model.MD_EXP_TIME: 1.2, # s
},
]
# Add wavelength metadata just to group them
for i in range(self.no_of_images):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i])
a[white[::-1]] = 124
ldata.append(a)
# export
stiff.export(FILENAME, ldata)
tokens = FILENAME.split(".0.", 1)
# Iterate through the files generated. Opening any of them should be
# returning _all_ the DAs
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY)
self.assertEqual(fmt_mng.FORMAT, "TIFF",
"For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT))
rdata = fmt_mng.read_data(fname)
# Assert all the DAs are there
self.assertEqual(len(rdata), len(ldata))
for da in rdata:
self.assertEqual(da[white[::-1]], 124)
rthumbnail = fmt_mng.read_thumbnail(fname)
# No thumbnail handling for now, so assert that is empty
self.assertEqual(rthumbnail, [])
def testExportMultiPage(self):
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
num = 2
metadata = {
model.MD_IN_WL: (500e-9, 520e-9), # m
}
# Add wavelength metadata just to group them
for i in range(num):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata)
a[white[::-1]] = 124
ldata.append(a)
# export
stiff.export(FILENAME, ldata)
tokens = FILENAME.split(".0.", 1)
self.no_of_images = 1
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
im = Image.open(fname)
self.assertEqual(im.format, "TIFF")
# check the number of pages
for i in range(num):
im.seek(i)
self.assertEqual(im.size, size)
self.assertEqual(im.getpixel(white), 124)
del im
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
os.remove(fname)
def testExportOpener(self):
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
num = 2
metadata = {
model.MD_IN_WL: (500e-9, 520e-9), # m
}
for i in range(num):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata)
a[white[::-1]] = 124
ldata.append(a)
# export
stiff.export(FILENAME, ldata)
tokens = FILENAME.split(".0.", 1)
self.no_of_images = 1
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY)
self.assertEqual(
fmt_mng.FORMAT, "TIFF",
"For '%s', expected format TIFF but got %s" %
(fname, fmt_mng.FORMAT))
rdata = fmt_mng.read_data(fname)
# Assert all the DAs are there
self.assertEqual(len(rdata[file_index]), len(ldata))
rthumbnail = fmt_mng.read_thumbnail(fname)
# No thumbnail handling for now, so assert that is empty
self.assertEqual(rthumbnail, [])
self.no_of_images = 1
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
os.remove(fname)
def testExportMultiPage(self):
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
num = 2
metadata = {
model.MD_IN_WL: (500e-9, 520e-9), # m
}
# Add wavelength metadata just to group them
for i in range(num):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata)
a[white[::-1]] = 124
ldata.append(a)
# export
stiff.export(FILENAME, ldata)
tokens = FILENAME.split(".0.", 1)
self.no_of_images = 1
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
im = Image.open(fname)
self.assertEqual(im.format, "TIFF")
# check the number of pages
for i in range(num):
im.seek(i)
self.assertEqual(im.size, size)
self.assertEqual(im.getpixel(white), 124)
del im
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
os.remove(fname)
def testExportOpener(self):
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
num = 2
metadata = {
model.MD_IN_WL: (500e-9, 520e-9), # m
}
for i in range(num):
a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata)
a[white[::-1]] = 124
ldata.append(a)
# export
stiff.export(FILENAME, ldata)
tokens = FILENAME.split(".0.", 1)
self.no_of_images = 1
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY)
self.assertEqual(fmt_mng.FORMAT, "TIFF",
"For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT))
rdata = fmt_mng.read_data(fname)
# Assert all the DAs are there
self.assertEqual(len(rdata[file_index]), len(ldata))
rthumbnail = fmt_mng.read_thumbnail(fname)
# No thumbnail handling for now, so assert that is empty
self.assertEqual(rthumbnail, [])
self.no_of_images = 1
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
os.remove(fname)
def testExportMultiArrayPyramid(self):
"""
Checks that we can export and read back the metadata and data of 1 SEM image,
2 optical images, 1 RGB imagem and a RGB thumnail
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "brightfield",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "blue dye",
model.MD_ACQ_DATE: time.time() + 1,
model.MD_BPP: 12,
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "green dye",
model.MD_ACQ_DATE: time.time() + 2,
model.MD_BPP: 12,
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "green dye",
model.MD_ACQ_DATE: time.time() + 2,
model.MD_BPP: 12,
model.MD_DIMS: "YXC",
# In order to test shear is applied even without rotation
# provided. And also check that *_COR is merged into its
# normal metadata brother.
# model.MD_SHEAR: 0.03,
model.MD_SHEAR_COR: 0.003,
},
]
# create 3 greyscale images of same size
size = (5120, 7680)
dtype = numpy.dtype("uint16")
ldata = []
# iterate on the first 3 metadata items
for i, md in enumerate(metadata[:-1]):
nparray = numpy.zeros(size[::-1], dtype)
a = model.DataArray(nparray, md.copy())
a[i, i + 10] = i # "watermark" it
ldata.append(a)
# write a RGB image
a = model.DataArray(numpy.zeros((514, 516, 3), dtype), metadata[3].copy())
a[8:24, 24:40] = [5, 8, 13] # "watermark" a square
ldata.append(a)
# thumbnail : small RGB completely green
tshape = (size[1] // 8, size[0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail.metadata[model.MD_DIMS] = "YXC"
thumbnail.metadata[model.MD_POS] = (13.7e-3, -30e-3)
thumbnail[:, :, 1] += 255 # green
# export
stiff.export(FILENAME, ldata, thumbnail, pyramid=True)
tokens = FILENAME.split(".0.", 1)
self.no_of_images = 4
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
f = libtiff.TIFF.open(FILENAME)
# read all images and subimages and store in main_images
main_images = []
count = 0
for im in f.iter_images():
zoom_level_images = []
zoom_level_images.append(im)
# get an array of offsets, one for each subimage
sub_ifds = f.GetField(T.TIFFTAG_SUBIFD)
if not sub_ifds:
main_images.append(zoom_level_images)
f.SetDirectory(count)
count += 1
continue
for n in xrange(len(sub_ifds)):
# set the offset of the current subimage
f.SetSubDirectory(sub_ifds[n])
# read the subimage
subim = f.read_image()
zoom_level_images.append(subim)
f.SetDirectory(count)
count += 1
main_images.append(zoom_level_images)
# check the total number of main images
self.assertEqual(len(main_images), 1)
# check the sizes of each grayscale pyramidal image
for main_image in main_images:
self.assertEqual(len(main_image), 6)
self.assertEqual(main_image[0].shape, (7680, 5120))
self.assertEqual(main_image[1].shape, (3840, 2560))
self.assertEqual(main_image[2].shape, (1920, 1280))
self.assertEqual(main_image[3].shape, (960, 640))
self.assertEqual(main_image[4].shape, (480, 320))
self.assertEqual(main_image[5].shape, (240, 160))
def testReadMDFluo(self):
"""
Checks that we can read back the metadata of a fluoresence image
The OME-TIFF file will contain just one big array, but three arrays
should be read back with the right data.
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "brightfield",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (400e-9, 630e-9), # m
model.MD_OUT_WL: (400e-9, 630e-9), # m
# correction metadata
model.MD_POS_COR: (-1e-6, 3e-6), # m
model.MD_PIXEL_SIZE_COR: (1.2, 1.2),
model.MD_ROTATION_COR: 6.27, # rad
model.MD_SHEAR_COR: 0.005,
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "blue dye",
model.MD_ACQ_DATE: time.time() + 1,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 522e-9), # m
model.MD_OUT_WL: (650e-9, 660e-9, 675e-9, 678e-9, 680e-9), # m
model.MD_USER_TINT: (255, 0, 65), # purple
model.MD_LIGHT_POWER: 100e-3 # W
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "green dye",
model.MD_ACQ_DATE: time.time() + 2,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1, # s
model.MD_IN_WL: (590e-9, 620e-9), # m
model.MD_OUT_WL: (620e-9, 650e-9), # m
model.MD_ROTATION: 0.1, # rad
model.MD_SHEAR: 0,
model.MD_BASELINE: 400.0
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "green dye",
model.MD_ACQ_DATE: time.time() + 2,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1, # s
model.MD_IN_WL: (600e-9, 630e-9), # m
model.MD_OUT_WL: (620e-9, 650e-9), # m
# In order to test shear is applied even without rotation
# provided. And also check that *_COR is merged into its
# normal metadata brother.
# model.MD_SHEAR: 0.03,
model.MD_SHEAR_COR: 0.003,
},
]
# create 3 greyscale images of same size
size = (512, 256)
dtype = numpy.dtype("uint16")
ldata = []
for i, md in enumerate(metadata):
a = model.DataArray(numpy.zeros(size[::-1], dtype), md.copy())
a[i, i] = i # "watermark" it
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (size[1] // 8, size[0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 1] += 255 # green
# export
stiff.export(FILENAME, ldata, thumbnail)
tokens = FILENAME.split(".0.", 1)
self.no_of_images = 4
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(fname)
self.assertEqual(len(rdata), len(ldata))
# TODO: rdata and ldata don't have to be in the same order
for i, im in enumerate(rdata):
md = metadata[i].copy()
img.mergeMetadata(md)
self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS], md[model.MD_POS], rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE], md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1)
self.assertEqual(im.metadata[model.MD_BPP], md[model.MD_BPP])
self.assertEqual(im.metadata[model.MD_BINNING], md[model.MD_BINNING])
if model.MD_USER_TINT in md:
self.assertEqual(im.metadata[model.MD_USER_TINT], md[model.MD_USER_TINT])
iwl = im.metadata[model.MD_IN_WL] # nm
self.assertTrue((md[model.MD_IN_WL][0] <= iwl[0] and
iwl[1] <= md[model.MD_IN_WL][-1]))
owl = im.metadata[model.MD_OUT_WL] # nm
self.assertTrue((md[model.MD_OUT_WL][0] <= owl[0] and
owl[1] <= md[model.MD_OUT_WL][-1]))
if model.MD_LIGHT_POWER in md:
self.assertEqual(im.metadata[model.MD_LIGHT_POWER], md[model.MD_LIGHT_POWER])
self.assertAlmostEqual(im.metadata.get(model.MD_ROTATION, 0), md.get(model.MD_ROTATION, 0))
self.assertAlmostEqual(im.metadata.get(model.MD_SHEAR, 0), md.get(model.MD_SHEAR, 0))
def testReadMDAR(self):
"""
Checks that we can read back the metadata of an Angular Resolved image
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "sem survey",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_LENS_MAG: 1200, # ratio
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake ccd",
model.MD_DESCRIPTION: "AR",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_SENSOR_PIXEL_SIZE: (13e-6, 13e-6), # m/px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1.2e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_AR_POLE: (253.1, 65.1), # px
model.MD_AR_XMAX: 12e-3,
model.MD_AR_HOLE_DIAMETER: 0.6e-3,
model.MD_AR_FOCUS_DISTANCE: 0.5e-3,
model.MD_AR_PARABOLA_F: 2e-3,
model.MD_LENS_MAG: 60, # ratio
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake ccd",
model.MD_DESCRIPTION: "AR",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_SENSOR_PIXEL_SIZE: (13e-6, 13e-6), # m/px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_AR_POLE: (253.1, 65.1), # px
model.MD_AR_XMAX: 12e-3,
model.MD_AR_HOLE_DIAMETER: 0.6e-3,
model.MD_AR_FOCUS_DISTANCE: 0.5e-3,
model.MD_AR_PARABOLA_F: 2e-3,
model.MD_LENS_MAG: 60, # ratio
},
]
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400), (500, 400)] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint16")
ldata = []
for s, md in zip(sizes, metadata):
a = model.DataArray(numpy.zeros(s[::-1], dtype), md)
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 1] += 255 # green
# export
stiff.export(FILENAME, ldata, thumbnail)
tokens = FILENAME.split(".0.", 1)
self.no_of_images = 2
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(fname)
self.assertEqual(len(rdata), len(ldata))
for im, md in zip(rdata, metadata):
self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS], md[model.MD_POS], rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE], md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1)
if model.MD_AR_POLE in md:
numpy.testing.assert_allclose(im.metadata[model.MD_AR_POLE], md[model.MD_AR_POLE])
if model.MD_AR_XMAX in md:
self.assertAlmostEqual(im.metadata[model.MD_AR_XMAX], md[model.MD_AR_XMAX])
if model.MD_AR_HOLE_DIAMETER in md:
self.assertAlmostEqual(im.metadata[model.MD_AR_HOLE_DIAMETER], md[model.MD_AR_HOLE_DIAMETER])
if model.MD_AR_FOCUS_DISTANCE in md:
self.assertAlmostEqual(im.metadata[model.MD_AR_FOCUS_DISTANCE], md[model.MD_AR_FOCUS_DISTANCE])
if model.MD_AR_PARABOLA_F in md:
self.assertAlmostEqual(im.metadata[model.MD_AR_PARABOLA_F], md[model.MD_AR_PARABOLA_F])
if model.MD_LENS_MAG in md:
self.assertAlmostEqual(im.metadata[model.MD_LENS_MAG], md[model.MD_LENS_MAG])
def testReadMDSpec(self):
"""
Checks that we can read back the metadata of a spectrum image
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_OUT_WL: (650e-9, 660e-9, 675e-9, 678e-9, 680e-9), # m
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake spec",
model.MD_DESCRIPTION: "test3d",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_WL_POLYNOMIAL: [500e-9, 1e-9], # m, m/px: wl polynomial
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
},
]
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400, 1, 1, 220)] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint8")
ldata = []
for i, s in enumerate(sizes):
a = model.DataArray(numpy.zeros(s[::-1], dtype), metadata[i])
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 1] += 255 # green
# export
stiff.export(FILENAME, ldata, thumbnail)
tokens = FILENAME.split(".0.", 1)
self.no_of_images = 2
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(fname)
self.assertEqual(len(rdata), len(ldata))
for i, im in enumerate(rdata):
md = metadata[i]
self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS], md[model.MD_POS], rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE], md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1)
self.assertEqual(im.metadata[model.MD_BPP], md[model.MD_BPP])
self.assertEqual(im.metadata[model.MD_BINNING], md[model.MD_BINNING])
if model.MD_WL_POLYNOMIAL in md:
pn = md[model.MD_WL_POLYNOMIAL]
# 2 formats possible
if model.MD_WL_LIST in im.metadata:
l = ldata[i].shape[0]
npn = polynomial.Polynomial(pn,
domain=[0, l - 1],
window=[0, l - 1])
wl = npn.linspace(l)[1]
numpy.testing.assert_allclose(im.metadata[model.MD_WL_LIST], wl)
else:
numpy.testing.assert_allclose(im.metadata[model.MD_WL_POLYNOMIAL], pn)
def testExportNoWL(self):
"""
Check it's possible to export/import a spectrum with missing wavelength
info
"""
dtype = numpy.dtype("uint16")
size3d = (512, 256, 220) # X, Y, C
size = (512, 256)
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "bad spec",
model.MD_DESCRIPTION: "test3d",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_WL_POLYNOMIAL: [0], # m, m/px: missing polynomial
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, #s
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: u"", # check empty unicode strings
model.MD_DESCRIPTION: u"tÉst", # tiff doesn't support É (but XML does)
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, #s
model.MD_IN_WL: (500e-9, 520e-9), #m
}]
ldata = []
# 3D data generation (+ metadata): gradient along the wavelength
data3d = numpy.empty(size3d[::-1], dtype=dtype)
end = 2 ** metadata[0][model.MD_BPP]
step = end // size3d[2]
lin = numpy.arange(0, end, step, dtype=dtype)[:size3d[2]]
lin.shape = (size3d[2], 1, 1) # to be able to copy it on the first dim
data3d[:] = lin
# introduce Time and Z dimension to state the 3rd dim is channel
data3d = data3d[:, numpy.newaxis, numpy.newaxis, :, :]
ldata.append(model.DataArray(data3d, metadata[0]))
# an additional 2D data, for the sake of it
ldata.append(model.DataArray(numpy.zeros(size[::-1], dtype), metadata[1]))
# export
stiff.export(FILENAME, ldata)
# check 3D data
tokens = FILENAME.split(".0.", 1)
self.no_of_images = 2
# Iterate through the files generated
for file_index in range(self.no_of_images):
fname = tokens[0] + "." + str(file_index) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
rdata = tiff.read_data(fname)
self.assertEqual(len(rdata), len(ldata))
for i, im in enumerate(rdata):
md = metadata[i]
self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION])
numpy.testing.assert_allclose(im.metadata[model.MD_POS], md[model.MD_POS], rtol=1e-4)
numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE], md[model.MD_PIXEL_SIZE])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1)
self.assertEqual(im.metadata[model.MD_BPP], md[model.MD_BPP])
self.assertEqual(im.metadata[model.MD_BINNING], md[model.MD_BINNING])
if model.MD_WL_POLYNOMIAL in md:
pn = md[model.MD_WL_POLYNOMIAL]
# either identical, or nothing at all
if model.MD_WL_POLYNOMIAL in im.metadata:
numpy.testing.assert_allclose(im.metadata[model.MD_WL_POLYNOMIAL], pn)
else:
self.assertNotIn(model.MD_WL_LIST, im.metadata)
def testExportCube(self):
"""
Check it's possible to export a 3D data (typically: 2D area with full
spectrum for each point)
"""
dtype = numpy.dtype("uint16")
size3d = (512, 256, 220) # X, Y, C
size = (512, 256)
metadata3d = {model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake spec",
model.MD_HW_VERSION: "1.23",
model.MD_DESCRIPTION: "test3d",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_WL_POLYNOMIAL: [500e-9, 1e-9], # m, m/px: wl polynomial
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, #s
model.MD_IN_WL: (500e-9, 520e-9), #m
}
metadata = {model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: u"", # check empty unicode strings
model.MD_DESCRIPTION: u"tÉst", # tiff doesn't support É (but XML does)
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, #s
model.MD_IN_WL: (500e-9, 520e-9), #m
}
ldata = []
# 3D data generation (+ metadata): gradient along the wavelength
data3d = numpy.empty(size3d[-1::-1], dtype=dtype)
end = 2**metadata3d[model.MD_BPP]
step = end // size3d[2]
lin = numpy.arange(0, end, step, dtype=dtype)[:size3d[2]]
lin.shape = (size3d[2], 1, 1) # to be able to copy it on the first dim
data3d[:] = lin
# introduce Time and Z dimension to state the 3rd dim is channel
data3d = data3d[:, numpy.newaxis, numpy.newaxis,:,:]
ldata.append(model.DataArray(data3d, metadata3d))
# an additional 2D data, for the sake of it
ldata.append(model.DataArray(numpy.zeros(size[-1::-1], dtype), metadata))
# export
stiff.export(FILENAME, ldata)
tokens = FILENAME.split(".0.", 1)
fname = tokens[0] + "." + str(0) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
im = Image.open(fname)
self.assertEqual(im.format, "TIFF")
# check the 3D data (one image per channel)
for i in range(size3d[2]):
im.seek(i)
self.assertEqual(im.size, size3d[0:2])
self.assertEqual(im.getpixel((1, 1)), i * step)
del im
fname = tokens[0] + "." + str(1) + "." + tokens[1]
# check it's here
st = os.stat(fname) # this test also that the file is created
self.assertGreater(st.st_size, 0)
im = Image.open(fname)
self.assertEqual(im.format, "TIFF")
# check the 2D data
self.assertEqual(im.size, size)
self.assertEqual(im.getpixel((1, 1)), 0)
