def testExportRead(self):
"""
Checks that we can read back an image and a thumbnail
"""
# 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
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
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]])
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [255, 0, 0])
self.assertEqual(im[blue[-1:-3:-1]].tolist(), [0, 0, 255])
def testExportRead(self):
"""
Checks that we can read back an image and a thumbnail
"""
# 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
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
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]])
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [255, 0, 0])
self.assertEqual(im[blue[-1:-3:-1]].tolist(), [0, 0, 255])
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_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, 520e-9), # m
model.MD_OUT_WL: (600e-9, 630e-9), # m
model.MD_USER_TINT: (255, 0, 65) # purple
},
{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, 620e-9), # m
model.MD_OUT_WL: (620e-9, 650e-9), # m
model.MD_ROTATION: 0.1, # rad
},
]
# 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)
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
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
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]))
self.assertAlmostEqual(im.metadata.get(model.MD_ROTATION, 0), md.get(model.MD_ROTATION, 0))
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [0, 255, 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_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_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
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
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_LENS_MAG in md:
self.assertAlmostEqual(im.metadata[model.MD_LENS_MAG], md[model.MD_LENS_MAG])
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [0, 255, 0])
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: (600e-9, 630e-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
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
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)
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [0, 255, 0])
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_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, 520e-9), # m
model.MD_OUT_WL: (600e-9, 630e-9), # m
model.MD_USER_TINT: (255, 0, 65) # purple
},
{
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, 620e-9), # m
model.MD_OUT_WL: (620e-9, 650e-9), # m
model.MD_ROTATION: 0.1, # rad
},
]
# 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)
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
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
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]))
self.assertAlmostEqual(im.metadata.get(model.MD_ROTATION, 0),
md.get(model.MD_ROTATION, 0))
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [0, 255, 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_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_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
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
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_LENS_MAG in md:
self.assertAlmostEqual(im.metadata[model.MD_LENS_MAG],
md[model.MD_LENS_MAG])
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [0, 255, 0])
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: (600e-9, 630e-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
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
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)
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [0, 255, 0])
def testReadMDTime(self):
"""
Checks that we can read back the metadata of an acquisition with time correlation
"""
shapes = [(512, 256), (1, 5220, 1, 50, 40), (1, 512, 1, 1, 1)]
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, 1e-6), # 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 time correlator",
model.MD_DESCRIPTION: "test3d",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 16,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-6), # m/px
model.MD_PIXEL_DUR: 1e-9, # s
model.MD_TIME_OFFSET:-20e-9, # s, of the first time value
model.MD_OUT_WL: "pass-through",
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: "fake time correlator",
model.MD_DESCRIPTION: "test1d",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 16,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_PIXEL_DUR: 10e-9, # s
model.MD_TIME_OFFSET:-500e-9, # s, of the first time value
model.MD_OUT_WL: (500e-9, 600e-9),
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
},
]
# create 1 simple greyscale image
ldata = []
a = model.DataArray(numpy.zeros(shapes[0], numpy.uint16), metadata[0])
ldata.append(a)
# Create 2D time correlated image
a = model.DataArray(numpy.zeros(shapes[1], numpy.uint32), metadata[1])
a[:, :, :, 1, 5] = 1
a[0, 10, 0, 1, 0] = 10000
ldata.append(a)
# Create time correlated spot acquisition
a = model.DataArray(numpy.zeros(shapes[2], numpy.uint32), metadata[2])
a[0, 10, 0, 0, 0] = 20000
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (400, 300, 3)
thumbnail = model.DataArray(numpy.zeros(tshape, numpy.uint8))
thumbnail[:, :, 1] += 255 # green
# export
tiff.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = tiff.read_data(FILENAME)
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])
self.assertAlmostEqual(im.metadata[model.MD_POS][0], md[model.MD_POS][0])
self.assertAlmostEqual(im.metadata[model.MD_POS][1], md[model.MD_POS][1])
self.assertAlmostEqual(im.metadata[model.MD_PIXEL_SIZE][0], md[model.MD_PIXEL_SIZE][0])
self.assertAlmostEqual(im.metadata[model.MD_PIXEL_SIZE][1], md[model.MD_PIXEL_SIZE][1])
self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1)
if model.MD_LENS_MAG in md:
self.assertEqual(im.metadata[model.MD_LENS_MAG], md[model.MD_LENS_MAG])
# None of the images are using light => no MD_IN_WL
self.assertFalse(model.MD_IN_WL in im.metadata,
"Reporting excitation wavelength while there is none")
if model.MD_PIXEL_DUR in md:
pxd = md[model.MD_PIXEL_DUR]
self.assertAlmostEqual(im.metadata[model.MD_PIXEL_DUR], pxd)
if model.MD_TIME_OFFSET in md:
tof = md[model.MD_TIME_OFFSET]
self.assertAlmostEqual(im.metadata[model.MD_TIME_OFFSET], tof)
# check thumbnail
rthumbs = tiff.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [0, 255, 0])
