def acquire(det, fn_beg):
scanner = model.getComponent(role="laser-mirror")
min_dt = scanner.dwellTime.range[0]
for zoom in (1, 50, 1.1, 1.25, 1.5, 1.75, 2, 4, 8, 16, 20, 30):
for kdt in (1, 2, 4, 8, 12, 16, 24, 32, 40, 64, 80):
dt = min_dt * kdt
if dt > scanner.dwellTime.range[1]:
continue
det.gain.value = int(GAIN_INIT - math.log(kdt, 2) * GAIN_DECREASE)
logging.info("Gain is now %g", det.gain.value)
for xres in (64, 128, 256, 512, 1024, 2048):
#for yres in (64, 128, 256, 512, 1024, 2048):
yres = xres # only square images
fn = "%s_z%g_d%g_r%dx%d.tiff" % (fn_beg, zoom, dt * 1e6, xres,
yres)
logging.info("Acquiring %s", fn)
im = acquire_settings(scanner, det, (xres, yres), zoom, dt)
if im is not None:
tiff.export(fn, im)
# Acquire at the end another time the first image, to check the drift
zoom = 1
dt = min_dt
xres = yres = 2048
im = acquire_settings(scanner, det, (xres, yres), zoom, dt)
fn = "%s_z%g_d%g_r%dx%d_after.tiff" % (fn_beg, zoom, dt * 1e6, xres, yres)
tiff.export(fn, im)
def _MakeReport(msg, data, optical_image=None, subimages=None):
"""
Creates failure report in case we cannot match the coordinates.
msg (str): error message
data (dict str->value): description of the value -> value
optical_image (2d array or None): Image from CCD
subimages (list of 2d array or None): List of Image from CCD
"""
path = os.path.join(os.path.expanduser(u"~"), u"odemis-overlay-report",
time.strftime(u"%Y%m%d-%H%M%S"))
os.makedirs(path)
report = open(os.path.join(path, u"report.txt"), 'w')
report.write("****Overlay Failure Report****\n")
report.write("%s\n" % (msg, ))
if optical_image is not None:
tiff.export(os.path.join(path, u"OpticalGrid.tiff"), optical_image)
report.write(
"The optical image of the grid can be seen in OpticalGrid.tiff\n")
if subimages is not None:
tiff.export(os.path.join(path, u"OpticalPartitions.tiff"), subimages)
report.write(
"The partitioned optical images can be seen in OpticalPartitions.tiff\n"
)
report.write("\n")
for desc, val in data.items():
report.write("%s:\t%s\n" % (desc, val))
report.close()
logging.warning(
"Failed to find overlay. Please check the failure report in %s.", path)
def testExportMultiPage(self):
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
num = 2
for i in range(num):
a = model.DataArray(numpy.zeros(size[::-1], dtype))
a[white[::-1]] = 124
ldata.append(a)
# export
tiff.export(FILENAME, ldata)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
im = Image.open(FILENAME)
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)
os.remove(FILENAME)
def testExportMultiPage(self):
# create a simple greyscale image
size = (512, 256)
white = (12, 52) # non symmetric position
dtype = numpy.uint16
ldata = []
num = 2
for i in range(num):
a = model.DataArray(numpy.zeros(size[::-1], dtype))
a[white[::-1]] = 124
ldata.append(a)
# export
tiff.export(FILENAME, ldata)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
im = Image.open(FILENAME)
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)
os.remove(FILENAME)
def _MakeReport(msg, data, optical_image=None, subimages=None):
"""
Creates failure report in case we cannot match the coordinates.
msg (str): error message
data (dict str->value): description of the value -> value
optical_image (2d array or None): Image from CCD
subimages (list of 2d array or None): List of Image from CCD
"""
path = os.path.join(os.path.expanduser(u"~"), u"odemis-overlay-report",
time.strftime(u"%Y%m%d-%H%M%S"))
os.makedirs(path)
report = open(os.path.join(path, u"report.txt"), 'w')
report.write("****Overlay Failure Report****\n")
report.write("%s\n" % (msg,))
if optical_image is not None:
tiff.export(os.path.join(path, u"OpticalGrid.tiff"), optical_image)
report.write("The optical image of the grid can be seen in OpticalGrid.tiff\n")
if subimages is not None:
tiff.export(os.path.join(path, u"OpticalPartitions.tiff"), subimages)
report.write("The partitioned optical images can be seen in OpticalPartitions.tiff\n")
report.write("\n")
for desc, val in data.items():
report.write("%s:\t%s\n" % (desc, val))
report.close()
logging.warning("Failed to find overlay. Please check the failure report in %s.",
path)
def _MakeReport(optical_image, repetitions, magnification, pixel_size,
dwell_time, electron_coordinates):
"""
Creates failure report in case we cannot match the coordinates.
optical_image (2d array): Image from CCD
repetitions (tuple of ints): The number of CL spots are used
dwell_time (float): Time to scan each spot (in s)
electron_coordinates (list of tuples): Coordinates of e-beam grid
"""
path = os.path.join(os.path.expanduser(u"~"), u"odemis-overlay-report",
time.strftime(u"%Y%m%d-%H%M%S"))
os.makedirs(path)
tiff.export(os.path.join(path, u"OpticalGrid.tiff"), optical_image)
report = open(os.path.join(path, u"report.txt"), 'w')
report.write(
"\n****Overlay Failure Report****\n\n" + "\nSEM magnification:\n" +
str(magnification) + "\nSEM pixel size:\n" + str(pixel_size) +
"\nGrid size:\n" + str(repetitions) +
"\n\nMaximum dwell time used:\n" + str(dwell_time) +
"\n\nElectron coordinates of the scanned grid:\n" +
str(electron_coordinates) +
"\n\nThe optical image of the grid can be seen in OpticalGrid.tiff\n\n"
)
report.close()
logging.warning(
"Failed to find overlay. Please check the failure report in %s.", path)
def setUp(self):
self.app = wx.App()
data = numpy.zeros((2160, 2560), dtype=numpy.uint16)
dataRGB = numpy.zeros((2160, 2560, 4))
metadata = {
'Hardware name': 'Andor ZYLA-5.5-USB3 (s/n: VSC-01959)',
'Exposure time': 0.3,
'Pixel size': (1.59604600574173e-07, 1.59604600574173e-07),
'Acquisition date': 1441361559.258568,
'Hardware version': "firmware: '14.9.16.0' (driver 3.10.30003.5)",
'Centre position': (-0.001203511795256, -0.000295338300158),
'Lens magnification': 40.0,
'Input wavelength range': (6.15e-07, 6.350000000000001e-07),
'Shear': -4.358492733391727e-16,
'Description': 'Filtered colour 1',
'Bits per pixel': 16,
'Binning': (1, 1),
'Pixel readout time': 1e-08,
'Gain': 1.1,
'Rotation': 6.279302551026012,
'Light power': 0.0,
'Display tint': (255, 0, 0),
'Output wavelength range': (6.990000000000001e-07, 7.01e-07)
}
image = model.DataArray(data, metadata)
fluo_stream = stream.StaticFluoStream(metadata['Description'], image)
fluo_stream_pj = stream.RGBSpatialProjection(fluo_stream)
data = numpy.zeros((1024, 1024), dtype=numpy.uint16)
dataRGB = numpy.zeros((1024, 1024, 4))
metadata = {
'Hardware name': 'pcie-6251',
'Description': 'Secondary electrons',
'Exposure time': 3e-06,
'Pixel size': (1e-6, 1e-6),
'Acquisition date': 1441361562.0,
'Hardware version':
'Unknown (driver 2.1-160-g17a59fb (driver ni_pcimio v0.7.76))',
'Centre position': (-0.001203511795256, -0.000295338300158),
'Lens magnification': 5000.0,
'Rotation': 0.0
}
image = model.DataArray(data, metadata)
# export
FILENAME = u"test" + tiff.EXTENSIONS[0]
tiff.export(FILENAME, image, pyramid=True)
# read back
acd = tiff.open_data(FILENAME)
sem_stream = stream.StaticSEMStream(metadata['Description'],
acd.content[0])
sem_stream_pj = stream.RGBSpatialProjection(sem_stream)
sem_stream_pj.mpp.value = 1e-6
self.streams = [fluo_stream_pj, sem_stream_pj]
self.min_res = (623, 432)
# Wait for all the streams to get an RGB image
time.sleep(0.5)
def testExportThumbnail(self):
# create a simple greyscale image
size = (512, 256)
dtype = numpy.uint16
ldata = []
num = 2
for i in range(num):
ldata.append(model.DataArray(numpy.zeros(size[::-1], dtype)))
# thumbnail : small RGB completely red
tshape = (size[1] // 8, size[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]] = [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)
im = Image.open(FILENAME)
self.assertEqual(im.format, "TIFF")
# first page should be thumbnail
im.seek(0)
self.assertEqual(im.size, (tshape[1], tshape[0]))
self.assertEqual(im.getpixel((0, 0)), (255, 0, 0))
self.assertEqual(im.getpixel(blue), (0, 0, 255))
# check the number of pages
for i in range(num):
im.seek(i + 1)
self.assertEqual(im.size, size)
# check OME-TIFF metadata
imo = libtiff.tiff.TIFFfile(FILENAME)
omemd = imo.IFD[0].get_value("ImageDescription")
self.assertTrue(
omemd.startswith('<?xml') or omemd[:4].lower() == '<ome')
# remove "xmlns" which is the default namespace and is appended everywhere
omemd = re.sub(
'xmlns="http://www.openmicroscopy.org/Schemas/OME/....-.."',
"",
omemd,
count=1)
root = ET.fromstring(omemd)
# check the IFD of each TIFFData is different
ifds = set()
for tdt in root.findall("Image/Pixels/TiffData"):
ifd = int(tdt.get("IFD", "0"))
self.assertNotIn(ifd, ifds, "Multiple times the same IFD %d" % ifd)
self.assertTrue(imo.IFD[ifd], "IFD %d doesn't exists" % ifd)
def testReadMDOutWlBands(self):
"""
Checks that we hand MD_OUT_WL if it contains multiple bands.
OME supports only one value, so it's ok to discard some info.
"""
metadata = [{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: ((650e-9, 660e-9), (675e-9, 680e-9)), # m
model.MD_USER_TINT: (255, 0, 65), # purple
model.MD_LIGHT_POWER: 100e-3 # W
},
]
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
a[i + 1, i + 5] = i + 1 # "watermark" it
ldata.append(a)
# export
tiff.export(FILENAME, ldata)
# check data
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
for i, im in enumerate(rdata):
self.assertEqual(im[i + 1, i + 5], i + 1)
im = rdata[0]
emd = metadata[0].copy()
rmd = im.metadata
img.mergeMetadata(emd)
img.mergeMetadata(rmd)
self.assertEqual(rmd[model.MD_DESCRIPTION], emd[model.MD_DESCRIPTION])
iwl = rmd[model.MD_IN_WL] # nm
self.assertTrue((emd[model.MD_IN_WL][0] <= iwl[0] and
iwl[1] <= emd[model.MD_IN_WL][-1]))
# It should be within at least one of the bands
owl = rmd[model.MD_OUT_WL] # nm
for eowl in emd[model.MD_OUT_WL]:
if (eowl[0] <= owl[0] and owl[1] <= eowl[-1]):
break
else:
self.fail("Out wl %s is not within original metadata" % (owl,))
def testExportThumbnail(self):
# create a simple greyscale image
size = (512, 256)
dtype = numpy.uint16
ldata = []
num = 2
for i in range(num):
ldata.append(model.DataArray(numpy.zeros(size[::-1], dtype)))
# thumbnail : small RGB completely red
tshape = (size[1]//8, size[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]] = [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)
im = Image.open(FILENAME)
self.assertEqual(im.format, "TIFF")
# first page should be thumbnail
im.seek(0)
self.assertEqual(im.size, (tshape[1], tshape[0]))
self.assertEqual(im.getpixel((0,0)), (255,0,0))
self.assertEqual(im.getpixel(blue), (0,0,255))
# check the number of pages
for i in range(num):
im.seek(i + 1)
self.assertEqual(im.size, size)
del im
# check OME-TIFF metadata
imo = libtiff.tiff.TIFFfile(FILENAME)
omemd = imo.IFD[0].get_value("ImageDescription")
self.assertTrue(omemd.startswith('<?xml') or omemd[:4].lower() == '<ome')
# remove "xmlns" which is the default namespace and is appended everywhere
omemd = re.sub('xmlns="http://www.openmicroscopy.org/Schemas/OME/....-.."',
"", omemd, count=1)
root = ET.fromstring(omemd)
# check the IFD of each TIFFData is different
ifds = set()
for tdt in root.findall("Image/Pixels/TiffData"):
ifd = int(tdt.get("IFD", "0"))
self.assertNotIn(ifd, ifds, "Multiple times the same IFD %d" % ifd)
self.assertTrue(imo.IFD[ifd], "IFD %d doesn't exists" % ifd)
imo.close()
def _save_overview(self, das):
"""
Save a set of DataArrays into a single TIFF file
das (list of DataArrays)
"""
fn = create_filename(self.conf.last_path, "{datelng}-{timelng}-overview",
".ome.tiff")
# We could use find_fittest_converter(), but as we always use tiff, it's not needed
tiff.export(fn, das, pyramid=True)
popup.show_message(self._tab.main_frame, "Overview saved", "Stored in %s" % (fn,),
timeout=3)
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
tiff.export(FILENAME,
ldata,
thumbnail,
multiple_files=True,
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 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 export(filename, data, thumbnail=None, compressed=True, pyramid=False):
'''
Write a collection of multiple OME-TIFF files with the given images and
metadata
filename (unicode): filename of the file to create (including path)
data (list of model.DataArray, or model.DataArray): the data to export.
Metadata is taken directly from the DA object. If it's a list, a multiple
files distribution is created.
thumbnail (None or numpy.array): Image used as thumbnail for the first file.
Can be of any (reasonable) size. Must be either 2D array (greyscale) or 3D
with last dimension of length 3 (RGB). If the exporter doesn't support it,
it will be dropped silently.
compressed (boolean): whether the file is compressed or not.
'''
tiff.export(filename, data, thumbnail, compressed, multiple_files=True, pyramid=pyramid)
def export(filename, data, thumbnail=None, compressed=True):
'''
Write a collection of multiple OME-TIFF files with the given images and
metadata
filename (unicode): filename of the file to create (including path)
data (list of model.DataArray, or model.DataArray): the data to export.
Metadata is taken directly from the DA object. If it's a list, a multiple
files distribution is created.
thumbnail (None or numpy.array): Image used as thumbnail for the first file.
Can be of any (reasonable) size. Must be either 2D array (greyscale) or 3D
with last dimension of length 3 (RGB). If the exporter doesn't support it,
it will be dropped silently.
compressed (boolean): whether the file is compressed or not.
'''
tiff.export(filename, data, thumbnail, compressed, multiple_files=True)
def testRGB(self):
"""
Checks that can both write and read back an RGB image
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "my exported image",
model.MD_ACQ_DATE: time.time() + 1,
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_DIMS: "YXC",
},
]
# TODO: test without alpha channel and with different DIMS order
shape = (5120, 2560, 4)
dtype = numpy.dtype("uint8")
ldata = []
for i, md in enumerate(metadata):
a = model.DataArray(numpy.zeros(shape, dtype), md.copy())
a[:, :, 3] = 255 # no transparency
a[i, i] = i # "watermark" it
a[i + 1, i + 5] = i + 1 # "watermark" it
ldata.append(a)
# export
tiff.export(FILENAME, ldata)
# check data
rdata = tiff.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
for i, im in enumerate(rdata):
for j in range(shape[-1]):
self.assertEqual(im[i + 1, i + 5, j], i + 1)
self.assertEqual(im.shape, shape)
emd = metadata[i].copy()
rmd = im.metadata
img.mergeMetadata(emd)
img.mergeMetadata(rmd)
self.assertEqual(rmd[model.MD_DESCRIPTION], emd[model.MD_DESCRIPTION])
self.assertEqual(rmd[model.MD_DIMS], emd[model.MD_DIMS])
self.assertAlmostEqual(rmd[model.MD_POS][0], emd[model.MD_POS][0])
self.assertAlmostEqual(rmd[model.MD_POS][1], emd[model.MD_POS][1])
self.assertAlmostEqual(rmd[model.MD_PIXEL_SIZE][0], emd[model.MD_PIXEL_SIZE][0])
self.assertAlmostEqual(rmd[model.MD_PIXEL_SIZE][1], emd[model.MD_PIXEL_SIZE][1])
def test_rgb_tiles(self):
def getSubData(dast, zoom, rect):
x1, y1, x2, y2 = rect
tiles = []
for x in range(x1, x2 + 1):
tiles_column = []
for y in range(y1, y2 + 1):
tiles_column.append(dast.getTile(x, y, zoom))
tiles.append(tiles_column)
return tiles
FILENAME = u"test" + tiff.EXTENSIONS[0]
POS = (5.0, 7.0)
size = (3, 2000, 1000)
dtype = numpy.uint8
md = {
model.MD_DIMS: 'YXC',
model.MD_POS: POS,
model.MD_PIXEL_SIZE: (1e-6, 1e-6),
}
arr = numpy.array(range(size[0] * size[1] * size[2])).reshape(
size[::-1]).astype(dtype)
print(arr.shape)
data = model.DataArray(arr, metadata=md)
# export
tiff.export(FILENAME, data, pyramid=True)
rdata = tiff.open_data(FILENAME)
tiles = getSubData(rdata.content[0], 0, (0, 0, 7, 3))
merged_img = img.mergeTiles(tiles)
self.assertEqual(merged_img.shape, (1000, 2000, 3))
self.assertEqual(merged_img.metadata[model.MD_POS], POS)
tiles = getSubData(rdata.content[0], 0, (0, 0, 3, 1))
merged_img = img.mergeTiles(tiles)
self.assertEqual(merged_img.shape, (512, 1024, 3))
numpy.testing.assert_almost_equal(merged_img.metadata[model.MD_POS],
(4.999512, 7.000244))
del rdata
os.remove(FILENAME)
def testExportOnePage(self):
# create a simple greyscale image
size = (256, 512)
dtype = numpy.uint16
data = model.DataArray(numpy.zeros(size[::-1], dtype))
white = (12, 52) # non symmetric position
# less that 2**15 so that we don't have problem with PIL.getpixel() always returning an signed int
data[white[::-1]] = 124
# export
tiff.export(FILENAME, data)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
im = Image.open(FILENAME)
self.assertEqual(im.format, "TIFF")
self.assertEqual(im.size, size)
self.assertEqual(im.getpixel(white), 124)
def test_rgb_tiles(self):
def getSubData(dast, zoom, rect):
x1, y1, x2, y2 = rect
tiles = []
for x in range(x1, x2 + 1):
tiles_column = []
for y in range(y1, y2 + 1):
tiles_column.append(dast.getTile(x, y, zoom))
tiles.append(tiles_column)
return tiles
FILENAME = u"test" + tiff.EXTENSIONS[0]
POS = (5.0, 7.0)
size = (3, 2000, 1000)
dtype = numpy.uint8
md = {
model.MD_DIMS: 'YXC',
model.MD_POS: POS,
model.MD_PIXEL_SIZE: (1e-6, 1e-6),
}
arr = numpy.array(range(size[0] * size[1] * size[2])).reshape(size[::-1]).astype(dtype)
print(arr.shape)
data = model.DataArray(arr, metadata=md)
# export
tiff.export(FILENAME, data, pyramid=True)
rdata = tiff.open_data(FILENAME)
tiles = getSubData(rdata.content[0], 0, (0, 0, 7, 3))
merged_img = img.mergeTiles(tiles)
self.assertEqual(merged_img.shape, (1000, 2000, 3))
self.assertEqual(merged_img.metadata[model.MD_POS], POS)
tiles = getSubData(rdata.content[0], 0, (0, 0, 3, 1))
merged_img = img.mergeTiles(tiles)
self.assertEqual(merged_img.shape, (512, 1024, 3))
numpy.testing.assert_almost_equal(merged_img.metadata[model.MD_POS], (4.999512, 7.000244))
del rdata
os.remove(FILENAME)
def testExportOnePage(self):
# create a simple greyscale image
size = (256, 512)
dtype = numpy.uint16
data = model.DataArray(numpy.zeros(size[::-1], dtype))
white = (12, 52) # non symmetric position
# less that 2**15 so that we don't have problem with PIL.getpixel() always returning an signed int
data[white[::-1]] = 124
# export
tiff.export(FILENAME, data)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
im = Image.open(FILENAME)
self.assertEqual(im.format, "TIFF")
self.assertEqual(im.size, size)
self.assertEqual(im.getpixel(white), 124)
def acquire(det, fn_beg):
scanner = model.getComponent(role="laser-mirror")
max_res = scanner.resolution.range[1]
dwell_times = []
dt = scanner.dwellTime.range[0]
while dt < min(scanner.dwellTime.range[1], MAX_DWELL_TIME):
dwell_times.append(dt)
dt *= 2
for zoom in (1, ):
det.gain.value = GAIN_INIT + GAIN_DECREASE
for dt in dwell_times:
det.gain.value -= GAIN_DECREASE
logging.info("Gain is now %g", det.gain.value)
for xres in (512, ):
for scan_delay in (90e-6, 100e-6):
# for yres in (64, 128, 256, 512, 1024, 2048):
yres = xres # only square images
fn = "%s_z%d_d%g_r%dx%d_%f.tiff" % (
fn_beg, zoom, dt * 1e6, xres, yres, scan_delay * 1e6)
res = (xres, yres)
scale = [m / (r * zoom) for m, r in zip(max_res, res)]
scanner.scale.value = scale
scanner.resolution.value = res
scanner.dwellTime.value = dt
if scanner.dwellTime.value > dt or scanner.dwellTime.value < dt * 0.8:
logging.info(
"Skipping %s because it doesn't support dwell time",
fn)
continue
scanner.scanDelay.value = scan_delay
logging.info("Acquiring %s", fn)
im = det.data.get()
if det.protection.value:
logging.warning("Protection activated")
det.protection.value = False
tiff.export(fn, im)
def setUp(self):
self.app = wx.App()
data = numpy.zeros((2160, 2560), dtype=numpy.uint16)
dataRGB = numpy.zeros((2160, 2560, 4))
metadata = {'Hardware name': 'Andor ZYLA-5.5-USB3 (s/n: VSC-01959)',
'Exposure time': 0.3, 'Pixel size': (1.59604600574173e-07, 1.59604600574173e-07),
'Acquisition date': 1441361559.258568, 'Hardware version': "firmware: '14.9.16.0' (driver 3.10.30003.5)",
'Centre position': (-0.001203511795256, -0.000295338300158), 'Lens magnification': 40.0,
'Input wavelength range': (6.15e-07, 6.350000000000001e-07), 'Shear':-4.358492733391727e-16,
'Description': 'Filtered colour 1', 'Bits per pixel': 16, 'Binning': (1, 1), 'Pixel readout time': 1e-08,
'Gain': 1.1, 'Rotation': 6.279302551026012, 'Light power': 0.0, 'Display tint': (255, 0, 0),
'Output wavelength range': (6.990000000000001e-07, 7.01e-07)}
image = model.DataArray(data, metadata)
fluo_stream = stream.StaticFluoStream(metadata['Description'], image)
fluo_stream_pj = stream.RGBSpatialProjection(fluo_stream)
data = numpy.zeros((1024, 1024), dtype=numpy.uint16)
dataRGB = numpy.zeros((1024, 1024, 4))
metadata = {'Hardware name': 'pcie-6251', 'Description': 'Secondary electrons',
'Exposure time': 3e-06, 'Pixel size': (1e-6, 1e-6),
'Acquisition date': 1441361562.0, 'Hardware version': 'Unknown (driver 2.1-160-g17a59fb (driver ni_pcimio v0.7.76))',
'Centre position': (-0.001203511795256, -0.000295338300158), 'Lens magnification': 5000.0, 'Rotation': 0.0}
image = model.DataArray(data, metadata)
# export
FILENAME = u"test" + tiff.EXTENSIONS[0]
tiff.export(FILENAME, image, pyramid=True)
# read back
acd = tiff.open_data(FILENAME)
sem_stream = stream.StaticSEMStream(metadata['Description'], acd.content[0])
sem_stream_pj = stream.RGBSpatialProjection(sem_stream)
sem_stream_pj.mpp.value = 1e-6
self.streams = [fluo_stream_pj, sem_stream_pj]
self.min_res = (623, 432)
# Wait for all the streams to get an RGB image
time.sleep(0.5)
def acquire(det, fn_beg):
scanner = model.getComponent(role="laser-mirror")
max_res = scanner.resolution.range[1]
dwell_times = []
dt = scanner.dwellTime.range[0]
while dt < min(scanner.dwellTime.range[1], MAX_DWELL_TIME):
dwell_times.append(dt)
dt *= 2
for zoom in (1,):
det.gain.value = GAIN_INIT + GAIN_DECREASE
for dt in dwell_times:
det.gain.value -= GAIN_DECREASE
logging.info("Gain is now %g", det.gain.value)
for xres in (512,):
for scan_delay in (90e-6, 100e-6):
# for yres in (64, 128, 256, 512, 1024, 2048):
yres = xres # only square images
fn = "%s_z%d_d%g_r%dx%d_%f.tiff" % (fn_beg, zoom, dt * 1e6, xres, yres, scan_delay * 1e6)
res = (xres, yres)
scale = [m / (r * zoom) for m, r in zip(max_res, res)]
scanner.scale.value = scale
scanner.resolution.value = res
scanner.dwellTime.value = dt
if scanner.dwellTime.value > dt or scanner.dwellTime.value < dt * 0.8:
logging.info("Skipping %s because it doesn't support dwell time", fn)
continue
scanner.scanDelay.value = scan_delay
logging.info("Acquiring %s", fn)
im = det.data.get()
if det.protection.value:
logging.warning("Protection activated")
det.protection.value = False
tiff.export(fn, im)
def testUnicodeName(self):
"""Try filename not fitting in ascii"""
# create a simple greyscale image
size = (256, 512)
dtype = numpy.uint16
data = model.DataArray(numpy.zeros(size[::-1], dtype))
white = (12, 52) # non symmetric position
# less that 2**15 so that we don't have problem with PIL.getpixel() always returning an signed int
data[white[::-1]] = 124
fn = u"𝔸𝔹ℂ" + FILENAME
# export
tiff.export(fn, data)
# check it's here
st = os.stat(fn) # this test also that the file is created
self.assertGreater(st.st_size, 0)
im = Image.open(fn)
self.assertEqual(im.format, "TIFF")
self.assertEqual(im.size, size)
self.assertEqual(im.getpixel(white), 124)
os.remove(fn)
def testUnicodeName(self):
"""Try filename not fitting in ascii"""
# create a simple greyscale image
size = (256, 512)
dtype = numpy.uint16
data = model.DataArray(numpy.zeros(size[::-1], dtype))
white = (12, 52) # non symmetric position
# less that 2**15 so that we don't have problem with PIL.getpixel() always returning an signed int
data[white[::-1]] = 124
fn = u"𝔸𝔹ℂ" + FILENAME
# export
tiff.export(fn, data)
# check it's here
st = os.stat(fn) # this test also that the file is created
self.assertGreater(st.st_size, 0)
im = Image.open(fn)
self.assertEqual(im.format, "TIFF")
self.assertEqual(im.size, size)
self.assertEqual(im.getpixel(white), 124)
os.remove(fn)
def test_one_tile(self):
def getSubData(dast, zoom, rect):
x1, y1, x2, y2 = rect
tiles = []
for x in range(x1, x2 + 1):
tiles_column = []
for y in range(y1, y2 + 1):
tiles_column.append(dast.getTile(x, y, zoom))
tiles.append(tiles_column)
return tiles
FILENAME = u"test" + tiff.EXTENSIONS[0]
POS = (5.0, 7.0)
size = (250, 200)
md = {
model.MD_DIMS: 'YX',
model.MD_POS: POS,
model.MD_PIXEL_SIZE: (1e-6, 1e-6),
}
arr = numpy.arange(size[0] * size[1],
dtype=numpy.uint8).reshape(size[::-1])
data = model.DataArray(arr, metadata=md)
# export
tiff.export(FILENAME, data, pyramid=True)
rdata = tiff.open_data(FILENAME)
tiles = getSubData(rdata.content[0], 0, (0, 0, 0, 0))
merged_img = img.mergeTiles(tiles)
self.assertEqual(merged_img.shape, (200, 250))
self.assertEqual(merged_img.metadata[model.MD_POS], POS)
del rdata
os.remove(FILENAME)
def _MakeReport(optical_image, repetitions, magnification, pixel_size, dwell_time, electron_coordinates):
"""
Creates failure report in case we cannot match the coordinates.
optical_image (2d array): Image from CCD
repetitions (tuple of ints): The number of CL spots are used
dwell_time (float): Time to scan each spot (in s)
electron_coordinates (list of tuples): Coordinates of e-beam grid
"""
path = os.path.join(os.path.expanduser(u"~"), u"odemis-overlay-report",
time.strftime(u"%Y%m%d-%H%M%S"))
os.makedirs(path)
tiff.export(os.path.join(path, u"OpticalGrid.tiff"), optical_image)
report = open(os.path.join(path, u"report.txt"), 'w')
report.write("\n****Overlay Failure Report****\n\n"
+ "\nSEM magnification:\n" + str(magnification)
+ "\nSEM pixel size:\n" + str(pixel_size)
+ "\nGrid size:\n" + str(repetitions)
+ "\n\nMaximum dwell time used:\n" + str(dwell_time)
+ "\n\nElectron coordinates of the scanned grid:\n" + str(electron_coordinates)
+ "\n\nThe optical image of the grid can be seen in OpticalGrid.tiff\n\n")
report.close()
logging.warning("Failed to find overlay. Please check the failure report in %s.",
path)
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
tiff.export(FILENAME, ldata)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
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]
# 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 test_data_to_stream(self):
"""
Check data_to_static_streams
"""
FILENAME = u"test" + tiff.EXTENSIONS[0]
# Create fake data of flurorescence acquisition
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "sem",
model.MD_ACQ_DATE: time.time() - 1,
model.MD_BPP: 16,
model.MD_PIXEL_SIZE: (1e-7, 1e-7), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_DWELL_TIME: 100e-6, # s
model.MD_LENS_MAG: 1200, # ratio
},
{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, 520e-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: (600e-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_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
# 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)
tiff.export(FILENAME, ldata)
# check data
rdata = tiff.read_data(FILENAME)
sts = data_to_static_streams(rdata)
# There should be 5 streams: 3 fluo + 1 SEM + 1 Brightfield
fluo = bright = sem = 0
for s in sts:
if isinstance(s, stream.StaticFluoStream):
fluo += 1
elif isinstance(s, stream.StaticBrightfieldStream):
bright += 1
elif isinstance(s, stream.EMStream):
sem += 1
self.assertEqual(fluo, 3)
self.assertEqual(bright, 1)
self.assertEqual(sem, 1)
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 test_pyramidal_zoom(self):
"""
Draws a view with two streams, one pyramidal stream square completely green,
and the other is a red square with a blue square in the center
"""
mpp = 0.00001
self.view.mpp.value = mpp
self.assertEqual(mpp, self.view.mpp.value)
self.view.show_crosshair.value = False
self.canvas.fit_view_to_next_image = False
# There is no viewport, so FoV is not updated automatically => display
# everything possible
self.view.fov_buffer.value = (1.0, 1.0)
init_pos = (200.5 * mpp, 199.5 * mpp)
FILENAME = u"test" + tiff.EXTENSIONS[0]
# 1 row of 2 tiles
w = 512
h = 250
md = {
model.MD_PIXEL_SIZE: (mpp, mpp),
model.MD_POS: init_pos,
model.MD_DIMS: 'YXC'
}
arr = model.DataArray(numpy.zeros((h, w, 3), dtype="uint8"))
# make it all green
arr[:, :] = [0, 255, 0]
data = model.DataArray(arr, metadata=md)
# export
tiff.export(FILENAME, data, pyramid=True)
acd = tiff.open_data(FILENAME)
stream1 = RGBStream("test", acd.content[0])
im2 = model.DataArray(numpy.zeros((201, 201, 3), dtype="uint8"))
# red background
im2[:, :] = [255, 0, 0]
# Blue square at center
im2[90:110, 90:110] = [0, 0, 255]
im2.metadata[model.MD_PIXEL_SIZE] = (mpp, mpp)
im2.metadata[model.MD_POS] = init_pos
im2.metadata[model.MD_DIMS] = "YXC"
stream2 = RGBStream("s2", im2)
self.view.addStream(stream1)
self.view.addStream(stream2)
self.canvas.shift_view((-200.5, 199.5))
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
px2 = get_rgb(result_im, result_im.Width // 2, result_im.Height // 2)
# center pixel, 2/3 green, 1/3 blue. The green image is the largest image
self.assertEqual(px2, (0, 179, 76))
px2 = get_rgb(result_im, result_im.Width // 2 - 30, result_im.Height // 2 - 30)
# background of the images, 2/3 green, 1/3 red
self.assertEqual(px2, (76, 179, 0))
self.view.mpp.value = mpp
shift = (63, 63)
self.canvas.shift_view(shift)
# merge the images
ratio = 0.5
self.view.merge_ratio.value = ratio
self.assertEqual(ratio, self.view.merge_ratio.value)
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
px = get_rgb(result_im, result_im.Width // 2, result_im.Height // 2)
# center pixel, now pointing to the background of the larger squares
# half red, half green
self.assertEqual(px, (128, 127, 0))
# copy the buffer into a nice image here
result_im = get_image_from_buffer(self.canvas)
px1 = get_rgb(result_im, result_im.Width // 2 + shift[0], result_im.Height // 2 + shift[1])
self.assertEqual(px1, (0, 127, 128)) # Ratio is at 0.5, so 255 becomes 128
px2 = get_rgb(result_im,
result_im.Width // 2 + 200 + shift[0],
result_im.Height // 2 - 200 + shift[1])
self.assertEqual(px2, (0, 0, 0))
self.assertAlmostEqual(1e-05, self.view.mpp.value)
numpy.testing.assert_almost_equal([0.001375, 0.002625], self.view.view_pos.value)
# Fit to content, and check it actually does
self.canvas.fit_view_to_content(recenter=True)
test.gui_loop(0.5)
exp_mpp = (mpp * w) / self.canvas.ClientSize[0]
self.assertAlmostEqual(exp_mpp, self.view.mpp.value)
# after fitting, the center of the view should be the center of the image
numpy.testing.assert_almost_equal(init_pos, self.view.view_pos.value)
# remove green picture
result_im = get_image_from_buffer(self.canvas)
# result_im.SaveFile('tmp3.bmp', wx.BITMAP_TYPE_BMP)
self.view.removeStream(stream1)
test.gui_loop(0.5)
# copy the buffer into a nice image here
result_im = get_image_from_buffer(self.canvas)
# result_im.SaveFile('tmp4.bmp', wx.BITMAP_TYPE_BMP)
self.canvas.fit_view_to_content(recenter=True)
# only .mpp changes, but the image keeps centered
exp_mpp = (mpp * im2.shape[0]) / self.canvas.ClientSize[0]
# TODO: check the precision
self.assertAlmostEqual(exp_mpp, self.view.mpp.value) # ,6
numpy.testing.assert_almost_equal(init_pos, self.view.view_pos.value)
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
# center of the translated red square with blue square on the center
# pixel must be completely blue
px2 = get_rgb(result_im,
result_im.Width // 2 + shift[0],
result_im.Height // 2 + shift[1])
# the center is red
self.assertEqual(px2, (255, 0, 0))
self.canvas.fit_to_content()
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])
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 test_pyramidal_3x2(self):
"""
Draws a view with two streams, one pyramidal stream square completely green,
and the other is a red square with a blue square in the center
"""
mpp = 0.00001
self.view.mpp.value = mpp
self.assertEqual(mpp, self.view.mpp.value)
self.view.show_crosshair.value = False
self.canvas.fit_view_to_next_image = False
# There is no viewport, so FoV is not updated automatically => display
# everything possible
self.view.fov_buffer.value = (1.0, 1.0)
init_pos = (1.0, 2.0)
FILENAME = u"test" + tiff.EXTENSIONS[0]
# 1 row of 2 tiles
w = 600
h = 300
md = {
model.MD_PIXEL_SIZE: (mpp, mpp),
model.MD_POS: init_pos,
model.MD_DIMS: 'YXC'
}
arr = model.DataArray(numpy.zeros((h, w, 3), dtype="uint8"))
# make it all green
arr[:, :] = [0, 255, 0]
data = model.DataArray(arr, metadata=md)
# export
tiff.export(FILENAME, data, pyramid=True)
acd = tiff.open_data(FILENAME)
stream1 = RGBStream("test", acd.content[0])
im2 = model.DataArray(numpy.zeros((800, 800, 3), dtype="uint8"))
# red background
im2[:, :] = [255, 0, 0]
# Blue square at center
im2[390:410, 390:410] = [0, 0, 255]
im2.metadata[model.MD_PIXEL_SIZE] = (mpp, mpp)
im2.metadata[model.MD_POS] = init_pos
im2.metadata[model.MD_DIMS] = "YXC"
stream2 = RGBStream("s2", im2)
self.view.addStream(stream1)
self.view.addStream(stream2)
self.canvas.shift_view((-init_pos[0] / mpp, init_pos[1] / mpp))
test.gui_loop(0.5)
self.view.mpp.value = mpp
# reset the mpp of the view, as it's automatically set to the first image
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
# result_im.SaveFile('big.bmp', wx.BITMAP_TYPE_BMP)
px2 = get_rgb(result_im, result_im.Width // 2, result_im.Height // 2)
# center pixel, 1/3 green, 2/3 blue. The red image is the largest image
self.assertEqual(px2, (0, 76, 179))
px2 = get_rgb(result_im, result_im.Width // 2 - 30, result_im.Height // 2 - 30)
# background of the images, 1/3 green, 2/3 red
self.assertEqual(px2, (179, 76, 0))
def test_pyramidal_zoom(self):
"""
Draws a view with two streams, one pyramidal stream square completely green,
and the other is a red square with a blue square in the center
"""
mpp = 0.00001
self.view.mpp.value = mpp
self.assertEqual(mpp, self.view.mpp.value)
self.view.show_crosshair.value = False
self.canvas.fit_view_to_next_image = False
# There is no viewport, so FoV is not updated automatically => display
# everything possible
self.view.fov_buffer.value = (1.0, 1.0)
init_pos = (200.5 * mpp, 199.5 * mpp)
FILENAME = u"test" + tiff.EXTENSIONS[0]
# 1 row of 2 tiles
w = 512
h = 250
md = {
model.MD_PIXEL_SIZE: (mpp, mpp),
model.MD_POS: init_pos,
model.MD_DIMS: 'YXC'
}
arr = model.DataArray(numpy.zeros((h, w, 3), dtype="uint8"))
# make it all green
arr[:, :] = [0, 255, 0]
data = model.DataArray(arr, metadata=md)
# export
tiff.export(FILENAME, data, pyramid=True)
acd = tiff.open_data(FILENAME)
stream1 = RGBStream("test", acd.content[0])
im2 = model.DataArray(numpy.zeros((201, 201, 3), dtype="uint8"))
# red background
im2[:, :] = [255, 0, 0]
# Blue square at center
im2[90:110, 90:110] = [0, 0, 255]
im2.metadata[model.MD_PIXEL_SIZE] = (mpp, mpp)
im2.metadata[model.MD_POS] = init_pos
im2.metadata[model.MD_DIMS] = "YXC"
stream2 = RGBStream("s2", im2)
self.view.addStream(stream1)
self.view.addStream(stream2)
self.canvas.shift_view((-200.5, 199.5))
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
px2 = get_rgb(result_im, result_im.Width // 2, result_im.Height // 2)
# center pixel, 2/3 green, 1/3 blue. The green image is the largest image
self.assertEqual(px2, (0, 179, 76))
px2 = get_rgb(result_im, result_im.Width // 2 - 30,
result_im.Height // 2 - 30)
# background of the images, 2/3 green, 1/3 red
self.assertEqual(px2, (76, 179, 0))
self.view.mpp.value = mpp
shift = (63, 63)
self.canvas.shift_view(shift)
# merge the images
ratio = 0.5
self.view.merge_ratio.value = ratio
self.assertEqual(ratio, self.view.merge_ratio.value)
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
px = get_rgb(result_im, result_im.Width // 2, result_im.Height // 2)
# center pixel, now pointing to the background of the larger squares
# half red, half green
self.assertEqual(px, (128, 127, 0))
# copy the buffer into a nice image here
result_im = get_image_from_buffer(self.canvas)
px1 = get_rgb(result_im, result_im.Width // 2 + shift[0],
result_im.Height // 2 + shift[1])
self.assertEqual(px1,
(0, 127, 128)) # Ratio is at 0.5, so 255 becomes 128
px2 = get_rgb(result_im, result_im.Width // 2 + 200 + shift[0],
result_im.Height // 2 - 200 + shift[1])
self.assertEqual(px2, (0, 0, 0))
self.assertAlmostEqual(1e-05, self.view.mpp.value)
numpy.testing.assert_almost_equal([0.001375, 0.002625],
self.view.view_pos.value)
# Fit to content, and check it actually does
self.canvas.fit_view_to_content(recenter=True)
test.gui_loop(0.5)
exp_mpp = (mpp * w) / self.canvas.ClientSize[0]
self.assertAlmostEqual(exp_mpp, self.view.mpp.value)
# after fitting, the center of the view should be the center of the image
numpy.testing.assert_almost_equal(init_pos, self.view.view_pos.value)
# remove green picture
result_im = get_image_from_buffer(self.canvas)
# result_im.SaveFile('tmp3.bmp', wx.BITMAP_TYPE_BMP)
self.view.removeStream(stream1)
test.gui_loop(0.5)
# copy the buffer into a nice image here
result_im = get_image_from_buffer(self.canvas)
# result_im.SaveFile('tmp4.bmp', wx.BITMAP_TYPE_BMP)
self.canvas.fit_view_to_content(recenter=True)
# only .mpp changes, but the image keeps centered
exp_mpp = (mpp * im2.shape[0]) / self.canvas.ClientSize[0]
# TODO: check the precision
self.assertAlmostEqual(exp_mpp, self.view.mpp.value) # ,6
numpy.testing.assert_almost_equal(init_pos, self.view.view_pos.value)
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
# center of the translated red square with blue square on the center
# pixel must be completely blue
px2 = get_rgb(result_im, result_im.Width // 2 + shift[0],
result_im.Height // 2 + shift[1])
# the center is red
self.assertEqual(px2, (255, 0, 0))
self.canvas.fit_to_content()
def test_data_to_stream_pyramidal(self):
"""
Check data_to_static_streams with pyramidal images using DataArrayShadows
"""
FILENAME = u"test" + tiff.EXTENSIONS[0]
# Create fake data of flurorescence acquisition
metadata = [
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "sem",
model.MD_ACQ_DATE: time.time() - 1,
model.MD_BPP: 16,
model.MD_PIXEL_SIZE: (1e-7, 1e-7), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_DWELL_TIME: 100e-6, # s
model.MD_LENS_MAG: 1200, # ratio
},
{
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: (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: (600e-9, 620e-9), # m
model.MD_OUT_WL: (620e-9, 650e-9), # m
model.MD_ROTATION: 0.1, # rad
model.MD_SHEAR: 0,
},
]
# 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)
tiff.export(FILENAME, ldata, pyramid=True)
# check data
rdata = open_acquisition(FILENAME)
sts = data_to_static_streams(rdata)
# There should be 3 streams: 2 fluo + 1 SEM
fluo = sem = 0
for s in sts:
if isinstance(s, stream.StaticFluoStream):
fluo += 1
elif isinstance(s, stream.EMStream):
sem += 1
self.assertEqual(fluo, 2)
self.assertEqual(sem, 1)
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_SW_VERSION: "aa 4.56",
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
tiff.export(FILENAME, ldata)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
im = Image.open(FILENAME)
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)
# check the 2D data
im.seek(i + 1)
self.assertEqual(im.size, size)
self.assertEqual(im.getpixel((1, 1)), 0)
def testReadMDMnchr(self):
"""
Checks that we can read back the metadata of a monochromator image.
It's 32 bits, and the same shape as the ETD
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake monochromator",
model.MD_SAMPLES_PER_PIXEL: 1,
model.MD_DESCRIPTION: "test",
model.MD_ACQ_DATE: time.time(),
model.MD_HW_VERSION: "Unknown",
model.MD_DWELL_TIME: 0.001, # s
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (1.2e-3, -30e-3), # m
model.MD_LENS_MAG: 100, # ratio
model.MD_OUT_WL: (2.8e-07, 3.1e-07)
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_VERSION: "Unknown",
model.MD_SAMPLES_PER_PIXEL: 1,
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "etd",
model.MD_ACQ_DATE: time.time(),
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_LENS_MAG: 100, # ratio
model.MD_DWELL_TIME: 1e-06, # s
},
{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_VERSION: "Unknown",
model.MD_SAMPLES_PER_PIXEL: 1,
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "Anchor region",
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (10e-3, 30e-3), # m
model.MD_LENS_MAG: 100, # ratio
model.MD_AD_LIST: (1437117571.733935, 1437117571.905051),
model.MD_DWELL_TIME: 1e-06, # s
},
]
# create 3 greyscale images
ldata = []
mnchr_size = (6, 5)
sem_size = (128, 128)
# Monochromator
mnchr_dtype = numpy.dtype("uint32")
a = model.DataArray(numpy.zeros(mnchr_size[::-1], mnchr_dtype), metadata[0])
ldata.append(a)
# Normal SEM
sem_dtype = numpy.dtype("uint16")
b = model.DataArray(numpy.zeros(mnchr_size[::-1], sem_dtype), metadata[1])
ldata.append(b)
# Anchor data
c = model.DataArray(numpy.zeros(sem_size[::-1], sem_dtype), metadata[2])
ldata.append(c)
# export
tiff.export(FILENAME, ldata)
# 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].copy()
img.mergeMetadata(md)
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])
# Check that output wavelength range was correctly read back
owl = rdata[0].metadata[model.MD_OUT_WL] # nm
md = metadata[0]
self.assertTrue((md[model.MD_OUT_WL][0] <= owl[0] and
owl[1] <= md[model.MD_OUT_WL][-1]))
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 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
tiff.export(FILENAME, ldata)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
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]
# 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 testMetadata(self):
"""
checks that the metadata is saved with every picture
"""
size = (512, 256, 1)
dtype = numpy.dtype("uint64")
metadata = {model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_HW_VERSION: "2.54",
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: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, #s
model.MD_IN_WL: (500e-9, 520e-9), #m
}
data = model.DataArray(numpy.zeros((size[1], size[0]), dtype), metadata=metadata)
# export
tiff.export(FILENAME, data)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
imo = libtiff.tiff.TIFFfile(FILENAME)
self.assertEqual(len(imo.IFD), 1, "Tiff file doesn't contain just one image")
ifd = imo.IFD[0]
# check format
self.assertEqual(size[2], ifd.get_value("SamplesPerPixel"))
# BitsPerSample is the actual format, not model.MD_BPP
self.assertEqual(dtype.itemsize * 8, ifd.get_value("BitsPerSample")[0])
self.assertEqual(T.SAMPLEFORMAT_UINT, ifd.get_value("SampleFormat")[0])
# check metadata
self.assertEqual("Odemis " + odemis.__version__, ifd.get_value("Software"))
self.assertEqual(metadata[model.MD_HW_NAME], ifd.get_value("Make"))
self.assertEqual(metadata[model.MD_HW_VERSION] + " (driver %s)" % metadata[model.MD_SW_VERSION],
ifd.get_value("Model"))
self.assertEqual(metadata[model.MD_DESCRIPTION], ifd.get_value("PageName"))
yres = rational2float(ifd.get_value("YResolution"))
self.assertAlmostEqual(1 / metadata[model.MD_PIXEL_SIZE][1], yres * 100)
ypos = rational2float(ifd.get_value("YPosition"))
self.assertAlmostEqual(metadata[model.MD_POS][1], (ypos / 100) - 1)
# check OME-TIFF metadata
omemd = imo.IFD[0].get_value("ImageDescription")
self.assertTrue(omemd.startswith('<?xml') or omemd[:4].lower()=='<ome')
# remove "xmlns" which is the default namespace and is appended everywhere
omemd = re.sub('xmlns="http://www.openmicroscopy.org/Schemas/OME/....-.."',
"", omemd, count=1)
root = ET.fromstring(omemd)
# ns = {"ome": root.tag.rsplit("}")[0][1:]} # read the default namespace
roottag = root.tag.split("}")[-1]
self.assertEqual(roottag.lower(), "ome")
detect_name = root.find("Instrument/Detector").get("Model")
self.assertEqual(metadata[model.MD_HW_NAME], detect_name)
self.assertEqual(len(root.findall("Image")), 1)
ime = root.find("Image")
ifdn = int(ime.find("Pixels/TiffData").get("IFD", "0"))
self.assertEqual(ifdn, 0)
sx = int(ime.find("Pixels").get("SizeX")) # px
self.assertEqual(size[0], sx)
psx = float(ime.find("Pixels").get("PhysicalSizeX")) # um
self.assertAlmostEqual(metadata[model.MD_PIXEL_SIZE][0], psx * 1e-6)
exp = float(ime.find("Pixels/Plane").get("ExposureTime")) # s
self.assertAlmostEqual(metadata[model.MD_EXP_TIME], exp)
iwl = float(ime.find("Pixels/Channel").get("ExcitationWavelength")) # nm
iwl *= 1e-9
self.assertTrue((metadata[model.MD_IN_WL][0] <= iwl and
iwl <= metadata[model.MD_IN_WL][1]))
bin_str = ime.find("Pixels/Channel/DetectorSettings").get("Binning")
exp_bin = "%dx%d" % metadata[model.MD_BINNING]
self.assertEqual(bin_str, exp_bin)
def test_data_to_stream(self):
"""
Check data_to_static_streams
"""
FILENAME = u"test" + tiff.EXTENSIONS[0]
# Create fake data of flurorescence acquisition
metadata = [
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "sem",
model.MD_ACQ_DATE: time.time() - 1,
model.MD_BPP: 16,
model.MD_PIXEL_SIZE: (1e-7, 1e-7), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_DWELL_TIME: 100e-6, # s
model.MD_LENS_MAG: 1200, # ratio
},
{
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, 520e-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: (600e-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_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
# 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)
tiff.export(FILENAME, ldata)
# check data
rdata = tiff.read_data(FILENAME)
sts = data_to_static_streams(rdata)
# There should be 5 streams: 3 fluo + 1 SEM + 1 Brightfield
fluo = bright = sem = 0
for s in sts:
if isinstance(s, stream.StaticFluoStream):
fluo += 1
elif isinstance(s, stream.StaticBrightfieldStream):
bright += 1
elif isinstance(s, stream.EMStream):
sem += 1
self.assertEqual(fluo, 3)
self.assertEqual(bright, 1)
self.assertEqual(sem, 1)
def _DoAlignSpot(future, ccd, stage, escan, focus, type, dfbkg, rng_f,
logpath):
"""
Adjusts settings until we have a clear and well focused optical spot image,
detects the spot and manipulates the stage so as to move the spot center to
the optical image center. If no spot alignment is achieved an exception is
raised.
future (model.ProgressiveFuture): Progressive future provided by the wrapper
ccd (model.DigitalCamera): The CCD
stage (model.Actuator): The stage
escan (model.Emitter): The e-beam scanner
focus (model.Actuator): The optical focus
type (string): Type of move in order to align
dfbkg (model.DataFlow): dataflow of se- or bs- detector
rng_f (tuple of floats): range to apply Autofocus on if needed
returns (float): Final distance to the center #m
raises:
CancelledError() if cancelled
IOError
"""
init_binning = ccd.binning.value
init_et = ccd.exposureTime.value
init_cres = ccd.resolution.value
init_scale = escan.scale.value
init_eres = escan.resolution.value
# TODO: allow to pass the precision as argument. As for the Delphi, we don't
# need such an accuracy on the alignment (as it's just for twin stage calibration).
# TODO: take logpath as argument, to store images later on
logging.debug("Starting Spot alignment...")
try:
if future._task_state == CANCELLED:
raise CancelledError()
# Configure CCD and set ebeam to spot mode
logging.debug("Configure CCD and set ebeam to spot mode...")
_set_blanker(escan, False)
ccd.binning.value = ccd.binning.clip((2, 2))
ccd.resolution.value = ccd.resolution.range[1]
ccd.exposureTime.value = 0.3
escan.scale.value = (1, 1)
escan.resolution.value = (1, 1)
if future._task_state == CANCELLED:
raise CancelledError()
logging.debug("Adjust exposure time...")
if dfbkg is None:
# Long exposure time to compensate for no background subtraction
ccd.exposureTime.value = 1.1
else:
# TODO: all this code to decide whether to pick exposure 0.3 or 1.5?
# => KISS! Use always 1s... or allow up to 5s?
# Estimate noise and adjust exposure time based on "Rose criterion"
image = AcquireNoBackground(ccd, dfbkg)
snr = MeasureSNR(image)
while snr < 5 and ccd.exposureTime.value < 1.5:
ccd.exposureTime.value = ccd.exposureTime.value + 0.2
image = AcquireNoBackground(ccd, dfbkg)
snr = MeasureSNR(image)
logging.debug("Using exposure time of %g s",
ccd.exposureTime.value)
if logpath:
tiff.export(os.path.join(logpath, "align_spot_init.tiff"),
[image])
hqet = ccd.exposureTime.value # exposure time for high-quality (binning == 1x1)
if ccd.binning.value == (2, 2):
hqet *= 4 # To compensate for smaller binning
logging.debug("Trying to find spot...")
for i in range(3):
if future._task_state == CANCELLED:
raise CancelledError()
if i == 0:
future._centerspotf = CenterSpot(ccd, stage, escan, ROUGH_MOVE,
type, dfbkg)
dist, vector = future._centerspotf.result()
elif i == 1:
logging.debug("Spot not found, auto-focusing...")
try:
# When Autofocus set binning 8 if possible, and use exhaustive
# method to be sure not to miss the spot.
ccd.binning.value = ccd.binning.clip((8, 8))
future._autofocusf = autofocus.AutoFocus(
ccd,
None,
focus,
dfbkg,
rng_focus=rng_f,
method=MTD_EXHAUSTIVE)
lens_pos, fm_level = future._autofocusf.result()
# Update progress of the future
future.set_progress(end=time.time() +
estimateAlignmentTime(hqet, dist, 1))
except IOError as ex:
logging.error("Autofocus on spot image failed: %s", ex)
raise IOError('Spot alignment failure. AutoFocus failed.')
logging.debug("Trying again to find spot...")
future._centerspotf = CenterSpot(ccd, stage, escan, ROUGH_MOVE,
type, dfbkg)
dist, vector = future._centerspotf.result()
elif i == 2:
if dfbkg is not None:
# In some case background subtraction goes wrong, and makes
# things worse, so try without.
logging.debug(
"Trying again to find spot, without background subtraction..."
)
dfbkg = None
future._centerspotf = CenterSpot(ccd, stage, escan,
ROUGH_MOVE, type, dfbkg)
dist, vector = future._centerspotf.result()
if dist is not None:
if logpath:
image = AcquireNoBackground(ccd, dfbkg)
tiff.export(os.path.join(logpath, "align_spot_found.tiff"),
[image])
break
else:
raise IOError('Spot alignment failure. Spot not found')
ccd.binning.value = (1, 1)
ccd.exposureTime.value = ccd.exposureTime.clip(hqet)
# Update progress of the future
future.set_progress(end=time.time() +
estimateAlignmentTime(hqet, dist, 1))
logging.debug("After rough alignment, spot center is at %s m", vector)
# Limit FoV to save time
logging.debug("Cropping FoV...")
CropFoV(ccd, dfbkg)
if future._task_state == CANCELLED:
raise CancelledError()
# Update progress of the future
future.set_progress(end=time.time() +
estimateAlignmentTime(hqet, dist, 0))
# Center spot
if future._task_state == CANCELLED:
raise CancelledError()
logging.debug("Aligning spot...")
# No need to be so precise with a stage move (eg, on the DELPHI), as the
# stage is quite imprecise anyway and the alignment is further adjusted
# using the beam shift (later).
mx_steps = FINE_MOVE if type != STAGE_MOVE else ROUGH_MOVE
future._centerspotf = CenterSpot(ccd, stage, escan, mx_steps, type,
dfbkg, logpath)
dist, vector = future._centerspotf.result()
if dist is None:
raise IOError('Spot alignment failure. Cannot reach the center.')
logging.info("After fine alignment, spot center is at %s m", vector)
return dist, vector
finally:
ccd.binning.value = init_binning
ccd.exposureTime.value = init_et
ccd.resolution.value = init_cres
escan.scale.value = init_scale
escan.resolution.value = init_eres
_set_blanker(escan, True)
with future._alignment_lock:
future._done.set()
if future._task_state == CANCELLED:
raise CancelledError()
future._task_state = FINISHED
def test_pyramidal_one_tile(self):
"""
Draws a view with two streams, one pyramidal stream square completely green,
and the other is a red square with a blue square in the center
"""
mpp = 0.00001
self.view.mpp.value = mpp
self.assertEqual(mpp, self.view.mpp.value)
self.view.show_crosshair.value = False
self.canvas.fit_view_to_next_image = False
FILENAME = u"test" + tiff.EXTENSIONS[0]
w = 201
h = 201
md = {
model.MD_PIXEL_SIZE: (mpp, mpp),
model.MD_POS: (200.5 * mpp, 199.5 * mpp),
model.MD_DIMS: 'YXC'
}
arr = model.DataArray(numpy.zeros((h, w, 3), dtype="uint8"))
# make it all green
arr[:, :] = [0, 255, 0]
data = model.DataArray(arr, metadata=md)
# export
tiff.export(FILENAME, data, pyramid=True)
acd = tiff.open_data(FILENAME)
stream1 = RGBStream("test", acd.content[0])
im2 = model.DataArray(numpy.zeros((201, 201, 3), dtype="uint8"))
# red background
im2[:, :] = [255, 0, 0]
# Blue square at center
im2[90:110, 90:110] = [0, 0, 255]
# 200, 200 => outside of the im1
# (+0.5, -0.5) to make it really in the center of the pixel
im2.metadata[model.MD_PIXEL_SIZE] = (mpp, mpp)
im2.metadata[model.MD_POS] = (200.5 * mpp, 199.5 * mpp)
im2.metadata[model.MD_DIMS] = "YXC"
stream2 = RGBStream("s2", im2)
self.view.addStream(stream1)
self.view.addStream(stream2)
# Ensure the merge ratio of the images is 0.5
ratio = 0.5
self.view.merge_ratio.value = ratio
self.assertEqual(ratio, self.view.merge_ratio.value)
test.gui_loop(0.5)
self.canvas.shift_view((-200.5, 199.5))
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
px2 = get_rgb(result_im, result_im.Width // 2, result_im.Height // 2)
# the center pixel should be half green and half blue
self.assertEqual(px2, (0, math.ceil(255 / 2), math.floor(255 / 2)))
px2 = get_rgb(result_im, result_im.Width // 2 - 30,
result_im.Height // 2 - 30)
# (-30, -30) pixels away from the center, the background of the images,
# should be half green and half red
self.assertEqual(px2, (math.floor(255 / 2), math.ceil(255 / 2), 0))
self.view.mpp.value = mpp
shift = (63, 63)
self.canvas.shift_view(shift)
# change the merge ratio of the images, take 1/3 of the first image and 2/3 of the second
ratio = 1 / 3
self.view.merge_ratio.value = ratio
self.assertEqual(ratio, self.view.merge_ratio.value)
# it's supposed to update in less than 0.5s
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
px = get_rgb(result_im, result_im.Width // 2, result_im.Height // 2)
# center pixel, now pointing to the background of the larger squares
# 2/3 red, 1/3 green
self.assertEqual(px, (255 * 2 / 3, 255 / 3, 0))
# copy the buffer into a nice image here
result_im = get_image_from_buffer(self.canvas)
px1 = get_rgb(result_im, result_im.Width // 2 + shift[0],
result_im.Height // 2 + shift[1])
self.assertEqual(px1, (0, 255 / 3, 255 * 2 / 3))
px2 = get_rgb(result_im, result_im.Width // 2 + 200 + shift[0],
result_im.Height // 2 - 200 + shift[1])
self.assertEqual(px2, (0, 0, 0))
# remove first picture with a green background, only the red image with blue center is left
self.view.removeStream(stream1)
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
# center of the translated red square with blue square on the center
# pixel must be completely blue
px2 = get_rgb(result_im, result_im.Width // 2 + shift[0],
result_im.Height // 2 + shift[1])
self.assertEqual(px2, (0, 0, 255))
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 test_pyramidal_zoom(self):
"""
Draws a view with two streams, one pyramidal stream square completely green,
and the other is a red square with a blue square in the center
"""
mpp = 0.00001
self.view.mpp.value = mpp
self.assertEqual(mpp, self.view.mpp.value)
self.view.show_crosshair.value = False
self.canvas.fit_view_to_next_image = False
# There is no viewport, so FoV is not updated automatically => display
# everything possible
self.view.fov_buffer.value = (1.0, 1.0)
init_pos = (200.5 * mpp, 199.5 * mpp)
FILENAME = u"test" + tiff.EXTENSIONS[0]
# 1 row of 2 tiles
w = 512
h = 250
md = {
model.MD_PIXEL_SIZE: (mpp, mpp),
model.MD_POS: init_pos,
model.MD_DIMS: 'YXC'
}
arr = model.DataArray(numpy.zeros((h, w, 3), dtype="uint8"))
# make it all green
arr[:, :] = [0, 255, 0]
data = model.DataArray(arr, metadata=md)
# export
tiff.export(FILENAME, data, pyramid=True)
acd = tiff.open_data(FILENAME)
stream1 = RGBStream("test", acd.content[0])
im2 = model.DataArray(numpy.zeros((201, 201, 3), dtype="uint8"))
# red background
im2[:, :] = [255, 0, 0]
# Blue square at center
im2[90:110, 90:110] = [0, 0, 255]
im2.metadata[model.MD_PIXEL_SIZE] = (mpp, mpp)
im2.metadata[model.MD_POS] = init_pos
im2.metadata[model.MD_DIMS] = "YXC"
stream2 = RGBStream("s2", im2)
self.view.addStream(
stream1
) # completely green background and a larger image than stream2
self.view.addStream(
stream2) # red background with blue square at the center
# Ensure the merge ratio of the images is 0.5
ratio = 0.5
self.view.merge_ratio.value = ratio
self.assertEqual(ratio, self.view.merge_ratio.value)
self.canvas.shift_view((-200.5, 199.5))
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
px2 = get_rgb(result_im, result_im.Width // 2, result_im.Height // 2)
# the center pixel should be half green and half blue
self.assertEqual(px2, (0, math.floor(255 / 2), math.ceil(255 / 2)))
px2 = get_rgb(result_im, result_im.Width // 2 - 30,
result_im.Height // 2 - 30)
# (-30, -30) pixels away from the center, the background of the images,
# should be half green and half red
self.assertEqual(px2, (math.ceil(255 / 2), math.floor(255 / 2), 0))
self.view.mpp.value = mpp
shift = (63, 63)
self.canvas.shift_view(shift)
# change the merge ratio of the images, take 1/3 of the first image and 2/3 of the second
ratio = 1 / 3
self.view.merge_ratio.value = ratio
self.assertEqual(ratio, self.view.merge_ratio.value)
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
px = get_rgb(result_im, result_im.Width // 2, result_im.Height // 2)
# center pixel, now pointing to the background of the larger squares
# 1/3 red, 2/3 green
self.assertEqual(px, (255 / 3, 255 * 2 / 3, 0))
# copy the buffer into a nice image here
result_im = get_image_from_buffer(self.canvas)
# because the canvas is shifted, getting the rgb value of the new center + shift
# should be the old center rgb value.
px1 = get_rgb(result_im, result_im.Width // 2 + shift[0],
result_im.Height // 2 + shift[1])
# the pixel should point to the old center values, 2/3 green and 1/3 blue
self.assertEqual(px1, (0, 255 * 2 / 3, 255 / 3))
px2 = get_rgb(result_im, result_im.Width // 2 + 200 + shift[0],
result_im.Height // 2 - 200 + shift[1])
self.assertEqual(px2, (0, 0, 0))
self.assertAlmostEqual(1e-05, self.view.mpp.value)
numpy.testing.assert_almost_equal([0.001375, 0.002625],
self.view.view_pos.value)
# Fit to content, and check it actually does
self.canvas.fit_view_to_content(recenter=True)
test.gui_loop(0.5)
exp_mpp = (mpp * w) / self.canvas.ClientSize[0]
self.assertAlmostEqual(exp_mpp, self.view.mpp.value)
# after fitting, the center of the view should be the center of the image
numpy.testing.assert_almost_equal(init_pos, self.view.view_pos.value)
# remove green picture
result_im = get_image_from_buffer(self.canvas)
# result_im.SaveFile('tmp3.bmp', wx.BITMAP_TYPE_BMP)
self.view.removeStream(stream1)
test.gui_loop(0.5)
# copy the buffer into a nice image here
result_im = get_image_from_buffer(self.canvas)
# result_im.SaveFile('tmp4.bmp', wx.BITMAP_TYPE_BMP)
self.canvas.fit_view_to_content(recenter=True)
# only .mpp changes, but the image keeps centered
exp_mpp = (mpp * im2.shape[1]) / self.canvas.ClientSize[1]
# The expected mpp is around 5e-6 m/px, therefore the default of checking
# 7 places does not test the required precision.
self.assertAlmostEqual(exp_mpp, self.view.mpp.value, places=16)
numpy.testing.assert_almost_equal(init_pos, self.view.view_pos.value)
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
# center of the translated red square with blue square on the center
# pixel must be completely blue
px2 = get_rgb(result_im, result_im.Width // 2 + shift[0],
result_im.Height // 2 + shift[1])
# the center is red
self.assertEqual(px2, (255, 0, 0))
self.canvas.fit_to_content()
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 test_pyramidal_3x2(self):
"""
Draws a view with two streams, one pyramidal stream square completely green,
and the other is a red square with a blue square in the center
"""
mpp = 0.00001
self.view.mpp.value = mpp
self.assertEqual(mpp, self.view.mpp.value)
self.view.show_crosshair.value = False
self.canvas.fit_view_to_next_image = False
# There is no viewport, so FoV is not updated automatically => display
# everything possible
self.view.fov_buffer.value = (1.0, 1.0)
init_pos = (1.0, 2.0)
FILENAME = u"test" + tiff.EXTENSIONS[0]
# 1 row of 2 tiles
w = 600
h = 300
md = {
model.MD_PIXEL_SIZE: (mpp, mpp),
model.MD_POS: init_pos,
model.MD_DIMS: 'YXC'
}
arr = model.DataArray(numpy.zeros((h, w, 3), dtype="uint8"))
# make it all green
arr[:, :] = [0, 255, 0]
data = model.DataArray(arr, metadata=md)
# export
tiff.export(FILENAME, data, pyramid=True)
acd = tiff.open_data(FILENAME)
stream1 = RGBStream("test", acd.content[0])
im2 = model.DataArray(numpy.zeros((800, 800, 3), dtype="uint8"))
# red background
im2[:, :] = [255, 0, 0]
# Blue square at center
im2[390:410, 390:410] = [0, 0, 255]
im2.metadata[model.MD_PIXEL_SIZE] = (mpp, mpp)
im2.metadata[model.MD_POS] = init_pos
im2.metadata[model.MD_DIMS] = "YXC"
stream2 = RGBStream("s2", im2)
self.view.addStream(stream1)
self.view.addStream(stream2)
self.canvas.shift_view((-init_pos[0] / mpp, init_pos[1] / mpp))
test.gui_loop(0.5)
self.view.mpp.value = mpp
# reset the mpp of the view, as it's automatically set to the first image
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
# result_im.SaveFile('big.bmp', wx.BITMAP_TYPE_BMP)
px2 = get_rgb(result_im, result_im.Width // 2, result_im.Height // 2)
# center pixel, half green, half blue. The red image is the largest image
self.assertEqual(px2, (0, math.ceil(255 / 2), math.floor(255 / 2)))
px2 = get_rgb(result_im, result_im.Width // 2 - 30,
result_im.Height // 2 - 30)
# background of the images, half green, half red
self.assertEqual(px2, (math.floor(255 / 2), math.ceil(255 / 2), 0))
def test_pyramidal_one_tile(self):
"""
Draws a view with two streams, one pyramidal stream square completely green,
and the other is a red square with a blue square in the center
"""
mpp = 0.00001
self.view.mpp.value = mpp
self.assertEqual(mpp, self.view.mpp.value)
self.view.show_crosshair.value = False
self.canvas.fit_view_to_next_image = False
FILENAME = u"test" + tiff.EXTENSIONS[0]
w = 201
h = 201
md = {
model.MD_PIXEL_SIZE: (mpp, mpp),
model.MD_POS: (200.5 * mpp, 199.5 * mpp),
model.MD_DIMS: 'YXC'
}
arr = model.DataArray(numpy.zeros((h, w, 3), dtype="uint8"))
# make it all green
arr[:, :] = [0, 255, 0]
data = model.DataArray(arr, metadata=md)
# export
tiff.export(FILENAME, data, pyramid=True)
acd = tiff.open_data(FILENAME)
stream1 = RGBStream("test", acd.content[0])
im2 = model.DataArray(numpy.zeros((201, 201, 3), dtype="uint8"))
# red background
im2[:, :] = [255, 0, 0]
# Blue square at center
im2[90:110, 90:110] = [0, 0, 255]
# 200, 200 => outside of the im1
# (+0.5, -0.5) to make it really in the center of the pixel
im2.metadata[model.MD_PIXEL_SIZE] = (mpp, mpp)
im2.metadata[model.MD_POS] = (200.5 * mpp, 199.5 * mpp)
im2.metadata[model.MD_DIMS] = "YXC"
stream2 = RGBStream("s2", im2)
self.view.addStream(stream1)
self.view.addStream(stream2)
test.gui_loop(0.5)
self.canvas.shift_view((-200.5, 199.5))
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
px2 = get_rgb(result_im, result_im.Width // 2, result_im.Height // 2)
# center pixel, 1/3 green, 2/3 blue
self.assertEqual(px2, (0, 76, 179))
px2 = get_rgb(result_im, result_im.Width // 2 - 30, result_im.Height // 2 - 30)
# background of the images, 1/3 green, 2/3 red
self.assertEqual(px2, (179, 76, 0))
self.view.mpp.value = mpp
shift = (63, 63)
self.canvas.shift_view(shift)
# merge the images
ratio = 0.5
self.view.merge_ratio.value = ratio
# self.assertEqual(ratio, self.view.merge_ratio.value)
# it's supposed to update in less than 0.5s
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
px = get_rgb(result_im, result_im.Width // 2, result_im.Height // 2)
# center pixel, now pointing to the background of the larger squares
# half red, half green
self.assertEqual(px, (127, 128, 0))
# copy the buffer into a nice image here
result_im = get_image_from_buffer(self.canvas)
px1 = get_rgb(result_im, result_im.Width // 2 + shift[0], result_im.Height // 2 + shift[1])
self.assertEqual(px1, (0, 128, 127)) # Ratio is at 0.5, so 255 becomes 128
px2 = get_rgb(result_im,
result_im.Width // 2 + 200 + shift[0],
result_im.Height // 2 - 200 + shift[1])
self.assertEqual(px2, (0, 0, 0))
# remove first picture
self.view.removeStream(stream1)
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
# center of the translated red square with blue square on the center
# pixel must be completely blue
px2 = get_rgb(result_im,
result_im.Width // 2 + shift[0],
result_im.Height // 2 + shift[1])
self.assertEqual(px2, (0, 0, 255))
def _DoCenterSpot(future, ccd, stage, escan, mx_steps, type, dfbkg, logpath):
"""
Iteratively acquires an optical image, finds the coordinates of the spot
(center) and moves the stage to this position. Repeats until the found
coordinates are at the center of the optical image or a maximum number of
steps is reached.
future (model.ProgressiveFuture): Progressive future provided by the wrapper
ccd (model.DigitalCamera): The CCD
stage (model.Actuator): The stage
escan (model.Emitter): The e-beam scanner
mx_steps (int): Maximum number of steps to reach the center
type (*_MOVE or BEAM_SHIFT): Type of move in order to align
dfbkg (model.DataFlow or None): If provided, will be used to start/stop
the e-beam emmision (it must be the dataflow of se- or bs-detector) in
order to do background subtraction. If None, no background subtraction is
performed.
returns (float or None): Final distance to the center (m)
(2 floats): vector to the spot from the center (m, m)
raises:
CancelledError() if cancelled
"""
try:
logging.debug("Aligning spot...")
steps = 0
# Stop once spot is found on the center of the optical image
dist = None
while True:
if future._spot_center_state == CANCELLED:
raise CancelledError()
# Wait to make sure no previous spot is detected
image = AcquireNoBackground(ccd, dfbkg)
if logpath:
tiff.export(
os.path.join(logpath, "center_spot_%d.tiff" % (steps, )),
[image])
try:
spot_pxs = FindSpot(image)
except LookupError:
return None, None
# Center of optical image
pixelSize = image.metadata[model.MD_PIXEL_SIZE]
center_pxs = (image.shape[1] / 2, image.shape[0] / 2)
# Epsilon distance below which the lens is considered centered. The worse of:
# * 1.5 pixels (because the CCD resolution cannot give us better)
# * 1 µm (because that's the best resolution of our actuators)
err_mrg = max(1.5 * pixelSize[0], 1e-06) # m
tab_pxs = [a - b for a, b in zip(spot_pxs, center_pxs)]
tab = (tab_pxs[0] * pixelSize[0], tab_pxs[1] * pixelSize[1])
logging.debug("Found spot @ %s px", spot_pxs)
# Stop if spot near the center or max number of steps is reached
dist = math.hypot(*tab)
if steps >= mx_steps or dist <= err_mrg:
break
# Move to the found spot
if type == OBJECTIVE_MOVE:
f = stage.moveRel({"x": tab[0], "y": -tab[1]})
f.result()
elif type == STAGE_MOVE:
f = stage.moveRel({"x": -tab[0], "y": tab[1]})
f.result()
else:
escan.translation.value = (-tab_pxs[0], -tab_pxs[1])
steps += 1
# Update progress of the future
future.set_progress(
end=time.time() +
estimateCenterTime(ccd.exposureTime.value, dist))
return dist, tab
finally:
with future._center_lock:
if future._spot_center_state == CANCELLED:
raise CancelledError()
future._spot_center_state = FINISHED
def test_data_to_stream_pyramidal(self):
"""
Check data_to_static_streams with pyramidal images using DataArrayShadows
"""
FILENAME = u"test" + tiff.EXTENSIONS[0]
# Create fake data of flurorescence acquisition
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "sem",
model.MD_ACQ_DATE: time.time() - 1,
model.MD_BPP: 16,
model.MD_PIXEL_SIZE: (1e-7, 1e-7), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_DWELL_TIME: 100e-6, # s
model.MD_LENS_MAG: 1200, # ratio
},
{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: (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: (600e-9, 620e-9), # m
model.MD_OUT_WL: (620e-9, 650e-9), # m
model.MD_ROTATION: 0.1, # rad
model.MD_SHEAR: 0,
},
]
# 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)
tiff.export(FILENAME, ldata, pyramid=True)
# check data
rdata = open_acquisition(FILENAME)
sts = data_to_static_streams(rdata)
# There should be 3 streams: 2 fluo + 1 SEM
fluo = sem = 0
for s in sts:
if isinstance(s, stream.StaticFluoStream):
fluo += 1
elif isinstance(s, stream.EMStream):
sem += 1
self.assertEqual(fluo, 2)
self.assertEqual(sem, 1)
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_SW_VERSION: "aa 4.56",
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
tiff.export(FILENAME, ldata)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
im = Image.open(FILENAME)
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)
# check the 2D data
im.seek(i + 1)
self.assertEqual(im.size, size)
self.assertEqual(im.getpixel((1,1)), 0)
def test_pyramidal_3x2(self):
"""
Draws a view with two streams, one pyramidal stream square completely green,
and the other is a red square with a blue square in the center
"""
mpp = 0.00001
self.view.mpp.value = mpp
self.assertEqual(mpp, self.view.mpp.value)
self.view.show_crosshair.value = False
init_pos = (1.0, 2.0)
FILENAME = u"test" + tiff.EXTENSIONS[0]
# 1 row of 2 tiles
w = 600
h = 300
size = (w, h, 3)
dtype = numpy.uint8
md = {
model.MD_PIXEL_SIZE: (mpp, mpp),
model.MD_POS: init_pos,
model.MD_DIMS: 'YXC'
}
arr = model.DataArray(numpy.zeros((h, w, 3), dtype="uint8"))
# make it all green
arr[:, :] = [0, 255, 0]
data = model.DataArray(arr, metadata=md)
# export
tiff.export(FILENAME, data, pyramid=True)
acd = tiff.open_data(FILENAME)
stream1 = RGBStream("test", acd.content[0])
im2 = model.DataArray(numpy.zeros((800, 800, 3), dtype="uint8"))
# red background
im2[:, :] = [255, 0, 0]
# Blue square at center
im2[390:410, 390:410] = [0, 0, 255]
im2.metadata[model.MD_PIXEL_SIZE] = (mpp, mpp)
im2.metadata[model.MD_POS] = init_pos
im2.metadata[model.MD_DIMS] = "YXC"
stream2 = RGBStream("s2", im2)
self.view.addStream(stream1)
self.view.addStream(stream2)
# insert a value greater than the maximu. This value will be croped
self.view.fov_buffer.value = (1.0, 1.0)
self.view.mpp.value = mpp
# reset the mpp of the view, as it's automatically set to the first image
test.gui_loop(0.5)
result_im = get_image_from_buffer(self.canvas)
result_im.SaveFile('/home/gstiebler/Projetos/Delmic/big.bmp',
wx.BITMAP_TYPE_BMP)
px2 = get_rgb(result_im, result_im.Width // 2, result_im.Height // 2)
# center pixel, 1/3 green, 2/3 blue. The red image is the largest image
self.assertEqual(px2, (0, 76, 179))
px2 = get_rgb(result_im, result_im.Width // 2 - 30,
result_im.Height // 2 - 30)
# background of the images, 1/3 green, 2/3 red
self.assertEqual(px2, (179, 76, 0))
def testMetadata(self):
"""
checks that the metadata is saved with every picture
"""
size = (512, 256, 1)
dtype = numpy.dtype("uint64")
metadata = {
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_HW_VERSION: "2.54",
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: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, #s
model.MD_IN_WL: (500e-9, 520e-9), #m
}
data = model.DataArray(numpy.zeros((size[1], size[0]), dtype),
metadata=metadata)
# export
tiff.export(FILENAME, data)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
imo = libtiff.tiff.TIFFfile(FILENAME)
self.assertEqual(len(imo.IFD), 1,
"Tiff file doesn't contain just one image")
ifd = imo.IFD[0]
# check format
self.assertEqual(size[2], ifd.get_value("SamplesPerPixel"))
# BitsPerSample is the actual format, not model.MD_BPP
self.assertEqual(dtype.itemsize * 8, ifd.get_value("BitsPerSample")[0])
self.assertEqual(T.SAMPLEFORMAT_UINT, ifd.get_value("SampleFormat")[0])
# check metadata
self.assertEqual("Odemis " + odemis.__version__,
ifd.get_value("Software"))
self.assertEqual(metadata[model.MD_HW_NAME], ifd.get_value("Make"))
self.assertEqual(
metadata[model.MD_HW_VERSION] +
" (driver %s)" % metadata[model.MD_SW_VERSION],
ifd.get_value("Model"))
self.assertEqual(metadata[model.MD_DESCRIPTION],
ifd.get_value("PageName"))
yres = rational2float(ifd.get_value("YResolution"))
self.assertAlmostEqual(1 / metadata[model.MD_PIXEL_SIZE][1],
yres * 100)
ypos = rational2float(ifd.get_value("YPosition"))
self.assertAlmostEqual(metadata[model.MD_POS][1], (ypos / 100) - 1)
# check OME-TIFF metadata
omemd = imo.IFD[0].get_value("ImageDescription")
self.assertTrue(
omemd.startswith('<?xml') or omemd[:4].lower() == '<ome')
# remove "xmlns" which is the default namespace and is appended everywhere
omemd = re.sub(
'xmlns="http://www.openmicroscopy.org/Schemas/OME/....-.."',
"",
omemd,
count=1)
root = ET.fromstring(omemd)
# ns = {"ome": root.tag.rsplit("}")[0][1:]} # read the default namespace
roottag = root.tag.split("}")[-1]
self.assertEqual(roottag.lower(), "ome")
detect_name = root.find("Instrument/Detector").get("Model")
self.assertEqual(metadata[model.MD_HW_NAME], detect_name)
self.assertEqual(len(root.findall("Image")), 1)
ime = root.find("Image")
ifdn = int(ime.find("Pixels/TiffData").get("IFD", "0"))
self.assertEqual(ifdn, 0)
sx = int(ime.find("Pixels").get("SizeX")) # px
self.assertEqual(size[0], sx)
psx = float(ime.find("Pixels").get("PhysicalSizeX")) # um
self.assertAlmostEqual(metadata[model.MD_PIXEL_SIZE][0], psx * 1e-6)
exp = float(ime.find("Pixels/Plane").get("ExposureTime")) # s
self.assertAlmostEqual(metadata[model.MD_EXP_TIME], exp)
iwl = float(
ime.find("Pixels/Channel").get("ExcitationWavelength")) # nm
iwl *= 1e-9
self.assertTrue((metadata[model.MD_IN_WL][0] <= iwl
and iwl <= metadata[model.MD_IN_WL][1]))
bin_str = ime.find("Pixels/Channel/DetectorSettings").get("Binning")
exp_bin = "%dx%d" % metadata[model.MD_BINNING]
self.assertEqual(bin_str, exp_bin)
