def on_acquisition_done(self, future, num):
"""
Callback called when the one overview image acquisition is finished.
"""
try:
da = future.result()
except CancelledError:
self._reset_acquisition_gui()
return
except Exception:
# leave the gauge, to give a hint on what went wrong.
logging.exception("Acquisition failed")
self._reset_acquisition_gui("Acquisition failed (see log panel).", level=logging.WARNING)
return
# Store DataArray as TIFF in pyramidal format and reopen as static stream (to be memory-efficient)
# TODO: pick a different name from previous acquisition?
fn = os.path.join(get_picture_folder(), "fastem_overview_%s.ome.tiff" % num)
dataio.tiff.export(fn, da, pyramid=True)
da = open_acquisition(fn)
s = data_to_static_streams(da)[0]
s = FastEMOverviewStream(s.name.value, s.raw[0])
# Dict VA needs to be explicitly copied, otherwise it doesn't detect the change
ovv_ss = self._main_data_model.overview_streams.value.copy()
ovv_ss[num] = s
self._main_data_model.overview_streams.value = ovv_ss
def save(self, dlg):
if not hasattr(self._spec_stream, "_orig_raw"):
box = wx.MessageDialog(self.main_app.main_frame,
"No correction was applied",
"No correction", wx.OK | wx.ICON_STOP)
box.ShowModal()
box.Destroy()
return
fn = self.tab_data.acq_fileinfo.value.file_name
# Store all the data present in the original file => just open it again.
das_orig = open_acquisition(fn)
das = []
for da in das_orig:
# Is it the stream that we've corrected?
if (self._spec_stream.raw[0].metadata == da.metadata and
self._spec_stream.raw[0].shape == da.shape):
das.append(self._spec_stream.raw[0])
else:
das.append(da)
# Ask for filename, with default to original filename + _corrected
# TODO: smart naming scheme if file already exists.
basefn, ext = os.path.splitext(fn)
cfn = basefn + "_corrected" + ext
cfn = ShowAcquisitionFileDialog(dlg, cfn)
exporter = dataio.find_fittest_converter(cfn)
if cfn is not None:
exporter.export(cfn, das)
else:
logging.debug("Saving cancelled")
dlg.Close()
def open_image(self, dlg):
tab = self.main_app.main_data.getTabByName("analysis")
tab_data = tab.tab_data_model
fi = tab_data.acq_fileinfo.value
if fi and fi.file_name:
path, _ = os.path.split(fi.file_name)
else:
config = get_acqui_conf()
path = config.last_path
# Find the available formats (and corresponding extensions)
formats_to_ext = dataio.get_available_formats(os.O_RDONLY)
wildcards, formats = guiutil.formats_to_wildcards(formats_to_ext,
include_all=True)
dialog = wx.FileDialog(dlg,
message="Choose a file to load",
defaultDir=path,
defaultFile="",
style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST,
wildcard=wildcards)
# Show the dialog and check whether is was accepted or cancelled
if dialog.ShowModal() != wx.ID_OK:
return None
# Detect the format to use
filename = dialog.GetPath()
data = udataio.open_acquisition(filename)[0]
try:
data = self._ensureGrayscale(data)
except ValueError as ex:
box = wx.MessageDialog(dlg, str(ex), "Failed to open image",
wx.OK | wx.ICON_STOP)
box.ShowModal()
box.Destroy()
return None
self.crop_top.range = (0, data.shape[0] // 2)
self.crop_bottom.range = (0, data.shape[0] // 2)
self.crop_left.range = (0, data.shape[1] // 2)
self.crop_right.range = (0, data.shape[1] // 2)
data.metadata[model.MD_POS] = (0, 0)
data.metadata[model.MD_PIXEL_SIZE] = (1e-9, 1e-9)
basename = os.path.splitext(os.path.split(filename)[1])[0]
return stream.StaticSEMStream(basename, data)
def open_image(self, dlg):
tab = self.main_app.main_data.getTabByName("analysis")
tab_data = tab.tab_data_model
fi = tab_data.acq_fileinfo.value
if fi and fi.file_name:
path, _ = os.path.split(fi.file_name)
else:
config = get_acqui_conf()
path = config.last_path
# Find the available formats (and corresponding extensions)
formats_to_ext = dataio.get_available_formats(os.O_RDONLY)
wildcards, formats = guiutil.formats_to_wildcards(formats_to_ext, include_all=True)
dialog = wx.FileDialog(dlg,
message="Choose a file to load",
defaultDir=path,
defaultFile="",
style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST,
wildcard=wildcards)
# Show the dialog and check whether is was accepted or cancelled
if dialog.ShowModal() != wx.ID_OK:
return None
# Detect the format to use
filename = dialog.GetPath()
data = udataio.open_acquisition(filename)[0]
try:
data = self._ensureGrayscale(data)
except ValueError as ex:
box = wx.MessageDialog(dlg, str(ex), "Failed to open image",
wx.OK | wx.ICON_STOP)
box.ShowModal()
box.Destroy()
return None
self.crop_top.range = (0, data.shape[0] // 2)
self.crop_bottom.range = (0, data.shape[0] // 2)
self.crop_left.range = (0, data.shape[1] // 2)
self.crop_right.range = (0, data.shape[1] // 2)
data.metadata[model.MD_POS] = (0, 0)
data.metadata[model.MD_PIXEL_SIZE] = (1e-9, 1e-9)
basename = os.path.splitext(os.path.split(filename)[1])[0]
return stream.StaticSEMStream(basename, data)
def main(args):
"""
Handles the command line arguments
args is the list of arguments passed
return (int): value to return to the OS as program exit code
"""
# arguments handling
parser = argparse.ArgumentParser(
description="Batch export of AR data to CSV")
parser.add_argument("--background",
"-b",
dest="background",
help="Name of background data")
parser.add_argument(dest="filenames",
nargs="+",
help="List of (HDF5) files containing the AR data")
options = parser.parse_args(args[1:])
try:
if options.background:
data = dataio.open_acquisition(options.background)
if not data:
return 1
# will raise exception if doesn't contain good calib data
bkg = calibration.get_ar_data(data)
else:
bkg = None
if os.name == 'nt':
# on Windows, the application is in charge of expanding "*".
filenames = []
for fn in options.filenames:
filenames.extend(glob.glob(fn))
else:
filenames = options.filenames
for fn in filenames:
export_ar_to_csv(fn, bkg)
except:
logging.exception("Unexpected error while performing action.")
return 127
return 0
def export_ar_to_csv(fn, background=None):
"""
fn (str): full path to the AR data file
background (DataArray or None): background data to subtract
"""
das = dataio.open_acquisition(fn)
if not das: # No such file or file doesn't contain data
return
streams = dataio.data_to_static_streams(das)
# Remove the extension of the filename, to extend the name with .csv
fn_base = dataio.splitext(fn)[0]
ar_streams = [s for s in streams if isinstance(s, ARStream)]
for s in ar_streams:
try:
s.background.value = background
except Exception as ex:
logging.error("Failed to use background data: %s", ex)
ar_proj = stream.ARRawProjection(s)
# Export every position separately
for p in s.point.choices:
if p == (None,
None): # Special "non-selected point" => not interesting
continue
s.point.value = p
# Project to "raw" = Theta vs phi array
exdata = img.ar_to_export_data([ar_proj], raw=True)
# Pick a good name
fn_csv = fn_base
if len(ar_streams) > 1: # Add the name of the stream
fn_csv += "-" + s.name.value
if len(s.point.choices) > 2:
# More than one point in the stream => add position (in µm)
fn_csv += f"-{p[0] * 1e6}-{p[1] * 1e6}"
fn_csv += ".csv"
# Save into a CSV file
logging.info("Exporting point %s to %s", p, fn_csv)
csv.export(fn_csv, exdata)
def _openImage(self, filename, tint, shiftX, shiftY):
""" Open the given filename and assign the tint of the corresponding channel. Add the stream to the dialog and
apply the crop functionality. Two sliders are displayed for every image to provide the option of shifting the
streams in x and y dimension. If there is no filename given return None.
Args:
filename(str) : the given filename with the R, G or B stream
tint(tuple): the color tint to be assigned
shiftX(ContinuousVA): shift x value in meters
shiftY(ContinuousVA): shift y value in meters
Returns (Stream or None): the displayed stream
"""
if filename == " ":
return None
try:
data = udataio.open_acquisition(filename)[0]
pxs = data.metadata.get(model.MD_PIXEL_SIZE, (1e-06, 1e-06))
if pxs[0] > 1e-04 or pxs[1] > 1e-04:
data.metadata[model.MD_PIXEL_SIZE] = (1e-06, 1e-06)
logging.warning(
"The given pixel size %s is too big, it got replaced to the default value %s",
pxs, (1e-06, 1e-06))
data = self._ensureRGB(data, tint)
except Exception as ex:
logging.exception("Failed to open %s", filename)
self._showErrorMessage("Failed to open image",
"Failed to open image:\n%s" % (ex, ))
return None
basename, ext = os.path.splitext(os.path.split(filename)[1])
stream_ch = stream.StaticFluoStream(basename, data)
self._raw_orig[stream_ch] = data
self._dlg.addStream(stream_ch)
self._setupStreambar()
self._cropBottom()
self._connectShift(stream_ch, 0, shiftX)
self._connectShift(stream_ch, 1, shiftY)
return stream_ch
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 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 test_image_pair(self):
''' Testing a pair of images
'''
# WARNING: if opencv is not compiled with SIFT support (ie, only ORB
# available), then this test case will fail.
# FIXME: these two images are very hard, and any tiny change in the
# algorithm or settings can cause the alignment to fail => not a good
# test case
# only one image will be used, but this structure helps to test
# different images
image_pairs = [
(
('Slice69_stretched.tif', True, (False, True), (0, 0, 0, 0), 6),
('g_009_cropped.tif', False, (False, False), (0, 0, 0, 0), 3)
),
# (
# ('001_CBS_010.tif', False, (False, False), (0, 0, 0, 0), 0),
# ('20141014-113042_1.tif', False, (False, False), (0, 0, 0, 0), 0)
# ),
# (
# ('t3 DELPHI.tiff', False, (False, False), (0, 200, 0, 0), 3),
# ('t3 testoutA3.tif', False, (False, False), (0, 420, 0, 0), 3)
# )
]
image_pair = image_pairs[0]
# open the images
tem_img = open_acquisition(os.path.join(IMG_PATH, image_pair[0][0]))[0].getData()
sem_img = open_acquisition(os.path.join(IMG_PATH, image_pair[1][0]))[0].getData()
# preprocess
tem_img = preprocess(tem_img, image_pair[0][1], image_pair[0][2],
image_pair[0][3], image_pair[0][4], True)
sem_img = preprocess(sem_img, image_pair[1][1], image_pair[1][2],
image_pair[1][3], image_pair[1][4], True)
# execute the algorithm to find the transform between the images
tmat, _, _, _, _ = keypoint.FindTransform(tem_img, sem_img)
# uncomment this if you want to see the keypoint images
'''tem_painted_kp = cv2.drawKeypoints(tem_img, tem_kp, None, color=(0,255,0), flags=0)
sem_painted_kp = cv2.drawKeypoints(sem_img, sem_kp, None, color=(0,255,0), flags=0)
cv2.imwrite(IMG_PATH + 'tem_kp.jpg', tem_painted_kp)
cv2.imwrite(IMG_PATH + 'sem_kp.jpg', sem_painted_kp)'''
# uncomment this if you want to see the warped image
'''warped_im = cv2.warpPerspective(tem_img, tmat, (sem_img.shape[1], sem_img.shape[0]))
merged_im = cv2.addWeighted(sem_img, 0.5, warped_im, 0.5, 0.0)
cv2.imwrite(IMG_PATH + 'merged_with_warped.jpg', merged_im)'''
tmetadata = get_img_transformation_md(tmat, tem_img, sem_img)
logging.debug("Computed metadata = %s", tmetadata)
# FIXME: these values are actually pretty bad
# comparing based on a successful alignment validated from the warped image
# self.assertAlmostEqual(8.7e-07, tmetadata[model.MD_PIXEL_SIZE][0], places=6)
# self.assertAlmostEqual(1.25e-06, tmetadata[model.MD_PIXEL_SIZE][1], places=6)
# self.assertAlmostEqual(0.085, tmetadata[model.MD_ROTATION], places=2)
# self.assertAlmostEqual(0.000166, tmetadata[model.MD_POS][0], places=5)
# self.assertAlmostEqual(-0.0001435, tmetadata[model.MD_POS][1], places=5)
# self.assertAlmostEqual(0.035, tmetadata[model.MD_SHEAR], places=2)
#
# Check that calling the function again with the same data returns the
# same results (bug happens when using FLANN-KDtree matcher)
for i in range(2):
tmatn, _, _, _, _ = keypoint.FindTransform(tem_img, sem_img)
tmetadatan = get_img_transformation_md(tmatn, tem_img, sem_img)
logging.debug("Computed metadata = %s", tmetadatan)
numpy.testing.assert_equal(tmatn, tmat)
self.assertEqual(tmetadatan, tmetadata)
def test_image_pair(self):
''' Testing a pair of images
'''
# WARNING: if opencv is not compiled with SIFT support (ie, only ORB
# available), then this test case will fail.
# FIXME: these two images are very hard, and any tiny change in the
# algorithm or settings can cause the alignment to fail => not a good
# test case
# only one image will be used, but this structure helps to test
# different images
image_pairs = [
(
('Slice69_stretched.tif', True, (False, True), (0, 0, 0, 0), 6),
('g_009_cropped.tif', False, (False, False), (0, 0, 0, 0), 3)
),
# (
# ('001_CBS_010.tif', False, (False, False), (0, 0, 0, 0), 0),
# ('20141014-113042_1.tif', False, (False, False), (0, 0, 0, 0), 0)
# ),
# (
# ('t3 DELPHI.tiff', False, (False, False), (0, 200, 0, 0), 3),
# ('t3 testoutA3.tif', False, (False, False), (0, 420, 0, 0), 3)
# )
]
image_pair = image_pairs[0]
# open the images
tem_img = open_acquisition(os.path.join(IMG_PATH, image_pair[0][0]))[0].getData()
sem_img = open_acquisition(os.path.join(IMG_PATH, image_pair[1][0]))[0].getData()
# preprocess
tem_img = preprocess(tem_img, image_pair[0][1], image_pair[0][2],
image_pair[0][3], image_pair[0][4], True)
sem_img = preprocess(sem_img, image_pair[1][1], image_pair[1][2],
image_pair[1][3], image_pair[1][4], True)
# execute the algorithm to find the transform between the images
try:
tmat, _, _, _, _ = keypoint.FindTransform(tem_img, sem_img)
except ValueError:
if not hasattr(cv2, 'SIFT') and not hasattr(cv2, 'SIFT_create'):
self.skipTest("Test only works with SIFT, not with ORB.")
else:
raise AssertionError("Failed to find transform between images.")
# uncomment this if you want to see the keypoint images
'''tem_painted_kp = cv2.drawKeypoints(tem_img, tem_kp, None, color=(0,255,0), flags=0)
sem_painted_kp = cv2.drawKeypoints(sem_img, sem_kp, None, color=(0,255,0), flags=0)
cv2.imwrite(IMG_PATH + 'tem_kp.jpg', tem_painted_kp)
cv2.imwrite(IMG_PATH + 'sem_kp.jpg', sem_painted_kp)'''
# uncomment this if you want to see the warped image
'''warped_im = cv2.warpPerspective(tem_img, tmat, (sem_img.shape[1], sem_img.shape[0]))
merged_im = cv2.addWeighted(sem_img, 0.5, warped_im, 0.5, 0.0)
cv2.imwrite(IMG_PATH + 'merged_with_warped.jpg', merged_im)'''
tmetadata = get_img_transformation_md(tmat, tem_img, sem_img)
logging.debug("Computed metadata = %s", tmetadata)
# FIXME: these values are actually pretty bad
# comparing based on a successful alignment validated from the warped image
# self.assertAlmostEqual(8.7e-07, tmetadata[model.MD_PIXEL_SIZE][0], places=6)
# self.assertAlmostEqual(1.25e-06, tmetadata[model.MD_PIXEL_SIZE][1], places=6)
# self.assertAlmostEqual(0.085, tmetadata[model.MD_ROTATION], places=2)
# self.assertAlmostEqual(0.000166, tmetadata[model.MD_POS][0], places=5)
# self.assertAlmostEqual(-0.0001435, tmetadata[model.MD_POS][1], places=5)
# self.assertAlmostEqual(0.035, tmetadata[model.MD_SHEAR], places=2)
#
# Check that calling the function again with the same data returns the
# same results (bug happens when using FLANN-KDtree matcher)
for i in range(2):
try:
tmatn, _, _, _, _ = keypoint.FindTransform(tem_img, sem_img)
except ValueError:
if not hasattr(cv2, 'SIFT') and not hasattr(cv2, 'SIFT_create'):
self.skipTest("Test only works with SIFT, not with ORB.")
else:
raise AssertionError("Failed to find transform between images.")
tmetadatan = get_img_transformation_md(tmatn, tem_img, sem_img)
logging.debug("Computed metadata = %s", tmetadatan)
numpy.testing.assert_equal(tmatn, tmat)
self.assertEqual(tmetadatan, tmetadata)
