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 align(self, dlg):
''' Executes the alignment. If the alignment is successful, the aligned stream is
added to the main window. If not, an error message is shown.
dlg (AlignmentAcquisitionDialog): The plugin dialog
'''
crop = (self.crop_top.value, self.crop_bottom.value,
self.crop_left.value, self.crop_right.value)
flip = (self.flip_x.value, self.flip_y.value)
tem_img = preprocess(self._nem_proj.raw[0], self.invert.value, flip,
crop, self.blur.value, True)
sem_raw = img.ensure2DImage(self._rem_proj.raw[0])
sem_img = preprocess(sem_raw, False, (False, False), (0, 0, 0, 0),
self.blur_ref.value, True)
try:
tmat, _, _, _, _ = keypoint.FindTransform(tem_img, sem_img)
# get the metadata corresponding to the transformation
transf_md = get_img_transformation_md(tmat, tem_img, sem_img)
logging.debug("Computed transformation metadata: %s", transf_md)
except ValueError as ex:
box = wx.MessageDialog(dlg, str(ex), "Failed to align images",
wx.OK | wx.ICON_STOP)
box.ShowModal()
box.Destroy()
return
# Shear is really big => something is gone wrong
if abs(transf_md[model.MD_SHEAR]) > 1:
logging.warning(
"Shear is %g, which means the alignment is probably wrong",
transf_md[model.MD_SHEAR])
transf_md[model.MD_SHEAR] = 0
# Pixel size ratio is more than 2 ? => something is gone wrong
# TODO: pixel size 100x bigger/smaller than the reference is also wrong
pxs = transf_md[model.MD_PIXEL_SIZE]
if not (0.5 <= pxs[0] / pxs[1] <= 2):
logging.warning(
"Pixel size is %s, which means the alignment is probably wrong",
pxs)
transf_md[model.MD_PIXEL_SIZE] = (pxs[0], pxs[0])
# The actual image inserted is not inverted and not blurred, but we still
# want it flipped and cropped.
raw = preprocess(self._nem_proj.raw[0], False, flip, crop, 0, False)
raw.metadata.update(transf_md)
# Add a new stream panel (removable)
analysis_tab = self.main_app.main_data.getTabByName('analysis')
aligned_stream = stream.StaticSEMStream(
self._nem_proj.stream.name.value, raw)
scont = analysis_tab.stream_bar_controller.addStream(aligned_stream,
add_to_view=True)
scont.stream_panel.show_remove_btn(True)
# Finish by closing the window
dlg.Close()
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.image.value = model.DataArray(dataRGB, metadata)
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': (5.9910982493639e-08, 6.0604642506361e-08),
'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,
'Shear': 0.003274715695854}
image = model.DataArray(data, metadata)
sem_stream = stream.StaticSEMStream(metadata['Description'], image)
#sem_stream.image.value = model.DataArray(dataRGB, metadata)
# create DataProjections for the streams
fluo_stream_pj = stream.RGBSpatialProjection(fluo_stream)
sem_stream_pj = stream.RGBSpatialProjection(sem_stream)
self.streams = [fluo_stream_pj, sem_stream_pj]
self.min_res = (623, 432)
# Spectrum stream
data = numpy.ones((251, 1, 1, 200, 300), dtype="uint16")
data[:, 0, 0, :, 3] = range(200)
data[:, 0, 0, :, 3] *= 3
data[:, 0, 0, 1, 3] = range(251)
data[2, :, :, :, :] = range(300)
data[200, 0, 0, 2] = range(300)
wld = 433e-9 + numpy.array(range(data.shape[0])) * 0.1e-9
md = {model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake ccd",
model.MD_DESCRIPTION: "Spectrum",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_PIXEL_SIZE: (2e-6, 2e-6), # m/px
model.MD_POS: (-0.001203511795256, -0.000295338300158), # m
model.MD_EXP_TIME: 0.2, # s
model.MD_LENS_MAG: 60, # ratio
model.MD_WL_LIST: wld,
}
spec_data = model.DataArray(data, md)
self.spec_stream = stream.StaticSpectrumStream("test spec", spec_data)
# Wait for all the streams to get an RGB image
time.sleep(0.5)
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)