def updateImage(self, dummy=None):
if self._images is None:
raise RuntimeError("No images set!")
images = list()
contours = dict()
ccolor = AtConfig().contours_complementary_color
for i, n in self.checkedChannels().iteritems():
# converting the gray image to the color defined in the button
color = self.widgetAt(i, 1).currentColor()
image = self._images[i]
lut = self.enhancer.lut_from_color(i, color, 256)
image.setColorTable(lut)
images.append(image)
if self._contours is not None:
if ccolor:
color = AtPainter.complementaryColor(color)
contours[color] = self._contours[n]
pixmap = AtPainter.blend(images)
# sometimes qt segfaults when drawing polygons
if AtConfig().draw_contours_in_pixmap:
pixmap = AtPainter.drawContours(pixmap, contours)
self.newContourImage.emit(pixmap, None)
else:
self.newContourImage.emit(pixmap, contours)
def __init__(self, file_):
super(ClfWriter, self).__init__()
self._compression = AtConfig().compression
self._copts = AtConfig().compression_opts
if isinstance(file_, HdfFile):
self.h5f = file_
elif isinstance(file_, basestring):
self.h5f = h5py.File(file_, HdfFile.READWRITECREATE)
def __init__(self, filename):
self._file = h5py.File(filename, "w")
self._cache = None
self._compression = AtConfig().compression
self._copts = AtConfig().compression_opts
self._save_raw_images = AtConfig().save_raw_images
self.dmodel = HdfDataModel("data")
# save a list of the training sets as attrib of the file
if self.dmodel.TRAININGDATA in self._file.attrs.keys():
tsets = self._file.attrs[self.dmodel.TRAININGDATA].tolist()
tsets.append(self.dmodel.data)
else:
tsets = self.dmodel.data
self._file.attrs[self.dmodel.TRAININGDATA] = [tsets, ]
def saveSettings(self):
atc = AtConfig()
atc.contours_complementary_color = self.complementary_colors.isChecked(
)
atc.default_sorter = self.sort_algorithm.currentText()
atc.default_feature_group = self.feature_grouping.currentText()
atc.max_sv_fraction = self.max_frac_outliers.value()
atc.interactive_item_limit = self.interactive_item_limit.value()
atc.compression = self.hdf_compression.currentText()
atc.compression_opts = self.hdf_compopts.itemData(
self.hdf_compopts.currentIndex())
atc.save_raw_images = self.saveRawImages.isChecked()
def loadSettings(self):
# singleton
atc = AtConfig()
self.complementary_colors.setChecked(atc.contours_complementary_color)
self.sort_algorithm.clear()
self.sort_algorithm.addItems(atc.Sorters)
self.sort_algorithm.setCurrentIndex(
self.sort_algorithm.findText(atc.default_sorter))
self.feature_grouping.clear()
self.feature_grouping.addItems(atc.FeatureGroups.keys())
self.feature_grouping.setCurrentIndex(
self.feature_grouping.findText(atc.default_feature_group))
self.max_frac_outliers.setValue(atc.max_sv_fraction)
self.interactive_item_limit.setValue(atc.interactive_item_limit)
self.hdf_compression.clear()
self.hdf_compression.addItems(atc.Compression.keys())
self.hdf_compression.setCurrentIndex(
self.hdf_compression.findText(atc.compression))
self.updateCompressionOptions(self.hdf_compression.currentText())
self.hdf_compopts.setCurrentIndex(
self.hdf_compopts.findText(str(atc.compression_opts)))
self.saveRawImages.setChecked(atc.save_raw_images)
def saveSettings(self):
atc = AtConfig()
atc.contours_complementary_color = self.complementary_colors.isChecked()
atc.default_sorter = self.sort_algorithm.currentText()
atc.default_feature_group = self.feature_grouping.currentText()
atc.max_sv_fraction = self.max_frac_outliers.value()
atc.interactive_item_limit = self.interactive_item_limit.value()
atc.compression = self.hdf_compression.currentText()
atc.compression_opts = self.hdf_compopts.itemData(
self.hdf_compopts.currentIndex())
atc.save_raw_images = self.saveRawImages.isChecked()
def setFeatureGroups(self, feature_groups):
self.clearFeatureGroups()
self._channels = feature_groups.keys()
for chn, tables in feature_groups.iteritems():
table = tables[AtConfig().default_feature_group]
fgw = AtChannelFeatureGroupsWidget(chn, table, self)
self.fbox.insertWidget(self.fbox.count(), fgw)
fgw.selectionChanged.connect(self.onSelectionChanged)
setattr(self, chn.lower(), fgw)
def _saveSettings(self):
settings = QtCore.QSettings(version.organisation, version.appname)
settings.beginGroup('Gui')
settings.setValue('state', self.saveState())
settings.setValue('geometry', self.saveGeometry())
settings.setValue('classifier',
self.annotation.classifiers.currentText())
settings.endGroup()
AtConfig().saveSettings()
def estimateParameters(self, testdata):
pp = PreProcessor(testdata)
# check all gammas from 2^-16 to 2^2
for gamma in 2**np.linspace(-16, 2, 100):
clf = sklearn.svm.OneClassSVM(kernel=self.KERNEL, nu=self.nu,
gamma=gamma)
clf.fit(pp(testdata))
sv_frac = clf.support_.size/float(pp.nsamples)
if sv_frac >= AtConfig().max_sv_fraction:
self._params.gamma.setValue(gamma)
break
def _restoreSettings(self):
settings = QtCore.QSettings(version.organisation, version.appname)
settings.beginGroup('Gui')
if settings.contains('geometry'):
geometry = settings.value('geometry')
self.restoreGeometry(geometry)
if settings.contains('state'):
state = settings.value('state')
self.restoreState(state)
if settings.contains('classifier'):
clfname = settings.value("classifier")
self.annotation.setCurrentClassifier(clfname)
AtConfig().restoreSettings()
settings.endGroup()
def loadItems(self):
if self._h5f is None:
return
# feature names of the last dataset loaded
self._feature_names = self._h5f.featureNames(
self._coordinate['region'])
try:
self._fgroups = self._h5f.featureGroups(self._coordinate['region'])
except KeyError:
pass
self.started.emit()
if AtConfig().interactive_item_limit < self._h5f.numberItems(
self._coordinate):
self._loadItemsSingle()
else:
self._loadItemsBulk()
self.progressUpdate.emit(-1)
self.finished.emit()
def saveSettings(self):
atc = AtConfig()
atc.contours_complementary_color = self.complementary_colors.isChecked()
atc.default_sorter = self.sort_algorithm.currentText()
atc.default_feature_group = self.feature_grouping.currentText()
atc.max_sv_fraction = self.max_frac_outliers.value()
atc.compression = self.hdf_compression.currentText()
atc.batch_size = self.batchsize.currentText()
atc.compression_opts = self.hdf_compopts.itemData(
self.hdf_compopts.currentIndex())
atc.save_raw_images = self.saveRawImages.isChecked()
atc.only_classifier_features = self.onlyClassifierFeatures.isChecked()
atc.features = self.featureGroups()
atc.enable_plugins = self.enablePlugins.isChecked()
def defaultSortAlgorithm(self):
return AtConfig().default_sorter
