class SectionClassification(SectionCore):
SECTION_NAME = SECTION_NAME_CLASSIFICATION
OPTIONS = [('%s_classification' % x, [ ('%s_classification_envpath' % x, \
StringTrait('', 1000, label='Classifier folder',
widget_info=StringTrait.STRING_PATH)),
('%s_classification_regionname' % x,
SelectionTrait2(None, [], label='Region name')),
('%s_classification_annotationfileext' % x,
StringTrait('xml', 50, label='Annotation ext.')) ]) \
for x in (CH_PRIMARY+CH_OTHER)] + \
[('collectsamples', [ ('collectsamples', BooleanTrait(False)),
('collectsamples_prefix', StringTrait('',100))]),
('merged_channel', [ ('merge_primary', BooleanTrait(True, label='primary')),
('merge_secondary', BooleanTrait(True, label='secondary')),
('merge_tertiary', BooleanTrait(True, label='tertiary')) ])
] + \
[('%s_classification' %CH_VIRTUAL[0],
[ ('%s_classification_envpath' %CH_VIRTUAL[0], \
StringTrait('', 1000, label='Classifier folder',
widget_info=StringTrait.STRING_PATH)),
('%s_primary_region' %CH_VIRTUAL[0],
SelectionTrait2(None, [], label='')),
('%s_secondary_region' %CH_VIRTUAL[0],
SelectionTrait2(None, [], label='')),
('%s_tertiary_region' %CH_VIRTUAL[0],
SelectionTrait2(None, [], label='')),
('%s_classification_annotationfileext' %CH_VIRTUAL[0],
StringTrait('xml', 50, label='Annotation ext.')),
('%s_classification_regionname' %CH_VIRTUAL[0],
SelectionTrait2(None, [], label="Region name"))
])]
class SectionGeneral(SectionCore):
SECTION_NAME = SECTION_NAME_GENERAL
OPTIONS = [('general', [
('pathin',
StringTrait('',
label='Image Directory',
widget_info=StringTrait.STRING_PATH)),
('has_multiple_plates', BooleanTrait(False, label='Multiple Plates')),
('pathout',
StringTrait('',
label='Output Directory',
widget_info=StringTrait.STRING_PATH)),
('plate_layout',
StringTrait('',
label='Plate Layout',
widget_info=StringTrait.STRING_PATH)),
('namingscheme',
SelectionTrait(CecogEnvironment.naming_schema.sections()[0],
CecogEnvironment.naming_schema.sections(),
label='Image Naming Scheme')),
('framerange', BooleanTrait(False, label='Timelapse')),
('framerange_begin', IntTrait(1, 0, 10000, label='first')),
('framerange_end', IntTrait(1, 0, 1000, label='last')),
('frameincrement', IntTrait(1, 1, 100, label='increment')),
('skip_finished', BooleanTrait(False,
label='Skip finished positions')),
('constrain_positions', BooleanTrait(False, label='Positions')),
('positions', StringTrait('', 1000, label="", mask='(\w+,)*\w+')),
('process_primary', BooleanTrait(True, label='primary')),
('process_secondary', BooleanTrait(False, label='secondary')),
('process_tertiary', BooleanTrait(False, label='tertiary')),
('process_merged', BooleanTrait(False, label='merged')),
('crop_image', BooleanTrait(False, label='Image cropping')),
('crop_image_x0', IntTrait(-1, -1, 4000, label='upper left x:')),
('crop_image_y0', IntTrait(-1, -1, 4000, label='upper left y')),
('crop_image_x1', IntTrait(-1, -1, 4000, label='lower right x')),
('crop_image_y1', IntTrait(-1, -1, 4000, label='lower right y')),
('crop_image', BooleanTrait(False, label='Image cropping')),
('rendering', DictTrait({}, label='Rendering')),
('version',
StringTrait('',
6,
label='Cecog %s, file version:' % version,
widget_info=StringTrait.STRING_GRAYED)),
('rendering_class', DictTrait({}, label='Rendering class')),
('primary_featureextraction_exportfeaturenames',
ListTrait(['n2_avg', 'n2_stddev', 'roisize'],
label='Primary channel')),
('secondary_featureextraction_exportfeaturenames',
ListTrait(['n2_avg', 'n2_stddev', 'roisize'],
label='Secondary channel')),
('tertiary_featureextraction_exportfeaturenames',
ListTrait(['n2_avg', 'n2_stddev', 'roisize'],
label='Tertiary channel')),
])]
class SectionEventSelection(SectionCore):
SECTION_NAME = SECTION_NAME_EVENT_SELECTION
OPTIONS = [
('event_selection',
[('eventchannel',
SelectionTrait2(None, [], label='Channel')),
('event_selection',
BooleanTrait(True, label='Event Selection')),
('backwardrange',
FloatTrait(0, -1, 4000, label='Duration [pre]')),
('forwardrange',
FloatTrait(0, -1, 4000, label='Duration [post]')),
('duration_unit',
SelectionTrait2(TimeConverter.FRAMES,
TimeConverter.units,
label='Duration unit')),
('backwardrange_min', BooleanTrait(False, label='Min.')),
('forwardrange_min', BooleanTrait(False, label='Min.')),
('maxindegree', IntTrait(1, 0, 4000, label='Max in-degree')),
('maxoutdegree', IntTrait(2, 0, 4000, label='Max out-degree')),
]),
('supervised_event_selection',
[('supervised_event_selection',
BooleanTrait(True, label='Supervised',
widget_info=BooleanTrait.RADIOBUTTON)),
('labeltransitions',
StringTrait('', 200, label='Class transition motif(s)')),
('backwardlabels',
StringTrait('', 200, label='Class filter [pre]',
mask='(\d+,)*\d+')),
('forwardlabels',
StringTrait('', 200, label='Class filter [post]',
mask='(\d+,)*\d+')),
('backwardcheck',
FloatTrait(2, 0, 4000, label='Filter duration [pre]')),
('forwardcheck',
FloatTrait(2, 0, 4000, label='Filter duration [post]')),
]),
('unsupervised_event_selection',
[('unsupervised_event_selection',
BooleanTrait(False, label='Unsupervised',
widget_info=BooleanTrait.RADIOBUTTON)),
('min_event_duration',
IntTrait(3, 1, 100, label='Min. event duration')),
('num_clusters',
IntTrait(6, 2, 15, label='Number of clusters',)),
('min_cluster_size',
IntTrait(2, 1, 10, label='Min. cluster size',)),
])
]
class SectionFeatureExtraction(SectionCore):
SECTION_NAME = SECTION_NAME_FEATURE_EXTRACTION
OPTIONS = [
('primary_features',
[('primary_featurecategory_%s' % name, BooleanTrait(True, label=desc))
for name, desc in zip(FEATURE_CATEGORIES, FEATURE_CATEGORY_DESC)] + [
('primary_dist_haralick',
StringTrait('1,2,4,8',
200,
label='Haralick: Distances for cooccurence')),
('primary_se_granugrey',
StringTrait('1,2,3,5,7',
200,
label='Granulometry Sizes (Structuring Element)')),
('primary_diameter_spotfeatures',
IntTrait(5, 1, 30, label="Diameter")),
('primary_thresh_spotfeatures',
IntTrait(8, 1, 255, label="Threshold")),
]),
('secondary_features',
[('secondary_featurecategory_%s' % name, BooleanTrait(True,
label=desc))
for name, desc in zip(FEATURE_CATEGORIES, FEATURE_CATEGORY_DESC)] + [
('secondary_dist_haralick',
StringTrait('1,2,4,8',
200,
label='Haralick: Distances for cooccurence')),
('secondary_se_granugrey',
StringTrait('1,2,3,5,7',
200,
label='Granulometry Sizes (Structuring Element)')),
('secondary_diameter_spotfeatures',
IntTrait(5, 1, 30, label="Diameter")),
('secondary_thresh_spotfeatures',
IntTrait(8, 1, 255, label="Threshold")),
]),
('tertiary_features',
[('tertiary_featurecategory_%s' % name, BooleanTrait(True,
label=desc))
for name, desc in zip(FEATURE_CATEGORIES, FEATURE_CATEGORY_DESC)] + [
('tertiary_dist_haralick',
StringTrait('1,2,4,8',
200,
label='Haralick: Distances for cooccurence')),
('tertiary_se_granugrey',
StringTrait('1,2,3,5,7',
200,
label='Granulometry Sizes (Structuring Element)')),
('tertiary_diameter_spotfeatures',
IntTrait(5, 1, 30, label="Diameter")),
('tertiary_thresh_spotfeatures',
IntTrait(8, 1, 255, label="Threshold")),
]),
]
class SectionFeatureExtraction(SectionCore):
SECTION_NAME = SECTION_NAME_FEATURE_EXTRACTION
OPTIONS = [
('primary_features', [
('primary_%s' % name, BooleanTrait(val, label=desc))
for name, (desc, val) in GUI_LABELS.iteritems()
] + [
('primary_dist_haralick',
StringTrait(
'1,2,4,8', 200, label='Haralick: Distances for cooccurence')),
('primary_se_granulometry',
StringTrait('1,2,3,5,7',
200,
label='Granulometry Sizes (Structuring Element)')),
('primary_diameter_spotfeatures',
IntTrait(5, 1, 30, label="Diameter")),
('primary_thresh_spotfeatures',
IntTrait(8, 1, 255, label="Threshold")),
]),
('secondary_features', [
('secondary_%s' % name, BooleanTrait(val, label=desc))
for name, (desc, val) in GUI_LABELS.iteritems()
] + [
('secondary_dist_haralick',
StringTrait(
'1,2,4,8', 200, label='Haralick: Distances for cooccurence')),
('secondary_se_granulometry',
StringTrait('1,2,3,5,7',
200,
label='Granulometry Sizes (Structuring Element)')),
('secondary_diameter_spotfeatures',
IntTrait(5, 1, 30, label="Diameter")),
('secondary_thresh_spotfeatures',
IntTrait(8, 1, 255, label="Threshold")),
]),
('tertiary_features', [
('tertiary_%s' % name, BooleanTrait(val, label=desc))
for name, (desc, val) in GUI_LABELS.iteritems()
] + [
('tertiary_dist_haralick',
StringTrait(
'1,2,4,8', 200, label='Haralick: Distances for cooccurence')),
('tertiary_se_granulometry',
StringTrait('1,2,3,5,7',
200,
label='Granulometry Sizes (Structuring Element)')),
('tertiary_diameter_spotfeatures',
IntTrait(5, 1, 30, label="Diameter")),
('tertiary_thresh_spotfeatures',
IntTrait(8, 1, 255, label="Threshold")),
]),
]
class SectionErrorcorrection(SectionCore):
SECTION_NAME = SECTION_NAME_ERRORCORRECTION
OPTIONS = [
('error_correction',
[('primary', BooleanTrait(True, label='primary')),
('secondary', BooleanTrait(False, label='secondary')),
('tertiary', BooleanTrait(False, label='tertiary')),
('merged', BooleanTrait(False, label='merged')),
('constrain_graph',
BooleanTrait(True, label='Constrain graph')),
('primary_graph',
StringTrait('', 1000, label='Primary file',
widget_info=StringTrait.STRING_FILE)),
('secondary_graph',
StringTrait('', 1000, label='Secondary file',
widget_info=StringTrait.STRING_FILE)),
('tertiary_graph',
StringTrait('', 1000, label='Tertiary file',
widget_info=StringTrait.STRING_FILE)),
('merged_graph',
StringTrait('', 1000, label='Merged ch. file',
widget_info=StringTrait.STRING_FILE)),
('groupby_position',
BooleanTrait(True, label='Position',
widget_info=BooleanTrait.RADIOBUTTON)),
('groupby_sirna',
BooleanTrait(False, label='siRNA',
widget_info=BooleanTrait.RADIOBUTTON)),
('hmm_smoothing',
BooleanTrait(True, label='Smoothing Model',
widget_info=BooleanTrait.RADIOBUTTON)),
('hmm_baumwelch',
BooleanTrait(False, label='Baum-Welch',
widget_info=BooleanTrait.RADIOBUTTON)),
('groupby_genesymbol',
BooleanTrait(False, label='Gene Symbol',
widget_info=BooleanTrait.RADIOBUTTON)),
('overwrite_time_lapse',
BooleanTrait(False, label='Overwrite time-lapse')),
('timelapse',
FloatTrait(1, 0, 2000, digits=2,
label='Time-lapse [min]')),
('max_time',
FloatTrait(-1, -1, 2000, digits=2,
label='Max. time in plot [min]')),
('ignore_tracking_branches',
BooleanTrait(False, label='Ignore tracking branches')),
('enable_sorting',
BooleanTrait(False, label='Sort by phase duration')),
('sorting_sequence',
StringTrait('', 1000, label='Label sequence',
mask='(\w+,)*\w+')),
('primary_sort',
StringTrait('', 100)),
('secondary_sort',
StringTrait('', 100)),
('compose_galleries',
BooleanTrait(False, label='Compose gallery images')),
('compose_galleries_sample',
IntTrait(-1, -1, 10000, label='Max. number of random samples')),
('resampling_factor',
FloatTrait(0.4, 0.01, 1.0, label="Resampling factor")),
('size_gallery_image',
IntTrait(60, 1, 1000, label='Size of gallery images (px)'))
])
]
class SectionGeneral(SectionCore):
SECTION_NAME = SECTION_NAME_GENERAL
OPTIONS = [('general', [
('pathin',
StringTrait('',
1000,
label='Image folder',
widget_info=StringTrait.STRING_PATH)),
('has_multiple_plates', BooleanTrait(False, label='Multiple plates')),
('pathout',
StringTrait('',
1000,
label='Analysis folder',
widget_info=StringTrait.STRING_PATH)),
('image_import_namingschema',
BooleanTrait(True,
label='Import via naming schema',
widget_info=BooleanTrait.RADIOBUTTON)),
('image_import_structurefile',
BooleanTrait(False,
label='Import via coordinate file',
widget_info=BooleanTrait.RADIOBUTTON)),
('namingscheme',
SelectionTrait(CecogEnvironment.naming_schema.sections()[0],
CecogEnvironment.naming_schema.sections(),
label='Naming scheme')),
('structure_filename',
StringTrait('',
1000,
label='Coordinate filename',
widget_info=StringTrait.STRING_FILE)),
('structure_file_pathin',
BooleanTrait(True,
label='Image folder',
widget_info=BooleanTrait.RADIOBUTTON)),
('structure_file_pathout',
BooleanTrait(False,
label='Analysis folder',
widget_info=BooleanTrait.RADIOBUTTON)),
('structure_file_extra_path',
BooleanTrait(False,
label='Different location',
widget_info=BooleanTrait.RADIOBUTTON)),
('structure_file_extra_path_name',
StringTrait('',
1000,
label='Path',
widget_info=StringTrait.STRING_PATH)),
('constrain_positions', BooleanTrait(False,
label='Constrain positions')),
('positions',
StringTrait('', 1000, label='Positions', mask='(\w+,)*\w+')),
('crop_image', BooleanTrait(False, label='Crop image')),
('crop_image_x0', IntTrait(-1, -1, 4000, label='Upper left X')),
('crop_image_y0', IntTrait(-1, -1, 4000, label='Upper left Y')),
('crop_image_x1', IntTrait(-1, -1, 4000, label='Lower right X')),
('crop_image_y1', IntTrait(-1, -1, 4000, label='Lower right Y')),
('crop_image', BooleanTrait(False, label='Crop image')),
('redofailedonly', BooleanTrait(True,
label='Skip processed positions')),
('framerange', BooleanTrait(False, label='Constrain timepoints')),
('framerange_begin', IntTrait(1, 0, 10000, label='Begin')),
('framerange_end', IntTrait(1, 0, 1000, label='End')),
('frameincrement', IntTrait(1, 1, 100, label='Timepoint increment')),
('rendering', DictTrait({}, label='Rendering')),
('version',
StringTrait('',
6,
label='Cecog %s, file version:' % VERSION,
widget_info=StringTrait.STRING_GRAYED)),
('rendering_class', DictTrait({}, label='Rendering class')),
('primary_featureextraction_exportfeaturenames',
ListTrait(['n2_avg', 'n2_stddev', 'roisize'],
label='Primary channel')),
('secondary_featureextraction_exportfeaturenames',
ListTrait(['n2_avg', 'n2_stddev', 'roisize'],
label='Secondary channel')),
('tertiary_featureextraction_exportfeaturenames',
ListTrait(['n2_avg', 'n2_stddev', 'roisize'],
label='Tertiary channel')),
])]
class SectionObjectdetection(SectionCore):
SECTION_NAME = SECTION_NAME_OBJECTDETECTION
OPTIONS = [
('primary_image',
[('primary_channelid',
SelectionTrait2(None, [], label='Primary channel ID')),
('primary_normalizemin',
IntTrait(0, -2**16, 2**16, label='Min.')),
('primary_normalizemax',
IntTrait(255, -2**16, 2**16, label='Max.')),
('primary_zslice_selection',
BooleanTrait(True, label='Z-slice selection',
widget_info=BooleanTrait.RADIOBUTTON)),
('primary_flat_field_correction',
BooleanTrait(False, label='Z-slice flat field correction',
widget_info=BooleanTrait.CHECKBOX)),
('primary_flat_field_correction_image_dir',
StringTrait('', 1000, label='Correction image directory',
widget_info=StringTrait.STRING_PATH)),
('primary_zslice_selection_slice',
IntTrait(1, 0, 1000, label='Slice')),
('primary_zslice_projection',
BooleanTrait(False, label='Z-slice projection',
widget_info=BooleanTrait.RADIOBUTTON)),
('primary_zslice_projection_method',
SelectionTrait(ZSLICE_PROJECTION_METHODS[0],
ZSLICE_PROJECTION_METHODS, label='Method')),
('primary_zslice_projection_begin',
IntTrait(1, 0, 1000, label='Begin')),
('primary_zslice_projection_end',
IntTrait(1, 0, 1000, label='End')),
('primary_zslice_projection_step',
IntTrait(1, 1, 1000, label='Step')),
# these two options are nopes, just to have no
# special casing in channel classes
('primary_channelregistration_x',
IntTrait(0, -99999, 99999, label='Shift X')),
('primary_channelregistration_y',
IntTrait(0, -99999, 99999, label='Shift Y')),
]),
] + \
unlist(
[[('%s_image' % prefix,
[('%s_channelid' % prefix,
SelectionTrait2(None, [], label='%s channel ID' % name)),
('%s_normalizemin' % prefix,
IntTrait(0, -2**16, 2**16, label='Min.')),
('%s_normalizemax' % prefix,
IntTrait(255, -2**16, 2**16, label='Max.')),
('%s_zslice_selection' % prefix,
BooleanTrait(True, label='Z-slice selection',
widget_info=BooleanTrait.RADIOBUTTON)),
('%s_flat_field_correction' % prefix,
BooleanTrait(False, label='Z-slice flat field correction',
widget_info=BooleanTrait.CHECKBOX)),
('%s_flat_field_correction_image_dir' % prefix,
StringTrait('', 1000, label='Correction image directory',
widget_info=StringTrait.STRING_PATH)),
('%s_zslice_selection_slice' % prefix,
IntTrait(1, 0, 1000, label='Slice')),
('%s_zslice_projection' % prefix,
BooleanTrait(False, label='Z-slice projection',
widget_info=BooleanTrait.RADIOBUTTON)),
('%s_zslice_projection_method' % prefix,
SelectionTrait(ZSLICE_PROJECTION_METHODS[0],
ZSLICE_PROJECTION_METHODS, label='Method')),
('%s_zslice_projection_begin' % prefix,
IntTrait(1, 0, 1000, label='Begin')),
('%s_zslice_projection_end' % prefix,
IntTrait(1, 0, 1000, label='End')),
('%s_zslice_projection_step' % prefix,
IntTrait(1, 1, 1000, label='Step')),
]),
('%s_registration' % prefix,
[('%s_channelregistration_x' % prefix,
IntTrait(0, -99999, 99999, label='Shift X')),
('%s_channelregistration_y' % prefix,
IntTrait(0, -99999, 99999, label='Shift Y')),
]),
]
for name, prefix in [('Secondary', 'secondary'),
('Tertiary', 'tertiary'),
# moste merged channel options are nopes
# to avoid special casing
('Merged', 'merged')
]]
)
class SegmentationPluginIlastik(SegmentationPluginPrimary):
LABEL = 'Local adaptive threshold w/ split&merge using trained ilastik classifier'
NAME = 'primary_ilastik'
COLOR = '#FF0000'
REQUIRES = None
PARAMS = [('ilastik_classifier', StringTrait('', 1000, label='ilastik Classifier File',
widget_info=StringTrait.STRING_FILE)),
('ilastik_class_selector', IntTrait(1, 0, 1000, label='Output class')),
('medianradius', IntTrait(2, 0, 1000, label='Median radius')),
('latwindowsize', IntTrait(20, 1, 1000, label='Window size')),
('latlimit', IntTrait(1, 0, 255, label='Min. contrast')),
('lat2', BooleanTrait(False, label='Local adaptive threshold 2')),
('latwindowsize2', IntTrait(20, 1, 1000, label='Window size')),
('latlimit2', IntTrait(1, 0, 255, label='Min. contrast')),
('shapewatershed', BooleanTrait(False, label='Split & merge by shape')),
('shapewatershed_gausssize', IntTrait(1, 0, 10000, label='Gauss radius')),
('shapewatershed_maximasize', IntTrait(1, 0, 10000, label='Min. seed distance')),
('shapewatershed_minmergesize', IntTrait(1, 0, 10000, label='Object size threshold')),
('intensitywatershed', BooleanTrait(False, label='Split & merge by intensity')),
('intensitywatershed_gausssize', IntTrait(1, 0, 10000, label='Gauss radius')),
('intensitywatershed_maximasize', IntTrait(1, 0, 10000, label='Min. seed distance')),
('intensitywatershed_minmergesize', IntTrait(1, 0, 10000, label='Object size threshold')),
('postprocessing', BooleanTrait(False, label='Object filter')),
('postprocessing_roisize_min', IntTrait(-1, -1, 10000, label='Min. object size')),
('postprocessing_roisize_max', IntTrait(-1, -1, 10000, label='Max. object size')),
('postprocessing_intensity_min', IntTrait(-1, -1, 10000, label='Min. average intensity')),
('postprocessing_intensity_max', IntTrait(-1, -1, 10000, label='Max. average intensity')),
('removeborderobjects', BooleanTrait(True, label='Remove border objects')),
('holefilling', BooleanTrait(True, label='Fill holes')),
]
# the : at the beginning indicates a QRC link with alias 'plugins/segmentation/local_adaptive_threshold'
DOC = ':local_adaptive_threshold'
@stopwatch()
def prefilter(self, img_in):
img = SegmentationPluginPrimary.prefilter(self, img_in)
np_img = img.toArray(True)
return self._predict_image_with_ilastik(np_img)
def threshold(self, img_in, *args):
np_img = img_in.toArray(True)
return ccore.numpy_to_image((np_img > 128).astype(numpy.uint8), True)
def render_to_gui(self, panel):
panel.add_group(None, [('ilastik_classifier', (0, 0, 1, 1)),
('ilastik_class_selector', (1, 0, 1, 1)),
], label='ilastik')
SegmentationPluginPrimary.render_to_gui(self, panel)
def _predict_image_with_ilastik(self, image_):
import ilastik
from ilastik.core.dataMgr import DataMgr, DataItemImage
from ilastik.modules.classification.core.featureMgr import FeatureMgr
from ilastik.modules.classification.core.classificationMgr import ClassificationMgr
from ilastik.modules.classification.core.features.featureBase import FeatureBase
from ilastik.modules.classification.core.classifiers.classifierRandomForest import ClassifierRandomForest
from ilastik.modules.classification.core.classificationMgr import ClassifierPredictThread
from ilastik.core.volume import DataAccessor
import numpy, h5py
dataMgr = DataMgr()
# Transform input image to ilastik convention s
# 3D = (time,x,y,z,channel)
# 2D = (time,1,x,y,channel)
# Note, this work for 2D images right now. Is there a need for 3D
image_.shape = (1,1) + image_.shape
# Check if image_ has channels, if not add singelton dimension
if len(image_.shape) == 4:
image_.shape = image_.shape + (1,)
# Add data item di to dataMgr
di = DataItemImage('')
di.setDataVol(DataAccessor(image_))
dataMgr.append(di, alreadyLoaded=True)
fileName = self.params["ilastik_classifier"]
ilastik_class = self.params["ilastik_class_selector"]
hf = h5py.File(fileName,'r')
temp = hf['classifiers'].keys()
# If hf is not closed this leads to an error in win64 and mac os x
hf.close()
del hf
classifiers = []
for cid in temp:
cidpath = 'classifiers/' + cid
classifiers.append(ClassifierRandomForest.loadRFfromFile(fileName, str(cidpath)))
dataMgr.module["Classification"]["classificationMgr"].classifiers = classifiers
# Restore user selection of feature items from hdf5
featureItems = []
f = h5py.File(fileName,'r')
for fgrp in f['features'].values():
featureItems.append(FeatureBase.deserialize(fgrp))
f.close()
del f
fm = FeatureMgr(dataMgr, featureItems)
# Create FeatureMgr
# Compute features
fm.prepareCompute(dataMgr)
fm.triggerCompute()
fm.joinCompute(dataMgr)
# Predict with loaded classifier
classificationPredict = ClassifierPredictThread(dataMgr)
classificationPredict.start()
classificationPredict.wait()
if ilastik_class >= classificationPredict._prediction[0].shape[-1]:
raise RuntimeError('ilastik output class not valid...')
# Produce output image and select the probability map
probMap = (classificationPredict._prediction[0][0,0,:,:, ilastik_class] * 255).astype(numpy.uint8)
img_out = ccore.numpy_to_image(probMap, True)
return img_out
class SegmentationPluginPrimaryLoadFromFile(SegmentationPluginPrimary):
LABEL = 'Load from file'
NAME = 'primary_from_file'
COLOR = '#FF00FF'
REQUIRES = None
PARAMS = [('segmentation_folder', StringTrait('', 1000, label='Segmentation folder',
widget_info=StringTrait.STRING_FILE)),
('loader_regex', StringTrait('^%(plate)s$/^%(pos)s$/.*P%(pos)s_T%(time)05d_C%(channel)s_Z%(zslice)d_S1.tif', 1000, label='Regex for loading')),
]
# the : at the beginning indicates a QRC link with alias 'plugins/segmentation/local_adaptive_threshold'
DOC = ':local_adaptive_threshold'
def render_to_gui(self, panel):
panel.add_group(None, [('segmentation_folder', (0, 0, 1, 1))])
panel.add_group(None, [('loader_regex', (0, 0, 1, 1))])
@stopwatch()
def _run(self, meta_image):
image = meta_image.image
coords = dict(
plate = meta_image.image_container.current_plate,
pos = meta_image.coordinate.position,
time = meta_image.coordinate.time,
zslice = meta_image.coordinate.zslice,
channel = meta_image.coordinate.channel,
)
main_folder = self.params['segmentation_folder']
#FIXME: This is useful enought to put into an reusable function, maybe in utils?
locator = self.params["loader_regex"] % coords
locator_split = locator.split('/')
locator_match = '/'
for loc in locator_split[:-1]:
try:
match_candidates = os.listdir(main_folder + locator_match)
if len(match_candidates) == 0:
raise RuntimeError
except:
raise RuntimeError('No files found in ' + main_folder + locator_match)
match_results = [m.group() for l in match_candidates for m in [re.search(loc, l)] if m]
if len(match_results) != 1:
raise RuntimeError('Could not match ' + match_candidates[0] + ' with ' + loc)
locator_match += match_results[0] + '/'
match_candidates = os.listdir(main_folder + locator_match)
match_results = [m.group() for l in match_candidates for m in [re.search(locator_split[-1], l)] if m]
if len(match_results) == 0:
raise RuntimeError('Could not match ', match_candidates[0], 'with', locator_split[-1])
match_result = match_results[0]
img = ccore.readImage(main_folder + locator_match + match_result)
# img_pre = SegmentationPluginPrimary.prefilter(self, img, 2)
# img_bin = SegmentationPluginPrimary.threshold(self, img_pre, 20, 3)
container = ccore.ImageMaskContainer(image, img, False)
return container
class SectionErrorcorrection(SectionCore):
SECTION_NAME = SECTION_NAME_ERRORCORRECTION
OPTIONS = [('error_correction', [
('filename_to_r',
StringTrait('',
1000,
label='R-project executable',
widget_info=StringTrait.STRING_FILE)),
('constrain_graph', BooleanTrait(True, label='Constrain graph')),
('primary_graph',
StringTrait('',
1000,
label='Primary file',
widget_info=StringTrait.STRING_FILE)),
('secondary_graph',
StringTrait('',
1000,
label='Secondary file',
widget_info=StringTrait.STRING_FILE)),
('skip_processed_plates',
BooleanTrait(False, label='Skip processed plates')),
('position_labels', BooleanTrait(False, label='Position labels')),
('mappingfile_path',
StringTrait('',
1000,
label='Path',
widget_info=StringTrait.STRING_PATH)),
('groupby_position',
BooleanTrait(True,
label='Position',
widget_info=BooleanTrait.RADIOBUTTON)),
('groupby_oligoid',
BooleanTrait(False,
label='Oligo ID',
widget_info=BooleanTrait.RADIOBUTTON)),
('groupby_genesymbol',
BooleanTrait(False,
label='Gene symbol',
widget_info=BooleanTrait.RADIOBUTTON)),
('overwrite_time_lapse',
BooleanTrait(False, label='Overwrite time-lapse')),
('timelapse', FloatTrait(1,
0,
2000,
digits=2,
label='Time-lapse [min]')),
('max_time',
FloatTrait(100, 1, 2000, digits=2, label='Max. time in plot [min]')),
('ignore_tracking_branches',
BooleanTrait(False, label='Ignore tracking branches')),
('show_html', BooleanTrait(True, label='Open in browser')),
('enable_sorting', BooleanTrait(False,
label='Sort by phase duration')),
('sorting_sequence',
StringTrait('', 1000, label='Label sequence', mask='(\w+,)*\w+')),
('primary_sort', StringTrait('', 100)),
('secondary_sort', StringTrait('', 100)),
('compose_galleries',
BooleanTrait(False, label='Compose gallery images')),
('compose_galleries_sample',
IntTrait(-1, -1, 10000, label='Max. number of random samples')),
])]
class SectionTracking(SectionCore):
SECTION_NAME = SECTION_NAME_TRACKING
OPTIONS = [
('tracking', [
('tracking_regionname',
SelectionTrait2(None, [], label='Region name')),
('tracking_maxobjectdistance',
IntTrait(0, 0, 4000, label='Max object x-y distance')),
('tracking_maxtrackinggap',
IntTrait(0, 0, 4000, label='Max time-point gap')),
('tracking_maxsplitobjects',
IntTrait(0, 0, 4000, label='Max split events')),
('tracking_maxindegree', IntTrait(1,
0,
4000,
label='Max in-degree')),
('tracking_maxoutdegree',
IntTrait(2, 0, 4000, label='Max out-degree')),
('tracking_exporttrackfeatures',
BooleanTrait(True, label='Export tracks')),
('tracking_compressiontrackfeatures',
SelectionTrait(COMPRESSION_FORMATS[0],
COMPRESSION_FORMATS,
label='Compression')),
]),
('event_selection', [
('tracking_labeltransitions',
StringTrait('',
200,
label='Class transition motif(s)',
mask='(\(\d+,\d+\),)*\(\d+,\d+\)')),
('tracking_backwardrange',
FloatTrait(0, -1, 4000, label='Duration [pre]')),
('tracking_forwardrange',
FloatTrait(0, -1, 4000, label='Duration [post]')),
('tracking_backwardlabels',
StringTrait('',
200,
label='Class filter [pre]',
mask='(\d+,)*\d+')),
('tracking_forwardlabels',
StringTrait('',
200,
label='Class filter [post]',
mask='(\d+,)*\d+')),
('tracking_backwardcheck',
FloatTrait(2, 0, 4000, label='Filter duration [pre]')),
('tracking_forwardcheck',
FloatTrait(2, 0, 4000, label='Filter duration [post]')),
('tracking_duration_unit',
SelectionTrait2(TRACKING_DURATION_UNITS_DEFAULT[0],
TRACKING_DURATION_UNITS_DEFAULT,
label='Duration unit')),
('tracking_backwardrange_min', BooleanTrait(False, label='Min.')),
('tracking_forwardrange_min', BooleanTrait(False, label='Min.')),
]),
('visualization', [
('tracking_visualization',
BooleanTrait(False, label='Visualization')),
('tracking_visualize_track_length',
IntTrait(5, -1, 10000, label='Max. time-points')),
('tracking_centroid_radius',
IntTrait(3, -1, 50, label='Centroid radius')),
]),
]
class SectionPostProcessing(SectionCore):
SECTION_NAME = SECTION_NAME_POST_PROCESSING
OPTIONS = [('post_processing', [
('ibb_analysis', BooleanTrait(True, label='IBB analysis')),
('mappingfile_path',
StringTrait('',
1000,
label='Mapping file path',
widget_info=StringTrait.STRING_PATH)),
('single_plot', BooleanTrait(
True,
label='Export single event',
)),
('single_plot_max_plots', IntTrait(
1,
1,
2000,
label='Max. number',
)),
('ibb_ratio_signal_threshold',
FloatTrait(
1.2,
0.5,
5,
label='IBB minimum ratio signal threshold',
)),
('ibb_range_signal_threshold',
FloatTrait(
3,
0.5,
5,
label='IBB minimum range threshold',
)),
('ibb_onset_factor_threshold',
FloatTrait(
1.2,
1,
5,
label='IBB onset slope threshold',
)),
('nebd_onset_factor_threshold',
FloatTrait(
1.2,
1,
5,
label='NEBD onset slope threshold',
)),
('plot_ylim1_low', IntTrait(
0,
0,
2000,
label='Y-axis limit (low)',
)),
('plot_ylim1_high',
IntTrait(
100,
1,
4000,
label='Y-axis limit (high)',
)),
('single_plot_ylim_low',
FloatTrait(
1,
0,
10,
label='Y-axis ratio range (low)',
)),
('single_plot_ylim_high',
IntTrait(
5,
1,
30,
label='Y-axis ratio range (high)',
)),
('group_by_position',
BooleanTrait(True,
label='Position',
widget_info=BooleanTrait.RADIOBUTTON)),
('group_by_oligoid',
BooleanTrait(False,
label='Oligo ID',
widget_info=BooleanTrait.RADIOBUTTON)),
('group_by_genesymbol',
BooleanTrait(False,
label='Gene symbol',
widget_info=BooleanTrait.RADIOBUTTON)),
('group_by_group',
BooleanTrait(False,
label='Group',
widget_info=BooleanTrait.RADIOBUTTON)),
('color_sort_by_position',
BooleanTrait(False,
label='Position',
widget_info=BooleanTrait.RADIOBUTTON)),
('color_sort_by_oligoid',
BooleanTrait(True,
label='Oligo ID',
widget_info=BooleanTrait.RADIOBUTTON)),
('color_sort_by_genesymbol',
BooleanTrait(False,
label='Gene symbol',
widget_info=BooleanTrait.RADIOBUTTON)),
('color_sort_by_group',
BooleanTrait(False,
label='Group',
widget_info=BooleanTrait.RADIOBUTTON)),
('securin_analysis', BooleanTrait(True, label='Securin analysis')),
])]