def apply_features(self):
self._features_calculated = True
for region_name, container in self.containers.iteritems():
object_holder = ObjectHolder(region_name)
if container is not None:
for group, params in self.feature_groups.iteritems():
# XXX special casing for feature parameters.
# call pattern like this: cnt.applyFeature(name, params)
if group.startswith('haralick'):
container.resetHaralick()
for dist in params['dist']:
container.addHaralickValue(dist)
elif group == "spotfeatures":
container.spot_diameter = params['diameter']
container.spot_threshold = params['thresh']
elif group == "granulometry":
container.resetGranulometry()
for val in params['se']:
container.addGranulometryValue(val)
container.applyFeature(group)
for obj_id, c_obj in container.getObjects().iteritems():
dctFeatures = c_obj.getFeatures()
# build a new ImageObject
obj = ImageObject(c_obj)
obj.iId = obj_id
ul = obj.oRoi.upperLeft
crack = [(pos[0] + ul[0], pos[1] + ul[1])
for pos in container.getCrackCoordinates(obj_id)]
obj.crack_contour = crack
# ORIENTATION TEST: orientation of objects (for tracking) #
# at the moment a bit of a hack #
# The problem is that orientation cannot be a feature #
# but moments need to be chosen to calculate the orientation. #
if self.feature_groups.has_key('moments'):
obj.orientation = Orientation(
angle=c_obj.orientation,
eccentricity=dctFeatures['eccentricity'])
# why do wo sort the features according to their names??
# does it matter?
if self.lstFeatureNames is None:
self.lstFeatureNames = sorted(dctFeatures.keys())
# assign feature values in sorted order as NumPy array
features = (dctFeatures[f] for f in self.lstFeatureNames)
obj.aFeatures = numpy.fromiter(features, dtype=float)
object_holder[obj_id] = obj
if self.lstFeatureNames is not None:
object_holder.feature_names = self.lstFeatureNames
self._regions[region_name] = object_holder
def apply_features(self):
self._features_calculated = True
for region_name, container in self.containers.iteritems():
object_holder = ObjectHolder(region_name)
if not container is None:
for strFeatureCategory in self.lstFeatureCategories:
container.applyFeature(strFeatureCategory)
# calculate set of haralick features
# (with differnt distances)
if 'haralick_categories' in self.dctFeatureParameters:
for strHaralickCategory in self.dctFeatureParameters[
'haralick_categories']:
for iHaralickDistance in self.dctFeatureParameters[
'haralick_distances']:
container.haralick_distance = iHaralickDistance
container.applyFeature(strHaralickCategory)
for obj_id, c_obj in container.getObjects().iteritems():
dctFeatures = c_obj.getFeatures()
# build a new ImageObject
obj = ImageObject(c_obj)
obj.iId = obj_id
ul = obj.oRoi.upperLeft
crack = [(pos[0] + ul[0], pos[1] + ul[1])
for pos in container.getCrackCoordinates(obj_id)]
obj.crack_contour = crack
# ORIENTATION TEST: orientation of objects (for tracking) #
# at the moment a bit of a hack #
# The problem is that orientation cannot be a feature #
# but moments need to be chosen to calculate the orientation. #
if 'moments' in self.lstFeatureCategories:
obj.orientation = Orientation(
angle=c_obj.orientation,
eccentricity=dctFeatures['eccentricity'])
# why do wo sort the features according to their names??
# does it matter?
if self.lstFeatureNames is None:
self.lstFeatureNames = sorted(dctFeatures.keys())
# assign feature values in sorted order as NumPy array
features = (dctFeatures[f] for f in self.lstFeatureNames)
obj.aFeatures = numpy.fromiter(features, dtype=float)
object_holder[obj_id] = obj
# print 'orientation %s (%i, %i): %f (%f deg)' % (obj_id,
# obj.oRoi.upperLeft[0],
# obj.oRoi.upperLeft[1],
# obj.orientation,
# 180.0 * obj.orientation / numpy.pi)
if self.lstFeatureNames is not None:
object_holder.feature_names = self.lstFeatureNames
self._regions[region_name] = object_holder
def apply_features(self):
self._features_calculated = True
for region_name, container in self.containers.iteritems():
object_holder = ObjectHolder(region_name)
if not container is None:
for strFeatureCategory in self.lstFeatureCategories:
container.applyFeature(strFeatureCategory)
# calculate set of haralick features
# (with differnt distances)
if 'haralick_categories' in self.dctFeatureParameters:
for strHaralickCategory in self.dctFeatureParameters['haralick_categories']:
for iHaralickDistance in self.dctFeatureParameters['haralick_distances']:
container.haralick_distance = iHaralickDistance
container.applyFeature(strHaralickCategory)
for obj_id, c_obj in container.getObjects().iteritems():
dctFeatures = c_obj.getFeatures()
# build a new ImageObject
obj = ImageObject(c_obj)
obj.iId = obj_id
ul = obj.oRoi.upperLeft
crack = [(pos[0] + ul[0], pos[1] + ul[1])
for pos in
container.getCrackCoordinates(obj_id)]
obj.crack_contour = crack
# ORIENTATION TEST: orientation of objects (for tracking) #
# at the moment a bit of a hack #
# The problem is that orientation cannot be a feature #
# but moments need to be chosen to calculate the orientation. #
if 'moments' in self.lstFeatureCategories:
obj.orientation = Orientation(angle = c_obj.orientation,
eccentricity = dctFeatures['eccentricity'])
# why do wo sort the features according to their names??
# does it matter?
if self.lstFeatureNames is None:
self.lstFeatureNames = sorted(dctFeatures.keys())
# assign feature values in sorted order as NumPy array
features = (dctFeatures[f] for f in self.lstFeatureNames)
obj.aFeatures = numpy.fromiter(features, dtype=float)
object_holder[obj_id] = obj
# print 'orientation %s (%i, %i): %f (%f deg)' % (obj_id,
# obj.oRoi.upperLeft[0],
# obj.oRoi.upperLeft[1],
# obj.orientation,
# 180.0 * obj.orientation / numpy.pi)
if self.lstFeatureNames is not None:
object_holder.feature_names = self.lstFeatureNames
self._regions[region_name] = object_holder
def apply_features(self):
self._features_calculated = True
for region_name, container in self.containers.iteritems():
object_holder = ObjectHolder(region_name)
if container is not None:
for group, params in self.feature_groups.iteritems():
if params is None:
container.applyFeature(group)
else: # special case for haralick features
for param in params:
container.haralick_distance = param
container.applyFeature(group)
for obj_id, c_obj in container.getObjects().iteritems():
dctFeatures = c_obj.getFeatures()
# build a new ImageObject
obj = ImageObject(c_obj)
obj.iId = obj_id
ul = obj.oRoi.upperLeft
crack = [(pos[0] + ul[0], pos[1] + ul[1])
for pos in
container.getCrackCoordinates(obj_id)]
obj.crack_contour = crack
# ORIENTATION TEST: orientation of objects (for tracking) #
# at the moment a bit of a hack #
# The problem is that orientation cannot be a feature #
# but moments need to be chosen to calculate the orientation. #
if self.feature_groups.has_key('moments'):
obj.orientation = Orientation(
angle = c_obj.orientation,
eccentricity = dctFeatures['eccentricity']
)
# why do wo sort the features according to their names??
# does it matter?
if self.lstFeatureNames is None:
self.lstFeatureNames = sorted(dctFeatures.keys())
# assign feature values in sorted order as NumPy array
features = (dctFeatures[f] for f in self.lstFeatureNames)
obj.aFeatures = numpy.fromiter(features, dtype=float)
object_holder[obj_id] = obj
if self.lstFeatureNames is not None:
object_holder.feature_names = self.lstFeatureNames
self._regions[region_name] = object_holder
def apply_features(self, *args, **kw):
"""Concatenate features of images objects of different channels"""
holder = ObjectHolder("-".join(self._merge_regions.values()))
for cname, region_name in self._merge_regions.iteritems():
channel = self._channels[cname]
holder0 = channel.get_region(region_name)
pfx = "%s_%s" %(cname, region_name)
feature_names = ["_".join((pfx, f)) for f in holder0.feature_names]
holder.cat_samples(holder0, feature_names)
removed = holder.remove_incomplete()
if len(removed) > 0:
self.logger.info("Found incomplete samples in merged channel")
self.logger.info("removed samples: %s" %",".join([str(r) for r in removed]))
self._regions[self.regkey] = holder
self.lstFeatureNames = holder.feature_names
def apply_features(self, *args, **kw):
"""Concatenate features of images objects of different channels"""
holder = ObjectHolder("-".join(self._merge_regions.values()))
for cname, region_name in self._merge_regions.iteritems():
channel = self._channels[cname]
holder0 = channel.get_region(region_name)
pfx = "%s_%s" % (cname, region_name)
feature_names = ["_".join((pfx, f)) for f in holder0.feature_names]
holder.cat_samples(holder0, feature_names)
removed = holder.remove_incomplete()
if len(removed) > 0:
self.logger.info("Found incomplete samples in merged channel")
self.logger.info("removed samples: %s" %
",".join([str(r) for r in removed]))
self._regions[self.regkey] = holder
self.lstFeatureNames = holder.feature_names
def annotate(self, sample_objects, learner, container, region):
"""Annotate predefined class labels to picked samples."""
training_set = ObjectHolder(region.name)
training_set.feature_names = region.feature_names
for class_label, object_ids in sample_objects.iteritems():
class_name = learner.classdef.names[class_label]
hex_color = learner.classdef.colors[class_name]
for obj_id in object_ids:
obj = region[obj_id]
obj.iLabel = class_label
obj.strClassName = class_name
obj.strHexColor = hex_color
training_set[obj_id] = obj
return training_set
def annotate(self, sample_objects, learner, container, region):
"""Annotate predefined class labels to picked samples."""
training_set = ObjectHolder(region.name)
training_set.feature_names = region.feature_names
for class_label, object_ids in sample_objects.iteritems():
class_name = learner.classdef.names[class_label]
hex_color = learner.classdef.colors[class_name]
for obj_id in object_ids:
obj = region[obj_id]
obj.iLabel = class_label
obj.strClassName = class_name
obj.strHexColor = hex_color
training_set[obj_id] = obj
return training_set
def apply_features(self):
self._features_calculated = True
for region_name, container in self.containers.iteritems():
object_holder = ObjectHolder(region_name)
if not container is None:
container.debug = False
if container.debug:
container.debug_folder = os.path.join("/Users/twalter/temp/spotfeatures",
self.meta_image.coordinate.position)
if not os.path.exists(container.debug_folder):
os.makedirs(container.debug_folder)
print 'generated ', container.debug_folder
container.debug_prefix = 'img'
# pass parameters to container
for feature in self.dctFeatureParameters:
if feature in ['featurecategory_haralick', 'featurecategory_haralick2']:
container.resetHaralick()
for haralick_size in self.dctFeatureParameters[feature]['dist']:
container.addHaralickValue(haralick_size)
elif feature == 'featurecategory_granulometry':
container.resetGranulometry()
for val in self.dctFeatureParameters[feature]['se']:
container.addGranulometryValue(val)
elif feature == 'featurecategory_spotfeatures':
container.spot_diameter = self.dctFeatureParameters[feature]['diameter']
container.spot_threshold = self.dctFeatureParameters[feature]['thresh']
# apply feature
for strFeatureCategory in self.lstFeatureCategories:
container.applyFeature(strFeatureCategory)
# ---
# to replace
# calculate set of haralick features
# (with differnt distances)
# if 'haralick_categories' in self.dctFeatureParameters:
# for strHaralickCategory in self.dctFeatureParameters['haralick_categories']:
# for iHaralickDistance in self.dctFeatureParameters['haralick_distances']:
# container.haralick_distance = iHaralickDistance
# container.applyFeature(strHaralickCategory)
# ---
for obj_id, c_obj in container.getObjects().iteritems():
dctFeatures = c_obj.getFeatures()
# build a new ImageObject
obj = ImageObject(c_obj)
obj.iId = obj_id
ul = obj.oRoi.upperLeft
crack = [(pos[0] + ul[0], pos[1] + ul[1])
for pos in
container.getCrackCoordinates(obj_id)]
obj.crack_contour = crack
# ORIENTATION TEST: orientation of objects (for tracking) #
# at the moment a bit of a hack #
# The problem is that orientation cannot be a feature #
# but moments need to be chosen to calculate the orientation. #
if 'moments' in self.lstFeatureCategories:
obj.orientation = Orientation(angle = c_obj.orientation,
eccentricity = dctFeatures['eccentricity'])
# why do wo sort the features according to their names??
# does it matter?
if self.lstFeatureNames is None:
self.lstFeatureNames = sorted(dctFeatures.keys())
# assign feature values in sorted order as NumPy array
features = (dctFeatures[f] for f in self.lstFeatureNames)
obj.aFeatures = numpy.fromiter(features, dtype=float)
object_holder[obj_id] = obj
if self.lstFeatureNames is not None:
object_holder.feature_names = self.lstFeatureNames
self._regions[region_name] = object_holder
def apply_features(self):
self._features_calculated = True
for region_name, container in self.containers.iteritems():
object_holder = ObjectHolder(region_name)
if container is not None:
for group, params in self.feature_groups.iteritems():
# XXX special casing for feature parameters.
# call pattern like this: cnt.applyFeature(name, params)
if group.startswith('haralick'):
container.resetHaralick()
for dist in params['dist']:
container.addHaralickValue(dist)
elif group == "spotfeatures":
container.spot_diameter = params['diameter']
container.spot_threshold = params['thresh']
elif group == "granulometry":
container.resetGranulometry()
for val in params['se']:
container.addGranulometryValue(val)
container.applyFeature(group)
for obj_id, c_obj in container.getObjects().iteritems():
dctFeatures = c_obj.getFeatures()
# build a new ImageObject
obj = ImageObject(c_obj)
obj.iId = obj_id
ul = obj.oRoi.upperLeft
crack = [(pos[0] + ul[0], pos[1] + ul[1])
for pos in
container.getCrackCoordinates(obj_id)]
obj.crack_contour = crack
# ORIENTATION TEST: orientation of objects (for tracking) #
# at the moment a bit of a hack #
# The problem is that orientation cannot be a feature #
# but moments need to be chosen to calculate the orientation. #
if self.feature_groups.has_key('moments'):
obj.orientation = Orientation(
angle = c_obj.orientation,
eccentricity = dctFeatures['eccentricity']
)
# why do wo sort the features according to their names??
# does it matter?
if self.lstFeatureNames is None:
self.lstFeatureNames = sorted(dctFeatures.keys())
# assign feature values in sorted order as NumPy array
features = (dctFeatures[f] for f in self.lstFeatureNames)
obj.aFeatures = numpy.fromiter(features, dtype=float)
object_holder[obj_id] = obj
if self.lstFeatureNames is not None:
object_holder.feature_names = self.lstFeatureNames
self._regions[region_name] = object_holder
def apply_features(self):
self._features_calculated = True
for region_name, container in self.containers.iteritems():
object_holder = ObjectHolder(region_name)
if not container is None:
container.debug = False
if container.debug:
container.debug_folder = os.path.join(
"/Users/twalter/temp/spotfeatures",
self.meta_image.coordinate.position)
if not os.path.exists(container.debug_folder):
os.makedirs(container.debug_folder)
print 'generated ', container.debug_folder
container.debug_prefix = 'img'
# pass parameters to container
for feature in self.dctFeatureParameters:
if feature in [
'featurecategory_haralick',
'featurecategory_haralick2'
]:
container.resetHaralick()
for haralick_size in self.dctFeatureParameters[
feature]['dist']:
container.addHaralickValue(haralick_size)
elif feature == 'featurecategory_granulometry':
container.resetGranulometry()
for val in self.dctFeatureParameters[feature]['se']:
container.addGranulometryValue(val)
elif feature == 'featurecategory_spotfeatures':
container.spot_diameter = self.dctFeatureParameters[
feature]['diameter']
container.spot_threshold = self.dctFeatureParameters[
feature]['thresh']
# apply feature
for strFeatureCategory in self.lstFeatureCategories:
container.applyFeature(strFeatureCategory)
# ---
# to replace
# calculate set of haralick features
# (with differnt distances)
# if 'haralick_categories' in self.dctFeatureParameters:
# for strHaralickCategory in self.dctFeatureParameters['haralick_categories']:
# for iHaralickDistance in self.dctFeatureParameters['haralick_distances']:
# container.haralick_distance = iHaralickDistance
# container.applyFeature(strHaralickCategory)
# ---
for obj_id, c_obj in container.getObjects().iteritems():
dctFeatures = c_obj.getFeatures()
# build a new ImageObject
obj = ImageObject(c_obj)
obj.iId = obj_id
ul = obj.oRoi.upperLeft
crack = [(pos[0] + ul[0], pos[1] + ul[1])
for pos in container.getCrackCoordinates(obj_id)]
obj.crack_contour = crack
# ORIENTATION TEST: orientation of objects (for tracking) #
# at the moment a bit of a hack #
# The problem is that orientation cannot be a feature #
# but moments need to be chosen to calculate the orientation. #
if 'moments' in self.lstFeatureCategories:
obj.orientation = Orientation(
angle=c_obj.orientation,
eccentricity=dctFeatures['eccentricity'])
# why do wo sort the features according to their names??
# does it matter?
if self.lstFeatureNames is None:
self.lstFeatureNames = sorted(dctFeatures.keys())
# assign feature values in sorted order as NumPy array
features = (dctFeatures[f] for f in self.lstFeatureNames)
obj.aFeatures = numpy.fromiter(features, dtype=float)
object_holder[obj_id] = obj
if self.lstFeatureNames is not None:
object_holder.feature_names = self.lstFeatureNames
self._regions[region_name] = object_holder
def apply_features(self):
for strKey, oContainer in self.dctContainers.iteritems():
oObjectHolder = ObjectHolder(strKey)
if not oContainer is None:
for strFeatureCategory in self.lstFeatureCategories:
#print strFeatureCategory
sys.stdout.flush()
oContainer.applyFeature(strFeatureCategory)
# calculate set of haralick features
# (with differnt distances)
if 'haralick_categories' in self.dctFeatureParameters:
for strHaralickCategory in self.dctFeatureParameters['haralick_categories']:
for iHaralickDistance in self.dctFeatureParameters['haralick_distances']:
oContainer.haralick_distance = iHaralickDistance
oContainer.applyFeature(strHaralickCategory)
lstValidObjectIds = []
lstRejectedObjectIds = []
for iObjectId, oObject in oContainer.getObjects().iteritems():
dctFeatures = oObject.getFeatures()
bAcceptObject = True
# # post-processing
# if self.bPostProcessing:
#
# if not eval(self.strPostprocessingConditions, dctFeatures):
# if self.bPostProcessDeleteObjects:
# #del dctObjects[iObjectId]
# oContainer.delObject(iObjectId)
# bAcceptObject = False
# lstRejectedObjectIds.append(iObjectId)
# else:
# lstValidObjectIds.append(iObjectId)
# else:
# lstValidObjectIds.append(iObjectId)
#
# oContainer.lstValidObjectIds = lstValidObjectIds
# oContainer.lstRejectedObjectIds = lstRejectedObjectIds
if bAcceptObject:
# build a new ImageObject
oImageObject = ImageObject(oObject)
oImageObject.iId = iObjectId
if self.lstFeatureNames is None:
self.lstFeatureNames = sorted(dctFeatures.keys())
# assign feature values in sorted order as NumPy array
oImageObject.aFeatures = \
numpy.asarray(dict_values(dctFeatures,
self.lstFeatureNames))
oObjectHolder[iObjectId] = oImageObject
if not self.lstFeatureNames is None:
oObjectHolder.setFeatureNames(self.lstFeatureNames)
self._dctRegions[strKey] = oObjectHolder