def _load_classifier(self, path):
learner = None
try:
learner = BaseLearner(path, None, None)
except:
exception(self, 'Error on loading classifier')
else:
state = learner.state
if state['has_definition']:
learner.loadDefinition()
return learner
def _load_classifier(self, path):
learner = None
try:
learner = BaseLearner(path, None, None)
except:
exception(self, 'Error on loading classifier')
else:
state = learner.state
if state['has_definition']:
learner.loadDefinition()
return learner
def _load_classifier(self, path):
learner = None
try:
learner = BaseLearner(path, None, None)
except:
exception(self, 'Error on loading classifier')
else:
result = learner.check()
#if result['has_arff']:
# self._learner.importFromArff()
if result['has_definition']:
learner.loadDefinition()
return learner
def _init_new_classifier(self):
learner = BaseLearner(None, None, None)
self._current_class = None
self._class_sbox.setValue(1)
self._class_text.setText('class1')
self._class_color_btn.set_color(QColor('red'))
class_table = self._class_table
class_table.clearContents()
class_table.setRowCount(0)
ann_table = self._ann_table
ann_table.clearContents()
ann_table.setRowCount(0)
if self._detect_objects:
self._activate_objects_for_image(False, clear=True)
self._annotations.remove_all()
return learner
def _loadInfos(self):
print "LOAD"
if not self.isInitialized:
self._oLearner = BaseLearner(strEnvPath=self.path)
try:
self._oLearner.importFromArff()
try:
self._oLearner.importSampleNames()
except IOError:
has_samples = False
else:
has_samples = True
#print has_samples
except IOError:
pass
else:
print "import %s" % self.name
for iLabel in self._oLearner.lstClassLabels:
strName = self._oLearner.dctClassNames[iLabel]
strHexColor = self._oLearner.dctHexColors[strName]
if strName in self._oLearner.dctFeatureData:
oClass = Class(strName, iLabel,
len(self._oLearner.dctFeatureData[strName]),
hexToFlexColor(strHexColor))
oClass.oClassifier = self
dctFeatures = {}
for iIdx, strFeatureName in enumerate(self._oLearner.lstFeatureNames):
dctFeatures[strFeatureName] = list(self._oLearner.dctFeatureData[strName][:,iIdx])
oClass.features = dctFeatures
if has_samples:
oClass.sample_names = [Sample(os.path.join(self._oLearner.dctEnvPaths['samples'], strName, filename))
for filename in self._oLearner.dctSampleNames[strName]]
#print strName,self._oLearner.dctSampleNames[strName]
self.dctClassInfos[strName] = oClass
self.isInitialized = True
for strFeatureName in self._oLearner.lstFeatureNames:
self.dctFeatureInfos[strFeatureName] = Feature(strFeatureName)
class Classifier(object):
def __init__(self, name=None, path=None):
#super(Classifier, self).__init__()
self.name = name
self.path = path
self.dctClassInfos = OrderedDict()
self.dctFeatureInfos = OrderedDict()
self.isInitialized = False
#self._loadInfos()
def _loadInfos(self):
print "LOAD"
if not self.isInitialized:
self._oLearner = BaseLearner(strEnvPath=self.path)
try:
self._oLearner.importFromArff()
try:
self._oLearner.importSampleNames()
except IOError:
has_samples = False
else:
has_samples = True
#print has_samples
except IOError:
pass
else:
print "import %s" % self.name
for iLabel in self._oLearner.lstClassLabels:
strName = self._oLearner.dctClassNames[iLabel]
strHexColor = self._oLearner.dctHexColors[strName]
if strName in self._oLearner.dctFeatureData:
oClass = Class(strName, iLabel,
len(self._oLearner.dctFeatureData[strName]),
hexToFlexColor(strHexColor))
oClass.oClassifier = self
dctFeatures = {}
for iIdx, strFeatureName in enumerate(self._oLearner.lstFeatureNames):
dctFeatures[strFeatureName] = list(self._oLearner.dctFeatureData[strName][:,iIdx])
oClass.features = dctFeatures
if has_samples:
oClass.sample_names = [Sample(os.path.join(self._oLearner.dctEnvPaths['samples'], strName, filename))
for filename in self._oLearner.dctSampleNames[strName]]
#print strName,self._oLearner.dctSampleNames[strName]
self.dctClassInfos[strName] = oClass
self.isInitialized = True
for strFeatureName in self._oLearner.lstFeatureNames:
self.dctFeatureInfos[strFeatureName] = Feature(strFeatureName)
@property
def classes(self):
self._loadInfos()
return self.dctClassInfos.values()
@property
def classInfos(self):
self._loadInfos()
logging.debug("classes: %s" % len(self.dctClassInfos.values()))
return self.dctClassInfos.values()
@property
def featureInfos(self):
self._loadInfos()
logging.debug("features: %s" % len(self.dctFeatureInfos.values()))
return self.dctFeatureInfos.values()
def getSamples(self, className):
self._loadInfos()
strPathSamples = os.path.join(self._oLearner.dctEnvPaths['samples'],
className)
lstResults = []
if os.path.isdir(strPathSamples):
#print len(self._oLearner.lstFeatureNames), len()
dctImagePairs = OrderedDict()
for strName, oMatch in collect_files_by_regex(strPathSamples, '(?P<prefix>.+?)__(?P<type>(img)|(msk)).+?', ['.png', '.jpg']):
strPrefix = oMatch.group('prefix')
strType = oMatch.group('type')
if not strPrefix in dctImagePairs:
dctImagePairs[strPrefix] = {}
dctImagePairs[strPrefix][strType] = strName
iIdx = 0
for dctPair in dctImagePairs.values():
#oContainer = ccore.SingleObjectContainer(dctPair['img'], dctPair['msk'])
#strCoords = ",".join(map(str,flatten(oContainer.getCrackCoordinates(1))))
#print dctPair['img'], dctPair['msk']
#dctFeatures = {}
#for iF, strFeatureName in enumerate(self._oLearner.lstFeatureNames):
# dctFeatures[strFeatureName] = self._oLearner.dctFeatureData[className][iIdx][iF]
oSample = Sample(dctPair['img'])
lstResults.append(oSample)
iIdx += 1
#break
return lstResults
def getFeatureData(self, featureNames):
self._loadInfos()
lstFeatureData = []
if len(featureNames) == 1:
strFeatureName = featureNames[0]
for strClassName, oClass in self.dctClassInfos.iteritems():
aY, aX = numpy.histogram(oClass.features[strFeatureName], normed=True)
lstData = [dict(x=fX, y=fY) for fX,fY in zip(aX, aY)]
lstFeatureData.append(lstData)
elif len(featureNames) == 2:
for strClassName, oClass in self.dctClassInfos.iteritems():
iSize = len(oClass.features.values()[0])
lstData = [dict([(strFeatureName, oClass.features[strFeatureName][iIdx])
for strFeatureName in featureNames])
for iIdx in range(iSize)]
lstFeatureData.append(lstData)
#print lstFeatureData
return lstFeatureData