def _save(self,grp,trail,root=None):
"""Save a group of files to disc by calling the save() method on each file. This internal method is called by walk_groups in turn
called from the public save() method. The trail of group keys is used to create a directory tree.
@param grp: A DataFolder group or a DataFile instance
@param trail: the trail of paths used to get here
@param root a replacement root directory
@return Saved Path
"""
trail=[self._removeDisallowedFilenameChars(t) for t in trail]
grp.filename=self._removeDisallowedFilenameChars(grp.filename)
if root is None:
root=self.directory
pth=path.join(root,*trail)
os.makesdirs(pth)
grp.save(path.join(pth,grp.filename))
return grp.filename
def _save(self,grp,trail,root=None):
"""Save a group of files to disc by calling the save() method on each file. This internal method is called by walk_groups in turn
called from the public save() method. The trail of group keys is used to create a directory tree.
Args:
grp (:py:class:`DataFolder` or :py:calss:`Stoner.DataFile`): A group or file to save
trail (list of strings): the trail of paths used to get here
root (string or None): a replacement root directory
Returns:
Saved Path
"""
trail=[self._removeDisallowedFilenameChars(t) for t in trail]
grp.filename=self._removeDisallowedFilenameChars(grp.filename)
if root is None:
root=self.directory
pth=path.join(root,*trail)
os.makesdirs(pth)
grp.save(path.join(pth,grp.filename))
return grp.filename
if __name__ == '__main__':
num_classes = 6
no_of_epochs = 5
emotion_models_path = '../trained_model/emotion_models/'
size = [64, 64]
pathToDataset = "../dataset/ferDataset/"
inputs, outputs = prepareData(size, pathToDataset)
inputs = inputs.reshape(inputs.shape[0], inputs.shape[1], inputs.shape[2],
1)
num_of_samples = len(inputs)
train_data_length = int(num_of_samples * 0.8)
x_train, x_test = inputs[0:train_data_length], inputs[train_data_length:]
y_train, y_test = outputs[0:train_data_length], outputs[train_data_length:]
model = buildCNNModel(inputs.shape[1:], num_classes, 32, (3, 3), 0.05,
(2, 2), 1)
#model = buildCnnModel(inputs.shape[1:], num_classes, 32, (3,3), 0.05, (2,2), 1)
print(model.summary())
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
history = model.fit(x_train,
y_train,
batch_size=32,
epochs=no_of_epochs,
validation_data=(x_test, y_test))
if not os.path.exists(emotion_models_path):
os.makesdirs(emotion_models_path)
model.save(emotion_models_path + 'emotion_recod_%d_acc-%f.model' %
(no_of_epochs, history.history['val_acc'][4]))
# read data
op1 = open(path1, 'r')
lin1 = op1.readlines()
op1.close()
data1 = [elem.split() for elem in lin1]
# build matrix
matrix = np.array([[float(item) for item in line] for line in data1[1:]])
# perform kpca
kpca = KernelPCA(kernel="linear", gamma=par_gamma)
X_kpca = kpca.fit_transform(matrix)
if not os.path.isdir('plots_' + case + '/'):
os.makesdirs('plots_' + case + '/')
for i in xrange(0,1):
for j in xrange(1, 2):
if i < j:
plt.figure()
plt.title('gamma = ' + str(par_gamma))
plt.scatter(X_kpca[:,i], X_kpca[:,j])
plt.xlabel('kPC' + str(i + 1))
plt.ylabel('kPC' + str(j + 1))
plt.savefig('plots_' + case + '/kpca_plot_' + str(i + 1)+ '_' + str(j + 1) + '_gamma_' + str(par_gamma) + '_' + case + '.png')
plt.figure()
plt.scatter(range(1, 11), kpca.lambdas_[:10]/sum(kpca.lambdas_))
plt.xlabel('kPCA')
def process(self, dataSource, progressBar):
skCase = Case.getCurrentCase().getSleuthkitCase()
#Create all the variables needed for show them in the autopsy interface
self.log(Level.INFO, "Begin Create New Artifacts")
try:
artID_eu = skCase.addArtifactType("TSK_ATPR", "ATPR Results")
except:
self.log(Level.INFO, "Artifacts Creation Error, ID ==> ")
try:
attID_filePath = skCase.addArtifactAttributeType(
"TSK_FILE_PATH",
BlackboardAttribute.TSK_BLACKBOARD_ATTRIBUTE_VALUE_TYPE.STRING,
"File Path")
except:
self.log(Level.INFO, "Attributes Creation Error, File path ==> ")
try:
attID_match = skCase.addArtifactAttributeType(
"TSK_WORD",
BlackboardAttribute.TSK_BLACKBOARD_ATTRIBUTE_VALUE_TYPE.STRING,
"Word")
except:
self.log(Level.INFO, "Attributes Creation Error, Word ==> ")
try:
attTotal_match = skCase.addArtifactAttributeType(
"TSK_TOTAL",
BlackboardAttribute.TSK_BLACKBOARD_ATTRIBUTE_VALUE_TYPE.STRING,
"Total")
except:
self.log(Level.INFO, "Attributes Creation Error, Total ==> ")
try:
attType_match = skCase.addArtifactAttributeType(
"TSK_TYPE",
BlackboardAttribute.TSK_BLACKBOARD_ATTRIBUTE_VALUE_TYPE.STRING,
"Type")
except:
self.log(Level.INFO, "Attributes Creation Error, Type ==> ")
try:
attDict_match = skCase.addArtifactAttributeType(
"TSK_DICT",
BlackboardAttribute.TSK_BLACKBOARD_ATTRIBUTE_VALUE_TYPE.STRING,
"Dict")
except:
self.log(Level.INFO, "Attributes Creation Error, Dict ==> ")
artID_eu = skCase.getArtifactTypeID("TSK_ATPR")
artID_eu_evt = skCase.getArtifactType("TSK_ATPR")
attID_fp = skCase.getAttributeType("TSK_FILE_PATH")
attID_match = skCase.getAttributeType("TSK_WORD")
attTotal_match = skCase.getAttributeType("TSK_TOTAL")
attType_match = skCase.getAttributeType("TSK_TYPE")
attDict_match = skCase.getAttributeType("TSK_DICT")
# we don't know how much work there will be
progressBar.switchToIndeterminate()
# Get the folder with de texts files
inputDir = Case.getCurrentCase().getModulesOutputDirAbsPath(
) + "\TextFiles"
try:
os.makesdirs(inputDir)
self.log(
Level.INFO,
"Find Text Directory must exists for launching the module" +
inputDir)
except:
self.log(Level.INFO, "Find Text Directory exists " + inputDir)
# We'll save our output to a file in the reports folder, named based on EXE and data source ID
reportPath = Case.getCurrentCase().getCaseDirectory(
) + "\\Reports\\atprResult-" + str(dataSource.getId()) + ".csv"
#reportPath = os.path.join(Case.getCurrentCase().getTempDirectory(), str(dataSource.getId()))
logPath = Case.getCurrentCase().getCaseDirectory(
) + "\\Reports\\log" + str(dataSource.getId()) + ".txt"
logHandle = open(logPath, 'w')
# Run the EXE, saving output to the report
self.log(Level.INFO, "Running program on data source")
self.log(
Level.INFO, self.path_to_exe + " -c match -i " + inputDir +
" -d " + self.path_to_dirs + " -o " + reportPath)
subprocess.Popen([
self.path_to_exe, "-c", "match", "-i", inputDir, "-d",
self.path_to_dirs, "-o", reportPath
],
stdout=logHandle).communicate()
logHandle.close()
# Add the report to the case, so it shows up in the tree
Case.getCurrentCase().addReport(reportPath, "Run EXE",
"ATPR result output")
self.log(Level.INFO, "Report created on " + reportPath)
self.log(Level.INFO, "Adding elements to Autopsy")
#Open with codecs for utf-8 parsing
result = codecs.open(reportPath, encoding='utf-8')
files_array = set()
for line in result:
fields = line.split(';')
fields_splitted = fields[0].split("\\")
files_array.add(fields_splitted[len(fields_splitted) - 1])
fileManager = Case.getCurrentCase().getServices().getFileManager()
result.close()
#Adding elements to autopsy reports interface
for item in files_array:
files = fileManager.findFiles(dataSource, item)
for file in files:
result = codecs.open(reportPath, encoding='utf-8')
for line in result:
fields = line.split(';')
if file.getName() in fields[0]:
art = file.newArtifact(artID_eu)
art.addAttributes(((BlackboardAttribute(attID_fp, RunExeIngestModuleFactory.moduleName, fields[0])), \
(BlackboardAttribute(attID_match, RunExeIngestModuleFactory.moduleName, fields[1])), \
(BlackboardAttribute(attTotal_match, RunExeIngestModuleFactory.moduleName, fields[2])), \
(BlackboardAttribute(attType_match, RunExeIngestModuleFactory.moduleName, fields[3])), \
(BlackboardAttribute(attDict_match, RunExeIngestModuleFactory.moduleName, fields[4]))))
IngestServices.getInstance().fireModuleDataEvent(
ModuleDataEvent(RunExeIngestModuleFactory.moduleName,
artID_eu_evt, None))
result.close()
self.log(Level.INFO, "Done")
return IngestModule.ProcessResult.OK