def test_link_tool(self):
loadPlugins()
model = scenario_model.loadProject(
self.locateFile('images/sample.json'))
model.mediaFromPlugin('OutputPNG::Foo')
lt = model.getLinkTool('sample', 'orig_input')
self.assertTrue(isinstance(lt, scenario_model.ImageImageLinkTool))
mask, analysis, errors = lt.compareImages('sample', 'orig_input',
model, 'OutputPng')
self.assertTrue(len(errors) == 0)
self.assertTrue('exifdiff' in analysis)
self.addFileToRemove('test_sm.csv')
model.toCSV('test_sm.csv', [
getPathValuesFunc('arguments.purpose'),
getPathValuesFunc('arguments.subject')
])
foundPasteSplice = False
with open('test_sm.csv', 'rb') as fp:
reader = csv.reader(fp)
for row in reader:
self.assertEqual(6, len(row))
if row[3] == 'PasteSplice':
foundPasteSplice = True
self.assertEqual('sample', row[0])
self.assertEqual('orig_input', row[1])
self.assertEqual('input_mod_1', row[2])
self.assertEqual('add', row[4])
self.assertEqual('man-made object', row[5])
self.assertTrue(foundPasteSplice)
def test_getDonorAndBaseNodeTuples(self):
model = scenario_model.loadProject(
self.locateFile('images/sample.json'))
r = model.getDonorAndBaseNodeTuples()
for t in r:
if ((u'hat_splice_crop', u'input_mod_1'), u'hat', [
u'hat_splice_crop', u'hat_splice_rot_1', u'hat_splice',
u'hat'
]) == t:
return
self.assertTrue(False, 'Should not be here')
def test_aproject(self):
model = loadProject(self.locateFile('images/sample.json'))
leafBaseTuple = model.getTerminalAndBaseNodeTuples()[0]
result = graph_rules.setFinalNodeProperties(model, leafBaseTuple[0])
self.assertEqual('yes', result['manmade'])
self.assertEqual('no', result['face'])
self.assertEqual('no', result['postprocesscropframes'])
self.assertEqual('no', result['spatialother'])
self.assertEqual('no', result['otherenhancements'])
self.assertEqual('yes', result['color'])
self.assertEqual('no', result['blurlocal'])
self.assertEqual('large', result['compositepixelsize'])
self.assertEqual('yes', result['imagecompression'])
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-u', '--username', help="optional username", required=False)
parser.add_argument('--projects', help='Directory of projects')
args = parser.parse_args()
initialize(maskGenPreferences, username=args.username)
project_list = pick_projects(args.projects)
with open(os.path.join(args.projects,'ErrorReport_' + str(os.getpid()) + '.csv'), 'wb') as csvfile:
error_writer = csv.writer(csvfile, delimiter = ' ', quotechar='|', quoting=csv.QUOTE_MINIMAL)
for project in project_list:
try:
validate_export(error_writer, project, scenario_model.loadProject(project))
except Exception as e:
print (project)
print (str(e))
def run_it(temp_folder=None,
expected_probes_directory='.',
project_dir='projects'):
"""
Store results in ErrorReport CSV file.
If a new project is found in project_dir that has not been processed (not in expected_probes_directory), the results are stored in expected_probes_directory.
If a project is found in expected_probes_directory, then the project is rerun to compoare to the expected results.
:param temp_folder: place to put exploded journals. Removed upon completion
:param expected_probes_directory: Expected probes store
:param project_dir: projects to test
:return:
"""
from time import strftime
if not os.path.exists(project_dir):
return
files_to_process = []
for item in os.listdir(project_dir):
if item.endswith('tgz'):
files_to_process.append(
os.path.abspath(os.path.join(project_dir, item)))
done_file_name = os.path.join(expected_probes_directory,
'it_tests_done.txt')
skips = []
if os.path.exists(done_file_name):
with open(done_file_name, 'r') as skip:
skips = skip.readlines()
skips = [x.strip() for x in skips]
count = 0
errorCount = 0
with open(done_file_name, 'a') as done_file:
with open(
os.path.join(
expected_probes_directory,
'ErrorReport_' + strftime('%b_%d_%Y_%H_%M_%S') + '.csv'),
'w') as csvfile:
error_writer = csv.writer(csvfile,
delimiter=' ',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
for file_to_process in files_to_process:
is_error_found = False
if file_to_process in skips:
count += 1
continue
logging.getLogger('maskgen').info(file_to_process)
process_dir = tempfile.mkdtemp(
dir=temp_folder) if temp_folder else tempfile.mkdtemp()
try:
extract_archive(file_to_process, process_dir)
for project in bulk_export.pick_projects(process_dir):
scModel = loadProject(project)
logging.getLogger('maskgen').info(
'Processing {} '.format(scModel.getName()))
expected_results_directory = os.path.abspath(
os.path.join(expected_probes_directory, 'expected',
scModel.getName()))
if not os.path.exists(expected_results_directory):
os.mkdir(expected_results_directory)
method = SeedTestMethod(expected_results_directory)
else:
method = RunTestMethod(expected_results_directory)
method.loadProbesData()
generator = ProbeGenerator(
scModel=scModel,
processors=[
ProbeSetBuilder(
scModel=scModel,
compositeBuilders=[EmptyCompositeBuilder])
])
probes = generator(keepFailures=True)
logging.getLogger('maskgen').info(
'Processing {} probes'.format(len(probes)))
for probe in probes:
for error in method.processProbe(probe):
errorCount += 1
is_error_found = True
error_writer.writerow(
(scModel.getName(), probe.targetBaseNodeId,
probe.finalNodeId, probe.edgeId, error))
method.saveProbesData()
for probe in method.probesMissed():
errorCount += 1
is_error_found = True
error_writer.writerow(
(scModel.getName(), probe.targetBaseNodeId,
probe.finalNodeId, probe.edgeId, "Missing"))
if not is_error_found:
done_file.write(file_to_process + '\n')
done_file.flush()
csvfile.flush()
except Exception as e:
logging.getLogger('maskgen').error(str(e))
errorCount += 1
sys.stdout.flush()
count += 1
shutil.rmtree(process_dir)
if errorCount == 0:
if os.path.exists(done_file_name):
os.remove(done_file_name)
return errorCount