def create_dataset(dataset_name, data_dir):
if not dataset_name:
dataset_name = 'mafa'
if dataset_name == 'widerface':
from morghulis.widerface import Wider
ds = Wider(data_dir)
elif dataset_name == 'fddb':
from morghulis.fddb import FDDB
ds = FDDB(data_dir)
elif dataset_name == 'afw':
from morghulis.afw import AFW
ds = AFW(data_dir)
elif dataset_name == 'pascal_faces':
from morghulis.pascal_faces import PascalFaces
ds = PascalFaces(data_dir)
elif dataset_name == 'mafa':
from morghulis.mafa import Mafa
ds = Mafa(data_dir)
elif dataset_name == 'caltech':
from morghulis.caltech_faces import CaltechFaces
ds = CaltechFaces(data_dir)
elif dataset_name == 'ufdd':
from morghulis.ufdd import UFDD
ds = UFDD(data_dir)
else:
logging.error('Invalid dataset name %s', dataset_name)
raise ValueError('Invalid dataset name %s' % dataset_name)
return ds
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset',
dest='dataset',
action='store',
required=True,
help='widerface, fddb or afw')
parser.add_argument('--output_dir',
dest='output_dir',
action='store',
required=True,
help='')
args = parser.parse_args()
dataset = args.dataset
output_dir = args.output_dir
if dataset == 'widerface':
from morghulis.widerface import Wider
ds = Wider(output_dir)
elif dataset == 'fddb':
from morghulis.fddb import FDDB
ds = FDDB(output_dir)
elif dataset == 'afw':
from morghulis.afw import AFW
ds = AFW(output_dir)
elif dataset == 'pascal_faces':
from morghulis.pascal_faces import PascalFaces
ds = PascalFaces(output_dir)
else:
logging.error('Invalid dataset name %s', dataset)
raise ValueError('Invalid dataset name %s' % dataset)
ds.download()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', dest='dataset', action='store', required=True, help='widerface or fddb')
parser.add_argument('--format', dest='format', action='store', required=True, help='darknet, tensorflow, caffe or coco')
parser.add_argument('--data_dir', dest='data_dir', action='store', required=True, help='')
parser.add_argument('--output_dir', dest='output_dir', action='store', required=True, help='')
args = parser.parse_args()
dataset = args.dataset
_format = args.format
data_dir = args.data_dir
output_dir = args.output_dir
if dataset == 'widerface':
from morghulis.widerface import Wider
ds = Wider(data_dir)
elif dataset == 'fddb':
from morghulis.fddb import FDDB
ds = FDDB(data_dir)
elif dataset == 'afw':
from morghulis.afw import AFW
ds = AFW(data_dir)
else:
logging.error('Invalid dataset name %s', dataset)
ds.export(output_dir, _format)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--input',
dest='input',
action='store',
required=True,
help='input csv containing the predictions')
parser.add_argument('--data_dir',
dest='data_dir',
action='store',
required=True,
help='')
parser.add_argument('--output_dir',
dest='output_dir',
action='store',
required=True,
help='')
args = parser.parse_args()
input_csv = args.input
data_dir = args.data_dir
output_dir = args.output_dir
ensure_dir(output_dir)
predictions = defaultdict(list)
with open(input_csv, 'r') as csvfile:
spamreader = csv.reader(csvfile, delimiter=' ')
for row in spamreader:
match = next(re.finditer(FILENAME_RE, row[0]))
_id = match.group(1)
score = float(row[1])
xmin = float(row[2])
ymin = float(row[3])
xmax = float(row[4])
ymax = float(row[5])
# left_x top_y width height detection_score
predictions[_id].append(
(xmin, ymin, xmax - xmin, ymax - ymin, score))
ds = FDDB(data_dir)
for fold_id, fold_file in ds.folds():
target_file = os.path.join(output_dir,
'fold-{}-out.txt'.format(fold_id))
with open(fold_file, 'r') as src, open(target_file, 'w') as dest:
for img_filename in src:
img_filename = img_filename.strip()
dest.write('{}\n'.format(img_filename))
if img_filename in predictions:
pred = predictions[img_filename]
dest.write('{}\n'.format(len(pred)))
for p in pred:
dest.write('{} {} {} {} {}\n'.format(*p))
else:
dest.write('{}\n'.format(0))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--input',
dest='input',
action='store',
required=True,
help='input tfrecord containing the predictions')
parser.add_argument('--data_dir',
dest='data_dir',
action='store',
required=True,
help='')
parser.add_argument('--output_dir',
dest='output_dir',
action='store',
required=True,
help='')
args = parser.parse_args()
input_file = args.input
data_dir = args.data_dir
output_dir = args.output_dir
ensure_dir(output_dir)
predictions = extract_predictions_from_tf_record(input_file)
ds = FDDB(data_dir)
for fold_id, fold_file in ds.folds():
target_file = os.path.join(output_dir,
'fold-{}-out.txt'.format(fold_id))
with open(fold_file, 'r') as src, open(target_file, 'w') as dest:
for img_filename in src:
img_filename = img_filename.strip()
dest.write('{}\n'.format(img_filename))
if img_filename in predictions:
pred = predictions[img_filename]
dest.write('{}\n'.format(len(pred)))
for p in pred:
dest.write('{} {} {} {} {}\n'.format(*p))
else:
dest.write('{}\n'.format(0))
def setUp(self):
rmtree(TMP_DIR, ignore_errors=True)
self.dataset = FDDB(FDDB_DIR)
self.tfExporter = TensorflowExporter(self.dataset)
def setUp(self):
rmtree(TMP_DIR, ignore_errors=True)
self.fddb = FDDB(FDDB_DIR)
self.darknetExporter = DarknetExporter(self.fddb)
def test_download(self):
self.fddb = FDDB(os.path.join(TMP_DIR, 'fddb_download/'))
self.fddb.download()
def setUp(self):
self.fddb = FDDB(FDDB_DIR)
def setUp(self):
rmtree(TMP_DIR, ignore_errors=True)
self.ds = FDDB(FDDB_DIR)