cover_path='/media/li/Li/cover/'
for split in ['dist_cover_train_single', 'dist_cover_test_single']:
name = split
__sets[name] = (lambda split=split: dist_fake(split,2007,cover_path))
nist_path='/media/li/Li/NIST2016'
for split in ['dist_NIST_train_new_2', 'dist_NIST_test_new_2']:
name = split
__sets[name] = (lambda split=split: nist(split,2007,nist_path))
casia_path='/media/li/Data/CASIA'
#for split in ['casia_train_all_single', 'casia_test_all_1']:
for split in ['casia_train_all_single', 'casia_test_all_single']:
name = split
__sets[name] = (lambda split=split: casia(split,2007,casia_path))
coco_path='./tamper'
for split in ['coco_train_filter_single', 'coco_test_filter_single']:
name = split
__sets[name] = (lambda split=split: coco(split,2007,coco_path))
def get_imdb(name):
"""Get an imdb (image database) by name."""
if name not in __sets:
raise KeyError('Unknown dataset: {}'.format(name))
return __sets[name]()
def list_imdbs():
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
#if self.config['matlab_eval']:
#self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
#os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.casia import casia
d = casia('trainval', '2007')
res = d.roidb
from IPython import embed
embed()