def _do_matlab_eval(self, output_dir = 'output'):
pass
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.caltech_ped import caltech_ped
d = caltech_ped('train04', '2009')
res = d.roidb
from IPython import embed; embed()
__sets[name] = (lambda split=split, year=year: coco(split, year))
# Set up coco_2015_<split>
for year in ['2015']:
for split in ['test', 'test-dev']:
name = 'coco_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: coco(split, year))
# Set up kaist_ped_2015_<split>
for year in ['2015']:
for split in ['train20', 'train02', 'test20', 'test01']: # test01: to generate result videos
name = 'kaist_ped_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: kaist_ped(split, year))
# Set up caltech_ped_2009_<split>
for year in ['2009']:
for split in ['train30', 'train04', 'test30', 'test01']: # test01: to generate result videos
name = 'caltech_ped_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: caltech_ped(split, year))
def get_imdb(name):
"""Get an imdb (image database) by name."""
if not __sets.has_key(name):
raise KeyError('Unknown dataset: {}'.format(name))
return __sets[name]()
def list_imdbs():
"""List all registered imdbs."""
return __sets.keys()
