.format(self._devkit_path,
self._image_set, output_dir)
print('Running:\n{}'.format(cmd))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_caltech_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.pascal_voc import caltech
d = caltech("trainval")
res = d.roidb
from IPython import embed; embed()
print('Running:\n{}'.format(cmd))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_caltech_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.pascal_voc import caltech
d = caltech("trainval")
res = d.roidb
from IPython import embed
embed()