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()