def selective_search_roidb(self): """ Return the database of selective search regions of interest. Ground-truth ROIs are also included. This function loads/saves from/to a cache file to speed up future calls. """ cache_file = os.path.join(self.cache_path, self.name + '_selective_search_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = pickle.load(fid) print('{} ss roidb loaded from {}'.format(self.name, cache_file)) return roidb if int(self._year) == 2007 or self._image_set != 'test': gt_roidb = self.gt_roidb() ss_roidb = self._load_selective_search_roidb(gt_roidb) roidb = imdb.merge_roidbs(gt_roidb, ss_roidb) else: roidb = self._load_selective_search_roidb(None) with open(cache_file, 'wb') as fid: pickle.dump(roidb, fid, pickle.HIGHEST_PROTOCOL) print('wrote ss roidb to {}'.format(cache_file)) return roidb
def _roidb_from_proposals(self, method): """ Creates a roidb from pre-computed proposals of a particular methods. """ top_k = self.config['top_k'] cache_file = osp.join( self.cache_path, self.name + '_{:s}_top{:d}'.format(method, top_k) + '_roidb.pkl') if osp.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{:s} {:s} roidb loaded from {:s}'.format( self.name, method, cache_file) return roidb if self._image_set in self._gt_splits: gt_roidb = self.gt_roidb() method_roidb = self._load_proposals(method, gt_roidb) roidb = imdb.merge_roidbs(gt_roidb, method_roidb) # Make sure we don't use proposals that are contained in crowds roidb = _filter_crowd_proposals(roidb, self.config['crowd_thresh']) else: roidb = self._load_proposals(method, None) with open(cache_file, 'wb') as fid: cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote {:s} roidb to {:s}'.format(method, cache_file) return roidb
def rpn_roidb(self): if self._image_set != 'test': gt_roidb = self.gt_roidb() rpn_roidb = self._load_rpn_roidb(gt_roidb) roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb) else: roidb = self._load_rpn_roidb(None) return roidb
def rpn_roidb(self): if int(self._year) == 2007 or self._image_set != 'valid': gt_roidb = self.gt_roidb() rpn_roidb = self._load_rpn_roidb(gt_roidb) roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb) else: roidb = self._load_rpn_roidb(None) return roidb
def rpn_roidb(self): # 在经过RPN网络产生了proposal以后,这个函数作用是将这些proposal # 的 # roi与groudtruth结合起来,送入网络训练。 # 那怎么个结合法呢?proposal # 的 # roidb格式与上面提到的gt_roidb一模一样,只不过overlap由1变成了与最接近的class的重合度。 # 如何判断是最接近的class呢?每个proposal的box都与groud - truth的box做一次重合度计算,与anchor # _target # _layer.py中类似 # overlap = (重合部分面积) / (proposal _box面积 + gt_boxes面积 - 重合部分面积) # 对于每个proposal,选出最大的那个gt # _boxes的值,然后填到相应的class # index下。 if int(self._year) == 2007 or self._image_set != 'test': gt_roidb = self.gt_roidb() rpn_roidb = self._load_rpn_roidb(gt_roidb) roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb) else: roidb = self._load_rpn_roidb(None) return roidb