我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用fast_rcnn.config.cfg.SUBCLS_NAME。
def gt_roidb(self): """ Return the database of ground-truth regions of interest. This function loads/saves from/to a cache file to speed up future calls. """ cache_file = os.path.join(self.cache_path, self.name + '_' + cfg.SUBCLS_NAME + '_gt_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} gt roidb loaded from {}'.format(self.name, cache_file) return roidb gt_roidb = [self._load_pascal3d_voxel_exemplar_annotation(index) for index in self.image_index] if cfg.IS_RPN: # print out recall for i in xrange(1, self.num_classes): print '{}: Total number of boxes {:d}'.format(self.classes[i], self._num_boxes_all[i]) print '{}: Number of boxes covered {:d}'.format(self.classes[i], self._num_boxes_covered[i]) print '{}: Recall {:f}'.format(self.classes[i], float(self._num_boxes_covered[i]) / float(self._num_boxes_all[i])) with open(cache_file, 'wb') as fid: cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote gt roidb to {}'.format(cache_file) return gt_roidb
def region_proposal_roidb(self): """ Return the database of 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 + '_' + cfg.SUBCLS_NAME + '_' + cfg.REGION_PROPOSAL + '_region_proposal_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} roidb loaded from {}'.format(self.name, cache_file) return roidb if self._image_set != 'test': gt_roidb = self.gt_roidb() print 'Loading region proposal network boxes...' model = cfg.REGION_PROPOSAL rpn_roidb = self._load_rpn_roidb(gt_roidb, model) print 'Region proposal network boxes loaded' roidb = datasets.imdb.merge_roidbs(rpn_roidb, gt_roidb) else: print 'Loading region proposal network boxes...' model = cfg.REGION_PROPOSAL roidb = self._load_rpn_roidb(None, model) print 'Region proposal network boxes loaded' print '{} region proposals per image'.format(self._num_boxes_proposal / len(self.image_index)) with open(cache_file, 'wb') as fid: cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote roidb to {}'.format(cache_file) return roidb
def evaluate_detections(self, all_boxes, output_dir): # load the mapping for subcalss the azimuth (viewpoint) filename = os.path.join(self._pascal3d_path, cfg.SUBCLS_NAME, 'mapping.txt') assert os.path.exists(filename), \ 'Path does not exist: {}'.format(filename) mapping = np.zeros(self._num_subclasses, dtype=np.float) with open(filename) as f: for line in f: words = line.split() subcls = int(words[0]) mapping[subcls] = float(words[2]) for cls_ind, cls in enumerate(self.classes): if cls == '__background__': continue print 'Writing {} VOC results file'.format(cls) filename = os.path.join(output_dir, 'det_' + self._image_set + '_' + cls + '.txt') print filename with open(filename, 'wt') as f: for im_ind, index in enumerate(self.image_index): dets = all_boxes[cls_ind][im_ind] if dets == []: continue # the VOCdevkit expects 1-based indices for k in xrange(dets.shape[0]): subcls = int(dets[k, 5]) cls_name = self.classes[self.subclass_mapping[subcls]] assert (cls_name == cls), 'subclass not in class' azimuth = mapping[subcls] f.write('{:s} {:.3f} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'. format(index, dets[k, 4], azimuth, dets[k, 0] + 1, dets[k, 1] + 1, dets[k, 2] + 1, dets[k, 3] + 1)) # evaluate detection results
def gt_roidb(self): """ Return the database of ground-truth regions of interest. This function loads/saves from/to a cache file to speed up future calls. """ cache_file = os.path.join(self.cache_path, self.name + '_' + cfg.SUBCLS_NAME + '_gt_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} gt roidb loaded from {}'.format(self.name, cache_file) return roidb gt_roidb = [self._load_imagenet3d_annotation(index) for index in self.image_index] if cfg.IS_RPN: # print out recall for i in xrange(1, self.num_classes): print '{}: Total number of boxes {:d}'.format(self.classes[i], self._num_boxes_all[i]) print '{}: Number of boxes covered {:d}'.format(self.classes[i], self._num_boxes_covered[i]) print '{}: Recall {:f}'.format(self.classes[i], float(self._num_boxes_covered[i]) / float(self._num_boxes_all[i])) with open(cache_file, 'wb') as fid: cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote gt roidb to {}'.format(cache_file) return gt_roidb
def gt_roidb(self): """ Return the database of ground-truth regions of interest. """ cache_file = os.path.join(self.cache_path, self.name + '_' + cfg.SUBCLS_NAME + '_gt_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} gt roidb loaded from {}'.format(self.name, cache_file) return roidb gt_roidb = [self._load_kitti_voxel_exemplar_annotation(index) for index in self.image_index] if cfg.IS_RPN: # print out recall for i in xrange(1, self.num_classes): print '{}: Total number of boxes {:d}'.format(self.classes[i], self._num_boxes_all[i]) print '{}: Number of boxes covered {:d}'.format(self.classes[i], self._num_boxes_covered[i]) print '{}: Recall {:f}'.format(self.classes[i], float(self._num_boxes_covered[i]) / float(self._num_boxes_all[i])) with open(cache_file, 'wb') as fid: cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote gt roidb to {}'.format(cache_file) return gt_roidb
def region_proposal_roidb(self): """ Return the database of 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 + '_' + cfg.SUBCLS_NAME + '_' + cfg.REGION_PROPOSAL + '_region_proposal_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} roidb loaded from {}'.format(self.name, cache_file) return roidb if self._image_set != 'testing': gt_roidb = self.gt_roidb() print 'Loading region proposal network boxes...' if self._image_set == 'trainval': model = cfg.REGION_PROPOSAL + '_trainval/' else: model = cfg.REGION_PROPOSAL + '_train/' rpn_roidb = self._load_rpn_roidb(gt_roidb, model) print 'Region proposal network boxes loaded' roidb = datasets.imdb.merge_roidbs(rpn_roidb, gt_roidb) else: print 'Loading region proposal network boxes...' model = cfg.REGION_PROPOSAL + '_trainval/' roidb = self._load_rpn_roidb(None, model) print 'Region proposal network boxes loaded' print '{} region proposals per image'.format(self._num_boxes_proposal / len(self.image_index)) with open(cache_file, 'wb') as fid: cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote roidb to {}'.format(cache_file) return roidb
def gt_roidb(self): """ Return the database of ground-truth regions of interest. This function loads/saves from/to a cache file to speed up future calls. """ #cache_file = os.path.join(self.cache_path, self.name + '_' + cfg.SUBCLS_NAME + '_gt_roidb.pkl') cache_file = os.path.join(self.cache_path, self.name + '_' + '_gt_roidb.pkl') # the path is: .../Faster-RCNN_TF/data/cache/building_train_gt_roidb.pkl if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} gt roidb loaded from {}'.format(self.name, cache_file) return roidb gt_roidb = [self._load_building_annotation(index) for index in self.image_index] """if cfg.IS_RPN: # print out recall for i in xrange(1, self.num_classes): print '{}: Total number of boxes {:d}'.format(self.classes[i], self._num_boxes_all[i]) print '{}: Number of boxes covered {:d}'.format(self.classes[i], self._num_boxes_covered[i]) print '{}: Recall {:f}'.format(self.classes[i], float(self._num_boxes_covered[i]) / float(self._num_boxes_all[i]))""" with open(cache_file, 'wb') as fid: cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote gt roidb to {}'.format(cache_file) return gt_roidb # ---------------------------------------------------------------------------------------------------------------- # ############## ??
def gt_roidb(self): """ Return the database of ground-truth regions of interest. This function loads/saves from/to a cache file to speed up future calls. """ cache_file = os.path.join(self.cache_path, self.name + '_' + cfg.SUBCLS_NAME + '_gt_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} gt roidb loaded from {}'.format(self.name, cache_file) return roidb gt_roidb = [self._load_kitti_voxel_exemplar_annotation(index) for index in self.image_index] if cfg.IS_RPN: # print out recall for i in xrange(1, self.num_classes): print '{}: Total number of boxes {:d}'.format(self.classes[i], self._num_boxes_all[i]) print '{}: Number of boxes covered {:d}'.format(self.classes[i], self._num_boxes_covered[i]) print '{}: Recall {:f}'.format(self.classes[i], float(self._num_boxes_covered[i]) / float(self._num_boxes_all[i])) with open(cache_file, 'wb') as fid: cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote gt roidb to {}'.format(cache_file) return gt_roidb
def gt_roidb(self): """ Return the database of ground-truth regions of interest. """ cache_file = os.path.join(self.cache_path, self.name + '_' + cfg.SUBCLS_NAME + '_gt_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} gt roidb loaded from {}'.format(self.name, cache_file) return roidb gt_roidb = [self._load_mot_voxel_exemplar_annotation(index) for index in self.image_index] if cfg.IS_RPN: # print out recall for i in xrange(1, self.num_classes): print '{}: Total number of boxes {:d}'.format(self.classes[i], self._num_boxes_all[i]) print '{}: Number of boxes covered {:d}'.format(self.classes[i], self._num_boxes_covered[i]) print '{}: Recall {:f}'.format(self.classes[i], float(self._num_boxes_covered[i]) / float(self._num_boxes_all[i])) with open(cache_file, 'wb') as fid: cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote gt roidb to {}'.format(cache_file) return gt_roidb
def region_proposal_roidb(self): """ Return the database of 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 + '_' + cfg.SUBCLS_NAME + '_' + cfg.REGION_PROPOSAL + '_region_proposal_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} roidb loaded from {}'.format(self.name, cache_file) return roidb if self._image_set != 'test': gt_roidb = self.gt_roidb() print 'Loading region proposal network boxes...' model = 'train/' rpn_roidb = self._load_rpn_roidb(gt_roidb, model) print 'Region proposal network boxes loaded' roidb = datasets.imdb.merge_roidbs(rpn_roidb, gt_roidb) else: print 'Loading region proposal network boxes...' model = 'test/' roidb = self._load_rpn_roidb(None, model) print 'Region proposal network boxes loaded' print '{} region proposals per image'.format(self._num_boxes_proposal / len(self.image_index)) with open(cache_file, 'wb') as fid: cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote roidb to {}'.format(cache_file) return roidb
def __init__(self, image_set, pascal3d_path = None): datasets.imdb.__init__(self, 'pascal3d_' + image_set) self._year = '2012' self._image_set = image_set self._pascal3d_path = self._get_default_path() if pascal3d_path is None \ else pascal3d_path self._data_path = os.path.join(self._pascal3d_path, 'VOCdevkit' + self._year, 'VOC' + self._year) self._classes = ('__background__', # always index 0 'aeroplane', 'bicycle', 'boat', 'bottle', 'bus', 'car', 'chair', 'diningtable', 'motorbike', 'sofa', 'train', 'tvmonitor') self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes))) self._image_ext = '.jpg' self._image_index = self._load_image_set_index() # Default to roidb handler if cfg.IS_RPN: self._roidb_handler = self.gt_roidb else: self._roidb_handler = self.region_proposal_roidb # num of subclasses if cfg.SUBCLS_NAME == 'voxel_exemplars': self._num_subclasses = 337 + 1 elif cfg.SUBCLS_NAME == 'pose_exemplars': self._num_subclasses = 260 + 1 else: assert (1), 'cfg.SUBCLS_NAME not supported!' # load the mapping for subcalss to class filename = os.path.join(self._pascal3d_path, cfg.SUBCLS_NAME, 'mapping.txt') assert os.path.exists(filename), 'Path does not exist: {}'.format(filename) mapping = np.zeros(self._num_subclasses, dtype=np.int) with open(filename) as f: for line in f: words = line.split() subcls = int(words[0]) mapping[subcls] = self._class_to_ind[words[1]] self._subclass_mapping = mapping # PASCAL specific config options self.config = {'cleanup' : True, 'use_salt' : True, 'top_k' : 2000} # statistics for computing recall self._num_boxes_all = np.zeros(self.num_classes, dtype=np.int) self._num_boxes_covered = np.zeros(self.num_classes, dtype=np.int) self._num_boxes_proposal = 0 assert os.path.exists(self._pascal3d_path), \ 'PASCAL3D path does not exist: {}'.format(self._pascal3d_path) assert os.path.exists(self._data_path), \ 'Path does not exist: {}'.format(self._data_path)
def __init__(self, image_set, kitti_path=None): datasets.imdb.__init__(self, 'kitti_' + image_set) self._image_set = image_set self._kitti_path = self._get_default_path() if kitti_path is None \ else kitti_path self._data_path = os.path.join(self._kitti_path, 'data_object_image_2') self._classes = ('__background__', 'Car', 'Pedestrian', 'Cyclist') self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes))) self._image_ext = '.png' self._image_index = self._load_image_set_index() # Default to roidb handler if cfg.IS_RPN: self._roidb_handler = self.gt_roidb else: self._roidb_handler = self.region_proposal_roidb # num of subclasses if image_set == 'train' or image_set == 'val': self._num_subclasses = 125 + 24 + 24 + 1 prefix = 'validation' else: self._num_subclasses = 227 + 36 + 36 + 1 prefix = 'test' # load the mapping for subcalss to class filename = os.path.join(self._kitti_path, cfg.SUBCLS_NAME, prefix, 'mapping.txt') assert os.path.exists(filename), 'Path does not exist: {}'.format(filename) mapping = np.zeros(self._num_subclasses, dtype=np.int) with open(filename) as f: for line in f: words = line.split() subcls = int(words[0]) mapping[subcls] = self._class_to_ind[words[1]] self._subclass_mapping = mapping self.config = {'top_k': 100000} # statistics for computing recall self._num_boxes_all = np.zeros(self.num_classes, dtype=np.int) self._num_boxes_covered = np.zeros(self.num_classes, dtype=np.int) self._num_boxes_proposal = 0 assert os.path.exists(self._kitti_path), \ 'KITTI path does not exist: {}'.format(self._kitti_path) assert os.path.exists(self._data_path), \ 'Path does not exist: {}'.format(self._data_path)
def evaluate_detections(self, all_boxes, output_dir): # load the mapping for subcalss the alpha (viewpoint) if self._image_set == 'val': prefix = 'validation' elif self._image_set == 'test': prefix = 'test' else: prefix = '' filename = os.path.join(self._kitti_path, cfg.SUBCLS_NAME, prefix, 'mapping.txt') assert os.path.exists(filename), \ 'Path does not exist: {}'.format(filename) mapping = np.zeros(self._num_subclasses, dtype=np.float) with open(filename) as f: for line in f: words = line.split() subcls = int(words[0]) mapping[subcls] = float(words[3]) # for each image for im_ind, index in enumerate(self.image_index): filename = os.path.join(output_dir, index + '.txt') print 'Writing KITTI results to file ' + filename with open(filename, 'wt') as f: # for each class for cls_ind, cls in enumerate(self.classes): if cls == '__background__': continue dets = all_boxes[cls_ind][im_ind] if dets == []: continue for k in xrange(dets.shape[0]): if cfg.TEST.SUBCLS: subcls = int(dets[k, 5]) cls_name = self.classes[self.subclass_mapping[subcls]] assert (cls_name == cls), 'subclass not in class' alpha = mapping[subcls] else: alpha = -10 f.write('{:s} -1 -1 {:f} {:f} {:f} {:f} {:f} -1 -1 -1 -1 -1 -1 -1 {:.32f}\n'.format(\ cls, alpha, dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], dets[k, 4])) # write detection results into one file
