faster rcnn源码解读（四）之数据类型imdb.py和pascal_voc.py（主要是imdb和roidb数据类型的解说）

转载自：faster rcnn源码解读（四）之数据类型imdb.py和pascal_voc.py（主要是imdb和roidb数据类型的解说） - 野孩子的专栏 - 博客频道 - CSDN.NET

http://blog.csdn.net/u010668907/article/details/51945719

faster用python版本的https://github.com/rbgirshick/py-faster-rcnn

imdb.py源码地址：https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/imdb.py

imdb源码：

 # --------------------------------------------------------
 # Fast R-CNN
 # Copyright (c) 2015 Microsoft
 # Licensed under The MIT License [see LICENSE for details]
 # Written by Ross Girshick
 # --------------------------------------------------------

 import os
 import os.path as osp
 import PIL
 from utils.cython_bbox import bbox_overlaps
 import numpy as np
 import scipy.sparse
 from fast_rcnn.config import cfg

 class imdb(object):
     """Image database."""

     def __init__(self, name):
         self._name = name
         self._num_classes = 0#类别的长度
         self._classes = []#类别定义
         self._image_index = []#a list of image name（read from eg：root/data + /VOCdevkit2007/VOC2007/ImageSets/Main/{image_set}.txt）
         self._obj_proposer = 'selective_search'
         self._roidb = None#gt_roidb（cfg.TRAIN.PROPOSAL_METHOD=gt导致了此操作）
         self._roidb_handler = self.default_roidb
         # Use this dict for storing dataset specific config options
         self.config = {}

     @property
     def name(self):
         return self._name

     @property
     def num_classes(self):
         return len(self._classes)

     @property
     def classes(self):
         return self._classes

     @property
     def image_index(self):
         return self._image_index

     @property
     def roidb_handler(self):
         return self._roidb_handler

     @roidb_handler.setter
     def roidb_handler(self, val):
         self._roidb_handler = val

     def set_proposal_method(self, method):
         method = eval('self.' + method + '_roidb')
         self.roidb_handler = method

     @property
     def roidb(self):
         # A roidb is a list of dictionaries, each with the following keys:
         #   boxes
         #   gt_overlaps
         #   gt_classes
         #   flipped
         if self._roidb is not None:
             return self._roidb
         self._roidb = self.roidb_handler()
         return self._roidb

     @property
     def cache_path(self):
         cache_path = osp.abspath(osp.join(cfg.DATA_DIR, 'cache'))
         if not os.path.exists(cache_path):
             os.makedirs(cache_path)
         return cache_path

     @property
     def num_images(self):
       return len(self.image_index)

     def image_path_at(self, i):
         raise NotImplementedError

     def default_roidb(self):
         raise NotImplementedError

     def evaluate_detections(self, all_boxes, output_dir=None):
         """
         all_boxes is a list of length number-of-classes.
         Each list element is a list of length number-of-images.
         Each of those list elements is either an empty list []
         or a numpy array of detection.

         all_boxes[class][image] = [] or np.array of shape #dets x 5
         """
         raise NotImplementedError

     def _get_widths(self):
       return [PIL.Image.open(self.image_path_at(i)).size[0]
               for i in xrange(self.num_images)]

     def append_flipped_images(self):
         num_images = self.num_images
         widths = self._get_widths()
         for i in xrange(num_images):
             boxes = self.roidb[i]['boxes'].copy()
             oldx1 = boxes[:, 0].copy()
             oldx2 = boxes[:, 2].copy()
             boxes[:, 0] = widths[i] - oldx2 - 1
             boxes[:, 2] = widths[i] - oldx1 - 1
             assert (boxes[:, 2] >= boxes[:, 0]).all()
             entry = {'boxes' : boxes,
                      'gt_overlaps' : self.roidb[i]['gt_overlaps'],
                      'gt_classes' : self.roidb[i]['gt_classes'],
                      'flipped' : True}
             self.roidb.append(entry)
         self._image_index = self._image_index * 2

     def evaluate_recall(self, candidate_boxes=None, thresholds=None,
                         area='all', limit=None):
         """Evaluate detection proposal recall metrics.

         Returns:
             results: dictionary of results with keys
                 'ar': average recall
                 'recalls': vector recalls at each IoU overlap threshold
                 'thresholds': vector of IoU overlap thresholds
                 'gt_overlaps': vector of all ground-truth overlaps
         """
         # Record max overlap value for each gt box
         # Return vector of overlap values
         areas = { 'all': 0, 'small': 1, 'medium': 2, 'large': 3,
                   '96-128': 4, '128-256': 5, '256-512': 6, '512-inf': 7}
         area_ranges = [ [0**2, 1e5**2],    # all
                         [0**2, 32**2],     # small
                         [32**2, 96**2],    # medium
                         [96**2, 1e5**2],   # large
                         [96**2, 128**2],   # 96-128
                         [128**2, 256**2],  # 128-256
                         [256**2, 512**2],  # 256-512
                         [512**2, 1e5**2],  # 512-inf
                       ]
         assert areas.has_key(area), 'unknown area range: {}'.format(area)
         area_range = area_ranges[areas[area]]
         gt_overlaps = np.zeros(0)
         num_pos = 0
         for i in xrange(self.num_images):
             # Checking for max_overlaps == 1 avoids including crowd annotations
             # (...pretty hacking :/)
             max_gt_overlaps = self.roidb[i]['gt_overlaps'].toarray().max(axis=1)
             gt_inds = np.where((self.roidb[i]['gt_classes'] > 0) &
                                (max_gt_overlaps == 1))[0]
             gt_boxes = self.roidb[i]['boxes'][gt_inds, :]
             gt_areas = self.roidb[i]['seg_areas'][gt_inds]
             valid_gt_inds = np.where((gt_areas >= area_range[0]) &
                                      (gt_areas <= area_range[1]))[0]
             gt_boxes = gt_boxes[valid_gt_inds, :]
             num_pos += len(valid_gt_inds)

             if candidate_boxes is None:
                 # If candidate_boxes is not supplied, the default is to use the
                 # non-ground-truth boxes from this roidb
                 non_gt_inds = np.where(self.roidb[i]['gt_classes'] == 0)[0]
                 boxes = self.roidb[i]['boxes'][non_gt_inds, :]
             else:
                 boxes = candidate_boxes[i]
             if boxes.shape[0] == 0:
                 continue
             if limit is not None and boxes.shape[0] > limit:
                 boxes = boxes[:limit, :]

             overlaps = bbox_overlaps(boxes.astype(np.float),
                                      gt_boxes.astype(np.float))

             _gt_overlaps = np.zeros((gt_boxes.shape[0]))
             for j in xrange(gt_boxes.shape[0]):
                 # find which proposal box maximally covers each gt box
                 argmax_overlaps = overlaps.argmax(axis=0)
                 # and get the iou amount of coverage for each gt box
                 max_overlaps = overlaps.max(axis=0)
                 # find which gt box is 'best' covered (i.e. 'best' = most iou)
                 gt_ind = max_overlaps.argmax()
                 gt_ovr = max_overlaps.max()
                 assert(gt_ovr >= 0)
                 # find the proposal box that covers the best covered gt box
                 box_ind = argmax_overlaps[gt_ind]
                 # record the iou coverage of this gt box
                 _gt_overlaps[j] = overlaps[box_ind, gt_ind]
                 assert(_gt_overlaps[j] == gt_ovr)
                 # mark the proposal box and the gt box as used
                 overlaps[box_ind, :] = -1
                 overlaps[:, gt_ind] = -1
             # append recorded iou coverage level
             gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps))

         gt_overlaps = np.sort(gt_overlaps)
         if thresholds is None:
             step = 0.05
             thresholds = np.arange(0.5, 0.95 + 1e-5, step)
         recalls = np.zeros_like(thresholds)
         # compute recall for each iou threshold
         for i, t in enumerate(thresholds):
             recalls[i] = (gt_overlaps >= t).sum() / float(num_pos)
         # ar = 2 * np.trapz(recalls, thresholds)
         ar = recalls.mean()
         return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds,
                 'gt_overlaps': gt_overlaps}

     def create_roidb_from_box_list(self, box_list, gt_roidb):
         assert len(box_list) == self.num_images, \
                 'Number of boxes must match number of ground-truth images'
         roidb = []
         for i in xrange(self.num_images):
             boxes = box_list[i]
             num_boxes = boxes.shape[0]
             overlaps = np.zeros((num_boxes, self.num_classes), dtype=np.float32)

             if gt_roidb is not None and gt_roidb[i]['boxes'].size > 0:
                 gt_boxes = gt_roidb[i]['boxes']
                 gt_classes = gt_roidb[i]['gt_classes']
                 gt_overlaps = bbox_overlaps(boxes.astype(np.float),
                                             gt_boxes.astype(np.float))
                 argmaxes = gt_overlaps.argmax(axis=1)
                 maxes = gt_overlaps.max(axis=1)
                 I = np.where(maxes > 0)[0]
                 overlaps[I, gt_classes[argmaxes[I]]] = maxes[I]

             overlaps = scipy.sparse.csr_matrix(overlaps)
             roidb.append({
                 'boxes' : boxes,
                 'gt_classes' : np.zeros((num_boxes,), dtype=np.int32),
                 'gt_overlaps' : overlaps,
                 'flipped' : False,
                 'seg_areas' : np.zeros((num_boxes,), dtype=np.float32),
             })
         return roidb

     @staticmethod
     def merge_roidbs(a, b):
         assert len(a) == len(b)
         for i in xrange(len(a)):
             a[i]['boxes'] = np.vstack((a[i]['boxes'], b[i]['boxes']))
             a[i]['gt_classes'] = np.hstack((a[i]['gt_classes'],
                                             b[i]['gt_classes']))
             a[i]['gt_overlaps'] = scipy.sparse.vstack([a[i]['gt_overlaps'],
                                                        b[i]['gt_overlaps']])
             a[i]['seg_areas'] = np.hstack((a[i]['seg_areas'],
                                            b[i]['seg_areas']))
         return a

     def competition_mode(self, on):
         """Turn competition mode on or off."""
         pass

get_imdb->factory->pascal_voc->(继承)imdb

factory

  year = ['2007', '2012']

  split = ['train', 'val', 'trainval', 'test']

 

imdb

  image_set: split

  devkit_path: config.DATA_DIR(root/data/) + VOCdevkit + year

  data_path: devkit_path + '/' + 'VOC' + year

  image_index: a list read image name from

      例如，root/data + /VOCdevkit2007/VOC2007/ImageSets/Main/{image_set}.txt

  roidb: gt_roidb得到（cfg.TRAIN.PROPOSAL_METHOD=gt导致了此操作）

  classes： 类别定义

  num_classes： 类别的长度

  class_to_ind：{类别名：类别索引}字典

  num_images（）: image_index'length，数据库中图片个数

  image_path_at（index）： 得到第index图片的地址，data_path + '/' + 'JPEGImages' + image_index[index] + image_ext(.jpg)

 

  在train_faster_rcnn_alt_opt.py的imdb.set_proposal_method之后一旦用imdb.roidb都会用gt_roidb读取xml中的内容中得到部分信息

xml的地址：data_path + '/' + 'Annotations' + '/' + index + '.xml'

          (root/data/) + VOCdevkit + year  + '/' + 'VOC' + year + '/' + 'Annotations' + '/' + index + '.xml'

get_training_roidb： 对得到的roi做是否反转（参见roidb的flipped，为了扩充数据库）和到roidb.py的prepare_roidb中计算得到roidb的其他数据

 

一张图有一个roidb，每个roidb是一个字典

roidb:

  boxes: four rows.the proposal.left-up,right-down

  gt_overlaps: len（box）*类别数（即，每个box对应的类别。初始化时，从xml读出来的类别对应类别值是1.0，被压缩保存）

  gt_classes: 每个box的类别索引

  flipped: true,代表图片被水平反转，改变了boxes里第一、三列的值（所有原图都这样的操作，imdb.image_index*2）(cfg.TRAIN.USE_FLIPPED会导致此操作的发生，见train.py 116行)

  seg_areas： box的面积

  （下面的值在roidb.py的prepare_roidb中得到）

  image：image_path_at（index），此roi的图片地址

  width：此图片的宽

  height： 高

  max_classes: box的类别=labels（gt_overlaps行最大值索引）

  max_overlaps: （gt_overlaps行最大值）（max_overlaps=0，max_classes=0，即都是背景，否则不正确）  

output_dir： ROOT_DIR + 'output' + EXP_DIR('faster_rcnn_alt_opt') + imdb.name("voc_2007_trainval" or "voc_2007_test")