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）

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 osimport os.path as ospimport PILfrom utils.cython_bbox import bbox_overlapsimport numpy as npimport scipy.sparsefrom fast_rcnn.config import cfgclass imdb(object):    """Image database."""    def __init__(self, name):        self._name = name        self._num_classes = 0#<span style="font-family: Arial, Helvetica, sans-serif;">类别的长度</span>        self._classes = []#<span style="font-family: Arial, Helvetica, sans-serif;">类别定义</span>        self._image_index = []#<span style="font-family: Arial, Helvetica, sans-serif;">a list of image name（read from eg：</span><span style="font-family: Arial, Helvetica, sans-serif;">root/data + /VOCdevkit2007/VOC2007/ImageSets/Main/{image_set}.txt）</span><span style="font-family: Arial, Helvetica, sans-serif;"></span>        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")