imdb对象是一个pascol _voc的类实例,该类继承自imdb,用于数据交互。
初始化函数
在初始化自身的同时,先调用了父类的初始化方法,将imdb _name传入,例如(‘voc_2007_trainval’)
class pascal _voc(imdb): def __init__(self, image_set, year, devkit_path=None): imdb.__init__(self, 'voc_' + year + '_' + image_set) self._year = year self._image_set = image_set self._devkit_path = self._get_default_path() if devkit_path is None else devkit_path self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year) self._classes = ('__background__', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', '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() self._roidb_handler = self.selective_search_roidb self._salt = str(uuid.uuid4()) self._comp_id = 'comp4' self.config = {'cleanup' : True, 'use_salt' : True, 'use_diff' : False, 'matlab_eval' : False, 'rpn_file' : None, 'min_size' : 2} assert os.path.exists(self._devkit_path), 'VOCdevkit path does not exist: {}'.format(self._devkit_path) assert os.path.exists(self._data_path), 'Path does not exist: {}'.format(self._data_path)
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image _path _from _index
根据图片的索引,比如‘000001’获取在JPEGImages下对应的图片路径
def image_path_at(self, i): """ Return the absolute path to image i in the image sequence. """ return self.image_path_from_index(self._image_index[i]) def image_path_from_index(self, index): """ Construct an image path from the image's "index" identifier. """ image_path = os.path.join(self._data_path, 'JPEGImages', index + self._image_ext) assert os.path.exists(image_path), 'Path does not exist: {}'.format(image_path) return image_path def _load_image_set_index(self): """ Load the indexes listed in this dataset's image set file. """ image_set_file = os.path.join(self._data_path, 'ImageSets', 'Main', self._image_set + '.txt') assert os.path.exists(image_set_file), 'Path does not exist: {}'.format(image_set_file) with open(image_set_file) as f: image_index = [x.strip() for x in f.readlines()] return image_index
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_get _default _path
返回默认的数据源路径,这里是放在data下的VOCDevkit2007
def _get_default_path(self): """ Return the default path where PASCAL VOC is expected to be installed. """ return os.path.join(cfg.DATA_DIR, 'VOCdevkit' + self._year)
gt _roidb
这个函数返回roidb数据对象。首先它会在cache路径下找到以扩展名’.pkl’结尾的缓存,这个文件是通过cPickle工具将roidb序列化存储的。
如果该文件存在,那么它会先读取这里的内容,以提高效率(所以如果你换数据集的时候,要先把cache文件给删除,否则会造成错误)。接着,它将调用 _load _pascal _annotation这个私有函数加载roidb中的数据,并将其保存在缓存文件中,返回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 + '_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_pascal_annotation(index) for index in self.image_index] 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
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_load _pascal _annotation
根据每个图像的索引,到Annotations这个文件夹下去找相应的xml标注数据,然后加载所有的bounding box对象。最后将这些成员变量组装成roidb返回
def _load_pascal_annotation(self, index): """ Load image and bounding boxes info from XML file in the PASCAL VOC format. """ filename = os.path.join(self._data_path, 'Annotations', index + '.xml') tree = ET.parse(filename) objs = tree.findall('object') if not self.config['use_diff']: non_diff_objs = [ obj for obj in objs if int(obj.find('difficult').text) == 0] objs = non_diff_objs num_objs = len(objs) boxes = np.zeros((num_objs, 4), dtype=np.uint16) gt_classes = np.zeros((num_objs), dtype=np.int32) overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32) seg_areas = np.zeros((num_objs), dtype=np.float32) for ix, obj in enumerate(objs): bbox = obj.find('bndbox') x1 = float(bbox.find('xmin').text) - 1 y1 = float(bbox.find('ymin').text) - 1 x2 = float(bbox.find('xmax').text) - 1 y2 = float(bbox.find('ymax').text) - 1 cls = self._class_to_ind[obj.find('name').text.lower().strip()] boxes[ix, :] = [x1, y1, x2, y2] gt_classes[ix] = cls overlaps[ix, cls] = 1.0 seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1) overlaps = scipy.sparse.csr_matrix(overlaps) return {'boxes' : boxes, 'gt_classes': gt_classes, 'gt_overlaps' : overlaps, 'flipped' : False, 'seg_areas' : seg_areas}
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rpn _roidb
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。
最后用merge _roidbs将gr _roidb与rpn _roidb合并输出。
def rpn_roidb(self): 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 def _load_rpn_roidb(self, gt_roidb): filename = self.config['rpn_file'] print 'loading {}'.format(filename) assert os.path.exists(filename), 'rpn data not found at: {}'.format(filename) with open(filename, 'rb') as f: box_list = cPickle.load(f) return self.create_roidb_from_box_list(box_list, gt_roidb)
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