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）
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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）
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轉載自：faster rcnn源碼解讀（四）之數據類型imdb.py和pascal_voc.py（主要是imdb和roidb數據類型的解說） - 野孩子的專欄 - 博客頻道 - CSDN.NET
#?--------------------------------------------------------?? #?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#<span?style="font-family:?Arial,?Helvetica,?sans-serif;">類別的長度</span>?? ????????self._classes?=?[]#<span?style="font-family:?Arial,?Helvetica,?sans-serif;">類別定義</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導致了此操作）?? ????????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??
《新程序員》：云原生和全面數字化實踐50位技術專家共同創作，文字、視頻、音頻交互閱讀
http://blog.csdn.net/u010668907/article/details/51945719
faster用python版本的https://github.com/rbgirshick/py-faster-rcnn
imdb.py源碼地址：https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/imdb.py
imdb源碼：
[python]?view plaincopy print?
get_imdb->factory->pascal_voc->(繼承)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導致了此操作）
??classes： 類別定義
??num_classes： 類別的長度
??class_to_ind：{類別名：類別索引}字典
??num_images（）: image_index'length，數據庫中圖片個數
??image_path_at（index）： 得到第index圖片的地址，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都會用gt_roidb讀取xml中的內容中得到部分信息
xml的地址：data_path + '/' + 'Annotations' + '/' + index + '.xml'
??????????(root/data/) + VOCdevkit + year ?+ '/' + 'VOC' + year + '/' + 'Annotations' + '/' + index + '.xml'
get_training_roidb： 對得到的roi做是否反轉（參見roidb的flipped，為了擴充數據庫）和到roidb.py的prepare_roidb中計算得到roidb的其他數據
?
一張圖有一個roidb，每個roidb是一個字典
roidb:
??boxes: four rows.the proposal.left-up,right-down
??gt_overlaps: len（box）*類別數（即，每個box對應的類別。初始化時，從xml讀出來的類別對應類別值是1.0，被壓縮保存）
??gt_classes:?每個box的類別索引
??flipped: true,代表圖片被水平反轉，改變了boxes里第一、三列的值（所有原圖都這樣的操作，imdb.image_index*2）(cfg.TRAIN.USE_FLIPPED會導致此操作的發生，見train.py 116行)
??seg_areas：?box的面積
??（下面的值在roidb.py的prepare_roidb中得到）
??image：image_path_at（index），此roi的圖片地址
??width：此圖片的寬
??height： 高
??max_classes: box的類別=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，否則不正確） ?
output_dir：?ROOT_DIR + 'output' + EXP_DIR('faster_rcnn_alt_opt') + imdb.name("voc_2007_trainval" or "voc_2007_test")
《新程序員》：云原生和全面數字化實踐50位技術專家共同創作，文字、視頻、音頻交互閱讀
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