py-faster-rcnn代码roidb.py的解读

roidb是比较复杂的数据结构，存放了数据集的roi信息。原始的roidb来自数据集，在trian.py的get_training_roidb(imdb)函数进行了水平翻转扩充数量，然后prepare_roidb(imdb)【定义在roidb.py】为roidb添加了一些说明性的属性。

在这里暂时记录下roidb的结构信息，后面继续看的时候可能会有些修正：

roidb是由字典组成的list，roidb[img_index]包含了该图片索引所包含到roi信息，下面以roidb[img_index]为例说明：

roidb[img_index]包含的key,	value
boxes	box位置信息，box_num*4的np array
gt_overlaps	所有box在不同类别的得分，box_num*class_num矩阵
gt_classes	所有box的真实类别，box_num长度的list
flipped	是否翻转
image	该图片的路径，字符串
width	图片的宽
height	图片的高
max_overlaps	每个box的在所有类别的得分最大值，box_num长度
max_classes	每个box的得分最高所对应的类，box_num长度
bbox_targets	每个box的类别，以及与最接近的gt-box的4个方位偏移（共5列）

def add_bbox_regression_targets(roidb):"""Add information needed to train bounding-box regressors."""assert len(roidb) > 0assert 'max_classes' in roidb[0], 'Did you call prepare_roidb first?'num_images = len(roidb)# Infer number of classes from the number of columns in gt_overlaps# 类别数，roidb[0]对应第0号图片上的roi,shape[1]多少列表示roi属于不同类上的概率num_classes = roidb[0]['gt_overlaps'].shape[1]for im_i in xrange(num_images):rois = roidb[im_i]['boxes']max_overlaps = roidb[im_i]['max_overlaps']max_classes = roidb[im_i]['max_classes']# bbox_targets：每个box的类别，以及与最接近的gt-box的4个方位偏移roidb[im_i]['bbox_targets'] = \_compute_targets(rois, max_overlaps, max_classes)# 这里config是falseif cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:# Use fixed / precomputed "means" and "stds" instead of empirical values# 使用固定的均值和方差代替经验值means = np.tile(np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS), (num_classes, 1))stds = np.tile(np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS), (num_classes, 1))else:# Compute values needed for means and stds# 计算所需的均值和方差# var(x) = E(x^2) - E(x)^2# 计数各个类别出现box的数量class_counts = np.zeros((num_classes, 1)) + cfg.EPS  #加上cfg.EPS防止除0出错# 21类*4个位置，如果出现box的类别与其中某一类相同，将该box的4个target加入4个列元素中sums = np.zeros((num_classes, 4)) # 21类*4个位置，如果出现box的类别与其中某一类相同，将该box的4个target的平方加入4个列元素中squared_sums = np.zeros((num_classes, 4))for im_i in xrange(num_images):targets = roidb[im_i]['bbox_targets']for cls in xrange(1, num_classes):cls_inds = np.where(targets[:, 0] == cls)[0]# box的类别与该类匹配，计入if cls_inds.size > 0:class_counts[cls] += cls_inds.sizesums[cls, :] += targets[cls_inds, 1:].sum(axis=0)squared_sums[cls, :] += \(targets[cls_inds, 1:] ** 2).sum(axis=0)means = sums / class_counts # 均值stds = np.sqrt(squared_sums / class_counts - means ** 2) #标准差print 'bbox target means:'print meansprint means[1:, :].mean(axis=0) # ignore bg classprint 'bbox target stdevs:'print stdsprint stds[1:, :].mean(axis=0) # ignore bg class# Normalize targets# 对每一box归一化targetif cfg.TRAIN.BBOX_NORMALIZE_TARGETS:print "Normalizing targets"for im_i in xrange(num_images):targets = roidb[im_i]['bbox_targets']for cls in xrange(1, num_classes):cls_inds = np.where(targets[:, 0] == cls)[0]roidb[im_i]['bbox_targets'][cls_inds, 1:] -= means[cls, :]roidb[im_i]['bbox_targets'][cls_inds, 1:] /= stds[cls, :]else:print "NOT normalizing targets"# 均值和方差也用于预测# These values will be needed for making predictions# (the predicts will need to be unnormalized and uncentered)return means.ravel(), stds.ravel()  # ravel()排序拉成一维def _compute_targets(rois, overlaps, labels):  # 参数rois只含有当前图片的box信息"""Compute bounding-box regression targets for an image."""# Indices目录 of ground-truth ROIs# ground-truth ROIsgt_inds = np.where(overlaps == 1)[0]if len(gt_inds) == 0:# Bail if the image has no ground-truth ROIs# 不存在gt ROI，返回空数组return np.zeros((rois.shape[0], 5), dtype=np.float32)# Indices of examples for which we try to make predictions# BBOX阈值，只有ROI与gt的重叠度大于阈值，这样的ROI才能用作bb回归的训练样本ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]# Get IoU overlap between each ex ROI and gt ROI# 计算ex ROI and gt ROI的IoUex_gt_overlaps = bbox_overlaps(# 变数据格式为floatnp.ascontiguousarray(rois[ex_inds, :], dtype=np.float),np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))# Find which gt ROI each ex ROI has max overlap with:# this will be the ex ROI's gt target# 这里每一行代表一个ex_roi,列代表gt_roi,元素数值代表两者的IoUgt_assignment = ex_gt_overlaps.argmax(axis=1) #按行求最大，返回索引.gt_rois = rois[gt_inds[gt_assignment], :]  #每个ex_roi对应的gt_rois，与下面ex_roi数量相同ex_rois = rois[ex_inds, :]targets = np.zeros((rois.shape[0], 5), dtype=np.float32)targets[ex_inds, 0] = labels[ex_inds]  #第一个元素是labeltargets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)  #后4个元素是ex_box与gt_box的4个方位的偏移return targets

下面解读一下这两个函数。

1. _compute_targets(rois, overlaps, labels)

这个函数用来计算roi的偏移量。基本的步骤就是先确认是否含有ground-truth rois,主要通过 ground-truth ROIs的overlaps=1来确认。

然后找到重叠度大于一定阈值的box，再进行计算。

对于满足条件的box,会调用程序bbox_overlaps重新计算box对应于ground-truth box的重叠度，根据最大的重叠度找对应的ground truth box.

这样就可以利用 fast_rcnn.bbox_transform 的bbox_transform计算4个偏移（分别是中心点的x,y坐标，w,d长度偏移）。

输出的是一个二维数组，横坐标是盒子的序号，纵坐标是5维，第一维是类别，第二维到第五维为偏移。

bbox_overlaps的代码如下：

def bbox_overlaps(np.ndarray[DTYPE_t, ndim=2] boxes,np.ndarray[DTYPE_t, ndim=2] query_boxes):"""Parameters----------boxes: (N, 4) ndarray of floatquery_boxes: (K, 4) ndarray of floatReturns-------overlaps: (N, K) ndarray of overlap between boxes and query_boxes"""cdef unsigned int N = boxes.shape[0]cdef unsigned int K = query_boxes.shape[0]cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE)cdef DTYPE_t iw, ih, box_areacdef DTYPE_t uacdef unsigned int k, nfor k in range(K):box_area = ((query_boxes[k, 2] - query_boxes[k, 0] + 1) *(query_boxes[k, 3] - query_boxes[k, 1] + 1))for n in range(N):iw = (min(boxes[n, 2], query_boxes[k, 2]) -max(boxes[n, 0], query_boxes[k, 0]) + 1)if iw > 0:ih = (min(boxes[n, 3], query_boxes[k, 3]) -max(boxes[n, 1], query_boxes[k, 1]) + 1)if ih > 0:ua = float((boxes[n, 2] - boxes[n, 0] + 1) *(boxes[n, 3] - boxes[n, 1] + 1) +box_area - iw * ih)overlaps[n, k] = iw * ih / uareturn overlaps

2. add_bbox_regression_targets

主要两个两件事： 1. 确定roidb每个图片的box的回归偏移量bbox_targets

2. 对于所有的类别，计算偏移量的均值和方差，这样输出的矩阵是二维，行坐标是种类（这里是21类），纵坐标是偏移量（这里是4）.

并且在需要正则化目标项（即cfg.TRAIN.BBOX_NORMALIZE_TARGETS=true）时，使每个偏移都减去均值除以标准差。并返回均值和方差的折叠一维向量，

用于预测（即逆操作，去正则化，则中心化）。

参考：