# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # --------------------------------------------------------
"""Transform a roidb into a trainable roidb by adding a bunch of metadata."""
import numpy as np from fast_rcnn.config import cfg from fast_rcnn.bbox_transform import bbox_transform from utils.cython_bbox import bbox_overlaps import PIL
defprepare_roidb(imdb): """Enrich the imdb's roidb by adding some derived quantities that are useful for training. This function precomputes the maximum overlap, taken over ground-truth boxes, between each ROI and each ground-truth box. The class with maximum overlap is also recorded. """ sizes = [PIL.Image.open(imdb.image_path_at(i)).size for i in xrange(imdb.num_images)] roidb = imdb.roidb #对所有的iamge（包含数据增强部分）进行迭代 for i in xrange(len(imdb.image_index)): #image信息记录图像全路径，width、heigth为图片宽和高 roidb[i]['image'] = imdb.image_path_at(i) roidb[i]['width'] = sizes[i][0] roidb[i]['height'] = sizes[i][1] # need gt_overlaps as a dense array for argmax #roidb[i]['gt_overlaps']为压缩后的one-hot矩阵，toarray()就为解压缩，复原了one_hot矩阵 gt_overlaps = roidb[i]['gt_overlaps'].toarray() # max overlap with gt over classes (columns) #取出最大值 max_overlaps = gt_overlaps.max(axis=1) #取出最大值对应引索 # gt class that had the max overlap max_classes = gt_overlaps.argmax(axis=1) #在roidb列表中的图片信息dict中添加两个信息 roidb[i]['max_classes'] = max_classes roidb[i]['max_overlaps'] = max_overlaps # sanity checks # max overlap of 0 => class should be zero (background) #最大值为0的为背景类（我在xml文件中没有找到定义background的，遗留问题），结果是找到backgound类为该副图像bboxes的引索 zero_inds = np.where(max_overlaps == 0)[0] #引入一个异常，没什么作用，就是保证上一步能正确操作 assert all(max_classes[zero_inds] == 0) # max overlap > 0 => class should not be zero (must be a fg class) #记录非零类在该副图像中的boxes引索 nonzero_inds = np.where(max_overlaps > 0)[0] #同样引入一个没什么用的异常 assert all(max_classes[nonzero_inds] != 0)
defadd_bbox_regression_targets(roidb): """Add information needed to train bounding-box regressors.""" assert len(roidb) > 0 assert'max_classes'in roidb[0], 'Did you call prepare_roidb first?' #图片个数（包括水平翻转） num_images = len(roidb) # Infer number of classes from the number of columns in gt_overlaps #分类数，21,包括背景 num_classes = roidb[0]['gt_overlaps'].shape[1] #取每个图片的信息 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'] #（标签，dx,dy,dw,dh） roidb[im_i]['bbox_targets'] = \ _compute_targets(rois, max_overlaps, max_classes) #False if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED: # Use fixed / precomputed "means" and "stds" instead of empirical values 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 # var(x) = E(x^2) - E(x)^2 #cfg.EPS 为1e-14 class_counts = np.zeros((num_classes, 1)) + cfg.EPS sums = np.zeros((num_classes, 4)) 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] if cls_inds.size > 0: #记录该图像上有几个当前类的roi class_counts[cls] += cls_inds.size #取出该类对应引索的targets，四个值纵向相加 sums[cls, :] += targets[cls_inds, 1:].sum(axis=0) #取出该类对应引索的targets，四个值平方和纵向相加 squared_sums[cls, :] += \ (targets[cls_inds, 1:] ** 2).sum(axis=0) #均值 means = sums / class_counts #方差 stds = np.sqrt(squared_sums / class_counts - means ** 2)
print'bbox target means:' print means print means[1:, :].mean(axis=0) # ignore bg class print'bbox target stdevs:' print stds print stds[1:, :].mean(axis=0) # ignore bg class
# Normalize targets #True #对于不同类，分别进行标准化（dx,dy,dw,dh） if 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"
# These values will be needed for making predictions # (the predicts will need to be unnormalized and uncentered) return means.ravel(), stds.ravel() #如果传进来的是rois（bboxes）, max_overlaps, max_classes，则这里全是GT def_compute_targets(rois, overlaps, labels): """Compute bounding-box regression targets for an image.""" #这个函数主要是计算一副图像bboxes回归信息，返回(rois.shape[0], 5) # Indices of ground-truth ROIs #那一行有1，len（gt_inds）表示所有行一共有几个1 gt_inds = np.where(overlaps == 1)[0] #GT情况：这种情况不存在,roidb已经筛选出没有任何fg与bg的图片，只要有一个，就会存在1,len(gt_inds)就不为0 if len(gt_inds) == 0: # Bail if the image has no ground-truth ROIs return np.zeros((rois.shape[0], 5), dtype=np.float32) # Indices of examples for which we try to make predictions #cfg.TRAIN.BBOX_THRESH为0.5 #情况为GT，则全满足 ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI #建立（len（ex_inds），len（gt_inds））大小的矩阵，内容为iou ex_gt_overlaps = bbox_overlaps( np.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 #找到与该ex_roi最佳匹配GT gt_assignment = ex_gt_overlaps.argmax(axis=1) #取出gt_rois与ex_rois（bboxes） gt_rois = rois[gt_inds[gt_assignment], :] ex_rois = rois[ex_inds, :] #targets：（标签，dx,dy,dw,dh） targets = np.zeros((rois.shape[0], 5), dtype=np.float32) #gt情况：就是max_classes，ex_inds就是全部引索，因为GT情况上面的条件全满足 targets[ex_inds, 0] = labels[ex_inds] targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois) return targets
