roidb2.py 文件源码

def add_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?'

    num_images = len(roidb)
    # Infer number of classes from the number of columns in gt_overlaps
    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']
        roidb[im_i]['bbox_targets'] = \
                _compute_targets(rois, max_overlaps, max_classes, num_classes)

    # Compute values needed for means and stds
    # var(x) = E(x^2) - E(x)^2
    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:
                class_counts[cls] += cls_inds.size
                sums[cls, :] += targets[cls_inds, 1:].sum(axis=0)
                squared_sums[cls, :] += (targets[cls_inds, 1:] ** 2).sum(axis=0)

    means = sums / class_counts
    stds = np.sqrt(squared_sums / class_counts - means ** 2)

    # Normalize 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, :]
            if stds[cls, 0] != 0:
                roidb[im_i]['bbox_targets'][cls_inds, 1:] /= stds[cls, :]

    # These values will be needed for making predictions
    # (the predicts will need to be unnormalized and uncentered)
    return means.ravel(), stds.ravel()