1 # --------------------------------------------------------
3 # Copyright (c) 2015 Microsoft
4 # Licensed under The MIT License [see LICENSE for details]
5 # Written by Ross Girshick and Xinlei Chen
6 # --------------------------------------------------------
7 from __future__ import absolute_import
8 from __future__ import division
9 from __future__ import print_function
12 from libs.configs import cfgs
14 import numpy.random as npr
15 from libs.box_utils.cython_utils.cython_bbox import bbox_overlaps
16 from libs.box_utils import encode_and_decode
19 def anchor_target_layer(
20 gt_boxes, img_shape, all_anchors, is_restrict_bg=False):
21 """Same as the anchor target layer in original Fast/er RCNN """
23 total_anchors = all_anchors.shape[0]
24 img_h, img_w = img_shape[1], img_shape[2]
25 gt_boxes = gt_boxes[:, :-1] # remove class label
28 # allow boxes to sit over the edge by a small amount
31 # only keep anchors inside the image
32 if cfgs.IS_FILTER_OUTSIDE_BOXES:
33 inds_inside = np.where(
34 (all_anchors[:, 0] >= -_allowed_border) &
35 (all_anchors[:, 1] >= -_allowed_border) &
36 (all_anchors[:, 2] < img_w + _allowed_border) & # width
37 (all_anchors[:, 3] < img_h + _allowed_border) # height
40 inds_inside = range(all_anchors.shape[0])
42 anchors = all_anchors[inds_inside, :]
44 # label: 1 is positive, 0 is negative, -1 is dont care
45 labels = np.empty((len(inds_inside),), dtype=np.float32)
48 # overlaps between the anchors and the gt boxes
49 overlaps = bbox_overlaps(
50 np.ascontiguousarray(anchors, dtype=np.float),
51 np.ascontiguousarray(gt_boxes, dtype=np.float))
53 argmax_overlaps = overlaps.argmax(axis=1)
54 max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
55 gt_argmax_overlaps = overlaps.argmax(axis=0)
56 gt_max_overlaps = overlaps[
57 gt_argmax_overlaps, np.arange(overlaps.shape[1])]
58 gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
60 if not cfgs.TRAIN_RPN_CLOOBER_POSITIVES:
61 labels[max_overlaps < cfgs.RPN_IOU_NEGATIVE_THRESHOLD] = 0
63 labels[gt_argmax_overlaps] = 1
64 labels[max_overlaps >= cfgs.RPN_IOU_POSITIVE_THRESHOLD] = 1
66 if cfgs.TRAIN_RPN_CLOOBER_POSITIVES:
67 labels[max_overlaps < cfgs.RPN_IOU_NEGATIVE_THRESHOLD] = 0
69 num_fg = int(cfgs.RPN_MINIBATCH_SIZE * cfgs.RPN_POSITIVE_RATE)
70 fg_inds = np.where(labels == 1)[0]
71 if len(fg_inds) > num_fg:
72 disable_inds = npr.choice(
73 fg_inds, size=(len(fg_inds) - num_fg), replace=False)
74 labels[disable_inds] = -1
76 num_bg = cfgs.RPN_MINIBATCH_SIZE - np.sum(labels == 1)
78 num_bg = max(num_bg, num_fg * 1.5)
79 bg_inds = np.where(labels == 0)[0]
80 if len(bg_inds) > num_bg:
81 disable_inds = npr.choice(
82 bg_inds, size=(len(bg_inds) - num_bg), replace=False)
83 labels[disable_inds] = -1
85 bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :])
87 # map up to original set of anchors
88 labels = _unmap(labels, total_anchors, inds_inside, fill=-1)
89 bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0)
91 # labels = labels.reshape((1, height, width, A))
92 rpn_labels = labels.reshape((-1, 1))
95 bbox_targets = bbox_targets.reshape((-1, 4))
96 rpn_bbox_targets = bbox_targets
98 return rpn_labels, rpn_bbox_targets
101 def _unmap(data, count, inds, fill=0):
102 """ Unmap a subset of item (data) back to the original set of items (of
104 if len(data.shape) == 1:
105 ret = np.empty((count,), dtype=np.float32)
109 ret = np.empty((count,) + data.shape[1:], dtype=np.float32)
115 def _compute_targets(ex_rois, gt_rois):
116 """Compute bounding-box regression targets for an image."""
117 # targets = bbox_transform(ex_rois, gt_rois[:, :4]).astype(
118 # np.float32, copy=False)
119 targets = encode_and_decode.encode_boxes(unencode_boxes=gt_rois,
120 reference_boxes=ex_rois,
121 scale_factors=cfgs.ANCHOR_SCALE_FACTORS)
122 # targets = encode_and_decode.encode_boxes(ex_rois=ex_rois,