-# --------------------------------------------------------
-# Faster R-CNN
-# Copyright (c) 2015 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Ross Girshick and Xinlei Chen
-# --------------------------------------------------------
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-from libs.configs import cfgs
-import numpy as np
-import numpy.random as npr
-from libs.box_utils.cython_utils.cython_bbox import bbox_overlaps
-from libs.box_utils import encode_and_decode
-
-
-def anchor_target_layer(
- gt_boxes, img_shape, all_anchors, is_restrict_bg=False):
- """Same as the anchor target layer in original Fast/er RCNN """
-
- total_anchors = all_anchors.shape[0]
- img_h, img_w = img_shape[1], img_shape[2]
- gt_boxes = gt_boxes[:, :-1] # remove class label
-
-
- # allow boxes to sit over the edge by a small amount
- _allowed_border = 0
-
- # only keep anchors inside the image
- if cfgs.IS_FILTER_OUTSIDE_BOXES:
- inds_inside = np.where(
- (all_anchors[:, 0] >= -_allowed_border) &
- (all_anchors[:, 1] >= -_allowed_border) &
- (all_anchors[:, 2] < img_w + _allowed_border) & # width
- (all_anchors[:, 3] < img_h + _allowed_border) # height
- )[0]
- else:
- inds_inside = range(all_anchors.shape[0])
-
- anchors = all_anchors[inds_inside, :]
-
- # label: 1 is positive, 0 is negative, -1 is dont care
- labels = np.empty((len(inds_inside),), dtype=np.float32)
- labels.fill(-1)
-
- # overlaps between the anchors and the gt boxes
- overlaps = bbox_overlaps(
- np.ascontiguousarray(anchors, dtype=np.float),
- np.ascontiguousarray(gt_boxes, dtype=np.float))
-
- argmax_overlaps = overlaps.argmax(axis=1)
- max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
- gt_argmax_overlaps = overlaps.argmax(axis=0)
- gt_max_overlaps = overlaps[
- gt_argmax_overlaps, np.arange(overlaps.shape[1])]
- gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
-
- if not cfgs.TRAIN_RPN_CLOOBER_POSITIVES:
- labels[max_overlaps < cfgs.RPN_IOU_NEGATIVE_THRESHOLD] = 0
-
- labels[gt_argmax_overlaps] = 1
- labels[max_overlaps >= cfgs.RPN_IOU_POSITIVE_THRESHOLD] = 1
-
- if cfgs.TRAIN_RPN_CLOOBER_POSITIVES:
- labels[max_overlaps < cfgs.RPN_IOU_NEGATIVE_THRESHOLD] = 0
-
- num_fg = int(cfgs.RPN_MINIBATCH_SIZE * cfgs.RPN_POSITIVE_RATE)
- fg_inds = np.where(labels == 1)[0]
- if len(fg_inds) > num_fg:
- disable_inds = npr.choice(
- fg_inds, size=(len(fg_inds) - num_fg), replace=False)
- labels[disable_inds] = -1
-
- num_bg = cfgs.RPN_MINIBATCH_SIZE - np.sum(labels == 1)
- if is_restrict_bg:
- num_bg = max(num_bg, num_fg * 1.5)
- bg_inds = np.where(labels == 0)[0]
- if len(bg_inds) > num_bg:
- disable_inds = npr.choice(
- bg_inds, size=(len(bg_inds) - num_bg), replace=False)
- labels[disable_inds] = -1
-
- bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :])
-
- # map up to original set of anchors
- labels = _unmap(labels, total_anchors, inds_inside, fill=-1)
- bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0)
-
- # labels = labels.reshape((1, height, width, A))
- rpn_labels = labels.reshape((-1, 1))
-
- # bbox_targets
- bbox_targets = bbox_targets.reshape((-1, 4))
- rpn_bbox_targets = bbox_targets
-
- return rpn_labels, rpn_bbox_targets
-
-
-def _unmap(data, count, inds, fill=0):
- """ Unmap a subset of item (data) back to the original set of items (of
- size count) """
- if len(data.shape) == 1:
- ret = np.empty((count,), dtype=np.float32)
- ret.fill(fill)
- ret[inds] = data
- else:
- ret = np.empty((count,) + data.shape[1:], dtype=np.float32)
- ret.fill(fill)
- ret[inds, :] = data
- return ret
-
-
-def _compute_targets(ex_rois, gt_rois):
- """Compute bounding-box regression targets for an image."""
- # targets = bbox_transform(ex_rois, gt_rois[:, :4]).astype(
- # np.float32, copy=False)
- targets = encode_and_decode.encode_boxes(unencode_boxes=gt_rois,
- reference_boxes=ex_rois,
- scale_factors=cfgs.ANCHOR_SCALE_FACTORS)
- # targets = encode_and_decode.encode_boxes(ex_rois=ex_rois,
- # gt_rois=gt_rois,
- # scale_factor=None)
- return targets