content: # --------------------------------------------------------
  # Faster R-CNN
  # Copyright (c) 2015 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ross Girshick and Sean Bell
  # --------------------------------------------------------
  
  import numpy as np
  import yaml
  
  from .generate_anchors import generate_anchors
  
  # TODO: make fast_rcnn irrelevant
  # >>>> obsolete, because it depends on sth outside of this project
  from ..fast_rcnn.config import cfg
  from ..fast_rcnn.bbox_transform import bbox_transform_inv, clip_boxes
  from ..fast_rcnn.nms_wrapper import nms
  
  # <<<< obsolete
  
  
  DEBUG = False
  """
  Outputs object detection proposals by applying estimated bounding-box
  transformations to a set of regular boxes (called "anchors").
  """
  
  
  def proposal_layer(rpn_cls_prob_reshape, rpn_bbox_pred, im_info, cfg_key, _feat_stride=[16, ],
                     anchor_scales=[8, 16, 32]):
      """
      Parameters
      ----------
      rpn_cls_prob_reshape: (1 , H , W , Ax2) outputs of RPN, prob of bg or fg
                           NOTICE: the old version is ordered by (1, H, W, 2, A) !!!!
      rpn_bbox_pred: (1 , H , W , Ax4), rgs boxes output of RPN
      im_info: a list of [image_height, image_width, scale_ratios]
      cfg_key: 'TRAIN' or 'TEST'
      _feat_stride: the downsampling ratio of feature map to the original input image
      anchor_scales: the scales to the basic_anchor (basic anchor is [16, 16])
      ----------
      Returns
      ----------
      rpn_rois : (1 x H x W x A, 5) e.g. [0, x1, y1, x2, y2]
  
      # Algorithm:
      #
      # for each (H, W) location i
      #   generate A anchor boxes centered on cell i
      #   apply predicted bbox deltas at cell i to each of the A anchors
      # clip predicted boxes to image
      # remove predicted boxes with either height or width < threshold
      # sort all (proposal, score) pairs by score from highest to lowest
      # take top pre_nms_topN proposals before NMS
      # apply NMS with threshold 0.7 to remaining proposals
      # take after_nms_topN proposals after NMS
      # return the top proposals (-> RoIs top, scores top)
      #layer_params = yaml.load(self.param_str_)
  
      """
      _anchors = generate_anchors(scales=np.array(anchor_scales))
      _num_anchors = _anchors.shape[0]
      # rpn_cls_prob_reshape = np.transpose(rpn_cls_prob_reshape,[0,3,1,2]) #-> (1 , 2xA, H , W)
      # rpn_bbox_pred = np.transpose(rpn_bbox_pred,[0,3,1,2])              # -> (1 , Ax4, H , W)
  
      # rpn_cls_prob_reshape = np.transpose(np.reshape(rpn_cls_prob_reshape,[1,rpn_cls_prob_reshape.shape[0],rpn_cls_prob_reshape.shape[1],rpn_cls_prob_reshape.shape[2]]),[0,3,2,1])
      # rpn_bbox_pred = np.transpose(rpn_bbox_pred,[0,3,2,1])
      im_info = im_info[0]
  
      assert rpn_cls_prob_reshape.shape[0] == 1, \
          'Only single item batches are supported'
      # cfg_key = str(self.phase) # either 'TRAIN' or 'TEST'
      # cfg_key = 'TEST'
      pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
      post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
      nms_thresh = cfg[cfg_key].RPN_NMS_THRESH
      min_size = cfg[cfg_key].RPN_MIN_SIZE
  
      # the first set of _num_anchors channels are bg probs
      # the second set are the fg probs, which we want
      scores = rpn_cls_prob_reshape[:, _num_anchors:, :, :]
      bbox_deltas = rpn_bbox_pred
      # im_info = bottom[2].data[0, :]
  
      if DEBUG:
          print 'im_size: ({}, {})'.format(im_info[0], im_info[1])
          print 'scale: {}'.format(im_info[2])
  
      # 1. Generate proposals from bbox deltas and shifted anchors
      height, width = scores.shape[-2:]
  
      if DEBUG:
          print 'score map size: {}'.format(scores.shape)
  
      # Enumerate all shifts
      shift_x = np.arange(0, width) * _feat_stride
      shift_y = np.arange(0, height) * _feat_stride
      shift_x, shift_y = np.meshgrid(shift_x, shift_y)
      shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),
                          shift_x.ravel(), shift_y.ravel())).transpose()
  
      # Enumerate all shifted anchors:
      #
      # add A anchors (1, A, 4) to
      # cell K shifts (K, 1, 4) to get
      # shift anchors (K, A, 4)
      # reshape to (K*A, 4) shifted anchors
      A = _num_anchors
      K = shifts.shape[0]
      anchors = _anchors.reshape((1, A, 4)) + \
                shifts.reshape((1, K, 4)).transpose((1, 0, 2))
      anchors = anchors.reshape((K * A, 4))
  
      # Transpose and reshape predicted bbox transformations to get them
      # into the same order as the anchors:
      #
      # bbox deltas will be (1, 4 * A, H, W) format
      # transpose to (1, H, W, 4 * A)
      # reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
      # in slowest to fastest order
      bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))
  
      # Same story for the scores:
      #
      # scores are (1, A, H, W) format
      # transpose to (1, H, W, A)
      # reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
      scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
  
      # Convert anchors into proposals via bbox transformations
      proposals = bbox_transform_inv(anchors, bbox_deltas)
  
      # 2. clip predicted boxes to image
      proposals = clip_boxes(proposals, im_info[:2])
  
      # 3. remove predicted boxes with either height or width < threshold
      # (NOTE: convert min_size to input image scale stored in im_info[2])
      keep = _filter_boxes(proposals, min_size * im_info[2])
      proposals = proposals[keep, :]
      scores = scores[keep]
  
      # # remove irregular boxes, too fat too tall
      # keep = _filter_irregular_boxes(proposals)
      # proposals = proposals[keep, :]
      # scores = scores[keep]
  
      # 4. sort all (proposal, score) pairs by score from highest to lowest
      # 5. take top pre_nms_topN (e.g. 6000)
      order = scores.ravel().argsort()[::-1]
      if pre_nms_topN > 0:
          order = order[:pre_nms_topN]
      proposals = proposals[order, :]
      scores = scores[order]
  
      # 6. apply nms (e.g. threshold = 0.7)
      # 7. take after_nms_topN (e.g. 300)
      # 8. return the top proposals (-> RoIs top)
      keep = nms(np.hstack((proposals, scores)), nms_thresh)
      if post_nms_topN > 0:
          keep = keep[:post_nms_topN]
      proposals = proposals[keep, :]
      scores = scores[keep]
      # Output rois blob
      # Our RPN implementation only supports a single input image, so all
      # batch inds are 0
      batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
      blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
      return blob
      # top[0].reshape(*(blob.shape))
      # top[0].data[...] = blob
  
      # [Optional] output scores blob
      # if len(top) > 1:
      #    top[1].reshape(*(scores.shape))
      #    top[1].data[...] = scores
  
  
  def _filter_boxes(boxes, min_size):
      """Remove all boxes with any side smaller than min_size."""
      ws = boxes[:, 2] - boxes[:, 0] + 1
      hs = boxes[:, 3] - boxes[:, 1] + 1
      keep = np.where((ws >= min_size) & (hs >= min_size))[0]
      return keep
  
  
  def _filter_irregular_boxes(boxes, min_ratio=0.2, max_ratio=5):
      """Remove all boxes with any side smaller than min_size."""
      ws = boxes[:, 2] - boxes[:, 0] + 1
      hs = boxes[:, 3] - boxes[:, 1] + 1
      rs = ws / hs
      keep = np.where((rs <= max_ratio) & (rs >= min_ratio))[0]
      return keep
  