content: # --------------------------------------------------------
  # Fast/er R-CNN
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ross Girshick
  # --------------------------------------------------------
  
  # from datasets.imdb import imdb
  # import datasets.ds_utils as ds_utils
  # from fast_rcnn.config import cfg
  import os.path as osp
  import sys
  import os
  import numpy as np
  import scipy.sparse
  import scipy.io as sio
  import cPickle
  import json
  import uuid
  # COCO API
  # TODO: add this part into this project
  from ..pycocotools.coco import COCO
  from ..pycocotools.cocoeval import COCOeval
  from ..pycocotools import mask as COCOmask
  
  from .imdb import imdb
  import ds_utils
  
  # TODO: make fast_rcnn irrelevant
  # >>>> obsolete, because it depends on sth outside of this project
  from ..fast_rcnn.config import cfg
  # <<<< obsolete
  
  def _filter_crowd_proposals(roidb, crowd_thresh):
      """
      Finds proposals that are inside crowd regions and marks them with
      overlap = -1 (for all gt rois), which means they will be excluded from
      training.
      """
      for ix, entry in enumerate(roidb):
          overlaps = entry['gt_overlaps'].toarray()
          crowd_inds = np.where(overlaps.max(axis=1) == -1)[0]
          non_gt_inds = np.where(entry['gt_classes'] == 0)[0]
          if len(crowd_inds) == 0 or len(non_gt_inds) == 0:
              continue
          iscrowd = [int(True) for _ in xrange(len(crowd_inds))]
          crowd_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][crowd_inds, :])
          non_gt_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][non_gt_inds, :])
          ious = COCOmask.iou(non_gt_boxes, crowd_boxes, iscrowd)
          bad_inds = np.where(ious.max(axis=1) > crowd_thresh)[0]
          overlaps[non_gt_inds[bad_inds], :] = -1
          roidb[ix]['gt_overlaps'] = scipy.sparse.csr_matrix(overlaps)
      return roidb
  
  class coco(imdb):
      def __init__(self, image_set, year):
          imdb.__init__(self, 'coco_' + year + '_' + image_set)
          # COCO specific config options
          self.config = {'top_k' : 2000,
                         'use_salt' : True,
                         'cleanup' : True,
                         'crowd_thresh' : 0.7,
                         'min_size' : 2}
          # name, paths
          self._year = year
          self._image_set = image_set
          self._data_path = osp.join(cfg.DATA_DIR, 'coco')
          # load COCO API, classes, class <-> id mappings
          self._COCO = COCO(self._get_ann_file())
          cats = self._COCO.loadCats(self._COCO.getCatIds())
          self._classes = tuple(['__background__'] + [c['name'] for c in cats])
          self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
          self._class_to_coco_cat_id = dict(zip([c['name'] for c in cats],
                                                self._COCO.getCatIds()))
          self._image_index = self._load_image_set_index()
          # Default to roidb handler
          self.set_proposal_method('selective_search')
          self.competition_mode(False)
  
          # Some image sets are "views" (i.e. subsets) into others.
          # For example, minival2014 is a random 5000 image subset of val2014.
          # This mapping tells us where the view's images and proposals come from.
          self._view_map = {
              'minival2014' : 'val2014',          # 5k val2014 subset
              'valminusminival2014' : 'val2014',  # val2014 \setminus minival2014
          }
          coco_name = image_set + year  # e.g., "val2014"
          self._data_name = (self._view_map[coco_name]
                             if self._view_map.has_key(coco_name)
                             else coco_name)
          # Dataset splits that have ground-truth annotations (test splits
          # do not have gt annotations)
          self._gt_splits = ('train', 'val', 'minival')
  
      def _get_ann_file(self):
          prefix = 'instances' if self._image_set.find('test') == -1 \
                               else 'image_info'
          return osp.join(self._data_path, 'annotations',
                          prefix + '_' + self._image_set + self._year + '.json')
  
      def _load_image_set_index(self):
          """
          Load image ids.
          """
          image_ids = self._COCO.getImgIds()
          return image_ids
  
      def _get_widths(self):
          anns = self._COCO.loadImgs(self._image_index)
          widths = [ann['width'] for ann in anns]
          return widths
  
      def image_path_at(self, i):
          """
          Return the absolute path to image i in the image sequence.
          """
          return self.image_path_from_index(self._image_index[i])
  
      def image_path_from_index(self, index):
          """
          Construct an image path from the image's "index" identifier.
          """
          # Example image path for index=119993:
          #   images/train2014/COCO_train2014_000000119993.jpg
          file_name = ('COCO_' + self._data_name + '_' +
                       str(index).zfill(12) + '.jpg')
          image_path = osp.join(self._data_path, 'images',
                                self._data_name, file_name)
          assert osp.exists(image_path), \
                  'Path does not exist: {}'.format(image_path)
          return image_path
  
      def selective_search_roidb(self):
          return self._roidb_from_proposals('selective_search')
  
      def edge_boxes_roidb(self):
          return self._roidb_from_proposals('edge_boxes_AR')
  
      def mcg_roidb(self):
          return self._roidb_from_proposals('MCG')
  
      def _roidb_from_proposals(self, method):
          """
          Creates a roidb from pre-computed proposals of a particular methods.
          """
          top_k = self.config['top_k']
          cache_file = osp.join(self.cache_path, self.name +
                                '_{:s}_top{:d}'.format(method, top_k) +
                                '_roidb.pkl')
  
          if osp.exists(cache_file):
              with open(cache_file, 'rb') as fid:
                  roidb = cPickle.load(fid)
              print '{:s} {:s} roidb loaded from {:s}'.format(self.name, method,
                                                              cache_file)
              return roidb
  
          if self._image_set in self._gt_splits:
              gt_roidb = self.gt_roidb()
              method_roidb = self._load_proposals(method, gt_roidb)
              roidb = imdb.merge_roidbs(gt_roidb, method_roidb)
              # Make sure we don't use proposals that are contained in crowds
              roidb = _filter_crowd_proposals(roidb, self.config['crowd_thresh'])
          else:
              roidb = self._load_proposals(method, None)
          with open(cache_file, 'wb') as fid:
              cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
          print 'wrote {:s} roidb to {:s}'.format(method, cache_file)
          return roidb
  
      def _load_proposals(self, method, gt_roidb):
          """
          Load pre-computed proposals in the format provided by Jan Hosang:
          http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-
            computing/research/object-recognition-and-scene-understanding/how-
            good-are-detection-proposals-really/
          For MCG, use boxes from http://www.eecs.berkeley.edu/Research/Projects/
            CS/vision/grouping/mcg/ and convert the file layout using
          lib/datasets/tools/mcg_munge.py.
          """
          box_list = []
          top_k = self.config['top_k']
          valid_methods = [
              'MCG',
              'selective_search',
              'edge_boxes_AR',
              'edge_boxes_70']
          assert method in valid_methods
  
          print 'Loading {} boxes'.format(method)
          for i, index in enumerate(self._image_index):
              if i % 1000 == 0:
                  print '{:d} / {:d}'.format(i + 1, len(self._image_index))
  
              box_file = osp.join(
                  cfg.DATA_DIR, 'coco_proposals', method, 'mat',
                  self._get_box_file(index))
  
              raw_data = sio.loadmat(box_file)['boxes']
              boxes = np.maximum(raw_data - 1, 0).astype(np.uint16)
              if method == 'MCG':
                  # Boxes from the MCG website are in (y1, x1, y2, x2) order
                  boxes = boxes[:, (1, 0, 3, 2)]
              # Remove duplicate boxes and very small boxes and then take top k
              keep = ds_utils.unique_boxes(boxes)
              boxes = boxes[keep, :]
              keep = ds_utils.filter_small_boxes(boxes, self.config['min_size'])
              boxes = boxes[keep, :]
              boxes = boxes[:top_k, :]
              box_list.append(boxes)
              # Sanity check
              im_ann = self._COCO.loadImgs(index)[0]
              width = im_ann['width']
              height = im_ann['height']
              ds_utils.validate_boxes(boxes, width=width, height=height)
          return self.create_roidb_from_box_list(box_list, gt_roidb)
  
      def gt_roidb(self):
          """
          Return the database of ground-truth regions of interest.
          This function loads/saves from/to a cache file to speed up future calls.
          """
          cache_file = osp.join(self.cache_path, self.name + '_gt_roidb.pkl')
          if osp.exists(cache_file):
              with open(cache_file, 'rb') as fid:
                  roidb = cPickle.load(fid)
              print '{} gt roidb loaded from {}'.format(self.name, cache_file)
              return roidb
  
          gt_roidb = [self._load_coco_annotation(index)
                      for index in self._image_index]
  
          with open(cache_file, 'wb') as fid:
              cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
          print 'wrote gt roidb to {}'.format(cache_file)
          return gt_roidb
  
      def _load_coco_annotation(self, index):
          """
          Loads COCO bounding-box instance annotations. Crowd instances are
          handled by marking their overlaps (with all categories) to -1. This
          overlap value means that crowd "instances" are excluded from training.
          """
          im_ann = self._COCO.loadImgs(index)[0]
          width = im_ann['width']
          height = im_ann['height']
  
          annIds = self._COCO.getAnnIds(imgIds=index, iscrowd=None)
          objs = self._COCO.loadAnns(annIds)
          # Sanitize bboxes -- some are invalid
          valid_objs = []
          for obj in objs:
              x1 = np.max((0, obj['bbox'][0]))
              y1 = np.max((0, obj['bbox'][1]))
              x2 = np.min((width - 1, x1 + np.max((0, obj['bbox'][2] - 1))))
              y2 = np.min((height - 1, y1 + np.max((0, obj['bbox'][3] - 1))))
              if obj['area'] > 0 and x2 >= x1 and y2 >= y1:
                  obj['clean_bbox'] = [x1, y1, x2, y2]
                  valid_objs.append(obj)
          objs = valid_objs
          num_objs = len(objs)
  
          boxes = np.zeros((num_objs, 4), dtype=np.uint16)
          gt_classes = np.zeros((num_objs), dtype=np.int32)
          overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
          seg_areas = np.zeros((num_objs), dtype=np.float32)
  
          # Lookup table to map from COCO category ids to our internal class
          # indices
          coco_cat_id_to_class_ind = dict([(self._class_to_coco_cat_id[cls],
                                            self._class_to_ind[cls])
                                           for cls in self._classes[1:]])
  
          for ix, obj in enumerate(objs):
              cls = coco_cat_id_to_class_ind[obj['category_id']]
              boxes[ix, :] = obj['clean_bbox']
              gt_classes[ix] = cls
              seg_areas[ix] = obj['area']
              if obj['iscrowd']:
                  # Set overlap to -1 for all classes for crowd objects
                  # so they will be excluded during training
                  overlaps[ix, :] = -1.0
              else:
                  overlaps[ix, cls] = 1.0
  
          ds_utils.validate_boxes(boxes, width=width, height=height)
          overlaps = scipy.sparse.csr_matrix(overlaps)
          return {'boxes' : boxes,
                  'gt_classes': gt_classes,
                  'gt_overlaps' : overlaps,
                  'flipped' : False,
                  'seg_areas' : seg_areas}
  
      def _get_box_file(self, index):
          # first 14 chars / first 22 chars / all chars + .mat
          # COCO_val2014_0/COCO_val2014_000000447/COCO_val2014_000000447991.mat
          file_name = ('COCO_' + self._data_name +
                       '_' + str(index).zfill(12) + '.mat')
          return osp.join(file_name[:14], file_name[:22], file_name)
  
      def _print_detection_eval_metrics(self, coco_eval):
          IoU_lo_thresh = 0.5
          IoU_hi_thresh = 0.95
          def _get_thr_ind(coco_eval, thr):
              ind = np.where((coco_eval.params.iouThrs > thr - 1e-5) &
                             (coco_eval.params.iouThrs < thr + 1e-5))[0][0]
              iou_thr = coco_eval.params.iouThrs[ind]
              assert np.isclose(iou_thr, thr)
              return ind
  
          ind_lo = _get_thr_ind(coco_eval, IoU_lo_thresh)
          ind_hi = _get_thr_ind(coco_eval, IoU_hi_thresh)
          # precision has dims (iou, recall, cls, area range, max dets)
          # area range index 0: all area ranges
          # max dets index 2: 100 per image
          precision = \
              coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, :, 0, 2]
          ap_default = np.mean(precision[precision > -1])
          print ('~~~~ Mean and per-category AP @ IoU=[{:.2f},{:.2f}] '
                 '~~~~').format(IoU_lo_thresh, IoU_hi_thresh)
          print '{:.1f}'.format(100 * ap_default)
          for cls_ind, cls in enumerate(self.classes):
              if cls == '__background__':
                  continue
              # minus 1 because of __background__
              precision = coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, cls_ind - 1, 0, 2]
              ap = np.mean(precision[precision > -1])
              print '{:.1f}'.format(100 * ap)
  
          print '~~~~ Summary metrics ~~~~'
          coco_eval.summarize()
  
      def _do_detection_eval(self, res_file, output_dir):
          ann_type = 'bbox'
          coco_dt = self._COCO.loadRes(res_file)
          coco_eval = COCOeval(self._COCO, coco_dt)
          coco_eval.params.useSegm = (ann_type == 'segm')
          coco_eval.evaluate()
          coco_eval.accumulate()
          self._print_detection_eval_metrics(coco_eval)
          eval_file = osp.join(output_dir, 'detection_results.pkl')
          with open(eval_file, 'wb') as fid:
              cPickle.dump(coco_eval, fid, cPickle.HIGHEST_PROTOCOL)
          print 'Wrote COCO eval results to: {}'.format(eval_file)
  
      def _coco_results_one_category(self, boxes, cat_id):
          results = []
          for im_ind, index in enumerate(self.image_index):
              dets = boxes[im_ind].astype(np.float)
              if dets == []:
                  continue
              scores = dets[:, -1]
              xs = dets[:, 0]
              ys = dets[:, 1]
              ws = dets[:, 2] - xs + 1
              hs = dets[:, 3] - ys + 1
              results.extend(
                [{'image_id' : index,
                  'category_id' : cat_id,
                  'bbox' : [xs[k], ys[k], ws[k], hs[k]],
                  'score' : scores[k]} for k in xrange(dets.shape[0])])
          return results
  
      def _write_coco_results_file(self, all_boxes, res_file):
          # [{"image_id": 42,
          #   "category_id": 18,
          #   "bbox": [258.15,41.29,348.26,243.78],
          #   "score": 0.236}, ...]
          results = []
          for cls_ind, cls in enumerate(self.classes):
              if cls == '__background__':
                  continue
              print 'Collecting {} results ({:d}/{:d})'.format(cls, cls_ind,
                                                            self.num_classes - 1)
              coco_cat_id = self._class_to_coco_cat_id[cls]
              results.extend(self._coco_results_one_category(all_boxes[cls_ind],
                                                             coco_cat_id))
          print 'Writing results json to {}'.format(res_file)
          with open(res_file, 'w') as fid:
              json.dump(results, fid)
  
      def evaluate_detections(self, all_boxes, output_dir):
          res_file = osp.join(output_dir, ('detections_' +
                                           self._image_set +
                                           self._year +
                                           '_results'))
          if self.config['use_salt']:
              res_file += '_{}'.format(str(uuid.uuid4()))
          res_file += '.json'
          self._write_coco_results_file(all_boxes, res_file)
          # Only do evaluation on non-test sets
          if self._image_set.find('test') == -1:
              self._do_detection_eval(res_file, output_dir)
          # Optionally cleanup results json file
          if self.config['cleanup']:
              os.remove(res_file)
  
      def competition_mode(self, on):
          if on:
              self.config['use_salt'] = False
              self.config['cleanup'] = False
          else:
              self.config['use_salt'] = True
              self.config['cleanup'] = True
  