text: # --------------------------------------------------------
  # Fast/er R-CNN
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ross Girshick
  # --------------------------------------------------------
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from datasets.imdb import imdb
  import datasets.ds_utils as ds_utils
  from model.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 pickle
  import json
  import uuid
  # COCO API
  from pycocotools.coco import COCO
  from pycocotools.cocoeval import COCOeval
  from pycocotools import mask as COCOmask
  
  
  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 range(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(list(zip(self.classes, list(range(self.num_classes)))))
      self._class_to_coco_cat_id = dict(list(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('gt')
      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
        'test-dev2015': 'test2015',
      }
      coco_name = image_set + year  # e.g., "val2014"
      self._data_name = (self._view_map[coco_name]
                         if coco_name in self._view_map
                         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 = pickle.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:
        pickle.dump(roidb, fid, pickle.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 = pickle.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:
        pickle.dump(gt_roidb, fid, pickle.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 {'width': width,
              'height': height,
              'boxes': boxes,
              'gt_classes': gt_classes,
              'gt_overlaps': overlaps,
              'flipped': False,
              'seg_areas': seg_areas}
  
    def _get_widths(self):
      return [r['width'] for r in self.roidb]
  
    def append_flipped_images(self):
      num_images = self.num_images
      widths = self._get_widths()
      for i in range(num_images):
        boxes = self.roidb[i]['boxes'].copy()
        oldx1 = boxes[:, 0].copy()
        oldx2 = boxes[:, 2].copy()
        boxes[:, 0] = widths[i] - oldx2 - 1
        boxes[:, 2] = widths[i] - oldx1 - 1
        assert (boxes[:, 2] >= boxes[:, 0]).all()
        entry = {'width': widths[i],
                 'height': self.roidb[i]['height'],
                 'boxes': boxes,
                 'gt_classes': self.roidb[i]['gt_classes'],
                 'gt_overlaps': self.roidb[i]['gt_overlaps'],
                 'flipped': True,
                 'seg_areas': self.roidb[i]['seg_areas']}
  
        self.roidb.append(entry)
      self._image_index = self._image_index * 2
  
    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:
        pickle.dump(coco_eval, fid, pickle.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 range(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
  