text: # --------------------------------------------------------
  # Fast/er R-CNN
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Bharath Hariharan
  # --------------------------------------------------------
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import xml.etree.ElementTree as ET
  import os
  import pickle
  import numpy as np
  from PIL import Image
  
  def parse_rec(filename, muscima, rescale_factor=1):
    """ Parse a PASCAL VOC xml file """
    if not muscima:
      tree = ET.parse(filename)
      for size in tree.findall('size'):
          width = int(round(float(size[0].text) * rescale_factor))
          height = int(round(float(size[1].text) * rescale_factor))
      objects = []
      for obj in tree.findall('object'):
        obj_struct = {}
        obj_struct['name'] = obj.find('name').text
        # obj_struct['pose'] = obj.find('pose').text
        bbox = obj.find('bndbox')
        obj_struct['bbox'] = [int(float(bbox.find('xmin').text) * width),
                              int(float(bbox.find('ymin').text) * height),
                              int(float(bbox.find('xmax').text) * width),
                              int(float(bbox.find('ymax').text) * height)]
    else:
      # Parse a muscima xml file
      tree = ET.parse(filename)
      objects = []
      for objs in tree.findall('CropObjects'):
        for obj in objs.findall('CropObject'):
          obj_struct = {}
          obj_struct['name'] = obj.find('ClassName').text
          obj_struct['bbox'] = [int(int(obj.find('Left').text) * rescale_factor),
                                int(int(obj.find('Top').text) * rescale_factor),
                                int((int(obj.find('Left').text) + int(obj.find('Width').text)) * rescale_factor),
                                int((int(obj.find('Top').text) + int(obj.find('Height').text)) * rescale_factor)]
          # we want to ignore the large objects
          if int((int(obj.find('Left').text) + int(obj.find('Width').text)) * rescale_factor) - \
                  int(int(obj.find('Left').text) * rescale_factor) < (400 * rescale_factor) and \
                  int((int(obj.find('Top').text) + int(obj.find('Height').text)) * rescale_factor) - \
                  int(int(obj.find('Top').text) * rescale_factor) < (400 * rescale_factor):
            objects.append(obj_struct)
  
    return objects
  
  
  def parse_rec_deepscores(filename, rescale_factor=0.5):
    """ Parse a DeepScores xml file """
  
    tree = ET.parse(filename)
    objects = []
  
    # get image size to scale gt bbox
    im_size = Image.open(filename.replace("xml_annotations", "images_png")[:-4] + ".png").convert('L').size
    for obj in tree.findall('object'):
      obj_struct = {}
      obj_struct['name'] = obj.find('name').text
      obj_struct['pose'] = "Unspecified"
      obj_struct['truncated'] = int(0)
      obj_struct['difficult'] = int(0)
      bbox = obj.find('bndbox')
      obj_struct['bbox'] = [(bbox.find('xmin').text),
                            (bbox.find('ymin').text),
                            (bbox.find('xmax').text),
                            (bbox.find('ymax').text)]
  
      obj_struct['bbox'][0] = int(round(float(obj_struct['bbox'][0]) * im_size[0]))
      obj_struct['bbox'][1] = int(round(float(obj_struct['bbox'][1]) * im_size[1]))
      obj_struct['bbox'][2] = int(round(float(obj_struct['bbox'][2]) * im_size[0]))
      obj_struct['bbox'][3] = int(round(float(obj_struct['bbox'][3]) * im_size[1]))
      objects.append(obj_struct)
      with open('bounding_boxes.pickle', 'w') as f:
        pickle.dump(objects, f)
  
    return objects
  
  
  def parse_rec_dota(filename, rescale_factor=0.5):
      objects = []
      with open(filename, 'r') as f:
          lines = f.readlines()
          splitlines = [x.strip().split(' ')  for x in lines]
          i = 0
          for splitline in splitlines:
              if i < 2:
                i += 1
                continue
              object_struct = {}
              object_struct['name'] = splitline[8]
              if (len(splitline) == 9):
                  object_struct['difficult'] = 0
              elif (len(splitline) == 10):
                  object_struct['difficult'] = int(splitline[9])
              # object_struct['difficult'] = 0
              object_struct['bbox'] = [int(float(splitline[0]) * rescale_factor),
                                           int(float(splitline[1]) * rescale_factor),
                                           int(float(splitline[4]) * rescale_factor),
                                           int(float(splitline[5]) * rescale_factor)]
              w = int(float(splitline[4]) * rescale_factor) - int(float(splitline[0]) * rescale_factor)
              h = int(float(splitline[5]) * rescale_factor) - int(float(splitline[1]) * rescale_factor)
              object_struct['area'] = w * h
              objects.append(object_struct)
      return objects
  
  
  def voc_ap(rec, prec, use_07_metric=False):
    """ ap = voc_ap(rec, prec, [use_07_metric])
    Compute VOC AP given precision and recall.
    If use_07_metric is true, uses the
    VOC 07 11 point method (default:False).
    """
    if use_07_metric:
      # 11 point metric
      ap = 0.
      for t in np.arange(0., 1.1, 0.1):
        if np.sum(rec >= t) == 0:
          p = 0
        else:
          p = np.max(prec[rec >= t])
        ap = ap + p / 11.
    else:
      # correct AP calculation
      # first append sentinel values at the end
      mrec = np.concatenate(([0.], rec, [1.]))
      mpre = np.concatenate(([0.], prec, [0.]))
  
      # compute the precision envelope
      for i in range(mpre.size - 1, 0, -1):
        mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
  
      # to calculate area under PR curve, look for points
      # where X axis (recall) changes value
      i = np.where(mrec[1:] != mrec[:-1])[0]
  
      # and sum (\Delta recall) * prec
      ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
    return ap
  
  
  def voc_eval(detpath,
               annopath,
               imagesetfile,
               classname,
               cachedir,
               ovthresh=0.5,
               use_07_metric=False):
    """rec, prec, ap = voc_eval(detpath,
                                annopath,
                                imagesetfile,
                                classname,
                                [ovthresh],
                                [use_07_metric])
    Top level function that does the PASCAL VOC evaluation.
    detpath: Path to detections
        detpath.format(classname) should produce the detection results file.
    annopath: Path to annotations
        annopath.format(imagename) should be the xml annotations file.
    imagesetfile: Text file containing the list of images, one image per line.
    classname: Category name (duh)
    cachedir: Directory for caching the annotations
    [ovthresh]: Overlap threshold (default = 0.5)
    [use_07_metric]: Whether to use VOC07's 11 point AP computation
        (default False)
    """
    # assumes detections are in detpath.format(classname)
    # assumes annotations are in annopath.format(imagename)
    # assumes imagesetfile is a text file with each line an image name
    # cachedir caches the annotations in a pickle file
  
    if classname == "notehead-full":
      print("nh-full")
  
    # first load gt
    if not os.path.isdir(cachedir):
      os.mkdir(cachedir)
    cachefile = os.path.join(cachedir, '%s_annots.pkl' % imagesetfile)
    # read list of images
    with open(imagesetfile, 'r') as f:
      lines = f.readlines()
    imagenames = [x.strip() for x in lines]
  
    # remove files not present on disk
    present_files = os.listdir(annopath[:-9])
    present_files = set([x[:-4] for x in present_files])
    imagenames = set(imagenames)
    imagenames = list(imagenames.intersection(present_files))
  
    if not os.path.isfile(cachefile):
      # load annotations
      recs = {}
      for i, imagename in enumerate(imagenames):
        if "DeepScores" in detpath:
          recs[imagename] = parse_rec_deepscores(annopath.format(imagename))
        elif "MUSICMA" in detpath:
          recs[imagename] = parse_rec(annopath.format(imagename), muscima = True)
  
        elif "Dota" in detpath:
          recs[imagename] = parse_rec_dota(annopath.format(imagename))
  
        else:
          recs[imagename] = parse_rec(annopath.format(imagename), musicma = False)
        if i % 100 == 0:
          print('Reading annotation for {:d}/{:d}'.format(
            i + 1, len(imagenames)))
      # save
      print('Saving cached annotations to {:s}'.format(cachefile))
      with open(cachefile, 'w') as f:
        pickle.dump(recs, f)
    else:
      # load
      with open(cachefile, 'rb') as f:
        try:
          recs = pickle.load(f)
        except:
          recs = pickle.load(f, encoding='bytes')
  
    # extract gt objects for this class
    class_recs = {}
    npos = 0
    for imagename in imagenames:
      R = [obj for obj in recs[imagename] if obj['name'] == classname]
      # if len(R) == 0:
      #   continue
  
      bbox = np.array([x['bbox'] for x in R])
  
  
      if len(R) > 0 and "difficult" not in R[0].keys():
        difficult = np.zeros(len(R)).astype(np.bool)
      else:
        difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
  
      det = [False] * len(R)
      npos = npos + sum(~difficult)
      class_recs[imagename] = {'bbox': bbox,
                               'difficult': difficult,
                               'det': det}
  
    # read dets
    detfile = detpath.format(classname)
    with open(detfile, 'r') as f:
      lines = f.readlines()
  
    splitlines = [x.strip().split(' ') for x in lines]
    image_ids = [x[0] for x in splitlines]
    confidence = np.array([float(x[1]) for x in splitlines])
    BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
  
    nd = len(image_ids)
    tp = np.zeros(nd)
    fp = np.zeros(nd)
  
    if BB.shape[0] > 0:
      # sort by confidence
      sorted_ind = np.argsort(-confidence)
      sorted_scores = np.sort(-confidence)
      BB = BB[sorted_ind, :]
      image_ids = [image_ids[x] for x in sorted_ind]
  
      # go down dets and mark TPs and FPs
      for d in range(nd):
        R = class_recs[image_ids[d]]
        bb = BB[d, :].astype(float)
        ovmax = -np.inf
        BBGT = R['bbox'].astype(float)
  
        if BBGT.size > 0:
          # compute overlaps
          # intersection
          ixmin = np.maximum(BBGT[:, 0], bb[0])
          iymin = np.maximum(BBGT[:, 1], bb[1])
          ixmax = np.minimum(BBGT[:, 2], bb[2])
          iymax = np.minimum(BBGT[:, 3], bb[3])
          iw = np.maximum(ixmax - ixmin + 1., 0.)
          ih = np.maximum(iymax - iymin + 1., 0.)
          inters = iw * ih
  
          # union
          uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
                 (BBGT[:, 2] - BBGT[:, 0] + 1.) *
                 (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
  
          overlaps = inters / uni
          ovmax = np.max(overlaps)
          jmax = np.argmax(overlaps)
  
        if ovmax > ovthresh:
          if not R['difficult'][jmax]:
            if not R['det'][jmax]:
              tp[d] = 1.
              R['det'][jmax] = 1
            else:
              fp[d] = 1.
        else:
          fp[d] = 1.
  
    # compute precision recall
    fp = np.cumsum(fp)
    tp = np.cumsum(tp)
    rec = tp / float(npos)
    # avoid divide by zero in case the first detection matches a difficult
    # ground truth
    prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
    ap = voc_ap(rec, prec, use_07_metric)
  
    return rec, prec, ap
  