tools/accuracy_checker/accuracy_checker/annotation_converters/bitvehicle.py

   1 """
   2 Copyright (c) 2019 Intel Corporation
   3
   4 Licensed under the Apache License, Version 2.0 (the "License");
   5 you may not use this file except in compliance with the License.
   6 You may obtain a copy of the License at
   7
   8       http://www.apache.org/licenses/LICENSE-2.0
   9
  10 Unless required by applicable law or agreed to in writing, software
  11 distributed under the License is distributed on an "AS IS" BASIS,
  12 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13 See the License for the specific language governing permissions and
  14 limitations under the License.
  15 """
  16
  17 from pathlib import Path
  18
  19 from ..representation import DetectionAnnotation
  20 from ..utils import get_key_by_value, read_json, read_xml
  21
  22 from .format_converter import FileBasedAnnotationConverter
  23
  24
  25 class BITVehicleJSON(FileBasedAnnotationConverter):
  26     __provider__ = 'bitvehicle_json'
  27
  28     def convert(self):
  29         annotations = []
  30         for annotation_image in read_json(self.annotation_file):
  31             labels, x_mins, y_mins, x_maxs, y_maxs, is_ignored, occluded = [], [], [], [], [], [], []
  32             for detection in annotation_image['objects']:
  33                 x_min, y_min, x_max, y_max = detection['bbox']
  34                 label = detection['label']
  35
  36                 if label == 'ignored':
  37                     for class_ in _CLASS_TO_IND.values():
  38                         is_ignored.append(len(labels))
  39                         labels.append(class_)
  40                         x_mins.append(x_min)
  41                         y_mins.append(y_min)
  42                         x_maxs.append(x_max)
  43                         y_maxs.append(y_max)
  44                 else:
  45                     is_occluded = detection.get('is_occluded', False) or detection.get('occluded', False)
  46                     is_difficult = detection.get('difficult', False)
  47                     if is_occluded or is_difficult:
  48                         occluded.append(len(labels))
  49
  50                     labels.append(_CLASS_TO_IND[label])
  51                     x_mins.append(x_min)
  52                     y_mins.append(y_min)
  53                     x_maxs.append(x_max)
  54                     y_maxs.append(y_max)
  55
  56             identifier = Path(annotation_image['image']).name
  57             annotation = DetectionAnnotation(identifier, labels, x_mins, y_mins, x_maxs, y_maxs)
  58             annotation.metadata['is_occluded'] = occluded
  59             annotation.metadata['difficult_boxes'] = is_ignored
  60
  61             annotations.append(annotation)
  62
  63         return annotations, get_meta()
  64
  65
  66 class BITVehicle(FileBasedAnnotationConverter):
  67     __provider__ = 'bitvehicle'
  68
  69     def convert(self):
  70         annotations = []
  71         for annotation_image in read_xml(self.annotation_file):
  72             if annotation_image.tag != 'image':
  73                 continue
  74
  75             identifier = annotation_image.get('name')
  76             labels, x_mins, y_mins, x_maxs, y_maxs, occluded = [], [], [], [], [], []
  77             for roi in annotation_image.findall('box'):
  78                 label = roi.get("label")
  79                 x_left = int(roi.get('xtl'))
  80                 x_right = int(roi.get('xbr'))
  81                 y_top = int(roi.get('ytl'))
  82                 y_bottom = int(roi.get('ybr'))
  83                 x_min, y_min, x_max, y_max = x_left, y_top, x_right - x_left, y_bottom - y_top
  84                 is_occluded = bool(int(roi.get('occluded')))
  85
  86                 labels.append(_CLASS_TO_IND[label])
  87                 x_mins.append(x_min)
  88                 y_mins.append(y_min)
  89                 x_maxs.append(x_max)
  90                 y_maxs.append(y_max)
  91                 if is_occluded:
  92                     occluded.append(len(labels) - 1)
  93
  94             annotation = DetectionAnnotation(identifier, labels, x_mins, y_mins, x_maxs, y_maxs)
  95             annotation.metadata['is_occluded'] = occluded
  96
  97             annotations.append(annotation)
  98
  99         return annotations, get_meta()
 100
 101
 102 _CLASSES = (
 103     '__background__',  # always index 0
 104     'vehicle',
 105     'plate'
 106 )
 107
 108 _CLASS_TO_IND = dict(zip(_CLASSES, list(range(len(_CLASSES)))))
 109
 110
 111 def get_meta():
 112     labels = dict(enumerate(_CLASSES))
 113     labels[-1] = 'ignored'
 114
 115     return {'label_map': labels, 'background_label': get_key_by_value(labels, '__background__')}