def _load_imagenet_annotation(self, index):
"""
Load image and bounding boxes info from txt files of imagenet.
"""
filename = os.path.join(self._data_path, 'Annotations', self._image_set, index + '.xml')
# print 'Loading: {}'.format(filename)
def get_data_from_tag(node, tag):
return node.getElementsByTagName(tag)[0].childNodes[0].data
with open(filename) as f:
data = minidom.parseString(f.read())
objs = data.getElementsByTagName('object')
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)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
x1 = float(get_data_from_tag(obj, 'xmin'))
y1 = float(get_data_from_tag(obj, 'ymin'))
x2 = float(get_data_from_tag(obj, 'xmax'))
y2 = float(get_data_from_tag(obj, 'ymax'))
cls = self._wnid_to_ind[
str(get_data_from_tag(obj, "name")).lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes' : boxes,
'gt_classes': gt_classes,
'gt_overlaps' : overlaps,
'flipped' : False}