def __init__(self, phase, single_object=False):
super(UVOSLoader, self).__init__()
self._phase = phase
self._single_object = single_object
self._db_sequences = db_read_sequences(self._phase)
# Load sequences
self.sequences = []
self.annotations = []
for s in self._db_sequences:
frames_dict = s['frames']
im_path = s['image_path']
anno_path = s['anno_path']
for label_id, frames in frames_dict.iteritems():
name = s['vid'] #get_sequence_name(s['vid'], label_id)
self.sequences.append(Sequence(name, im_path, frames,
label_id))
self.annotations.append(
Annotation(name, anno_path, frames, label_id))
self._keys = dict(
zip([s.name for s in self.sequences], range(len(self.sequences))))
try:
self.color_palette = np.array(
Image.open(self.annotations[0].files[0]).getpalette()).reshape(
-1, 3)
except Exception as e:
self.color_palette = np.array([[0, 255, 0]])
def __init__(self, year, phase, single_object=False):
super(DAVISLoader, self).__init__()
self._year = year
self._phase = phase
self._single_object = single_object
assert year == "2017" or year == "2016"
# check the phase
if year == '2016':
if not (self._phase == phase.TRAIN or self._phase == phase.VAL or \
self._phase == phase.TRAINVAL):
raise Exception(
"Set \'{}\' not available in DAVIS 2016 ({},{},{})".format(
self._phase.name, phase.TRAIN.name, phase.VAL.name,
phase.TRAINVAL.name))
# Check single_object if False iif year is 2016
if self._single_object:
assert self._year == '2016'
self._db_sequences = db_read_sequences(year, self._phase)
# Load sequences
self.sequences = [Sequence(s.name) for s in self._db_sequences]
# Load sequences
"""
if self._phase==phase.TESTDEV:
self.annotations = [None for s in self._db_sequences]
else:
"""
self.annotations = [
Annotation(s.name, self._single_object) for s in self._db_sequences
]
self._keys = dict(
zip([s.name for s in self.sequences], range(len(self.sequences))))
# Check number of frames is correct
for sequence, db_sequence in zip(self.sequences, self._db_sequences):
assert len(sequence) == db_sequence.num_frames
# Check number of annotations is correct
for annotation, db_sequence in zip(self.sequences, self._db_sequences):
if (self._phase == phase.TRAIN) or (self._phase == phase.VAL):
assert len(annotation) == db_sequence.num_frames
elif self._phase == phase.TESTDEV:
pass
try:
self.color_palette = np.array(
Image.open(self.annotations[0].files[0]).getpalette()).reshape(
-1, 3)
except Exception as e:
self.color_palette = np.array([[0, 255, 0]])
def _create_sentence_objects(self):
'''Returns a list of Sentence objects from the raw text.
'''
sentence_objects = []
sent_tokenizer = SentenceTokenizer(locale=self.language.code)
seq = Sequence(self.raw)
seq = sent_tokenizer.transform(seq)
for start_index, end_index in zip(seq.idx[:-1], seq.idx[1:]):
# Sentences share the same models as their parent blob
sent = seq.text[start_index:end_index].strip()
if not sent: continue
s = Sentence(sent, start_index=start_index, end_index=end_index)
s.detected_languages = self.detected_languages
sentence_objects.append(s)
return sentence_objects
def tokens(self):
"""Return a list of tokens, using this blob's tokenizer object
(defaults to :class:`WordTokenizer <textblob.tokenizers.WordTokenizer>`).
"""
seq = self.word_tokenizer.transform(Sequence(self.raw))
return WordList(seq.tokens(), parent=self, language=self.language.code)