def download_dataset(path: str, otb50: bool = False):
from vot.utilities.net import download_uncompress, join_url, NetworkException
dataset = _SEQUENCES
if otb50:
dataset = {k: v for k, v in dataset.items() if k in _OTB50_SUBSET}
with Progress("Downloading", len(dataset)) as progress:
for name, metadata in dataset.items():
name = metadata.get("base", name)
if not os.path.isdir(os.path.join(path, name)):
try:
download_uncompress(join_url(_BASE_URL, "%s.zip" % name),
path)
except NetworkException as ex:
raise DatasetException(
"Unable do download sequence data") from ex
except IOError as ex:
raise DatasetException(
"Unable to extract sequence data, is the target directory writable and do you have enough space?"
) from ex
progress.relative(1)
def _read(self):
channels = {}
tags = {}
values = {}
groundtruth = []
channels["color"] = load_channel(os.path.join(self._base, "%08d.jpg"))
self._metadata["channel.default"] = "color"
self._metadata["width"], self._metadata["height"] = six.next(
six.itervalues(channels)).size
groundtruth_file = os.path.join(
self._base, self.metadata("groundtruth", "groundtruth.txt"))
with open(groundtruth_file, 'r') as filehandle:
for region in filehandle.readlines():
groundtruth.append(parse(region))
self._metadata["length"] = len(groundtruth)
tagfiles = glob.glob(os.path.join(self._base, '*.label'))
for tagfile in tagfiles:
with open(tagfile, 'r') as filehandle:
tagname = os.path.splitext(os.path.basename(tagfile))[0]
tag = [line.strip() == "1" for line in filehandle.readlines()]
while not len(tag) >= len(groundtruth):
tag.append(False)
tags[tagname] = tag
valuefiles = glob.glob(os.path.join(self._base, '*.value'))
for valuefile in valuefiles:
with open(valuefile, 'r') as filehandle:
valuename = os.path.splitext(os.path.basename(valuefile))[0]
value = [
float(line.strip()) for line in filehandle.readlines()
]
while not len(value) >= len(groundtruth):
value.append(0.0)
values[valuename] = value
for name, channel in channels.items():
if not channel.length == len(groundtruth):
raise DatasetException("Length mismatch for channel %s" % name)
for name, tag in tags.items():
if not len(tag) == len(groundtruth):
tag_tmp = len(groundtruth) * [False]
tag_tmp[:len(tag)] = tag
tag = tag_tmp
for name, value in values.items():
if not len(value) == len(groundtruth):
raise DatasetException("Length mismatch for value %s" % name)
return channels, groundtruth, tags, values
def _read(self):
channels = {}
groundtruth = []
metadata = _SEQUENCES[self.name]
channels["color"] = PatternFileListChannel(
os.path.join(self._base, "img", "%04d.jpg"),
start=metadata.get("start", 1),
end=metadata.get("end", None))
self._metadata["channel.default"] = "color"
self._metadata["width"], self._metadata["height"] = six.next(
six.itervalues(channels)).size
groundtruth_file = os.path.join(self._base, "groundtruth_rect.txt")
with open(groundtruth_file, 'r') as filehandle:
for region in filehandle.readlines():
groundtruth.append(parse(region))
self._metadata["length"] = len(groundtruth)
if not channels["color"].length == len(groundtruth):
raise DatasetException(
"Length mismatch between groundtruth and images")
return channels, groundtruth, {}, {}
def __init__(self, path, splits=False):
super().__init__(path)
if not splits and not TrackingNetDataset.check(path):
raise DatasetException(
"Unsupported dataset format, expected TrackingNet")
sequences = []
if not splits:
for file in glob.glob(os.path.join(path, "anno", "*.txt")):
sequences.append(file)
else:
# Special mode to load all training splits
for split in ["TRAIN_%d" % i for i in range(0, 12)]:
for file in glob.glob(
os.path.join(path, split, "anno", "*.txt")):
sequences.append(file)
self._sequences = OrderedDict()
with Progress("Loading dataset", len(sequences)) as progress:
for sequence in sequences:
name = os.path.splitext(os.path.basename(sequence))[0]
self._sequences[name] = TrackingNetSequence(sequence,
dataset=self)
progress.relative(1)
def convert_frame(frame: Frame, channels: list) -> dict:
tlist = dict()
for channel in channels:
image = frame.filename(channel)
if image is None:
raise DatasetException("Frame does not have information for channel: {}".format(channel))
tlist[channel] = FileImage.create(image)
return tlist
def __init__(self, path):
super().__init__(path)
if not os.path.isfile(os.path.join(path, "list.txt")):
raise DatasetException("Dataset not available locally")
with open(os.path.join(path, "list.txt"), 'r') as fd:
names = fd.readlines()
self._sequences = {
name.strip(): VOTSequence(os.path.join(path, name.strip()),
dataset=self)
for name in Progress(
names, desc="Loading dataset", unit="sequences")
}
def __init__(self, path):
super().__init__(path)
if not os.path.isfile(os.path.join(path, "list.txt")):
raise DatasetException("Dataset not available locally")
with open(os.path.join(path, "list.txt"), 'r') as fd:
names = fd.readlines()
self._sequences = OrderedDict()
with Progress("Loading dataset", len(names)) as progress:
for name in names:
self._sequences[name.strip()] = VOTSequence(os.path.join(
path, name.strip()),
dataset=self)
progress.relative(1)
def __init__(self, path, sequence_list="list.txt"):
super().__init__(path)
if not os.path.isabs(sequence_list):
sequence_list = os.path.join(path, sequence_list)
if not os.path.isfile(sequence_list):
raise DatasetException("Sequence list does not exist")
with open(sequence_list, 'r') as handle:
names = handle.readlines()
self._sequences = OrderedDict()
with Progress("Loading dataset", len(names)) as progress:
for name in names:
self._sequences[name.strip()] = GOT10kSequence(os.path.join(
path, name.strip()),
dataset=self)
progress.relative(1)
def download(self, url, path="."):
from vot.utilities.net import download_uncompress, download_json, get_base_url, join_url, NetworkException
if os.path.splitext(url)[1] == '.zip':
logger.info(
'Downloading sequence bundle from "%s". This may take a while ...',
url)
try:
download_uncompress(url, path)
except NetworkException as e:
raise DatasetException(
"Unable do download dataset bundle, Please try to download the bundle manually from {} and uncompress it to {}'"
.format(url, path))
except IOError as e:
raise DatasetException(
"Unable to extract dataset bundle, is the target directory writable and do you have enough space?"
)
else:
meta = download_json(url)
logger.info('Downloading sequence dataset "%s" with %s sequences.',
meta["name"], len(meta["sequences"]))
base_url = get_base_url(url) + "/"
with Progress("Donwloading", len(meta["sequences"])) as progress:
for sequence in meta["sequences"]:
sequence_directory = os.path.join(path, sequence["name"])
os.makedirs(sequence_directory, exist_ok=True)
data = {
'name': sequence["name"],
'fps': sequence["fps"],
'format': 'default'
}
annotations_url = join_url(base_url,
sequence["annotations"]["url"])
try:
download_uncompress(annotations_url,
sequence_directory)
except NetworkException as e:
raise DatasetException(
"Unable do download annotations bundle")
except IOError as e:
raise DatasetException(
"Unable to extract annotations bundle, is the target directory writable and do you have enough space?"
)
for cname, channel in sequence["channels"].items():
channel_directory = os.path.join(
sequence_directory, cname)
os.makedirs(channel_directory, exist_ok=True)
channel_url = join_url(base_url, channel["url"])
try:
download_uncompress(channel_url, channel_directory)
except NetworkException as e:
raise DatasetException(
"Unable do download channel bundle")
except IOError as e:
raise DatasetException(
"Unable to extract channel bundle, is the target directory writable and do you have enough space?"
)
if "pattern" in channel:
data["channels." +
cname] = cname + os.path.sep + channel[
"pattern"]
else:
data["channels." + cname] = cname + os.path.sep
write_properties(
os.path.join(sequence_directory, 'sequence'), data)
progress.relative(1)
with open(os.path.join(path, "list.txt"), "w") as fp:
for sequence in meta["sequences"]:
fp.write('{}\n'.format(sequence["name"]))
def _read(self):
channels = {}
tags = {}
values = {}
groundtruth = []
channels["color"] = load_channel(os.path.join(self._base, "%08d.jpg"))
self._metadata["channel.default"] = "color"
self._metadata["width"], self._metadata["height"] = six.next(
six.itervalues(channels)).size
groundtruth_file = os.path.join(
self._base, self.metadata("groundtruth", "groundtruth.txt"))
groundtruth = read_trajectory(groundtruth_file)
if len(groundtruth) == 1 and channels["color"].length > 1:
# We are dealing with testing dataset, only first frame is available, so we pad the
# groundtruth with unknowns. Only unsupervised experiment will work, but it is ok
groundtruth.extend([Special(Special.UNKNOWN)] *
(channels["color"].length - 1))
self._metadata["length"] = len(groundtruth)
tagfiles = glob.glob(os.path.join(self._base, '*.label'))
for tagfile in tagfiles:
with open(tagfile, 'r') as filehandle:
tagname = os.path.splitext(os.path.basename(tagfile))[0]
tag = [line.strip() == "1" for line in filehandle.readlines()]
while not len(tag) >= len(groundtruth):
tag.append(False)
tags[tagname] = tag
valuefiles = glob.glob(os.path.join(self._base, '*.value'))
for valuefile in valuefiles:
with open(valuefile, 'r') as filehandle:
valuename = os.path.splitext(os.path.basename(valuefile))[0]
value = [
float(line.strip()) for line in filehandle.readlines()
]
while not len(value) >= len(groundtruth):
value.append(0.0)
values[valuename] = value
for name, channel in channels.items():
if not channel.length == len(groundtruth):
raise DatasetException("Length mismatch for channel %s" % name)
for name, tag in tags.items():
if not len(tag) == len(groundtruth):
tag_tmp = len(groundtruth) * [False]
tag_tmp[:len(tag)] = tag
tag = tag_tmp
for name, value in values.items():
if not len(value) == len(groundtruth):
raise DatasetException("Length mismatch for value %s" % name)
return channels, groundtruth, tags, values
def _read(self):
channels = {}
tags = {}
values = {}
groundtruth = []
for c in ["color", "depth", "ir"]:
channel_path = self.metadata("channels.%s" % c, None)
if not channel_path is None:
channels[c] = load_channel(
os.path.join(self._base, localize_path(channel_path)))
# Load default channel if no explicit channel data available
if len(channels) == 0:
channels["color"] = load_channel(
os.path.join(self._base, "color", "%08d.jpg"))
else:
self._metadata["channel.default"] = next(iter(channels.keys()))
self._metadata["width"], self._metadata["height"] = six.next(
six.itervalues(channels)).size
groundtruth_file = os.path.join(
self._base, self.metadata("groundtruth", "groundtruth.txt"))
groundtruth = read_trajectory(groundtruth_file)
self._metadata["length"] = len(groundtruth)
tagfiles = glob.glob(os.path.join(self._base, '*.tag')) + glob.glob(
os.path.join(self._base, '*.label'))
for tagfile in tagfiles:
with open(tagfile, 'r') as filehandle:
tagname = os.path.splitext(os.path.basename(tagfile))[0]
tag = [line.strip() == "1" for line in filehandle.readlines()]
while not len(tag) >= len(groundtruth):
tag.append(False)
tags[tagname] = tag
valuefiles = glob.glob(os.path.join(self._base, '*.value'))
for valuefile in valuefiles:
with open(valuefile, 'r') as filehandle:
valuename = os.path.splitext(os.path.basename(valuefile))[0]
value = [
float(line.strip()) for line in filehandle.readlines()
]
while not len(value) >= len(groundtruth):
value.append(0.0)
values[valuename] = value
for name, channel in channels.items():
if not channel.length == len(groundtruth):
raise DatasetException(
"Length mismatch for channel %s (%d != %d)" %
(name, channel.length, len(groundtruth)))
for name, tag in tags.items():
if not len(tag) == len(groundtruth):
tag_tmp = len(groundtruth) * [False]
tag_tmp[:len(tag)] = tag
tag = tag_tmp
for name, value in values.items():
if not len(value) == len(groundtruth):
raise DatasetException("Length mismatch for value %s" % name)
return channels, groundtruth, tags, values
def _scan(self, base):
metadata_file = os.path.join(base, 'sequence')
data = read_properties(metadata_file)
for c in ["color", "depth", "ir"]:
if "channels.%s" % c in data:
self._channels[c] = load_channel(
os.path.join(self._base,
localize_path(data["channels.%s" % c])))
# Load default channel if no explicit channel data available
if len(self._channels) == 0:
self._channels["color"] = load_channel(
os.path.join(self._base, "color", "%08d.jpg"))
else:
self._metadata["channel.default"] = next(
iter(self._channels.keys()))
self._metadata["width"], self._metadata["height"] = six.next(
six.itervalues(self._channels)).size
groundtruth_file = os.path.join(
self._base, data.get("groundtruth", "groundtruth.txt"))
with open(groundtruth_file, 'r') as groundtruth:
for region in groundtruth.readlines():
self._groundtruth.append(parse(region))
self._metadata["length"] = len(self._groundtruth)
tagfiles = glob.glob(os.path.join(self._base, '*.tag')) + glob.glob(
os.path.join(self._base, '*.label'))
for tagfile in tagfiles:
with open(tagfile, 'r') as filehandle:
tagname = os.path.splitext(os.path.basename(tagfile))[0]
tag = [line.strip() == "1" for line in filehandle.readlines()]
while not len(tag) >= len(self._groundtruth):
tag.append(False)
self._tags[tagname] = tag
valuefiles = glob.glob(os.path.join(self._base, '*.value'))
for valuefile in valuefiles:
with open(valuefile, 'r') as filehandle:
valuename = os.path.splitext(os.path.basename(valuefile))[0]
value = [
float(line.strip()) for line in filehandle.readlines()
]
while not len(value) >= len(self._groundtruth):
value.append(0.0)
self._values[valuename] = value
for name, channel in self._channels.items():
if not channel.length == len(self._groundtruth):
raise DatasetException("Length mismatch for channel %s" % name)
for name, tags in self._tags.items():
if not len(tags) == len(self._groundtruth):
tag_tmp = len(self._groundtruth) * [False]
tag_tmp[:len(tags)] = tags
tags = tag_tmp
for name, values in self._values.items():
if not len(values) == len(self._groundtruth):
raise DatasetException("Length mismatch for value %s" % name)