def load_predictions_csv(fp):
'''Loads output of tracker from CSV file.
Args:
fp: File-like object with read() and seek().
Returns:
List of (time, prediction-dict) pairs.
'''
# has_header = csv.Sniffer().has_header(fp.read(4<<10)) # 4 kB
# fp.seek(0)
reader = _dict_reader_optional_fieldnames(fp, PREDICTION_FIELD_NAMES)
predictions = util.SparseTimeSeries()
for row in reader:
present = util.str2bool(row['present'])
t = int(row['frame_num'])
predictions[t] = make_prediction(
present=present,
score=float(row['score']),
xmin=float(row['xmin']) if present else None,
xmax=float(row['xmax']) if present else None,
ymin=float(row['ymin']) if present else None,
ymax=float(row['ymax']) if present else None)
return predictions
def load_dataset_annotations_csv(fp):
'''Loads the annotations for an entire dataset from one CSV file.'''
reader = csv.DictReader(fp, fieldnames=TRACK_FIELDS)
rows = [row for row in reader]
# Group rows by object.
rows_by_track = {}
for row in rows:
vid_id = row['video_id']
obj_id = row['object_id']
rows_by_track.setdefault((vid_id, obj_id), []).append(row)
tracks = VideoObjectDict()
for vid_obj in rows_by_track.keys():
# vid_id, obj_id = vid_obj
frames = util.SparseTimeSeries()
for row in rows_by_track[vid_obj]:
present = _parse_is_present(row['object_presence'])
# TODO: Support 'exemplar' field in CSV format?
t = int(row['frame_num'])
frames[t] = make_frame_label(
present=present,
xmin=float(row['xmin']) if present else None,
xmax=float(row['xmax']) if present else None,
ymin=float(row['ymin']) if present else None,
ymax=float(row['ymax']) if present else None)
assert len(frames) >= 2
first_row = rows_by_track[vid_obj][0]
tracks[vid_obj] = make_track_label(
category=first_row['class_name'],
frames=frames,
contains_cuts=first_row['contains_cuts'],
always_visible=first_row['always_visible'])
return tracks
def make_task_from_track(track):
'''Creates a tracking task from a track annotation dict (oxuva.annot.make_track_label).
The first frame is adopted as initialization.
The remaining frames become the ground-truth rectangles.
'''
frames = list(track['frames'].sorted_items())
init_time, init_annot = frames[0]
labels = util.SparseTimeSeries(frames[1:])
# TODO: Check that init_annot['exemplar'] is True.
init_rect = {k: init_annot[k] for k in ['xmin', 'xmax', 'ymin', 'ymax']}
attributes = {k: v for k, v in track.items() if k not in {'frames'}}
return Task(init_time, init_rect, labels=labels, attributes=attributes)
def assess_sequence(gt, pred, iou_threshold):
'''Evaluate predicted track against ground-truth annotations.
Args:
gt: SparseTimeSeries of annotation dicts.
pred: SparseTimeSeries of prediction dicts.
iou_threshold: Threshold for determining true positive.
Returns:
An assessment of each frame with ground-truth.
This is a TimeSeries of frame assessment dicts.
'''
times = gt.sorted_keys()
# if pred.sorted_keys() != times:
pred = subset_using_previous_if_missing(pred, times)
return util.SparseTimeSeries({t: assess_frame(gt[t], pred[t], iou_threshold) for t in times})
def subset_using_previous_if_missing(series, times):
'''Extracts a subset of values at the given times.
If there is no data for a particular time, then the last value is used.
Args:
series: SparseTimeSeries of data.
times: List of times.
Returns:
Time series sampled at specified times.
Examples:
>> subset_using_previous_if_missing([(2, 'hi'), (4, 'bye')], [2, 3, 4, 5])
['hi', 'hi', 'bye', 'bye']
Raises an exception if asked for a time before the first element in series.
'''
assert isinstance(series, util.SparseTimeSeries)
series = list(series.sorted_items())
subset = [None for _ in times]
t_curr, x_curr = None, None
for i, t in enumerate(times):
# Read from series until we have read all elements <= t.
read_all = False
while not read_all:
if len(series) == 0:
read_all = True
else:
# Peek at next element.
t_next, x_next = series[0]
if t_next > t:
# We have gone past t.
read_all = True
else:
# Keep going.
t_curr, x_curr = t_next, x_next
series = series[1:]
if t_curr is None:
raise ValueError('no value for time: {}'.format(t))
if t_curr != t:
logger.warning(
'no prediction for time %d: use prediction for time %s', t,
t_curr)
subset[i] = x_curr
return util.SparseTimeSeries(zip(times, subset))
def test_idempotent(self):
source = util.SparseTimeSeries({1: 'one', 3: 'three'})
times = [1, 2, 3, 4]
once = subset_using_previous_if_missing(source, times)
twice = subset_using_previous_if_missing(once, times)
self.assertEqual(list(once.sorted_items()), list(twice.sorted_items()))
def test_before_start(self):
source = util.SparseTimeSeries({3: 'three'})
times = [1, 2, 3]
self.assertRaises(
Exception, lambda: subset_using_previous_if_missing(source, times))
def test_beyond_end(self):
source = util.SparseTimeSeries({1: 'one', 3: 'three'})
times = [2, 4, 6]
got = subset_using_previous_if_missing(source, times)
want = [(2, 'one'), (4, 'three'), (6, 'three')]
self.assertEqual(list(got.sorted_items()), want)
def test_missing(self):
source = util.SparseTimeSeries({1: 'one', 3: 'three'})
times = [1, 2, 3]
got = subset_using_previous_if_missing(source, times)
want = [(1, 'one'), (2, 'one'), (3, 'three')]
self.assertEqual(list(got.sorted_items()), want)
