def _plot_tpr_tnr_overall(assessments,
trackers,
names=None,
colors=None,
markers=None):
bootstrap_modes = [False, True] if args.bootstrap else [False]
for iou in args.iou_thresholds:
for bootstrap in bootstrap_modes:
for posthoc in ([False] if bootstrap else [False, True]):
_plot_tpr_tnr(('tpr_tnr_iou_' + oxuva.float2str(iou) +
('_posthoc' if posthoc else '') +
('_bootstrap' if bootstrap else '')),
assessments,
trackers,
iou,
bootstrap,
posthoc,
names=names,
colors=colors,
markers=markers,
min_time_sec=None,
max_time_sec=None,
include_score=True)
def _plot_present_absent(assessments,
trackers,
iou_threshold,
bootstrap,
names=None,
colors=None,
markers=None):
names = names or {}
colors = colors or {}
markers = markers or {}
stats_whole = {
tracker:
oxuva.dataset_quality(assessments[tracker][iou_threshold]['totals'],
enable_bootstrap=bootstrap,
num_trials=args.bootstrap_trials)
for tracker in trackers
}
stats_all_present = {
tracker: oxuva.dataset_quality_filter(
assessments[tracker][iou_threshold]['totals'],
require_none_absent=True,
enable_bootstrap=bootstrap,
num_trials=args.bootstrap_trials)
for tracker in trackers
}
stats_any_absent = {
tracker: oxuva.dataset_quality_filter(
assessments[tracker][iou_threshold]['totals'],
require_some_absent=True,
enable_bootstrap=bootstrap,
num_trials=args.bootstrap_trials)
for tracker in trackers
}
order = _sort_quality(stats_whole)
tpr_key = 'TPR_mean' if bootstrap else 'TPR'
max_tpr = max(
max([stats_all_present[tracker][tpr_key] for tracker in trackers]),
max([stats_any_absent[tracker][tpr_key] for tracker in trackers]))
plt.figure(figsize=(args.width_inches, args.height_inches))
plt.xlabel('TPR (tracks without absent labels)')
plt.ylabel('TPR (tracks with some absent labels)')
for tracker in order:
if bootstrap:
plot_func = functools.partial(
_errorbar,
xerr=args.errorbar_size *
np.sqrt([stats_all_present[tracker]['TPR_var']]),
yerr=args.errorbar_size *
np.sqrt([stats_any_absent[tracker]['TPR_var']]),
capsize=3)
else:
plot_func = plt.plot
plot_func([stats_all_present[tracker][tpr_key]],
[stats_any_absent[tracker][tpr_key]],
label=names.get(tracker, tracker),
color=colors.get(tracker, None),
marker=markers.get(tracker, None),
markerfacecolor='none',
markeredgewidth=2,
clip_on=False)
plt.xlim(xmin=0, xmax=_ceil_nearest(CLEARANCE * max_tpr, 0.1))
plt.ylim(ymin=0, ymax=_ceil_nearest(CLEARANCE * max_tpr, 0.1))
plt.grid(color=GRID_COLOR, clip_on=False)
_hide_spines()
# Draw a diagonal line.
plt.plot([0, 1], [0, 1], color=GRID_COLOR, linewidth=1, linestyle='dotted')
plot_dir = os.path.join('analysis', args.data, args.challenge)
_ensure_dir_exists(plot_dir)
base_name = (
'present_absent_iou_{}'.format(oxuva.float2str(iou_threshold)) +
('_bootstrap' if bootstrap else ''))
_save_fig(os.path.join(plot_dir, base_name + '_no_legend.pdf'))
_legend_outside()
_save_fig(os.path.join(plot_dir, base_name + '.pdf'))
def _plot_intervals(assessments,
trackers,
iou_threshold,
bootstrap,
names=None,
colors=None,
markers=None):
# TODO: Add errorbars using bootstrap sampling?
names = names or {}
colors = colors or {}
markers = markers or {}
times_sec = range(0, args.max_time + 1, args.time_step)
# Get overall stats for order in legend.
overall_stats = {
tracker:
oxuva.dataset_quality(assessments[tracker][iou_threshold]['totals'],
enable_bootstrap=bootstrap,
num_trials=args.bootstrap_trials)
for tracker in trackers
}
order = _sort_quality(overall_stats, use_bootstrap_mean=False)
intervals_sec = {}
points = {}
for mode in INTERVAL_TYPES:
intervals_sec[mode], points[mode] = _make_intervals(times_sec, mode)
stats = {
mode: {
tracker: [
oxuva.dataset_quality_interval(
assessments[tracker][iou_threshold]['quantized_totals'],
min_time=None if min_time is None else FRAME_RATE *
min_time,
max_time=None if max_time is None else FRAME_RATE *
max_time,
enable_bootstrap=bootstrap,
num_trials=args.bootstrap_trials)
for min_time, max_time in intervals_sec[mode]
]
for tracker in trackers
}
for mode in INTERVAL_TYPES
}
tpr_key = 'TPR_mean' if bootstrap else 'TPR'
# Find maximum TPR value over all plots (to have same axes).
max_tpr = {
mode:
max(s[tpr_key] for tracker in trackers for s in stats[mode][tracker])
for mode in INTERVAL_TYPES
}
for mode in INTERVAL_TYPES:
plt.figure(figsize=(args.width_inches, args.height_inches))
plt.xlabel(INTERVAL_AXIS_LABEL[mode])
plt.ylabel('True Positive Rate')
for tracker in order:
tpr = [s.get(tpr_key, None) for s in stats[mode][tracker]]
if bootstrap:
tpr_var = [
s.get('TPR_var', None) for s in stats[mode][tracker]
]
plot_func = functools.partial(_errorbar,
yerr=args.errorbar_size *
np.sqrt(tpr_var),
capsize=3)
else:
plot_func = plt.plot
plot_func(1 / 60.0 * np.asarray(points[mode]),
tpr,
label=names.get(tracker, tracker),
marker=markers.get(tracker, None),
color=colors.get(tracker, None),
markerfacecolor='none',
markeredgewidth=2,
clip_on=False)
plt.xlim(xmin=0, xmax=args.max_time / 60.0)
ymax = max(max_tpr.values()) if args.same_axes else max_tpr[mode]
plt.ylim(ymin=0, ymax=_ceil_nearest(CLEARANCE * ymax, 0.1))
plt.grid(color=GRID_COLOR, clip_on=False)
_hide_spines()
plot_dir = os.path.join('analysis', args.data, args.challenge)
_ensure_dir_exists(plot_dir)
base_name = ('tpr_time_iou_{}_interval_{}'.format(
oxuva.float2str(iou_threshold), mode) +
('_bootstrap' if bootstrap else ''))
_save_fig(os.path.join(plot_dir, base_name + '_no_legend.pdf'))
_legend_outside()
_save_fig(os.path.join(plot_dir, base_name + '.pdf'))
def _plot_tpr_tnr_intervals(assessments,
trackers,
names=None,
colors=None,
markers=None):
modes = ['before', 'after']
intervals_sec = {}
for mode in modes:
intervals_sec[mode], _ = _make_intervals(args.times, mode)
bootstrap_modes = [False, True] if args.bootstrap else [False]
for bootstrap in bootstrap_modes:
for iou in args.iou_thresholds:
# Order by performance on all frames.
stats = {
tracker:
oxuva.dataset_quality(assessments[tracker][iou]['totals'],
enable_bootstrap=bootstrap,
num_trials=args.bootstrap_trials)
for tracker in trackers
}
order = _sort_quality(stats, use_bootstrap_mean=False)
tpr_key = 'TPR_mean' if bootstrap else 'TPR'
# Get stats for all plots to establish axis range.
# Note: This means that dataset_quality_interval() is called twice.
max_tpr = max([
max([
max([
oxuva.dataset_quality_interval(
assessments[tracker][iou]['quantized_totals'],
min_time=None if min_time is None else FRAME_RATE *
min_time,
max_time=None if max_time is None else FRAME_RATE *
max_time,
enable_bootstrap=bootstrap,
num_trials=args.bootstrap_trials)[tpr_key]
for tracker in trackers
]) for min_time, max_time in intervals_sec[mode]
]) for mode in modes
])
for mode in modes:
for min_time_sec, max_time_sec in intervals_sec[mode]:
base_name = '_'.join([
'tpr_tnr', 'iou_' + oxuva.float2str(iou),
'interval_{}_{}'.format(oxuva.float2str(min_time_sec),
oxuva.float2str(max_time_sec))
] + (['bootstrap'] if bootstrap else []))
_plot_tpr_tnr(base_name,
assessments,
trackers,
iou,
bootstrap,
posthoc=False,
min_time_sec=min_time_sec,
max_time_sec=max_time_sec,
max_tpr=max_tpr,
order=order,
names=names,
colors=colors,
markers=markers)
def _get_assessments(dataset, trackers):
'''
Args:
dataset: String that identifies dataset ("dev" or "test").
trackers: List of tracker names.
Returns:
Dictionary that maps [tracker][iou] to dataset assessment.
Only returns assessments for subset of trackers that were successful.
'''
# Create functions to load tasks with annotations on demand.
# (Do not need annotations if using cached assessments.)
# TODO: Code would be easier to read using a class with lazy-cached elements as members?
get_annotations = oxuva.LazyCacheCaller(
functools.partial(
_load_tasks_with_annotations,
os.path.join(REPO_DIR, 'dataset', 'annotations',
dataset + '.csv')))
assessments = {}
for tracker_ind, tracker in enumerate(trackers):
try:
log_context = 'tracker {}/{} {}'.format(tracker_ind + 1,
len(trackers), tracker)
tracker_assessments = {}
# Load predictions at most once for all IOU thresholds (can be slow).
get_predictions = oxuva.LazyCacheCaller(
lambda: oxuva.load_predictions_and_select_frames(
get_annotations(),
os.path.join('predictions', dataset, tracker),
permissive=args.permissive,
log_prefix=log_context + ': '))
# Obtain results at all IOU thresholds in order to make axes equal in all graphs.
# TODO: Is it unsafe to use float (iou) as dictionary key?
for iou in args.iou_thresholds:
logger.info('assess tracker "%s" with iou %g', tracker, iou)
assess_func = lambda: oxuva.assess_dataset(get_annotations(),
get_predictions(),
iou,
resolution_seconds=
30)
if args.use_summary:
tracker_assessments[iou] = oxuva.cache(
oxuva.Protocol(dump=oxuva.dump_dataset_assessment_json,
load=oxuva.load_dataset_assessment_json,
binary=False),
os.path.join(
'assess', dataset, tracker,
'iou_{}.json'.format(oxuva.float2str(iou))),
assess_func)
else:
# When it is not cached, it will include frame_assessments.
# TODO: Could cache (selected frames of) predictions to file if this is slow.
tracker_assessments[iou] = assess_func()
except IOError as ex:
logger.warning(
'could not obtain assessment of tracker "%s" on dataset "%s": %s',
tracker, dataset, ex)
else:
assessments[tracker] = tracker_assessments
return assessments
