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track_map.py
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track_map.py
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import numpy as np
from ._base_metric import _BaseMetric
from .. import _timing
from functools import partial
from .. import utils
from ..utils import TrackEvalException
class TrackMAP(_BaseMetric):
"""Class which implements the TrackMAP metrics"""
@staticmethod
def get_default_metric_config():
"""Default class config values"""
default_config = {
'USE_AREA_RANGES': True, # whether to evaluate for certain area ranges
'AREA_RANGES': [[0 ** 2, 32 ** 2], # additional area range sets for which TrackMAP is evaluated
[32 ** 2, 96 ** 2], # (all area range always included), default values for TAO
[96 ** 2, 1e5 ** 2]], # evaluation
'AREA_RANGE_LABELS': ["area_s", "area_m", "area_l"], # the labels for the area ranges
'USE_TIME_RANGES': True, # whether to evaluate for certain time ranges (length of tracks)
'TIME_RANGES': [[0, 3], [3, 10], [10, 1e5]], # additional time range sets for which TrackMAP is evaluated
# (all time range always included) , default values for TAO evaluation
'TIME_RANGE_LABELS': ["time_s", "time_m", "time_l"], # the labels for the time ranges
'IOU_THRESHOLDS': np.arange(0.5, 0.96, 0.05), # the IoU thresholds
'RECALL_THRESHOLDS': np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01) + 1), endpoint=True),
# recall thresholds at which precision is evaluated
'MAX_DETECTIONS': 0, # limit the maximum number of considered tracks per sequence (0 for unlimited)
'PRINT_CONFIG': True
}
return default_config
def __init__(self, config=None):
super().__init__()
self.config = utils.init_config(config, self.get_default_metric_config(), self.get_name())
self.num_ig_masks = 1
self.lbls = ['all']
self.use_area_rngs = self.config['USE_AREA_RANGES']
if self.use_area_rngs:
self.area_rngs = self.config['AREA_RANGES']
self.area_rng_lbls = self.config['AREA_RANGE_LABELS']
self.num_ig_masks += len(self.area_rng_lbls)
self.lbls += self.area_rng_lbls
self.use_time_rngs = self.config['USE_TIME_RANGES']
if self.use_time_rngs:
self.time_rngs = self.config['TIME_RANGES']
self.time_rng_lbls = self.config['TIME_RANGE_LABELS']
self.num_ig_masks += len(self.time_rng_lbls)
self.lbls += self.time_rng_lbls
self.array_labels = self.config['IOU_THRESHOLDS']
self.rec_thrs = self.config['RECALL_THRESHOLDS']
self.maxDet = self.config['MAX_DETECTIONS']
self.float_array_fields = ['AP_' + lbl for lbl in self.lbls] + ['AR_' + lbl for lbl in self.lbls]
self.fields = self.float_array_fields
self.summary_fields = self.float_array_fields
@_timing.time
def eval_sequence(self, data):
"""Calculates GT and Tracker matches for one sequence for TrackMAP metrics. Adapted from
https://github.com/TAO-Dataset/"""
# Initialise results to zero for each sequence as the fields are only defined over the set of all sequences
res = {}
for field in self.fields:
res[field] = [0 for _ in self.array_labels]
gt_ids, dt_ids = data['gt_track_ids'], data['dt_track_ids']
if len(gt_ids) == 0 and len(dt_ids) == 0:
for idx in range(self.num_ig_masks):
res[idx] = None
return res
# get track data
gt_tr_areas = data.get('gt_track_areas', None) if self.use_area_rngs else None
gt_tr_lengths = data.get('gt_track_lengths', None) if self.use_time_rngs else None
gt_tr_iscrowd = data.get('gt_track_iscrowd', None)
dt_tr_areas = data.get('dt_track_areas', None) if self.use_area_rngs else None
dt_tr_lengths = data.get('dt_track_lengths', None) if self.use_time_rngs else None
is_nel = data.get('not_exhaustively_labeled', False)
# compute ignore masks for different track sets to eval
gt_ig_masks = self._compute_track_ig_masks(len(gt_ids), track_lengths=gt_tr_lengths, track_areas=gt_tr_areas,
iscrowd=gt_tr_iscrowd)
dt_ig_masks = self._compute_track_ig_masks(len(dt_ids), track_lengths=dt_tr_lengths, track_areas=dt_tr_areas,
is_not_exhaustively_labeled=is_nel, is_gt=False)
boxformat = data.get('boxformat', 'xywh')
ious = self._compute_track_ious(data['dt_tracks'], data['gt_tracks'], iou_function=data['iou_type'],
boxformat=boxformat)
for mask_idx in range(self.num_ig_masks):
gt_ig_mask = gt_ig_masks[mask_idx]
# Sort gt ignore last
gt_idx = np.argsort([g for g in gt_ig_mask], kind="mergesort")
gt_ids = [gt_ids[i] for i in gt_idx]
ious_sorted = ious[:, gt_idx] if len(ious) > 0 else ious
num_thrs = len(self.array_labels)
num_gt = len(gt_ids)
num_dt = len(dt_ids)
# Array to store the "id" of the matched dt/gt
gt_m = np.zeros((num_thrs, num_gt)) - 1
dt_m = np.zeros((num_thrs, num_dt)) - 1
gt_ig = np.array([gt_ig_mask[idx] for idx in gt_idx])
dt_ig = np.zeros((num_thrs, num_dt))
for iou_thr_idx, iou_thr in enumerate(self.array_labels):
if len(ious_sorted) == 0:
break
for dt_idx, _dt in enumerate(dt_ids):
iou = min([iou_thr, 1 - 1e-10])
# information about best match so far (m=-1 -> unmatched)
# store the gt_idx which matched for _dt
m = -1
for gt_idx, _ in enumerate(gt_ids):
# if this gt already matched continue
if gt_m[iou_thr_idx, gt_idx] > 0:
continue
# if _dt matched to reg gt, and on ignore gt, stop
if m > -1 and gt_ig[m] == 0 and gt_ig[gt_idx] == 1:
break
# continue to next gt unless better match made
if ious_sorted[dt_idx, gt_idx] < iou - np.finfo('float').eps:
continue
# if match successful and best so far, store appropriately
iou = ious_sorted[dt_idx, gt_idx]
m = gt_idx
# No match found for _dt, go to next _dt
if m == -1:
continue
# if gt to ignore for some reason update dt_ig.
# Should not be used in evaluation.
dt_ig[iou_thr_idx, dt_idx] = gt_ig[m]
# _dt match found, update gt_m, and dt_m with "id"
dt_m[iou_thr_idx, dt_idx] = gt_ids[m]
gt_m[iou_thr_idx, m] = _dt
dt_ig_mask = dt_ig_masks[mask_idx]
dt_ig_mask = np.array(dt_ig_mask).reshape((1, num_dt)) # 1 X num_dt
dt_ig_mask = np.repeat(dt_ig_mask, num_thrs, 0) # num_thrs X num_dt
# Based on dt_ig_mask ignore any unmatched detection by updating dt_ig
dt_ig = np.logical_or(dt_ig, np.logical_and(dt_m == -1, dt_ig_mask))
# store results for given video and category
res[mask_idx] = {
"dt_ids": dt_ids,
"gt_ids": gt_ids,
"dt_matches": dt_m,
"gt_matches": gt_m,
"dt_scores": data['dt_track_scores'],
"gt_ignore": gt_ig,
"dt_ignore": dt_ig,
}
return res
def combine_sequences(self, all_res):
"""Combines metrics across all sequences. Computes precision and recall values based on track matches.
Adapted from https://github.com/TAO-Dataset/
"""
num_thrs = len(self.array_labels)
num_recalls = len(self.rec_thrs)
# -1 for absent categories
precision = -np.ones(
(num_thrs, num_recalls, self.num_ig_masks)
)
recall = -np.ones((num_thrs, self.num_ig_masks))
for ig_idx in range(self.num_ig_masks):
ig_idx_results = [res[ig_idx] for res in all_res.values() if res[ig_idx] is not None]
# Remove elements which are None
if len(ig_idx_results) == 0:
continue
# Append all scores: shape (N,)
# limit considered tracks for each sequence if maxDet > 0
if self.maxDet == 0:
dt_scores = np.concatenate([res["dt_scores"] for res in ig_idx_results], axis=0)
dt_idx = np.argsort(-dt_scores, kind="mergesort")
dt_m = np.concatenate([e["dt_matches"] for e in ig_idx_results],
axis=1)[:, dt_idx]
dt_ig = np.concatenate([e["dt_ignore"] for e in ig_idx_results],
axis=1)[:, dt_idx]
elif self.maxDet > 0:
dt_scores = np.concatenate([res["dt_scores"][0:self.maxDet] for res in ig_idx_results], axis=0)
dt_idx = np.argsort(-dt_scores, kind="mergesort")
dt_m = np.concatenate([e["dt_matches"][:, 0:self.maxDet] for e in ig_idx_results],
axis=1)[:, dt_idx]
dt_ig = np.concatenate([e["dt_ignore"][:, 0:self.maxDet] for e in ig_idx_results],
axis=1)[:, dt_idx]
else:
raise Exception("Number of maximum detections must be >= 0, but is set to %i" % self.maxDet)
gt_ig = np.concatenate([res["gt_ignore"] for res in ig_idx_results])
# num gt anns to consider
num_gt = np.count_nonzero(gt_ig == 0)
if num_gt == 0:
continue
tps = np.logical_and(dt_m != -1, np.logical_not(dt_ig))
fps = np.logical_and(dt_m == -1, np.logical_not(dt_ig))
tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
for iou_thr_idx, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
tp = np.array(tp)
fp = np.array(fp)
num_tp = len(tp)
rc = tp / num_gt
if num_tp:
recall[iou_thr_idx, ig_idx] = rc[-1]
else:
recall[iou_thr_idx, ig_idx] = 0
# np.spacing(1) ~= eps
pr = tp / (fp + tp + np.spacing(1))
pr = pr.tolist()
# Ensure precision values are monotonically decreasing
for i in range(num_tp - 1, 0, -1):
if pr[i] > pr[i - 1]:
pr[i - 1] = pr[i]
# find indices at the predefined recall values
rec_thrs_insert_idx = np.searchsorted(rc, self.rec_thrs, side="left")
pr_at_recall = [0.0] * num_recalls
try:
for _idx, pr_idx in enumerate(rec_thrs_insert_idx):
pr_at_recall[_idx] = pr[pr_idx]
except IndexError:
pass
precision[iou_thr_idx, :, ig_idx] = (np.array(pr_at_recall))
res = {'precision': precision, 'recall': recall}
# compute the precision and recall averages for the respective alpha thresholds and ignore masks
for lbl in self.lbls:
res['AP_' + lbl] = np.zeros((len(self.array_labels)), dtype=np.float)
res['AR_' + lbl] = np.zeros((len(self.array_labels)), dtype=np.float)
for a_id, alpha in enumerate(self.array_labels):
for lbl_idx, lbl in enumerate(self.lbls):
p = precision[a_id, :, lbl_idx]
if len(p[p > -1]) == 0:
mean_p = -1
else:
mean_p = np.mean(p[p > -1])
res['AP_' + lbl][a_id] = mean_p
res['AR_' + lbl][a_id] = recall[a_id, lbl_idx]
return res
def combine_classes_class_averaged(self, all_res, ignore_empty_classes=True):
"""Combines metrics across all classes by averaging over the class values
Note mAP is not well defined for 'empty classes' so 'ignore empty classes' is always true here.
"""
res = {}
for field in self.fields:
res[field] = np.zeros((len(self.array_labels)), dtype=np.float)
field_stacked = np.array([res[field] for res in all_res.values()])
for a_id, alpha in enumerate(self.array_labels):
values = field_stacked[:, a_id]
if len(values[values > -1]) == 0:
mean = -1
else:
mean = np.mean(values[values > -1])
res[field][a_id] = mean
return res
def combine_classes_det_averaged(self, all_res):
"""Combines metrics across all classes by averaging over the detection values"""
res = {}
for field in self.fields:
res[field] = np.zeros((len(self.array_labels)), dtype=np.float)
field_stacked = np.array([res[field] for res in all_res.values()])
for a_id, alpha in enumerate(self.array_labels):
values = field_stacked[:, a_id]
if len(values[values > -1]) == 0:
mean = -1
else:
mean = np.mean(values[values > -1])
res[field][a_id] = mean
return res
def _compute_track_ig_masks(self, num_ids, track_lengths=None, track_areas=None, iscrowd=None,
is_not_exhaustively_labeled=False, is_gt=True):
"""
Computes ignore masks for different track sets to evaluate
:param num_ids: the number of track IDs
:param track_lengths: the lengths of the tracks (number of timesteps)
:param track_areas: the average area of a track
:param iscrowd: whether a track is marked as crowd
:param is_not_exhaustively_labeled: whether the track category is not exhaustively labeled
:param is_gt: whether it is gt
:return: the track ignore masks
"""
# for TAO tracks for classes which are not exhaustively labeled are not evaluated
if not is_gt and is_not_exhaustively_labeled:
track_ig_masks = [[1 for _ in range(num_ids)] for i in range(self.num_ig_masks)]
else:
# consider all tracks
track_ig_masks = [[0 for _ in range(num_ids)]]
# consider tracks with certain area
if self.use_area_rngs:
for rng in self.area_rngs:
track_ig_masks.append([0 if rng[0] - np.finfo('float').eps <= area <= rng[1] + np.finfo('float').eps
else 1 for area in track_areas])
# consider tracks with certain duration
if self.use_time_rngs:
for rng in self.time_rngs:
track_ig_masks.append([0 if rng[0] - np.finfo('float').eps <= length
<= rng[1] + np.finfo('float').eps else 1 for length in track_lengths])
# for YouTubeVIS evaluation tracks with crowd tag are not evaluated
if is_gt and iscrowd:
track_ig_masks = [np.logical_or(mask, iscrowd) for mask in track_ig_masks]
return track_ig_masks
@staticmethod
def _compute_bb_track_iou(dt_track, gt_track, boxformat='xywh'):
"""
Calculates the track IoU for one detected track and one ground truth track for bounding boxes
:param dt_track: the detected track (format: dictionary with frame index as keys and
numpy arrays as values)
:param gt_track: the ground truth track (format: dictionary with frame index as keys and
numpy array as values)
:param boxformat: the format of the boxes
:return: the track IoU
"""
intersect = 0
union = 0
image_ids = set(gt_track.keys()) | set(dt_track.keys())
for image in image_ids:
g = gt_track.get(image, None)
d = dt_track.get(image, None)
if boxformat == 'xywh':
if d is not None and g is not None:
dx, dy, dw, dh = d
gx, gy, gw, gh = g
w = max(min(dx + dw, gx + gw) - max(dx, gx), 0)
h = max(min(dy + dh, gy + gh) - max(dy, gy), 0)
i = w * h
u = dw * dh + gw * gh - i
intersect += i
union += u
elif d is None and g is not None:
union += g[2] * g[3]
elif d is not None and g is None:
union += d[2] * d[3]
elif boxformat == 'x0y0x1y1':
if d is not None and g is not None:
dx0, dy0, dx1, dy1 = d
gx0, gy0, gx1, gy1 = g
w = max(min(dx1, gx1) - max(dx0, gx0), 0)
h = max(min(dy1, gy1) - max(dy0, gy0), 0)
i = w * h
u = (dx1 - dx0) * (dy1 - dy0) + (gx1 - gx0) * (gy1 - gy0) - i
intersect += i
union += u
elif d is None and g is not None:
union += (g[2] - g[0]) * (g[3] - g[1])
elif d is not None and g is None:
union += (d[2] - d[0]) * (d[3] - d[1])
else:
raise TrackEvalException('BoxFormat not implemented')
if intersect > union:
raise TrackEvalException("Intersection value > union value. Are the box values corrupted?")
return intersect / union if union > 0 else 0
@staticmethod
def _compute_mask_track_iou(dt_track, gt_track):
"""
Calculates the track IoU for one detected track and one ground truth track for segmentation masks
:param dt_track: the detected track (format: dictionary with frame index as keys and
pycocotools rle encoded masks as values)
:param gt_track: the ground truth track (format: dictionary with frame index as keys and
pycocotools rle encoded masks as values)
:return: the track IoU
"""
# only loaded when needed to reduce minimum requirements
from pycocotools import mask as mask_utils
intersect = .0
union = .0
image_ids = set(gt_track.keys()) | set(dt_track.keys())
for image in image_ids:
g = gt_track.get(image, None)
d = dt_track.get(image, None)
if d and g:
intersect += mask_utils.area(mask_utils.merge([d, g], True))
union += mask_utils.area(mask_utils.merge([d, g], False))
elif not d and g:
union += mask_utils.area(g)
elif d and not g:
union += mask_utils.area(d)
if union < 0.0 - np.finfo('float').eps:
raise TrackEvalException("Union value < 0. Are the segmentaions corrupted?")
if intersect > union:
raise TrackEvalException("Intersection value > union value. Are the segmentations corrupted?")
iou = intersect / union if union > 0.0 + np.finfo('float').eps else 0.0
return iou
@staticmethod
def _compute_track_ious(dt, gt, iou_function='bbox', boxformat='xywh'):
"""
Calculate track IoUs for a set of ground truth tracks and a set of detected tracks
"""
if len(gt) == 0 and len(dt) == 0:
return []
if iou_function == 'bbox':
track_iou_function = partial(TrackMAP._compute_bb_track_iou, boxformat=boxformat)
elif iou_function == 'mask':
track_iou_function = partial(TrackMAP._compute_mask_track_iou)
else:
raise Exception('IoU function not implemented')
ious = np.zeros([len(dt), len(gt)])
for i, j in np.ndindex(ious.shape):
ious[i, j] = track_iou_function(dt[i], gt[j])
return ious
@staticmethod
def _row_print(*argv):
"""Prints results in an evenly spaced rows, with more space in first row"""
if len(argv) == 1:
argv = argv[0]
to_print = '%-40s' % argv[0]
for v in argv[1:]:
to_print += '%-12s' % str(v)
print(to_print)