def test_center_distance(self):
"""Test for center_distance()."""
# Same boxes.
sa = DetectionBox(translation=(4, 4, 5))
sr = DetectionBox(translation=(4, 4, 5))
self.assertAlmostEqual(center_distance(sa, sr), 0)
# When no translation given
sa = DetectionBox(size=(4, 4, 4))
sr = DetectionBox(size=(3, 3, 3))
self.assertAlmostEqual(center_distance(sa, sr), 0)
# Different z translation (z should be ignored).
sa = DetectionBox(translation=(4, 4, 4))
sr = DetectionBox(translation=(3, 3, 3))
self.assertAlmostEqual(center_distance(sa, sr),
np.sqrt((3 - 4)**2 + (3 - 4)**2))
# Negative values.
sa = DetectionBox(translation=(-1, -1, -1))
sr = DetectionBox(translation=(1, 1, 1))
self.assertAlmostEqual(center_distance(sa, sr),
np.sqrt((1 + 1)**2 + (1 + 1)**2))
# Arbitrary values.
sa = DetectionBox(translation=(4.2, 2.8, 4.2))
sr = DetectionBox(translation=(-1.45, 3.5, 3.9))
self.assertAlmostEqual(center_distance(sa, sr),
np.sqrt((-1.45 - 4.2)**2 + (3.5 - 2.8)**2))
def accumulate(gt_boxes: EvalBoxes,
pred_boxes: EvalBoxes,
class_name: str,
dist_fcn: Callable,
dist_th: float,
verbose: bool = False) -> DetectionMetricData:
"""
Average Precision over predefined different recall thresholds for a single distance threshold.
The recall/conf thresholds and other raw metrics will be used in secondary metrics.
:param gt_boxes: Maps every sample_token to a list of its sample_annotations.
:param pred_boxes: Maps every sample_token to a list of its sample_results.
:param class_name: Class to compute AP on.
:param dist_fcn: Distance function used to match detections and ground truths.
:param dist_th: Distance threshold for a match.
:param verbose: If true, print debug messages.
:return: (average_prec, metrics). The average precision value and raw data for a number of metrics.
"""
# ---------------------------------------------
# Organize input and initialize accumulators.
# ---------------------------------------------
# Count the positives.
npos = len([1 for gt_box in gt_boxes.all if gt_box.detection_name == class_name])
if verbose:
print("Found {} GT of class {} out of {} total across {} samples.".
format(npos, class_name, len(gt_boxes.all), len(gt_boxes.sample_tokens)))
# For missing classes in the GT, return a data structure corresponding to no predictions.
if npos == 0:
return DetectionMetricData.no_predictions()
# Organize the predictions in a single list.
pred_boxes_list = [box for box in pred_boxes.all if box.detection_name == class_name]
pred_confs = [box.detection_score for box in pred_boxes_list]
if verbose:
print("Found {} PRED of class {} out of {} total across {} samples.".
format(len(pred_confs), class_name, len(pred_boxes.all), len(pred_boxes.sample_tokens)))
# Sort by confidence.
sortind = [i for (v, i) in sorted((v, i) for (i, v) in enumerate(pred_confs))][::-1]
# Do the actual matching.
tp = [] # Accumulator of true positives.
fp = [] # Accumulator of false positives.
conf = [] # Accumulator of confidences.
# match_data holds the extra metrics we calculate for each match.
match_data = {'trans_err': [],
'vel_err': [],
'scale_err': [],
'orient_err': [],
'attr_err': [],
'conf': [],
'ego_dist': [],
'vel_magn': []}
# ---------------------------------------------
# Match and accumulate match data.
# ---------------------------------------------
taken = set() # Initially no gt bounding box is matched.
for ind in sortind:
pred_box = pred_boxes_list[ind]
min_dist = np.inf
match_gt_idx = None
for gt_idx, gt_box in enumerate(gt_boxes[pred_box.sample_token]):
# Find closest match among ground truth boxes
if gt_box.detection_name == class_name and not (pred_box.sample_token, gt_idx) in taken:
this_distance = dist_fcn(gt_box, pred_box)
if this_distance < min_dist:
min_dist = this_distance
match_gt_idx = gt_idx
# If the closest match is close enough according to threshold we have a match!
is_match = min_dist < dist_th
if is_match:
taken.add((pred_box.sample_token, match_gt_idx))
# Update tp, fp and confs.
tp.append(1)
fp.append(0)
conf.append(pred_box.detection_score)
# Since it is a match, update match data also.
gt_box_match = gt_boxes[pred_box.sample_token][match_gt_idx]
match_data['trans_err'].append(center_distance(gt_box_match, pred_box))
match_data['vel_err'].append(velocity_l2(gt_box_match, pred_box))
match_data['scale_err'].append(1 - scale_iou(gt_box_match, pred_box))
# Barrier orientation is only determined up to 180 degree. (For cones orientation is discarded later)
period = np.pi if class_name == 'barrier' else 2 * np.pi
match_data['orient_err'].append(yaw_diff(gt_box_match, pred_box, period=period))
match_data['attr_err'].append(1 - attr_acc(gt_box_match, pred_box))
match_data['conf'].append(pred_box.detection_score)
# For debugging only.
match_data['ego_dist'].append(gt_box_match.ego_dist)
match_data['vel_magn'].append(np.sqrt(np.sum(np.array(gt_box_match.velocity) ** 2)))
else:
# No match. Mark this as a false positive.
tp.append(0)
fp.append(1)
conf.append(pred_box.detection_score)
# Check if we have any matches. If not, just return a "no predictions" array.
if len(match_data['trans_err']) == 0:
return DetectionMetricData.no_predictions()
# ---------------------------------------------
# Calculate and interpolate precision and recall
# ---------------------------------------------
# Accumulate.
tp = np.cumsum(tp).astype(np.float)
fp = np.cumsum(fp).astype(np.float)
conf = np.array(conf)
# Calculate precision and recall.
prec = tp / (fp + tp)
rec = tp / float(npos)
rec_interp = np.linspace(0, 1, DetectionMetricData.nelem) # 101 steps, from 0% to 100% recall.
prec = np.interp(rec_interp, rec, prec, right=0)
conf = np.interp(rec_interp, rec, conf, right=0)
rec = rec_interp
# ---------------------------------------------
# Re-sample the match-data to match, prec, recall and conf.
# ---------------------------------------------
for key in match_data.keys():
if key == "conf":
continue # Confidence is used as reference to align with fp and tp. So skip in this step.
else:
# For each match_data, we first calculate the accumulated mean.
tmp = cummean(np.array(match_data[key]))
# Then interpolate based on the confidences. (Note reversing since np.interp needs increasing arrays)
match_data[key] = np.interp(conf[::-1], match_data['conf'][::-1], tmp[::-1])[::-1]
# ---------------------------------------------
# Done. Instantiate MetricData and return
# ---------------------------------------------
return DetectionMetricData(recall=rec,
precision=prec,
confidence=conf,
trans_err=match_data['trans_err'],
vel_err=match_data['vel_err'],
scale_err=match_data['scale_err'],
orient_err=match_data['orient_err'],
attr_err=match_data['attr_err'])