def extract_auprc(targets, scores, radius, threshold, match_radius=None, pool=None):
N = 0
mse = 0
hits = []
preds = []
if pool is not None:
process = ExtractMatches(radius, threshold, match_radius)
iterator = iterate_score_target_pairs(scores, targets)
for assignment,score,this_mse,n in pool.imap_unordered(process, iterator):
mse += this_mse
hits.append(assignment)
preds.append(score)
N += n
else:
for score,target in iterate_score_target_pairs(scores, targets):
score,coords = non_maximum_suppression(score, radius, threshold=threshold)
if match_radius is None:
assignment, dist = match_coordinates(target, coords, radius)
else:
assignment, dist = match_coordinates(target, coords, match_radius)
mse += np.sum(dist[assignment==1]**2)
hits.append(assignment)
preds.append(score)
N += len(target)
hits = np.concatenate(hits, 0)
preds = np.concatenate(preds, 0)
auprc = average_precision(hits, preds, N=N)
rmse = np.sqrt(mse/hits.sum())
return auprc, rmse, int(hits.sum()), N
def nms_iterator(scores, radius, threshold, pool=None):
process = NonMaximumSuppression(radius, threshold)
if pool is not None:
for name,score,coords in pool.imap_unordered(process, scores):
yield name,score,coords
else:
for name,score in scores:
score,coords = non_maximum_suppression(score, radius, threshold=threshold)
yield name,score,coords
def __call__(self, args):
score,target = args
score,coords = non_maximum_suppression(score, self.radius, threshold=self.threshold)
if self.match_radius is None:
assignment, dist = match_coordinates(target, coords, self.radius)
else:
assignment, dist = match_coordinates(target, coords, self.match_radius)
mse = np.sum(dist[assignment==1]**2)
return assignment, score, mse, len(target)
def __call__(self, args):
name, score = args
score, coords = non_maximum_suppression(score,
self.radius,
threshold=self.threshold)
return name, score, coords
