def gen_dist_error(model, methods, dataset_id):
"""Generates the horizontal and vertical 3D error statistics."""
session = SESSION()
try:
# pylint: disable-msg=E1101
model_id, = session.query(Model.id) \
.filter_by(filename=model) \
.one()
todo, = session.query(func.count(Photo.id)) \
.outerjoin((
Detection,
and_(
Detection.pid == Photo.id,
Detection.mid == model_id
)
)) \
.filter(Photo.test == True) \
.filter(Detection.id == None) \
.filter(Photo.dataset_id == dataset_id) \
.one()
# pylint: enable-msg=E1101
if todo > 0:
msg = '%s is not ready. %d photos remaining' % (model, todo)
logging.info(msg)
return
not_ready = False
for name in methods:
nms_method = scores.METHODS[name]
# pylint: disable-msg=E1101
todo, = session.query(func.count(Detection.id)) \
.join(Model) \
.join(Photo) \
.filter(Photo.test == True) \
.filter(Model.filename == model) \
.filter(Photo.dataset_id == dataset_id) \
.filter(nms_method.output == None) \
.one()
# pylint: enable-msg=E1101
if todo > 0:
msg = '%s is not ready. %d %s NMS remaining' % (
model, todo, name)
logging.info(msg)
not_ready = True
if not_ready:
return
# pylint: disable-msg=E1101
dataset_id = [Photo.dataset_id == dataset_id]
# pylint: enable-msg=E1101
for method in methods:
nms_method = scores.METHODS[method]
selected = [nms_method.output == True]
msg = '%s method: %s' % (model, method)
logging.info(msg)
points = precision_recall(
session,
nms_method.score,
dataset_id + selected,
dataset_id, model
)
idx = numpy.abs(points[:, 0] - 0.9).argmin()
logging.info(points[idx, :])
labels = get_labels(
session, nms_method.score, selected, [], model, points[idx, 2])
dists = [l.dist for l in labels]
dist_zs = [l.height_diff for l in labels]
logging.info(
(numpy.array(dists).mean(), numpy.array(dist_zs).mean()))
logging.info('done')
except:
raise
finally:
session.close()
def gen_results(model, methods, aos, dataset_id):
"""Computes PR curve and optionally OS-R curve."""
session = SESSION()
try:
difficulties = {'full': []}
daynights = {
'both': [],
}
# pylint: disable-msg=E1101
model_id, = session.query(Model.id) \
.filter(Model.filename == model) \
.one()
todo, = session.query(func.count(Photo.id)) \
.outerjoin((
Detection,
and_(
Detection.pid == Photo.id,
Detection.mid == model_id
)
)) \
.filter(Photo.test == True) \
.filter(Detection.id == None) \
.filter(Photo.dataset_id == dataset_id) \
.one()
# pylint: enable-msg=E1101
if todo > 0:
msg = '%s is not ready. %d photos remaining' % (model, todo)
logging.info(msg)
return
not_ready = False
for name in methods:
nms_method = scores.METHODS[name]
# pylint: disable-msg=E1101
todo, = session.query(func.count(Detection.id)) \
.join(Model) \
.join(Photo) \
.filter(Photo.test == True) \
.filter(Model.filename == model) \
.filter(Photo.dataset_id == dataset_id) \
.filter(nms_method.output == None) \
.one()
# pylint: enable-msg=E1101
if todo > 0:
msg = '%s is not ready. %d %s NMS remaining' % (model, todo,
name)
logging.info(msg)
not_ready = True
if not_ready:
return
# pylint: disable-msg=E1101
dataset_id = [Photo.dataset_id == dataset_id]
# pylint: enable-msg=E1101
for daynight, difficulty in itertools.product(daynights, difficulties):
for method in methods:
nms_method = scores.METHODS[method]
selected = [nms_method.output == True]
msg = '%s daynight: %s, difficulty: %s, method: %s' % (
model, daynight, difficulty, method)
logging.info(msg)
points = precision_recall(
session, nms_method.score,
dataset_id + daynights[daynight] + selected, dataset_id +
daynights[daynight] + difficulties[difficulty], model)
name = '%s, %s' % (model, method)
if aos:
points_aos = orientation_similarity(
session, nms_method.score,
dataset_id + daynights[daynight] + selected,
dataset_id + daynights[daynight] +
difficulties[difficulty], model)
else:
points_aos = None
print(points.shape)
print(scipy.integrate.trapz(points[:, 0], points[:, 1]))
filename = '%s-%s-%s-%s-pr.txt' % (model, daynight, difficulty,
method)
print(filename)
numpy.savetxt(filename, points)
if points_aos is not None:
print(
scipy.integrate.trapz(points_aos[:, 0], points_aos[:,
1]))
filename = '%s-%s-%s-%s-aos.txt' % (model, daynight,
difficulty, method)
print(filename)
numpy.savetxt(filename, points_aos)
logging.info('done')
except:
raise
finally:
session.close()
def gen_results(model, methods, aos, dataset_id):
"""Computes PR curve and optionally OS-R curve."""
session = SESSION()
try:
difficulties = {
'full': []
}
daynights = {
'both': [],
}
# pylint: disable-msg=E1101
model_id, = session.query(Model.id) \
.filter(Model.filename == model) \
.one()
todo, = session.query(func.count(Photo.id)) \
.outerjoin((
Detection,
and_(
Detection.pid == Photo.id,
Detection.mid == model_id
)
)) \
.filter(Photo.test == True) \
.filter(Detection.id == None) \
.filter(Photo.dataset_id == dataset_id) \
.one()
# pylint: enable-msg=E1101
if todo > 0:
msg = '%s is not ready. %d photos remaining' % (model, todo)
logging.info(msg)
return
not_ready = False
for name in methods:
nms_method = scores.METHODS[name]
# pylint: disable-msg=E1101
todo, = session.query(func.count(Detection.id)) \
.join(Model) \
.join(Photo) \
.filter(Photo.test == True) \
.filter(Model.filename == model) \
.filter(Photo.dataset_id == dataset_id) \
.filter(nms_method.output == None) \
.one()
# pylint: enable-msg=E1101
if todo > 0:
msg = '%s is not ready. %d %s NMS remaining' % (
model, todo, name)
logging.info(msg)
not_ready = True
if not_ready:
return
# pylint: disable-msg=E1101
dataset_id = [Photo.dataset_id == dataset_id]
# pylint: enable-msg=E1101
for daynight, difficulty in itertools.product(daynights, difficulties):
for method in methods:
nms_method = scores.METHODS[method]
selected = [nms_method.output == True]
msg = '%s daynight: %s, difficulty: %s, method: %s' % (
model, daynight, difficulty, method)
logging.info(msg)
points = precision_recall(
session,
nms_method.score,
dataset_id + daynights[daynight] + selected,
dataset_id +
daynights[daynight] + difficulties[difficulty],
model
)
name = '%s, %s' % (model, method)
if aos:
points_aos = orientation_similarity(session,
nms_method.score,
dataset_id +
daynights[
daynight] + selected,
dataset_id +
daynights[daynight] + difficulties[
difficulty],
model
)
else:
points_aos = None
print(points.shape)
print(scipy.integrate.trapz(points[:, 0], points[:, 1]))
filename = '%s-%s-%s-%s-pr.txt' % (
model, daynight, difficulty, method)
print(filename)
numpy.savetxt(filename, points)
if points_aos is not None:
print(scipy.integrate.trapz(
points_aos[:, 0], points_aos[:, 1]))
filename = '%s-%s-%s-%s-aos.txt' % (
model, daynight, difficulty, method)
print(filename)
numpy.savetxt(filename, points_aos)
logging.info('done')
except:
raise
finally:
session.close()