def update_changed_objects(changed_objects):
""" This function is automatically called by
mturk.tasks.mturk_update_votes_cubam_task
with all objects that were changed by new votes. """
from photos.tasks import update_photos_num_shapes
changed_photo_ids = [
s.photo_id for s in changed_objects
if has_foreign_key(s, 'photo')]
update_photos_num_shapes(set(changed_photo_ids))
def update_changed_objects(changed_objects):
""" This function is automatically called by
mturk.tasks.mturk_update_votes_cubam_task
with all objects that were changed by new votes. """
from photos.tasks import update_photos_num_shapes
changed_photo_ids = [
s.photo_id for s in changed_objects if has_foreign_key(s, 'photo')
]
update_photos_num_shapes(set(changed_photo_ids))
def triangulate_submitted_shapes_impl(photo, user, mturk_assignment,
shape_model, submitted_shapes):
if not submitted_shapes:
return
if not os.path.isfile(settings.TRIANGULATE_BIN):
raise RuntimeError(
"ERROR: '%s' (settings.TRIANGULATE_BIN) does not exist -- "
"check that it is compiled" % settings.TRIANGULATE_BIN)
input_lines = [
('%s ' % s.id) + ' '.join(filter(None, s.vertices.split(',')))
for s in submitted_shapes
]
input_txt = '\n'.join(input_lines) + '\nEND'
process = None
try:
process = subprocess.Popen(args=settings.TRIANGULATE_BIN,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True)
output_txt, errors_txt = process.communicate(input_txt)
except:
if process:
process.kill()
process.wait()
raise
if not output_txt:
raise ValueError(
"Error with triangulate. Bin:%s\nInput:\n%s\n\nOutput:\n%s\n\nErrors:\n%s"
% (settings.TRIANGULATE_BIN, input_txt, output_txt, errors_txt))
if errors_txt:
print errors_txt
#print("Bin:%s\nInput:\n%s\n\nOutput:\n%s\n\nErrors:\n%s" % (
#settings.TRIANGULATE_BIN, input_txt, output_txt, errors_txt))
new_content_tuples = []
output_lines = output_txt.split('\n')
with transaction.atomic():
for line in output_lines:
line = line.strip()
if not line:
continue
fields = line.split('|')
if len(fields) != 4:
raise ValueError("Invalid output: %s" % repr(output_txt))
ids = [int(f) for f in filter(None, fields[0].split(' '))]
if not ids:
print 'Discarding shape not contained in input'
continue
verts, tris, segs = [
','.join(filter(None, f.split(' '))) for f in fields[1:4]
]
# compute polygon area and discard small polygons
area = complex_polygon_area(verts, tris)
# 0.0002 is roughly a 32x32 patch for a 2400x2400 image
if area < 0.0001:
print 'Discarding: verts: "%s", tris: "%s", segs: "%s", area: %s' % (
verts, tris, segs, area)
continue
# convert area to pixels
pixel_area = area * photo.image_orig.width * \
photo.image_orig.height
# extract segmentation times
time_ms_list = []
ss_list = []
for ss in submitted_shapes:
if int(ss.id) in ids:
ss_list.append(ss)
time_ms_list.append(ss.time_ms)
if not ss_list or not time_ms_list:
print 'Discarding shape not mapping to input shapes'
# use the average time of the submitted shapes
time_ms = sum(time_ms_list) / float(len(time_ms_list))
# auto-grant high quality for users with qualifications
quality_method = None
correct = None
if pixel_area >= 12000:
from mturk.models import MtQualificationAssignment
try:
correct = bool(
MtQualificationAssignment.objects.get(
worker=user, qualification__slug="mat_seg").value)
if correct:
quality_method = 'Q'
except MtQualificationAssignment.DoesNotExist:
correct = False
new_obj, created = shape_model.objects.get_or_create(
photo=photo,
user=user,
mturk_assignment=mturk_assignment,
vertices=verts,
triangles=tris,
segments=segs,
area=area,
pixel_area=pixel_area,
time_ms=time_ms,
defaults={
'added': ss_list[0].added,
'correct': correct,
'quality_method': quality_method,
})
if created:
for ss in ss_list:
new_obj.submitted_shapes.add(ss)
new_content_tuples.append(get_content_tuple(new_obj))
# these are created outside of the mturk view response, so we need to
# manually add them to the pending objects queue
# (imported here to avoid circular imports)
for (ct_id, obj_id) in new_content_tuples:
mturk_assignment.submitted_contents.get_or_create(
content_type=ContentType.objects.get_for_id(ct_id),
object_id=obj_id,
)
# update photo shape count synchronously
from photos.tasks import update_photos_num_shapes
update_photos_num_shapes([photo.id])
new_content_tuples.append(get_content_tuple(photo))
# new pending objects
from mturk.tasks import add_pending_objects_task
add_pending_objects_task.delay(new_content_tuples)
def triangulate_submitted_shapes_impl(
photo, user, mturk_assignment, shape_model, submitted_shapes):
if not submitted_shapes:
return
if not os.path.isfile(settings.TRIANGULATE_BIN):
raise RuntimeError("ERROR: '%s' (settings.TRIANGULATE_BIN) does not exist -- "
"check that it is compiled" % settings.TRIANGULATE_BIN)
input_lines = [('%s ' % s.id) + ' '.join(
filter(None, s.vertices.split(','))) for s in submitted_shapes]
input_txt = '\n'.join(input_lines) + '\nEND'
process = None
try:
process = subprocess.Popen(
args=settings.TRIANGULATE_BIN,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True)
output_txt, errors_txt = process.communicate(input_txt)
except:
if process:
process.kill()
process.wait()
raise
if not output_txt:
raise ValueError(
"Error with triangulate. Bin:%s\nInput:\n%s\n\nOutput:\n%s\n\nErrors:\n%s" % (
settings.TRIANGULATE_BIN, input_txt, output_txt, errors_txt)
)
if errors_txt:
print errors_txt
#print("Bin:%s\nInput:\n%s\n\nOutput:\n%s\n\nErrors:\n%s" % (
#settings.TRIANGULATE_BIN, input_txt, output_txt, errors_txt))
new_content_tuples = []
output_lines = output_txt.split('\n')
with transaction.atomic():
for line in output_lines:
line = line.strip()
if not line:
continue
fields = line.split('|')
if len(fields) != 4:
raise ValueError("Invalid output: %s" % repr(output_txt))
ids = [int(f) for f in filter(None, fields[0].split(' '))]
if not ids:
print 'Discarding shape not contained in input'
continue
verts, tris, segs = [','.join(filter(None, f.split(' ')))
for f in fields[1:4]]
# compute polygon area and discard small polygons
area = complex_polygon_area(verts, tris)
# 0.0002 is roughly a 32x32 patch for a 2400x2400 image
if area < 0.0001:
print 'Discarding: verts: "%s", tris: "%s", segs: "%s", area: %s' % (
verts, tris, segs, area)
continue
# convert area to pixels
pixel_area = area * photo.image_orig.width * \
photo.image_orig.height
# extract segmentation times
time_ms_list = []
ss_list = []
for ss in submitted_shapes:
if int(ss.id) in ids:
ss_list.append(ss)
time_ms_list.append(ss.time_ms)
if not ss_list or not time_ms_list:
print 'Discarding shape not mapping to input shapes'
# use the average time of the submitted shapes
time_ms = sum(time_ms_list) / float(len(time_ms_list))
# auto-grant high quality for users with qualifications
quality_method = None
correct = None
if pixel_area >= 12000:
from mturk.models import MtQualificationAssignment
try:
correct = bool(MtQualificationAssignment.objects.get(
worker=user, qualification__slug="mat_seg").value)
if correct:
quality_method = 'Q'
except MtQualificationAssignment.DoesNotExist:
correct = False
new_obj, created = shape_model.objects.get_or_create(
photo=photo,
user=user,
mturk_assignment=mturk_assignment,
vertices=verts,
triangles=tris,
segments=segs,
area=area,
pixel_area=pixel_area,
time_ms=time_ms,
defaults={
'added': ss_list[0].added,
'correct': correct,
'quality_method': quality_method,
}
)
if created:
for ss in ss_list:
new_obj.submitted_shapes.add(ss)
new_content_tuples.append(get_content_tuple(new_obj))
# these are created outside of the mturk view response, so we need to
# manually add them to the pending objects queue
# (imported here to avoid circular imports)
for (ct_id, obj_id) in new_content_tuples:
mturk_assignment.submitted_contents.get_or_create(
content_type=ContentType.objects.get_for_id(ct_id),
object_id=obj_id,
)
# update photo shape count synchronously
from photos.tasks import update_photos_num_shapes
update_photos_num_shapes([photo.id])
new_content_tuples.append(get_content_tuple(photo))
# new pending objects
from mturk.tasks import add_pending_objects_task
add_pending_objects_task.delay(new_content_tuples)