def auto_rectify_shape(shape_id):
""" Attempt to automatically rectify the shape based on vanishing points """
if ShapeRectifiedNormalLabel.objects.filter(shape_id=shape_id, automatic=True).exists():
print "shape already automatically rectified"
return
shape = MaterialShape.objects.get(id=shape_id)
from normals.perspective import estimate_uvnb_from_vanishing_points
uvnb, method, num_vanishing_lines = estimate_uvnb_from_vanishing_points(
shape)
if not uvnb:
print "Could not estimate uvnb matrix"
return
admin_user = User.objects.get_or_create(
username='******')[0].get_profile()
print 'method: %s, uvnb: %s' % (method, uvnb)
obj = ShapeRectifiedNormalLabel.objects.create(
user=admin_user,
shape=shape,
uvnb=json.dumps(uvnb),
automatic=True,
method=method,
num_vanishing_lines=num_vanishing_lines
)
from mturk.tasks import add_pending_objects_task
add_pending_objects_task.delay([get_content_tuple(obj)])
def mturk_submit(user, hit_contents, results, time_ms,
time_active_ms, version,
mturk_assignment=None, **kwargs):
if unicode(version) != u'1.0':
raise ValueError(
"Unknown version: '%s' (type: %s)" % (version, type(version)))
new_objects = {}
for point in hit_contents:
key = unicode(point.id)
new_obj, created = point.opacities.get_or_create(
user=user,
mturk_assignment=mturk_assignment,
time_ms=time_ms[key],
time_active_ms=time_active_ms[key],
opaque=results[key][u'opaque'],
zoom=results[key][u'zoom'],
)
if created:
new_objects[get_content_tuple(point)] = [new_obj]
# atomically update comparison objects
with transaction.atomic():
qset = IntrinsicPoint.objects \
.select_for_update() \
.filter(id__in=[c.id for c in hit_contents])
for point in qset:
point.update_opacity()
return new_objects
def mturk_submit(user,
hit_contents,
results,
time_ms,
time_active_ms,
version,
mturk_assignment=None,
**kwargs):
if unicode(version) != u'1.0':
raise ValueError("Unknown version: '%s' (type: %s)" %
(version, type(version)))
if not hit_contents:
return {}
# best we can do is average
avg_time_ms = time_ms / len(hit_contents)
avg_time_active_ms = time_active_ms / len(hit_contents)
new_objects = {}
for photo in hit_contents:
correct = (str(results[unicode(photo.id)]).lower() == 'true')
new_obj, created = photo.scene_qualities.get_or_create(
user=user,
mturk_assignment=mturk_assignment,
time_ms=avg_time_ms,
time_active_ms=avg_time_active_ms,
correct=correct)
if created:
new_objects[get_content_tuple(photo)] = [new_obj]
return new_objects
def mturk_submit(user, hit_contents, results, time_ms, time_active_ms, version,
mturk_assignment=None, **kwargs):
""" Add new instances from a mturk HIT after the user clicks [submit] """
if unicode(version) != u'1.0':
raise ValueError("Unknown version: '%s'" % version)
if not hit_contents:
return {}
# best we can do is average
avg_time_ms = time_ms / len(hit_contents)
avg_time_active_ms = time_active_ms / len(hit_contents)
new_objects = {}
for shape in hit_contents:
selected = (
str(results[unicode(shape.id)]['selected']).lower() == 'true')
canttell = (
str(results[unicode(shape.id)]['canttell']).lower() == 'true')
new_obj, created = shape.planarities.get_or_create(
user=user,
mturk_assignment=mturk_assignment,
time_ms=avg_time_ms,
time_active_ms=avg_time_active_ms,
planar=selected,
canttell=canttell,
)
if created:
new_objects[get_content_tuple(shape)] = [new_obj]
return new_objects
def auto_rectify_shape(shape_id):
""" Attempt to automatically rectify the shape based on vanishing points """
if ShapeRectifiedNormalLabel.objects.filter(shape_id=shape_id,
automatic=True).exists():
print "shape already automatically rectified"
return
shape = MaterialShape.objects.get(id=shape_id)
from normals.perspective import estimate_uvnb_from_vanishing_points
uvnb, method, num_vanishing_lines = estimate_uvnb_from_vanishing_points(
shape)
if not uvnb:
print "Could not estimate uvnb matrix"
return
admin_user = User.objects.get_or_create(username='******')[0].get_profile()
print 'method: %s, uvnb: %s' % (method, uvnb)
obj = ShapeRectifiedNormalLabel.objects.create(
user=admin_user,
shape=shape,
uvnb=json.dumps(uvnb),
automatic=True,
method=method,
num_vanishing_lines=num_vanishing_lines)
from mturk.tasks import add_pending_objects_task
add_pending_objects_task.delay([get_content_tuple(obj)])
def add_photo(path, must_have_fov=False, must_have_exif=False, **args):
""" Add a photo to the database """
if not os.path.exists(path):
raise ValueError("File does not exist")
if 'license' not in args:
args['license'] = License.objects.get_or_create(
name='All Rights Reserved')[0]
# md5: check for duplicates
md5 = md5sum(path)
duplicate = True
try:
photo = Photo.objects.get(md5=md5)
except Photo.DoesNotExist:
duplicate = False
except Photo.MultipleObjectsReturned:
duplicates = Photo.objects.filter(md5=md5).order_by('id')
for d in duplicates[1:]:
d.delete()
if duplicate:
raise ValueError("Duplicate photo import: '%s'" % path)
# parse exif
if 'exif' not in args:
print 'Obtaining EXIF...'
exif = get_exif(path)
if exif:
args['exif'] = exif
elif must_have_exif:
raise ValueError("Photo has no EXIF: %s" % path)
if 'fov' not in args:
print 'Obtaining FOV...'
fov = get_fov(args['exif'])
if fov:
args['fov'] = fov
elif must_have_fov:
raise ValueError("Could not obtain photo FOV: %s" % path)
photo = None
# use a transaction so that it is only committed to the database
# after save() returns. otherwise, there's a small time betwee
# when the photo is added and it has an image attached.
with transaction.atomic():
with open(path, 'rb') as f:
print 'Uploading photo...'
photo = Photo.objects.create(image_orig=ImageFile(f),
md5=md5,
**args)
from mturk.tasks import add_pending_objects_task
add_pending_objects_task.delay([get_content_tuple(photo)])
return photo
def mturk_submit(user, hit_contents, results, time_ms, time_active_ms, version,
experiment, mturk_assignment=None, **kwargs):
""" Add new instances from a mturk HIT after the user clicks [submit] """
if unicode(version) != u'1.0':
raise ValueError("Unknown version: '%s'" % version)
if not hit_contents:
return {}
new_objects = {}
for shape in hit_contents:
d = results[unicode(shape.id)]
shape_time_ms = time_ms[unicode(shape.id)]
shape_time_active_ms = time_active_ms[unicode(shape.id)]
edit_dict = d[u'edit']
edit_sum = sum(int(edit_dict[k]) for k in edit_dict)
edit_nnz = sum(int(int(edit_dict[k]) > 0) for k in edit_dict)
init_method = 'KR'
envmap = EnvironmentMap.objects.get(
id=json.loads(experiment.variant)['envmap_id'])
doi = int(d[u'doi'])
contrast = float(d[u'contrast'])
metallic = (int(d[u'type']) == 1)
color = d['color']
give_up = d[u'give_up']
give_up_msg = d[u'give_up_msg']
bsdf, bsdf_created = shape.bsdfs_wd.get_or_create(
user=user,
mturk_assignment=mturk_assignment,
time_ms=shape_time_ms,
time_active_ms=shape_time_active_ms,
doi=doi,
contrast=contrast,
metallic=metallic,
color=color,
give_up=give_up,
give_up_msg=give_up_msg,
edit_dict=json.dumps(edit_dict),
edit_sum=edit_sum,
edit_nnz=edit_nnz,
envmap=envmap,
init_method=init_method,
)
if bsdf_created:
new_objects[get_content_tuple(shape)] = [bsdf]
if ((not bsdf.image_blob) and 'screenshot' in d and d['screenshot'].startswith('data:image/')):
save_obj_attr_base64_image(bsdf, 'image_blob', d['screenshot'])
return new_objects
def add_photo(path, must_have_fov=False, must_have_exif=False, **args):
""" Add a photo to the database """
if not os.path.exists(path):
raise ValueError("File does not exist")
if "license" not in args:
args["license"] = License.objects.get_or_create(name="All Rights Reserved")[0]
# md5: check for duplicates
md5 = md5sum(path)
duplicate = True
try:
photo = Photo.objects.get(md5=md5)
except Photo.DoesNotExist:
duplicate = False
except Photo.MultipleObjectsReturned:
duplicates = Photo.objects.filter(md5=md5).order_by("id")
for d in duplicates[1:]:
d.delete()
if duplicate:
raise ValueError("Duplicate photo import: '%s'" % path)
# parse exif
if "exif" not in args:
print "Obtaining EXIF..."
exif = get_exif(path)
if exif:
args["exif"] = exif
elif must_have_exif:
raise ValueError("Photo has no EXIF: %s" % path)
if "fov" not in args:
print "Obtaining FOV..."
fov = get_fov(args["exif"])
if fov:
args["fov"] = fov
elif must_have_fov:
raise ValueError("Could not obtain photo FOV: %s" % path)
photo = None
# use a transaction so that it is only committed to the database
# after save() returns. otherwise, there's a small time betwee
# when the photo is added and it has an image attached.
with transaction.atomic():
with open(path, "rb") as f:
print "Uploading photo..."
photo = Photo.objects.create(image_orig=ImageFile(f), md5=md5, **args)
from mturk.tasks import add_pending_objects_task
add_pending_objects_task.delay([get_content_tuple(photo)])
return photo
def mturk_submit(user,
hit_contents,
results,
time_ms,
time_active_ms,
version,
mturk_assignment=None,
**kwargs):
if unicode(version) != u'1.0':
raise ValueError("Unknown version: '%s' (type: %s)" %
(version, type(version)))
new_objects = {}
for comparison in hit_contents:
key = unicode(comparison.id)
darker = results[key][u'darker']
confidence = results[key][u'confidence']
reflectance_dd = None
if darker == "E":
reflectance_eq = True
else:
reflectance_eq = False
if comparison.point1_image_darker is not None:
if darker == "1":
reflectance_dd = comparison.point1_image_darker
elif darker == "2":
reflectance_dd = not comparison.point1_image_darker
new_obj, created = comparison.responses.get_or_create(
user=user,
mturk_assignment=mturk_assignment,
time_ms=time_ms[key],
time_active_ms=time_active_ms[key],
darker=darker,
confidence=confidence,
reflectance_eq=reflectance_eq,
reflectance_dd=reflectance_dd,
)
if created:
new_objects[get_content_tuple(comparison)] = [new_obj]
# clear any existing aggregation
IntrinsicPointComparison.objects \
.filter(id__in=[c.id for c in hit_contents]) \
.update(darker=None, darker_score=None,
reflectance_dd=None, reflectance_dd_score=None,
reflectance_eq=None, reflectance_eq_score=None)
return new_objects
def mturk_submit(user,
hit_contents,
results,
time_ms,
time_active_ms,
version,
experiment,
mturk_assignment=None,
**kwargs):
""" Add new instances from a mturk HIT after the user clicks [submit] """
if unicode(version) != u'1.0':
raise ValueError("Unknown version: '%s'" % version)
if not hit_contents:
return {}
# best we can do is average
avg_time_ms = time_ms / len(hit_contents)
avg_time_active_ms = time_active_ms / len(hit_contents)
new_objects = {}
for bsdf in hit_contents:
selected = (str(results[unicode(
bsdf.id)]['selected']).lower() == 'true')
canttell = (str(results[unicode(
bsdf.id)]['canttell']).lower() == 'true')
color_correct = None
gloss_correct = None
if 'color' in experiment.slug:
color_correct = selected
elif 'gloss' in experiment.slug:
gloss_correct = selected
content_tuple = get_content_tuple(bsdf)
new_obj, created = ShapeBsdfQuality.objects.get_or_create(
content_type=ContentType.objects.get_for_id(content_tuple[0]),
object_id=content_tuple[1],
user=user,
mturk_assignment=mturk_assignment,
time_ms=avg_time_ms,
time_active_ms=avg_time_active_ms,
color_correct=color_correct,
gloss_correct=gloss_correct,
canttell=canttell)
if created:
new_objects[content_tuple] = [new_obj]
return new_objects
def mturk_submit(user, hit_contents, results, time_ms, time_active_ms, version,
mturk_assignment=None, **kwargs):
if unicode(version) != u'1.0':
raise ValueError(
"Unknown version: '%s' (type: %s)" % (version, type(version)))
new_objects = {}
for comparison in hit_contents:
key = unicode(comparison.id)
darker = results[key][u'darker']
confidence = results[key][u'confidence']
reflectance_dd = None
if darker == "E":
reflectance_eq = True
else:
reflectance_eq = False
if comparison.point1_image_darker is not None:
if darker == "1":
reflectance_dd = comparison.point1_image_darker
elif darker == "2":
reflectance_dd = not comparison.point1_image_darker
new_obj, created = comparison.responses.get_or_create(
user=user,
mturk_assignment=mturk_assignment,
time_ms=time_ms[key],
time_active_ms=time_active_ms[key],
darker=darker,
confidence=confidence,
reflectance_eq=reflectance_eq,
reflectance_dd=reflectance_dd,
)
if created:
new_objects[get_content_tuple(comparison)] = [new_obj]
# clear any existing aggregation
IntrinsicPointComparison.objects \
.filter(id__in=[c.id for c in hit_contents]) \
.update(darker=None, darker_score=None,
reflectance_dd=None, reflectance_dd_score=None,
reflectance_eq=None, reflectance_eq_score=None)
return new_objects
def mturk_submit(user,
hit_contents,
results,
time_ms,
time_active_ms,
version,
mturk_assignment=None,
**kwargs):
""" Add new instances from a mturk HIT after the user clicks [submit] """
if unicode(version) != u'1.0':
raise ValueError("Unknown version: '%s'" % version)
if not hit_contents:
return {}
new_objects = {}
for shape in hit_contents:
normal = results[unicode(shape.id)]
shape_time_ms = time_ms[unicode(shape.id)]
shape_time_active_ms = time_active_ms[unicode(shape.id)]
new_obj, created = shape.rectified_normals.get_or_create(
user=user,
mturk_assignment=mturk_assignment,
time_ms=shape_time_ms,
time_active_ms=shape_time_active_ms,
uvnb=json.dumps(normal['uvnb']),
method=normal['method'],
focal_pixels=normal['focal_pixels'],
canvas_width=normal['canvas_width'],
canvas_height=normal['canvas_height'],
pos_x=normal['pos_x'],
pos_y=normal['pos_y'],
)
if created:
new_objects[get_content_tuple(shape)] = [new_obj]
# rectify synchronously (since this is a transaction and will
# not be visible in another thread)
from normals.tasks import auto_rectify_shape
auto_rectify_shape(shape.id)
return new_objects
def mturk_submit(user, hit_contents, results, time_ms, time_active_ms, version,
experiment, mturk_assignment=None, **kwargs):
""" Add new instances from a mturk HIT after the user clicks [submit] """
if unicode(version) != u'1.0':
raise ValueError("Unknown version: '%s'" % version)
if not hit_contents:
return {}
# best we can do is average
avg_time_ms = time_ms / len(hit_contents)
avg_time_active_ms = time_active_ms / len(hit_contents)
new_objects = {}
for bsdf in hit_contents:
selected = (str(results[unicode(bsdf.id)]['selected']).lower()
== 'true')
canttell = (str(results[unicode(bsdf.id)]['canttell']).lower()
== 'true')
color_correct = None
gloss_correct = None
if 'color' in experiment.slug:
color_correct = selected
elif 'gloss' in experiment.slug:
gloss_correct = selected
content_tuple = get_content_tuple(bsdf)
new_obj, created = ShapeBsdfQuality.objects.get_or_create(
content_type=ContentType.objects.get_for_id(content_tuple[0]),
object_id=content_tuple[1],
user=user,
mturk_assignment=mturk_assignment,
time_ms=avg_time_ms,
time_active_ms=avg_time_active_ms,
color_correct=color_correct,
gloss_correct=gloss_correct,
canttell=canttell
)
if created:
new_objects[content_tuple] = [new_obj]
return new_objects
def add_pending_objects_task(list_or_model, show_progress=False):
""" Adds/updates modified objects as inputs to experiments """
if not list_or_model:
return
try:
experiments = Experiment.objects.all() \
.filter(new_hit_settings__auto_add_hits=True) \
.prefetch_related('new_hit_settings')
if isinstance(list_or_model, list):
# convert list to a content_tuple list
tuple_list = [
(x if isinstance(x, tuple) else get_content_tuple(x))
for x in list_or_model
]
for exp in experiments:
# find objects that match the experiment content type
exp_ct = exp.new_hit_settings.content_type
id_list = [obj_id for (ct_id, obj_id) in tuple_list
if ct_id == exp_ct.id]
if id_list:
queryset = exp_ct.model_class().objects \
.filter(id__in=id_list)
add_pending_objects_impl(exp, queryset, show_progress)
else:
model = list_or_model
ct = ContentType.objects.get_for_model(model)
queryset = model.objects.all()
for exp in experiments.filter(new_hit_settings__content_type=ct):
add_pending_objects_impl(exp, queryset, show_progress)
except Exception as exc:
# Re-add to the queue so that we don't lose tasks
print 'Exception (%s) -- will retry in 5 minutes' % exc
traceback.print_exc()
raise add_pending_objects_task.retry(exc=exc, countdown=60 * 5)
def mturk_submit(user,
hit_contents,
results,
time_ms,
time_active_ms,
version,
mturk_assignment=None,
**kwargs):
if unicode(version) != u'1.0':
raise ValueError("Unknown version: '%s' (type: %s)" %
(version, type(version)))
new_objects = {}
for point in hit_contents:
key = unicode(point.id)
new_obj, created = point.opacities.get_or_create(
user=user,
mturk_assignment=mturk_assignment,
time_ms=time_ms[key],
time_active_ms=time_active_ms[key],
opaque=results[key][u'opaque'],
zoom=results[key][u'zoom'],
)
if created:
new_objects[get_content_tuple(point)] = [new_obj]
# atomically update comparison objects
with transaction.atomic():
qset = IntrinsicPoint.objects \
.select_for_update() \
.filter(id__in=[c.id for c in hit_contents])
for point in qset:
point.update_opacity()
return new_objects
def mturk_submit(user, hit_contents, results, time_ms, time_active_ms, version,
mturk_assignment=None, **kwargs):
""" Add new instances from a mturk HIT after the user clicks [submit] """
if unicode(version) != u'1.0':
raise ValueError("Unknown version: '%s'" % version)
if not hit_contents:
return {}
new_objects = {}
for shape in hit_contents:
normal = results[unicode(shape.id)]
shape_time_ms = time_ms[unicode(shape.id)]
shape_time_active_ms = time_active_ms[unicode(shape.id)]
new_obj, created = shape.rectified_normals.get_or_create(
user=user,
mturk_assignment=mturk_assignment,
time_ms=shape_time_ms,
time_active_ms=shape_time_active_ms,
uvnb=json.dumps(normal['uvnb']),
method=normal['method'],
focal_pixels=normal['focal_pixels'],
canvas_width=normal['canvas_width'],
canvas_height=normal['canvas_height'],
pos_x=normal['pos_x'],
pos_y=normal['pos_y'],
)
if created:
new_objects[get_content_tuple(shape)] = [new_obj]
# rectify synchronously (since this is a transaction and will
# not be visible in another thread)
from normals.tasks import auto_rectify_shape
auto_rectify_shape(shape.id)
return new_objects
def mturk_submit(user, hit_contents, results, time_ms, time_active_ms, version,
mturk_assignment=None, **kwargs):
""" Add new instances from a mturk HIT after the user clicks [submit] """
if unicode(version) != u'1.0':
raise ValueError("Unknown version: '%s'" % version)
if not hit_contents:
return {}
if not user:
raise ValueError("Null user")
new_objects = {}
for shape in hit_contents:
name_string = results[unicode(shape.id)]
shape_time_ms = time_ms[unicode(shape.id)]
shape_time_active_ms = time_active_ms[unicode(shape.id)]
# normalize case
name_string = name_string.lower()
name_string = name_string[0].upper() + name_string[1:]
name = ShapeName.objects.get_or_create(
name=name_string,
)[0]
new_obj, created = shape.names.get_or_create(
user=user,
mturk_assignment=mturk_assignment,
time_ms=shape_time_ms,
time_active_ms=shape_time_active_ms,
name=name)
if created and name:
shape.update_entropy(save=True)
new_objects[get_content_tuple(shape)] = [new_obj]
return new_objects
def mturk_update_votes_cubam_task(show_progress=False):
# use a lock directory to ensure only one thread is running
LOCK_DIR = '.mturk_update_votes_cubam_task'
try:
os.mkdir(LOCK_DIR)
except:
print ("Already running! If you are *sure* that " +
"mturk_update_votes_cubam_task is not running, " +
"delete the .mturk_update_votes_cubam_task directory")
return
try:
from common.utils import import_modules
modules = import_modules(settings.MTURK_MODULES)
for mt1 in modules:
if not hasattr(mt1, 'update_votes_cubam'):
continue
print '\nStarting: %s.update_votes_cubam()' % mt1.__name__
changed_objects = mt1.update_votes_cubam(
show_progress=show_progress)
if changed_objects:
# update pending contents
add_pending_objects_task.delay(
[get_content_tuple(c) for c in changed_objects])
# other updates
for mt2 in modules:
if hasattr(mt2, 'update_changed_objects'):
mt2.update_changed_objects(changed_objects)
print '\nDone: %s.update_votes_cubam()' % mt1.__name__
finally:
os.rmdir(LOCK_DIR)
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 mturk_submit(user,
hit_contents,
results,
time_ms,
time_active_ms,
version,
mturk_assignment=None,
**kwargs):
if unicode(version) != u'1.0':
raise ValueError("Unknown version: '%s'" % version)
if not hit_contents:
return {}
new_objects = {}
for photo in hit_contents:
points = results[unicode(photo.id)].strip()
photo_time_ms = time_ms[unicode(photo.id)]
photo_time_active_ms = time_active_ms[unicode(photo.id)]
# null by default
chroma_median = None
# count points
points_list = points.split(',')
num_points = len(points_list)
if num_points < 2:
num_points = 0
else:
if num_points % 2 != 0:
raise ValueError("Odd number of coordinates (%d)" %
num_points)
num_points //= 2
# compute median chromaticity
if num_points > 0:
pil = photo.open_image(width='300')
chromaticities = []
for idx in xrange(num_points):
x = float(points_list[idx * 2]) * pil.size[0]
y = float(points_list[idx * 2 + 1]) * pil.size[1]
rgb = pil.getpixel((x, y))
if rgb[0] >= 253 and rgb[1] >= 253 and rgb[2] >= 253:
continue # oversaturated
lab = RGBColor(rgb[0], rgb[1], rgb[2]).convert_to('lab')
chroma = math.sqrt(lab.lab_a * lab.lab_a +
lab.lab_b * lab.lab_b)
chromaticities.append(chroma)
if chromaticities:
chromaticities.sort()
chroma_median = chromaticities[len(chromaticities) // 2]
# add whitebalance label
new_obj, created = photo.whitebalances.get_or_create(
user=user,
mturk_assignment=mturk_assignment,
time_ms=photo_time_ms,
time_active_ms=photo_time_active_ms,
points=points,
num_points=num_points,
chroma_median=chroma_median)
# update photo filter status
if created:
new_objects[get_content_tuple(photo)] = [new_obj]
return new_objects
def mturk_submit(user,
hit_contents,
results,
time_ms,
time_active_ms,
version,
mturk_assignment=None,
**kwargs):
""" Add new instances from a mturk HIT after the user clicks [submit] """
if unicode(version) != u'2.0':
raise ValueError("Unknown version: %s" % version)
new_objects = {}
for task in hit_contents:
task_id = str(task.id)
scribbles = results[task_id][u'scribbles']
person_time_ms = time_ms[task_id]
person_time_active_ms = time_active_ms[task_id]
# check if the scribbles make sense
for scribble in scribbles:
for point in scribble[u'points']:
if len(point) != 2:
raise ValueError("Point with more than 2 coordinates")
# check if the results contain a segmentation already, if so do not
# recalculate the segmentation
overlay_img = None
if u'segmentation' in results[task_id]:
try:
overlay_img_data = base64.standard_b64decode(
results[task_id][u'segmentation'])
overlay_img = Image.open(StringIO(overlay_img_data))
print("reusing segmentation data")
except:
overlay_img = None
if not overlay_img:
# generate the segmentation image
overlay_img = calc_person_overlay_img(task, scribbles)
print("NOT reusing segmentation data")
with transaction.atomic():
with NamedTemporaryFile(prefix=u'segmentation_' +
task.person.photo.name + u'_',
suffix=u'.png') as f:
overlay_img.save(f, u"PNG")
f.seek(0)
segmentation = ImageFile(f)
new_obj, created = task.responses.get_or_create(
user=user,
segmentation=segmentation,
mturk_assignment=mturk_assignment,
time_ms=person_time_ms,
time_active_ms=person_time_active_ms,
# (repr gives more float digits)
scribbles=json.dumps(scribbles),
num_scribbles=len(scribbles),
)
if created:
new_objects[get_content_tuple(task)] = [new_obj]
return new_objects
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 mturk_submit_impl(**kwargs):
#slug = kwargs['experiment'].slug
#print '%s time_ms: %s, time_active_ms: %s, time_load_ms: %s' % (
#slug, kwargs['time_ms'], kwargs['time_active_ms'],
#kwargs['time_load_ms'])
#print '%s results: %s' % (slug, kwargs['results'])
#if kwargs['mturk_assignment'].feedback:
#print '%s feedback: %s' % (slug, kwargs['mturk_assignment'].feedback)
# fetch objects if passed by ID
if 'user_id' in kwargs:
kwargs['user'] = UserProfile.objects.get(user_id=kwargs['user_id'])
if 'mturk_hit_id' in kwargs:
kwargs['mturk_hit'] = MtHit.objects.get(id=kwargs['mturk_hit_id'])
if 'mturk_assignment_id' in kwargs:
kwargs['mturk_assignment'] = MtAssignment.objects.get(id=kwargs['mturk_assignment_id'])
if 'experiment_id' in kwargs:
kwargs['experiment'] = Experiment.objects.get(id=kwargs['experiment_id'])
# fetch experiment settings
hit_type = kwargs['mturk_hit'].hit_type
exp_settings = hit_type.experiment_settings
if not exp_settings:
# if the settings are somehow missing, update all records with the
# newest experiment settings
exp_settings = kwargs['experiment'].new_hit_settings
MtHitType.objects.filter(id=hit_type.id) \
.update(experiment_settings=exp_settings)
# fetch hit contents
if 'hit_contents' not in kwargs:
kwargs['hit_contents'] = fetch_hit_contents(kwargs['mturk_hit'])
hit_contents = kwargs['hit_contents']
# new_objects_dict: {(content_type_id, content_id): [created items]}
# (if [created items] is empty, the entry may be omitted)
if hit_contents:
new_objects_dict = exp_settings.out_content_model() \
.mturk_submit(**kwargs)
else:
print "WARNING: no hit_contents in %s" % kwargs['mturk_hit'].id
new_objects_dict = {}
# sanity check
if not all(isinstance(k, tuple) for k in new_objects_dict):
raise ValueError(
"Invalid new_objects_dict: %s" % repr(new_objects_dict))
# flatten all items into one list
new_objects_list = []
for obj_list in new_objects_dict.values():
new_objects_list += obj_list
# attach objects to assignment
for obj in new_objects_list:
MtSubmittedContent.objects.get_or_create(
assignment=kwargs['mturk_assignment'],
object_id=obj.id,
content_type=ContentType.objects.get_for_model(obj),
)
for content in hit_contents:
# content_tuple: (content type id, object id)
content_tuple = get_content_tuple(content)
if content_tuple not in new_objects_dict:
# print '%s: no new objects generated' % repr(content_tuple)
continue
delta_completed = len(new_objects_dict[content_tuple])
delta_scheduled = exp_settings.out_count_ratio
# update the fact that some outputs have been completed
PendingContent.objects \
.filter(
experiment=kwargs['experiment'],
content_type=ContentType.objects.get_for_id(content_tuple[0]),
object_id=content_tuple[1],
).update(
num_outputs_completed=F(
'num_outputs_completed') + delta_completed,
num_outputs_scheduled=F(
'num_outputs_scheduled') - delta_scheduled,
)
# consider all affected objects for new experiments
pending_objects = list(set(hit_contents + new_objects_list))
add_pending_objects_task.delay(
[get_content_tuple(c) for c in pending_objects])
# mark experiment as dirty
Experiment.objects.filter(id=kwargs['experiment'].id) \
.update(cubam_dirty=True)
# here, "complete" means that the user actually submitted (and is not a
# "partial submission", i.e. a background auto-submit performed by the
# experiment script)
if not kwargs['complete']:
return
# sync with mturk 30 minutes from now (it can take a while to update the
# status; 1 minute is not enough)
sync_hit_task.apply_async(
args=[kwargs['mturk_hit'].id],
countdown=30 * 60)
# mark as done
MtAssignment.objects.filter(id=kwargs['mturk_assignment'].id) \
.update(submission_complete=True)
def mturk_submit(user, hit_contents, results, time_ms, time_active_ms,
experiment, version, mturk_assignment=None, **kwargs):
""" Add new instances from a mturk HIT after the user clicks [submit] """
if unicode(version) != u'1.0':
raise ValueError("Unknown version: %s" % version)
photo = hit_contents[0]
poly_list = results[str(photo.id)]
time_ms_list = time_ms[str(photo.id)]
time_active_ms_list = time_active_ms[str(photo.id)]
if len(poly_list) != len(time_ms_list):
raise ValueError("Result length mismatch (%s polygons, %s times)" % (
len(poly_list), len(time_ms_list)))
shape_model = MaterialShape
slug = experiment.slug
if slug == "segment_material":
shape_type = 'M'
elif slug == "segment_object":
shape_type = 'O'
else:
raise ValueError("Unknown slug: %s" % slug)
# store results in SubmittedShape objects
new_objects_list = []
for idx in xrange(len(poly_list)):
poly_vertices = poly_list[idx]
poly_time_ms = time_ms_list[idx]
poly_time_active_ms = time_active_ms_list[idx]
num_vertices = len(poly_vertices)
if num_vertices % 2 != 0:
raise ValueError("Odd number of vertices (%d)" % num_vertices)
num_vertices //= 2
new_obj, created = photo.submitted_shapes.get_or_create(
user=user,
mturk_assignment=mturk_assignment,
time_ms=poly_time_ms,
time_active_ms=poly_time_active_ms,
# (repr gives more float digits)
vertices=','.join([repr(f) for f in poly_vertices]),
num_vertices=num_vertices,
shape_type=shape_type
)
if created:
new_objects_list.append(new_obj)
# triangulate polygons (creates instances of shape_model)
if new_objects_list:
from shapes.tasks import triangulate_submitted_shapes_task
triangulate_submitted_shapes_task.delay(
photo, user, mturk_assignment, shape_model, new_objects_list)
if new_objects_list:
return {get_content_tuple(photo): new_objects_list}
else:
return {}
def mturk_submit(user,
hit_contents,
results,
time_ms,
time_active_ms,
version,
experiment,
mturk_assignment=None,
**kwargs):
""" Add new instances from a mturk HIT after the user clicks [submit] """
if unicode(version) != u'1.0':
raise ValueError("Unknown version: '%s'" % version)
if not hit_contents:
return {}
new_objects = {}
for shape in hit_contents:
d = results[unicode(shape.id)]
shape_time_ms = time_ms[unicode(shape.id)]
shape_time_active_ms = time_active_ms[unicode(shape.id)]
edit_dict = d[u'edit']
edit_sum = sum(int(edit_dict[k]) for k in edit_dict)
edit_nnz = sum(int(int(edit_dict[k]) > 0) for k in edit_dict)
init_method = 'KR'
envmap = EnvironmentMap.objects.get(
id=json.loads(experiment.variant)['envmap_id'])
doi = int(d[u'doi'])
contrast = float(d[u'contrast'])
metallic = (int(d[u'type']) == 1)
color = d['color']
give_up = d[u'give_up']
give_up_msg = d[u'give_up_msg']
bsdf, bsdf_created = shape.bsdfs_wd.get_or_create(
user=user,
mturk_assignment=mturk_assignment,
time_ms=shape_time_ms,
time_active_ms=shape_time_active_ms,
doi=doi,
contrast=contrast,
metallic=metallic,
color=color,
give_up=give_up,
give_up_msg=give_up_msg,
edit_dict=json.dumps(edit_dict),
edit_sum=edit_sum,
edit_nnz=edit_nnz,
envmap=envmap,
init_method=init_method,
)
if bsdf_created:
new_objects[get_content_tuple(shape)] = [bsdf]
if ((not bsdf.image_blob) and 'screenshot' in d
and d['screenshot'].startswith('data:image/')):
save_obj_attr_base64_image(bsdf, 'image_blob', d['screenshot'])
return new_objects
