def source_main(request, source_id):
"""
Main page for a particular source.
"""
source = get_object_or_404(Source, id=source_id)
members = source.get_members_ordered_by_role()
memberDicts = [dict(username=member.username,
role=source.get_member_role(member))
for member in members]
all_images = source.get_all_images()
latest_images = all_images.order_by('-upload_date')[:5]
image_stats = dict(
total = all_images.count(),
anno_completed = all_images.filter(status__annotatedByHuman=True).count(),
)
if source.enable_robot_classifier:
image_stats.update( dict(
need_comp_anno = all_images.filter(status__annotatedByHuman=False, status__annotatedByRobot=False).count(),
need_human_anno = all_images.filter(status__annotatedByHuman=False, status__annotatedByRobot=True).count(),
need_human_anno_first = get_first_image(source, dict(status__annotatedByHuman=False, status__annotatedByRobot=True)),
))
else:
image_stats.update( dict(
need_anno = all_images.filter(status__annotatedByHuman=False).count(),
need_anno_first = get_first_image(source, dict(status__annotatedByHuman=False)),
))
latestRobot = source.get_latest_robot()
if latestRobot == None:
robotStats = dict(
hasRobot=False,
)
else:
f = open(latestRobot.path_to_model + '.meta.json')
jsonstring = f.read()
f.close()
meta=json.loads(jsonstring)
robotStats = dict(
version=latestRobot.version,
trainTime=round(meta['totalRuntime']),
precision = 100 * (1 - meta['hp']['estPrecision']),
hasRobot=True,
)
return render_to_response('images/source_main.html', {
'source': source,
'loc_keys': ', '.join(source.get_key_list()),
'members': memberDicts,
'latest_images': latest_images,
'image_stats': image_stats,
'robotStats':robotStats,
},
context_instance=RequestContext(request)
)
def annotation_tool(request, image_id):
"""
View for the annotation tool.
"""
image = get_object_or_404(Image, id=image_id)
source = image.source
metadata = image.metadata
# Image navigation history.
# Establish default values for the history, first.
nav_history_form =\
annotations_forms.AnnotationToolNavHistoryForm(
initial=dict(back="[]", forward="[]", from_image_id=image_id)
)
nav_history = dict(
form=nav_history_form,
back=None,
forward=None,
)
# We made a POST request if the user is going to another image,
# going back, or going forward.
# (It's non-POST if they came from a non annotation tool page, or
# came via URL typing.)
if request.method == 'POST':
nav_history_form_submitted =\
annotations_forms.AnnotationToolNavHistoryForm(request.POST)
if nav_history_form_submitted.is_valid():
# Nav history is a serialized list of image ids.
# For example: "[258,259,268,109]"
form_data = nav_history_form_submitted.cleaned_data
back_submitted_list = json.loads(form_data['back'])
forward_submitted_list = json.loads(form_data['forward'])
from_image_id = form_data['from_image_id']
# Construct new back and forward lists based on
# where we're navigating.
if request.POST.get('nav_next', None):
back_list = back_submitted_list + [from_image_id]
forward_list = []
elif request.POST.get('nav_back', None):
back_list = back_submitted_list[:-1]
forward_list = [from_image_id] + forward_submitted_list
else: # 'nav_forward'
back_list = back_submitted_list + [from_image_id]
forward_list = forward_submitted_list[1:]
limit = 10
nav_history_form = \
annotations_forms.AnnotationToolNavHistoryForm(
initial=dict(
back=json.dumps(back_list[-limit:]),
forward=json.dumps(forward_list[:limit]),
from_image_id=image_id,
)
)
if len(back_list) > 0:
back = Image.objects.get(pk=back_list[-1])
else:
back = None
if len(forward_list) > 0:
forward = Image.objects.get(pk=forward_list[0])
else:
forward = None
nav_history = dict(
form=nav_history_form,
back=back,
forward=forward,
)
else:
# Invalid form for some reason.
# Fail silently, I guess? That is, use an empty history.
pass
# Get the settings object for this user.
# If there is no such settings object, then create it.
settings_obj, created = AnnotationToolSettings.objects.get_or_create(user=request.user)
settings_form = AnnotationToolSettingsForm(instance=settings_obj)
# Get all labels, ordered first by functional group, then by short code.
labels = source.labelset.labels.all().order_by('group', 'code')
# Get labels in the form {'code': <short code>, 'group': <functional group>, 'name': <full name>}.
# Convert from a ValuesQuerySet to a list to make the structure JSON-serializable.
labelValues = list(labels.values('code', 'group', 'name'))
error_message = []
# Get the machine's label probabilities, if applicable.
if not settings_obj.show_machine_annotations:
label_probabilities = None
elif not image.status.annotatedByRobot:
label_probabilities = None
else:
label_probabilities = task_utils.get_label_probabilities_for_image(image_id)
# label_probabilities can still be None here if something goes wrong.
# But if not None, apply Alleviate.
if label_probabilities:
annotations_utils.apply_alleviate(image_id, label_probabilities)
else:
error_message.append('Woops! Could not read the label probabilities. Manual annotation still works.')
# Get points and annotations.
form = AnnotationForm(
image=image,
user=request.user,
show_machine_annotations=settings_obj.show_machine_annotations
)
pointValues = Point.objects.filter(image=image).values(
'point_number', 'row', 'column')
annotationValues = Annotation.objects.filter(image=image).values(
'point__point_number', 'label__name', 'label__code')
# annotationsDict
# keys: point numbers
# values: dicts containing the values in pointValues and
# annotationValues (if the point has an annotation) above
annotationsDict = dict()
for p in pointValues:
annotationsDict[p['point_number']] = p
for a in annotationValues:
annotationsDict[a['point__point_number']].update(a)
# Get a list of the annotationsDict values (the keys are discarded)
# Sort by point_number
annotations = list(annotationsDict.values())
annotations.sort(key=lambda x:x['point_number'])
# Now we've gotten all the relevant points and annotations
# from the database, in a list of dicts:
# [{'point_number':1, 'row':294, 'column':749, 'label__name':'Porites', 'label__code':'Porit', 'user_is_robot':False},
# {'point_number':2, ...},
# ...]
# TODO: Are we even using anything besides row, column, and point_number? If not, discard the annotation fields to avoid confusion.
# Image tools form (brightness, contrast, etc.)
image_options_form = AnnotationImageOptionsForm()
# Image dimensions.
IMAGE_AREA_WIDTH = 850
IMAGE_AREA_HEIGHT = 650
source_images = dict(full=dict(
url=image.original_file.url,
width=image.original_file.width,
height=image.original_file.height,
))
if image.original_width > IMAGE_AREA_WIDTH:
# Set scaled image's dimensions (Specific width, height that keeps the aspect ratio)
thumbnail_dimensions = (IMAGE_AREA_WIDTH, 0)
# Generate the thumbnail if it doesn't exist, and get the thumbnail's URL and dimensions.
thumbnailer = get_thumbnailer(image.original_file)
thumb = thumbnailer.get_thumbnail(dict(size=thumbnail_dimensions))
source_images.update(dict(scaled=dict(
url=thumb.url,
width=thumb.width,
height=thumb.height,
)))
# Get the next image to annotate.
# This'll be the next image that needs annotation;
# or if we're at the last image, wrap around to the first image.
next_image_to_annotate = get_next_image(image, dict(status__annotatedByHuman=False))
if next_image_to_annotate is None:
next_image_to_annotate = get_first_image(
image.source, dict(status__annotatedByHuman=False)
)
# Don't allow getting the current image as the next image to annotate.
if next_image_to_annotate is not None and next_image_to_annotate.id == image.id:
next_image_to_annotate = None
# Record this access of the annotation tool page.
access = AnnotationToolAccess(image=image, source=source, user=request.user)
access.save()
return render_to_response('annotations/annotation_tool.html', {
'source': source,
'image': image,
'next_image_to_annotate': next_image_to_annotate,
'nav_history': nav_history,
'metadata': metadata,
'labels': labelValues,
'form': form,
'settings_form': settings_form,
'image_options_form': image_options_form,
'annotations': annotations,
'annotationsJSON': simplejson.dumps(annotations),
'label_probabilities': label_probabilities,
'IMAGE_AREA_WIDTH': IMAGE_AREA_WIDTH,
'IMAGE_AREA_HEIGHT': IMAGE_AREA_HEIGHT,
'source_images': source_images,
'num_of_points': len(annotations),
'num_of_annotations': len(annotationValues),
'messages': error_message,
},
context_instance=RequestContext(request)
)
