def visualize_attack():
"""Run selected attack on uploaded image.
"""
form = VisualizeAttackForm()
if form.validate_on_submit():
index_model = int(form.model.data)
index_attack = int(form.attacks.data)
img = Image.open(form.image.data)
width, height = img.size
classifier_id = int(form.model.data)
attack_id = int(form.attacks.data)
ratio = min(MAX_WIDTH / 100, MAX_HEIGHT / 100)
(original_image, result_image, _, _, _) = run_attack(img,
classifier_id,
attack_id,
scale=ratio)
result_file = save_image(result_image,
path="tmp",
output_size=(MAX_WIDTH, MAX_HEIGHT))
original_file = save_image(original_image,
path="tmp",
output_size=(MAX_WIDTH, MAX_HEIGHT))
# Get predictions before and after running
# the attack
preds = []
probs = []
size = 5 # top 5 predictions
if form.classify.data:
_, original_label = predict(img, classifier_id)
_, result_label = predict(result_image, classifier_id)
preds.append([original_label[i][1] for i in range(size)])
preds.append([result_label[i][1] for i in range(size)])
probs.append(
[np.round(original_label[i][2] / 100, 4) for i in range(size)])
probs.append(
[np.round(result_label[i][2] / 100, 4) for i in range(size)])
flash("Attack successully run!", 'success')
return render_template('visualize_attack.html',
form=form,
image_file=original_file,
result_file=result_file,
index_model=index_model,
index_attack=index_attack,
preds=preds,
probs=probs,
size=size)
return render_template('visualize_attack.html', form=form)
def form_predict():
form = PredictForm()
if form.validate_on_submit():
index_model = int(form.model.data)
paper = pretrained_classifiers[index_model].paper
name = pretrained_classifiers[index_model].name
img = Image.open(form.image.data)
width, height = img.size
# ratio is used to preserve the aspect-ratio
ratio = min(MAX_WIDTH / 100, MAX_HEIGHT / 100)
image, label = predict(img, int(form.model.data), scale=ratio)
image_file = save_image(image,
path="tmp",
output_size=(MAX_WIDTH, MAX_HEIGHT))
flash("Done!", 'success')
return render_template('predict.html',
title='Classify an image.',
image_file=image_file,
form=form,
label=label,
index_model=index_model,
paper=paper,
name=name)
return render_template('predict.html',
title='Classify an image.',
form=form)
def account():
form = UpdateAccountForm()
if form.validate_on_submit():
if form.image.data:
image_file = save_image(form.image.data, path="profile_pictures")
current_user.image_file = image_file
current_user.username = form.username.data
current_user.email = form.email.data
db.session.commit()
flash("Your account has been successfully updated!", 'success')
return redirect(url_for('account'))
elif request.method == 'GET':
form.username.data = current_user.username
form.email.data = current_user.email
image_file = url_for('static',
filename=f"profile_pictures/"
f"{current_user.image_file}")
page = request.args.get('page', 1, type=int)
models = Classifier.query.filter_by(user=current_user)\
.order_by(Classifier.upload_date.desc())\
.paginate(page=page, per_page=5)
return render_template('account.html',
title='Account',
image_file=image_file,
form=form,
models=models)
def visualize_attack():
"""Run selected attack on uploaded image.
TODO
----
- For now, we don't have any logic that goes over what specific
attack was selected. This can be easily fixed, but because we ran
out of time, we didn't implement it.
- The way the path is passed to the attack method is also messy:
this can be done more elegantly using url_for to avoid problems
that would happen on deployment (I think Docker is weird with this
sort of stuff), and anyway it's bad practice to hardcode a path like
this. I'll add it as an issue after the commit.
"""
form = VisualizeAttackForm()
if form.validate_on_submit():
index = len(form.model.choices) - int(form.model.data)
image_file = save_image(form.image.data,
path="tmp",
output_size=(400, 400))
result_file = gray(f"./loki/static/tmp/" f"{image_file}")
flash("Attack successully run!", 'success')
return render_template('visualize_attack.html',
form=form,
image_file=image_file,
result_file=result_file,
index=index)
return render_template('visualize_attack.html', form=form)
def predict():
form = PredictForm()
if form.validate_on_submit():
index = int(form.model.data) - 1
image_file = save_image(form.image.data, path="tmp")
path = url_for('static', filename=f"tmp/" f"{image_file}")
classifier = IR()
label = classifier.predict(path)
flash("Done!", 'success')
return render_template('predict.html',
title='Classify an image.',
image_file=image_file,
form=form,
label=label,
index=index)
return render_template('predict.html',
title='Classify an image.',
form=form)