def _create_workstation(users):
w = Workstation.objects.create(
title=settings.DEFAULT_WORKSTATION_SLUG,
logo=create_uploaded_image(),
public=True,
)
w.add_editor(user=users["workstation"])
def _create_challenge(*, creator, participant, archive, suffix, inputs,
outputs):
c = Challenge.objects.create(
short_name=f"algorithm-evaluation-{suffix}",
creator=creator,
hidden=False,
logo=create_uploaded_image(),
)
c.add_participant(participant)
p = c.phase_set.first()
p.algorithm_inputs.set(inputs)
p.algorithm_outputs.set(outputs)
p.title = "Algorithm Evaluation"
p.submission_kind = p.SubmissionKind.ALGORITHM
p.archive = archive
p.score_jsonpath = "score"
p.save()
m = Method(creator=creator, phase=p)
with _uploaded_container_image() as container:
m.image.save("algorithm_io.tar", container)
def _create_archive(*, creator, interfaces, suffix, items=5):
a = Archive.objects.create(
title=f"Algorithm Evaluation {suffix} Test Set",
logo=create_uploaded_image(),
workstation=Workstation.objects.get(
slug=settings.DEFAULT_WORKSTATION_SLUG),
)
a.add_editor(creator)
for n in range(items):
ai = ArchiveItem.objects.create(archive=a)
for interface in interfaces:
v = ComponentInterfaceValue.objects.create(interface=interface)
im = Image.objects.create(
name=f"Test Image {n}",
width=10,
height=10,
)
im_file = ImageFile.objects.create(image=im)
with _uploaded_image_file() as f:
im_file.file.save(f"test_image_{n}.mha", f)
im_file.save()
v.image = im
v.save()
ai.values.add(v)
return a
def _try_create_challenge(self,
user,
short_name,
description="test project"):
url = reverse("challenges:create")
data = {
"short_name": short_name,
"description": description,
"logo": create_uploaded_image(),
"banner": create_uploaded_image(),
"prefix": "form",
"page_set-TOTAL_FORMS": "0",
"page_set-INITIAL_FORMS": "0",
"page_set-MAX_NUM_FORMS": "",
}
self._login(user)
response = self.client.post(url, data)
return response
def create_archive(name, user):
a = Archive.objects.get_or_create(
title=name,
defaults={
"logo": create_uploaded_image(),
"workstation": Workstation.objects.first(),
},
)[0]
a.add_editor(user)
return a
def _create_algorithm(*, creator, inputs, outputs, suffix):
algorithm = Algorithm.objects.create(
title=f"Test Algorithm Evaluation {suffix}",
logo=create_uploaded_image(),
use_flexible_inputs=True,
)
algorithm.inputs.set(inputs)
algorithm.outputs.set(outputs)
algorithm.add_editor(creator)
algorithm_image = AlgorithmImage(creator=creator, algorithm=algorithm)
with _uploaded_container_image() as container:
algorithm_image.image.save("algorithm_io.tar", container)
def _create_reader_studies(users):
reader_study = ReaderStudy.objects.create(
title="Reader Study",
workstation=Workstation.objects.last(),
logo=create_uploaded_image(),
description="Test reader study",
)
reader_study.editors_group.user_set.add(users["readerstudy"])
reader_study.readers_group.user_set.add(users["demo"])
question = Question.objects.create(
reader_study=reader_study,
question_text="foo",
answer_type=Question.AnswerType.SINGLE_LINE_TEXT,
)
answer = Answer.objects.create(creator=users["readerstudy"],
question=question,
answer="foo")
answer.images.add(Image.objects.first())
answer.save()
def _create_algorithm_demo(users):
cases_image = Image(
name="test_image.mha",
modality=ImagingModality.objects.get(modality="MR"),
width=128,
height=128,
color_space="RGB",
)
cases_image.save()
algorithm = Algorithm.objects.create(title="Test Algorithm",
logo=create_uploaded_image())
algorithm.editors_group.user_set.add(users["algorithm"], users["demo"])
algorithm.users_group.user_set.add(users["algorithmuser"])
algorithm.result_template = (
"{% for key, value in results.metrics.items() -%}"
"{{ key }} {{ value }}"
"{% endfor %}")
detection_interface = ComponentInterface(
store_in_database=False,
relative_path="detection_results.json",
slug="detection-results",
title="Detection Results",
kind=ComponentInterface.Kind.ANY,
)
detection_interface.save()
algorithm.outputs.add(detection_interface)
algorithm_image = AlgorithmImage(creator=users["algorithm"],
algorithm=algorithm)
if os.path.isfile(settings.DEMO_ALGORITHM_IMAGE_PATH):
with open(settings.DEMO_ALGORITHM_IMAGE_PATH, "rb") as f:
container = File(f)
algorithm_image.image.save("test_algorithm.tar", container)
algorithm_image.image_sha256 = settings.DEMO_ALGORITHM_SHA256
else:
container = ContentFile(base64.b64decode(b""))
algorithm_image.image.save("test_algorithm.tar", container)
algorithm_image.save()
results = [
{
"cancer_score": 0.5
},
{
"cancer_score": 0.6
},
{
"cancer_score": 0.7
},
]
detections = [
{
"detected points": [{
"type": "Point",
"start": [0, 1, 2],
"end": [3, 4, 5]
}]
},
{
"detected points": [{
"type": "Point",
"start": [6, 7, 8],
"end": [9, 10, 11]
}]
},
{
"detected points": [{
"type": "Point",
"start": [12, 13, 14],
"end": [15, 16, 17]
}]
},
]
for res, det in zip(results, detections):
_create_job_result(users, algorithm_image, cases_image, res, det)