def _create_algorithm_demo(self):
cases_image = grandchallenge.cases.models.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=get_temporary_image())
algorithm.editors_group.user_set.add(self.users["algorithm"],
self.users["demo"])
algorithm.users_group.user_set.add(self.users["algorithmuser"])
algorithm_image = AlgorithmImage(creator=self.users["algorithm"],
algorithm=algorithm)
container = ContentFile(base64.b64decode(b""))
algorithm_image.image.save("test_algorithm.tar", container)
algorithm_image.save()
algorithms_job = grandchallenge.algorithms.models.Job(
creator=self.users["algorithm"],
algorithm_image=algorithm_image,
image=cases_image,
status=Evaluation.SUCCESS,
)
algorithms_job.save()
algorithms_job.create_result(result={"cancer_score": 0.5})
def _create_io_algorithm(self):
algorithm = Algorithm.objects.create(
title="Test Algorithm IO",
logo=get_temporary_image(),
use_flexible_inputs=True,
)
algorithm.editors_group.user_set.add(
self.users["algorithm"], self.users["demo"]
)
algorithm.users_group.user_set.add(self.users["algorithmuser"])
algorithm_image = AlgorithmImage(
creator=self.users["algorithm"], algorithm=algorithm, ready=True
)
algorithm_image_path = (
"tests/resources/gc_demo_algorithm/algorithm_io.tar"
)
if os.path.exists(algorithm_image_path):
with open(
os.path.join(settings.SITE_ROOT, algorithm_image_path,), "rb",
) as f:
container = File(f)
algorithm_image.image.save("algorithm_io.tar", container)
algorithm_image.save()
def _create_algorithm_demo(self):
cases_image = grandchallenge.cases.models.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=get_temporary_image())
algorithm.editors_group.user_set.add(self.users["algorithm"],
self.users["demo"])
algorithm.users_group.user_set.add(self.users["algorithmuser"])
algorithm.result_template = (
"{% for key, value in result_dict.metrics.items() -%}"
"{{ key }} {{ value }}"
"{% endfor %}")
algorithm_image = AlgorithmImage(creator=self.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)
else:
container = ContentFile(base64.b64decode(b""))
algorithm_image.image.save("test_algorithm.tar", container)
algorithm_image.save()
for res in [
{
"cancer_score": 0.5
},
{
"cancer_score": 0.6
},
{
"cancer_score": 0.7
},
]:
self.create_job_result(algorithm_image, cases_image, res)
def _create_algorithm(*, creator, inputs, outputs, suffix):
algorithm = Algorithm.objects.create(
title=f"Test Algorithm Evaluation {suffix}",
logo=create_uploaded_image(),
)
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_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)