Пример #1
0
def generate_annotation_set(retina_grader=False):
    grader = UserFactory()

    if retina_grader:
        add_to_graders_group([grader])

    measurement = MeasurementAnnotationFactory(grader=grader)
    boolean = BooleanClassificationAnnotationFactory(grader=grader)
    integer = IntegerClassificationAnnotationFactory(grader=grader)
    polygon = PolygonAnnotationSetFactory(grader=grader)
    coordinatelist = CoordinateListAnnotationFactory(grader=grader)
    landmark = LandmarkAnnotationSetFactory(grader=grader)
    etdrs = ETDRSGridAnnotationFactory(grader=grader)

    # Create child models for polygon annotation set
    SinglePolygonAnnotationFactory.create_batch(10, annotation_set=polygon)

    # Create child models for landmark annotation set (3 per image)
    for i in range(5):
        image = ImageFactory()
        SingleLandmarkAnnotationFactory(annotation_set=landmark, image=image)

    return AnnotationSet(
        grader=grader,
        measurement=measurement,
        boolean=boolean,
        polygon=polygon,
        coordinatelist=coordinatelist,
        landmark=landmark,
        etdrs=etdrs,
        integer=integer,
    )
Пример #2
0
def generate_annotation_set(retina_grader=False, image=False):
    grader = UserFactory()

    if retina_grader:
        add_to_graders_group([grader])

    create_options = {"grader": grader}
    if image:
        create_options_with_image = {"image": image, **create_options}
    else:
        create_options_with_image = create_options

    measurement = MeasurementAnnotationFactory(**create_options_with_image)
    boolean = BooleanClassificationAnnotationFactory(
        **create_options_with_image
    )
    integer = IntegerClassificationAnnotationFactory(
        **create_options_with_image
    )
    polygon = PolygonAnnotationSetFactory(**create_options_with_image)
    coordinatelist = CoordinateListAnnotationFactory(
        **create_options_with_image
    )
    etdrs = ETDRSGridAnnotationFactory(**create_options_with_image)
    landmark = LandmarkAnnotationSetFactory(**create_options)

    # Create child models for polygon annotation set
    SinglePolygonAnnotationFactory.create_batch(10, annotation_set=polygon)

    # Create child models for landmark annotation set (3 per image)
    single_landmarks = []
    for i in range(5):
        if i > 0 or not image:
            image = ImageFactory()
        single_landmarks.append(
            SingleLandmarkAnnotationFactory(
                annotation_set=landmark, image=image
            )
        )

    return AnnotationSet(
        grader=grader,
        measurement=measurement,
        boolean=boolean,
        polygon=polygon,
        coordinatelist=coordinatelist,
        landmark=landmark,
        singlelandmarks=single_landmarks,
        etdrs=etdrs,
        integer=integer,
    )
Пример #3
0
def create_load_data(data_type, ds, grader):
    if data_type == "Registration":
        model = LandmarkAnnotationSetFactory(grader=grader)
        SingleLandmarkAnnotationFactory(
            annotation_set=model, image=ds["image_cf"]
        ),
        if ds["archive"].name == "Australia":
            # Australia does not allow obs images so create a new cf image for Australia test
            img = ImageFactory(study=ds["study"])
            SingleLandmarkAnnotationFactory(annotation_set=model, image=img)
        else:
            SingleLandmarkAnnotationFactory(
                annotation_set=model, image=ds["image_obs"]
            ),
    elif data_type == "ETDRS":
        model = ETDRSGridAnnotationFactory(grader=grader, image=ds["image_cf"])
    elif data_type == "GA" or data_type == "kappa":
        model_macualar = PolygonAnnotationSetFactory(
            grader=grader, image=ds["image_cf"], name="macular"
        )
        SinglePolygonAnnotationFactory(annotation_set=model_macualar)
        SinglePolygonAnnotationFactory(annotation_set=model_macualar)
        SinglePolygonAnnotationFactory(annotation_set=model_macualar)

        model_peripapillary = PolygonAnnotationSetFactory(
            grader=grader, image=ds["image_cf"], name="peripapillary"
        )
        SinglePolygonAnnotationFactory(annotation_set=model_peripapillary)
        SinglePolygonAnnotationFactory(annotation_set=model_peripapillary)
        SinglePolygonAnnotationFactory(annotation_set=model_peripapillary)
        model = [model_macualar, model_peripapillary]
    elif data_type == "Measure":
        model = [
            MeasurementAnnotationFactory(grader=grader, image=ds["image_cf"]),
            MeasurementAnnotationFactory(grader=grader, image=ds["image_cf"]),
            MeasurementAnnotationFactory(grader=grader, image=ds["image_cf"]),
        ]
    elif data_type == "Fovea":
        model = BooleanClassificationAnnotationFactory(
            grader=grader, image=ds["image_cf"], name="fovea_affected"
        )

    return model