def test_median_filter(self):
self.image.median_filter(3)
original_image = retina_grayscale.Retina_grayscale(
None, _image_path, 1)
assert_array_equal(
self.image.np_image,
cv2.medianBlur(original_image.np_image.astype(np.uint8), 3))
def test_opening(self):
self.image.opening(3)
original_image = retina_grayscale.Retina_grayscale(
None, _image_path, 1)
assert_array_equal(
self.image.np_image,
ndimage.grey_opening(original_image.np_image, size=(3, 3)))
def test_gaussian_filter(self):
self.image.gaussian_filter(17, 1.82)
original_image = retina_grayscale.Retina_grayscale(
None, _image_path, 1)
assert_array_equal(
self.image.np_image,
cv2.GaussianBlur(original_image.np_image, (17, 17), 1.82))
def test_equalize_histogram(self):
self.image.equalize_histogram()
original_image = retina_grayscale.Retina_grayscale(None, _image_path, 1)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(3, 3))
original_image.np_image = clahe.apply(original_image.np_image)
original_image.restore_mask()
assert_array_equal(self.image.np_image, original_image.np_image)
def test_restore_mask(self):
self.image.np_image[self.image.mask == 0] = 5
self.image.restore_mask()
original_image = retina_grayscale.Retina_grayscale(None, _image_path, 1)
original_image.np_image[original_image.mask == 0] = 5
original_image.restore_mask()
assert_array_equal(self.image.np_image, original_image.np_image)
def test_top_hat(self):
self.image.top_hat(3)
original_image = retina_grayscale.Retina_grayscale(None, _image_path, 1)
assert_array_equal(self.image.np_image,
cv2.morphologyEx(original_image.np_image, cv2.MORPH_TOPHAT,
cv2.getStructuringElement(cv2.MORPH_RECT, (
3, 3))))
def test_calculate_roc(self):
double_segmentation = self.image.normal_vessels_segmentation()
original_image = retina_grayscale.Retina_grayscale(
None, _manual_result_path, 1)
self.image.calculate_roc(double_segmentation / 255,
original_image.np_image / 255)
np.testing.assert_allclose(self.image.roc,
[[425., 304., 423., 299., 1451.]], 1e-2)
def post_segmentation_double_segmentation():
data = {"success": False} # pragma: no cover
if flask.request.method == "POST": # pragma: no cover
json = flask.request.get_json(silent=True)
if json is not None: # pragma: no cover
image = base64.b64decode(json["image"])
image = Image.open(io.BytesIO(image))
retina = retina_grayscale.Retina_grayscale(np.array(image), None)
data = {"segmentation": retina.double_segmentation()}
return flask.jsonify(data) # pragma: no cover
def test_constructor_image_no_type_2(self):
"""Test the constructor with an existing image"""
image = io.imread(_image_path_2)
none_constructor_image = retina_grayscale.Retina_grayscale(
image, _image_file_name)
def setUp(self):
self.image = retina_grayscale.Retina_grayscale(None, _image_path, 1)
def test_double_vessels_segmentation(self):
double_segmentation = self.image.double_segmentation()
other_segmentation = retina_grayscale.Retina_grayscale(
None, _image_path, 1).double_segmentation()
assert_array_equal(double_segmentation, other_segmentation)
def test_mean_filter(self):
self.image.mean_filter(3)
original_image = retina_grayscale.Retina_grayscale(
None, _image_path, 1)
assert_array_equal(self.image.np_image,
cv2.blur(original_image.np_image, (3, 3)))
