def test_dpmm(self):
arr = np.arange(42).reshape(6, 7)
ai = ArrayImage(arr)
self.assertIsNone(ai.dpi)
ai2 = ArrayImage(arr, dpi=20)
self.assertEqual(ai2.dpi, 20)
self.assertEqual(ai2.dpmm, 20/25.4)
def test_dpmm(self):
arr = np.arange(42).reshape(6, 7)
ai = ArrayImage(arr)
# errors if not passed in
with self.assertRaises(AttributeError):
ai.dpi
ai2 = ArrayImage(arr, dpi=20)
self.assertEqual(ai2.dpi, 20)
self.assertEqual(ai2.dpmm, 20 / 25.4)
def _getThresholdMask(self, img, proz):
""" Eine Bildmaske erstellen
img : image.array
proz : [ min, max]
"""
min_value, max_value = np.percentile(img, proz)
threshold_value = (max_value - min_value) / 2 + min_value
array = np.where(img >= threshold_value, 1, 0)
threshold_img = ArrayImage(array)
return threshold_img
distTA_pixels = ref_image.dpmm * distTA
# construct image gradient using sobel filter
img_x = spf.sobel(ref_img.as_type(np.float32), 1)
img_y = spf.sobel(ref_img.as_type(np.float32), 0)
img_z = spf.sobel(ref_img.as_type(np.float32), 2)
grad_img = np.sqrt(img_x**2 + img_y**2 + img_z**2)
# equation: (measurement - reference) / sqrt ( doseTA^2 + distTA^2 * image_gradient^2 )
subtracted_img = np.abs(comp_img - ref_img)
denominator = np.sqrt((doseTA / 100.0**2) + ((distTA_pixels**2) *
(grad_img**2)))
gamma_map = subtracted_img / denominator
return gamma_map
test_array = np.zeros((5, 5, 5))
test_array[2, 2, 2] = 5
test_img = ArrayImage(test_array, dpi=1)
ref_array = np.zeros((5, 5, 5))
ref_array[2, 2, 2] = 1
ref_image = ArrayImage(ref_array, dpi=1)
gamma_map = gamma3d(ref_image, test_img, threshold=0)
# max gamma value
print(np.max(gamma_map))
print(np.mean(gamma_map))