def testMovingAverageWithNan(self):
# ------------
# single image
# ------------
img1 = np.array([[1, np.nan, 3], [np.nan, 4, 5]], dtype=np.float32)
img2 = np.array([[2, 3, 4], [np.nan, 5, 6]], dtype=np.float32)
movingAvgImageData(img1, img2, 2)
ma_gt = np.array([[1.5, np.nan, 3.5], [np.nan, 4.5, 5.5]],
dtype=np.float32)
np.testing.assert_array_equal(ma_gt, img1)
# ------------
# train images
# ------------
imgs1 = np.array(
[[[1, np.nan, 3], [np.nan, 4, 5]], [[1, 2, 3], [np.nan, 4, 5]]],
dtype=np.float32)
imgs2 = np.array([[[2, 3, 4], [4, 5, 6]], [[2, 3, 4], [4, 5, 6]]],
dtype=np.float32)
movingAvgImageData(imgs1, imgs2, 2)
ma_gt = np.array([[[1.5, np.nan, 3.5], [np.nan, 4.5, 5.5]],
[[1.5, 2.5, 3.5], [np.nan, 4.5, 5.5]]],
dtype=np.float32)
np.testing.assert_array_equal(ma_gt, imgs1)
def _run_moving_average_image_array(data_type):
imgs = np.ones((64, 1024, 512), dtype=data_type)
t0 = time.perf_counter()
movingAvgImageData(imgs, imgs, 5)
dt_cpp = time.perf_counter() - t0
t0 = time.perf_counter()
imgs + (imgs - imgs) / 5
dt_py = time.perf_counter() - t0
print(f"\nmoving average with {data_type} - "
f"dt (cpp para): {dt_cpp:.4f}, dt (numpy): {dt_py:.4f}")
def _run_moving_average_image_array(data, new_data, data_type):
data = data.astype(data_type)
new_data = new_data.astype(data_type)
data_cpp = data.copy()
t0 = time.perf_counter()
movingAvgImageData(data_cpp, new_data, 5)
dt_cpp = time.perf_counter() - t0
data_py = data.copy()
t0 = time.perf_counter()
data_py += (new_data - data_py) / 5
dt_py = time.perf_counter() - t0
np.testing.assert_array_equal(data_cpp, data_py)
print(f"\nmoving average with {data_type} - "
f"dt (cpp para): {dt_cpp:.4f}, dt (numpy): {dt_py:.4f}")
def __set__(self, instance, data):
if data is None:
self._data = None
self._count = 0
return
if self._data is not None and self._window > 1 and \
self._count <= self._window and data.shape == self._data.shape:
if self._count < self._window:
self._count += 1
if data.ndim in (2, 3):
movingAvgImageData(self._data, data, self._count)
else:
self._data += (data - self._data) / self._count
else: # self._count == self._window
# here is an approximation
if data.ndim in (2, 3):
movingAvgImageData(self._data, data, self._count)
else:
self._data += (data - self._data) / self._count
else:
self._data = data
self._count = 1
def testMovingAverage(self):
arr1d = np.ones(2, dtype=np.float32)
arr2d = np.ones((2, 2), dtype=np.float32)
arr3d = np.ones((2, 2, 2), dtype=np.float32)
arr4d = np.ones((2, 2, 2, 2), dtype=np.float32)
# test invalid input
with self.assertRaises(TypeError):
movingAvgImageData()
with self.assertRaises(TypeError):
movingAvgImageData(arr1d, arr1d, 2)
with self.assertRaises(TypeError):
movingAvgImageData(arr4d, arr4d, 2)
# count is 0
with self.assertRaises(ValueError):
movingAvgImageData(arr2d, arr2d, 0)
with self.assertRaises(ValueError):
movingAvgImageData(arr3d, arr3d, 0)
# inconsistent shape
with self.assertRaises(TypeError):
movingAvgImageData(arr2d, arr3d)
with self.assertRaises(ValueError):
movingAvgImageData(arr2d, np.ones((2, 3), dtype=np.float32), 2)
with self.assertRaises(ValueError):
movingAvgImageData(arr3d, np.ones((2, 3, 2), dtype=np.float32), 2)
# inconsistent dtype
with self.assertRaises(TypeError):
movingAvgImageData(arr2d, np.ones((2, 2), dtype=np.float64), 2)
with self.assertRaises(TypeError):
movingAvgImageData(arr3d, np.ones((2, 2, 2), dtype=np.float64), 2)
# ------------
# single image
# ------------
img1 = np.array([[1, 2, 3], [3, 4, 5]], dtype=np.float32)
img2 = np.array([[2, 3, 4], [4, 5, 6]], dtype=np.float32)
movingAvgImageData(img1, img2, 2)
ma_gt = np.array([[1.5, 2.5, 3.5], [3.5, 4.5, 5.5]], dtype=np.float32)
np.testing.assert_array_equal(ma_gt, img1)
# ------------
# train images
# ------------
imgs1 = np.array([[[1, 2, 3], [3, 4, 5]], [[1, 2, 3], [3, 4, 5]]],
dtype=np.float32)
imgs2 = np.array([[[2, 3, 4], [4, 5, 6]], [[2, 3, 4], [4, 5, 6]]],
dtype=np.float32)
movingAvgImageData(imgs1, imgs2, 2)
ma_gt = np.array([[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5]],
[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5]]],
dtype=np.float32)
np.testing.assert_array_equal(ma_gt, imgs1)