def test_get_footprint(self):
output = utils.get_footprint(size=5)
reference = np.array(
[
[0, 1, 1, 1, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[0, 1, 1, 1, 0],
],
dtype="b1",
)
self.assertTrue(np.equal(output, reference).all())
def process(data, size=None):
if data is None or size is None or "values" not in data:
return data
radius = int(size // 2)
footprint = get_footprint(size)[np.newaxis]
# put absolute minimum on no data pixels
array = data["values"].copy()
minimum = get_dtype_min(array.dtype)
no_data_mask = array == data["no_data_value"]
array[no_data_mask] = minimum
# apply maximum filter
filtered = ndimage.maximum_filter(array, footprint=footprint)
# replace absolute minimum with original fillvalue
filtered[(filtered == minimum) & no_data_mask] = data["no_data_value"]
# cut out the result
filtered = filtered[:, radius:-radius, radius:-radius]
return {"values": filtered, "no_data_value": data["no_data_value"]}