def test_most_common_class_exclude(self):
a = np.array([[10, 20, 30] * 6] * 6, dtype=np.uint8)
expected = (10, 36)
actual = most_common_class(a, exclude=(20, 30))
self.assertEqual(expected, actual)
def test_most_common_class_with_valid(self):
*_, gl30_10 = random_test_data()
expected = 50
actual = most_common_class(gl30_10)[0]
self.assertEqual(expected, actual)
def test_most_common_class_with_fallback(self):
a = np.array([[20, 20, 10] * 5] * 5, dtype=np.uint8)
expected = 10
actual = most_common_class(a)[0]
self.assertEqual(expected, actual)
def test_most_common_class_with_zeros(self):
a = np.zeros((10, 10), dtype=np.uint8)
expected = None
actual = most_common_class(a)
self.assertEqual(expected, actual)
def reclassify(driver, clustering=SETTINGS['clustering'],
reject=SETTINGS['reject'], side_length=SETTINGS['buffer'], res=(1, 1)):
"""Reclassify pixels in proximate deforestation stratum.
Approach: Cluster pixels with values ``clustering``; create square sized buffer around the cluster center;
count most frequent class within the buffer by exclusion of ``reject``; reassign cluster to most frequent class
Args:
driver (ndarray): Proximate deforestation driver stratum.
clustering (list(int): Values to cluster.
reject (list(int): Values to reject for reclassification.
side_length (int): Edge length of the buffer.
res(int or tuple(int, int)): Cell size.
Returns:
ndarray: The reclassified stratum.
"""
mask = np.isin(driver, clustering)
clusters = []
for cluster, _ in shapes(driver, mask=mask):
polygon = Polygon(cluster['coordinates'][0])
point = polygon.centroid
center = int(point.y), int(point.x)
LOGGER.debug('Cluster centroid at (%s, %s)', int(point.x), int(point.y))
buffer = extract_square(driver, center, side_length, res)
LOGGER.debug('Buffer size (%s, %s)', buffer.shape[0], buffer.shape[1])
mc = most_common_class(buffer, reject)
if mc:
cls, count = mc
clusters.append((cluster, cls))
if clusters:
return rasterize(clusters, out_shape=driver.shape, dtype=driver.dtype)
return np.zeros(shape=driver.shape, dtype=driver.dtype)