diff --git a/skais/ais/ais_trajectory.py b/skais/ais/ais_trajectory.py
index e6ba885dcafca82f0a023eee0196003472ce28b4..7e1307e18352bf3caf025b5d89c6d73f1f02faba 100644
--- a/skais/ais/ais_trajectory.py
+++ b/skais/ais/ais_trajectory.py
@@ -62,7 +62,7 @@ def apply_time_sequence(dat, time, func):
def __get_image_value__(features, bounds):
value = []
for f, b in zip(features, bounds):
- value.append(1 - (b[1] - f) / b[1])
+ value.append(1 - (b[1] - f - b[0]) / (b[1] - b[0]))
return value
@@ -236,22 +236,42 @@ class AISTrajectory(AISPoints):
node_size=0):
nb_channels = 1
- if bounding_box != 'fit':
- raise ValueError("feature not implemented")
positions = self.df[['longitude', 'latitude']].to_numpy()
- min_lon, max_lon = (min(positions[:, 0]), max(positions[:, 0]))
- min_lat, max_lat = (min(positions[:, 1]), max(positions[:, 1]))
- if min_lat == max_lat:
- min_lat -= 1
- max_lat += 1
- if min_lon == max_lon:
- min_lon -= 1
- max_lon += 1
+ if bounding_box == 'fit':
+ lower_lon, upper_lon = (min(positions[:, 0]), max(positions[:, 0]))
+ lower_lat, upper_lat = (min(positions[:, 1]), max(positions[:, 1]))
+ elif bounding_box == 'centered':
+ center_lon, center_lat = positions[-1]
+ min_lon, max_lon = (min(positions[:, 0]), max(positions[:, 0]))
+ min_lat, max_lat = (min(positions[:, 1]), max(positions[:, 1]))
+
+ distance_to_center = max(center_lon - min_lon, max_lon - center_lon, center_lat - min_lat,
+ max_lat - center_lat)
+
+ upper_lat = center_lat + distance_to_center
+ lower_lat = center_lat - distance_to_center
+ upper_lon = center_lon + distance_to_center
+ lower_lon = center_lon - distance_to_center
+ elif type(bounding_box) is list:
+ upper_lon = bounding_box[1][0]
+ lower_lon = bounding_box[0][0]
+ upper_lat = bounding_box[1][1]
+ lower_lat = bounding_box[0][1]
+ else:
+ raise ValueError(f"Option not supported: {bounding_box}")
+
+ if lower_lat == upper_lat:
+ lower_lat -= 1
+ upper_lat += 1
+ if lower_lon == upper_lon:
+ lower_lon -= 1
+ upper_lon += 1
if features is None:
data = np.zeros((height, width, nb_channels), dtype=np.uint8)
for longitude, latitude in positions:
- x_coord, y_coord = get_coord(latitude, longitude, height, width, min_lat, max_lat, min_lon, max_lon)
+ x_coord, y_coord = get_coord(latitude, longitude, height, width, lower_lat, upper_lat, lower_lon,
+ upper_lon)
x_lower_bound = max(0, x_coord - node_size)
x_upper_bound = min(height - 1, x_coord + node_size)
@@ -265,30 +285,38 @@ class AISTrajectory(AISPoints):
if link:
lon, lat = positions[0, 0], positions[0, 1]
for longitude, latitude in positions[1:]:
- x_prv, y_prev = get_coord(lat, lon, height, width, min_lat, max_lat, min_lon, max_lon)
- x_nxt, y_nxt = get_coord(latitude, longitude, height, width, min_lat, max_lat, min_lon, max_lon)
+ x_prv, y_prev = get_coord(lat, lon, height, width, lower_lat, upper_lat, lower_lon, upper_lon)
+ x_nxt, y_nxt = get_coord(latitude, longitude, height, width, lower_lat, upper_lat, lower_lon,
+ upper_lon)
lon, lat = longitude, latitude
for x, y in bresenham(x_prv, y_prev, x_nxt, y_nxt):
data[x, y] = [1]
else:
+ bounds = []
if type(features) is list:
nb_channels = len(features)
+ features_vectors = self.df[features].to_numpy()
+ for c in features_vectors.T:
+ bounds.append((0, max(c)))
elif type(features) is str:
- features = [features]
+ features_vectors = self.df[[features]].to_numpy()
+ for c in features_vectors.T:
+ bounds.append((0, max(c)))
+ elif type(features) is dict:
+ bounds = list(features.values())
+ features_vectors = self.df[features.keys()].to_numpy()
else:
raise TypeError("Type not supported")
data = np.zeros((height, width, nb_channels), dtype=np.float)
- features_vectors = self.df[features].to_numpy()
- bounds = []
- for c in features_vectors.T:
- bounds.append((min(c), max(c)))
+
for pos, f in zip(positions, features_vectors):
latitude = pos[1]
longitude = pos[0]
- x_coord, y_coord = get_coord(latitude, longitude, height, width, min_lat, max_lat, min_lon, max_lon)
+ x_coord, y_coord = get_coord(latitude, longitude, height, width, lower_lat, upper_lat, lower_lon,
+ upper_lon)
value = __get_image_value__(f, bounds)
x_lower_bound = max(0, x_coord - node_size)
x_upper_bound = min(height - 1, x_coord + node_size)
@@ -307,8 +335,9 @@ class AISTrajectory(AISPoints):
for pos, f in zip(positions[1:], features_vectors[1:]):
latitude = pos[1]
longitude = pos[0]
- x_prv, y_prev = get_coord(lat, lon, height, width, min_lat, max_lat, min_lon, max_lon)
- x_nxt, y_nxt = get_coord(latitude, longitude, height, width, min_lat, max_lat, min_lon, max_lon)
+ x_prv, y_prev = get_coord(lat, lon, height, width, lower_lat, upper_lat, lower_lon, upper_lon)
+ x_nxt, y_nxt = get_coord(latitude, longitude, height, width, lower_lat, upper_lat, lower_lon,
+ upper_lon)
lon, lat = longitude, latitude
for x, y in bresenham(x_prv, y_prev, x_nxt, y_nxt):
for i, v in enumerate(value):
diff --git a/skais/tests/ais/test_ais_trajectory.py b/skais/tests/ais/test_ais_trajectory.py
index d5e64a0e87a67f15f19289c2f1d527f215b178db..56aa71cd0dfbc24deed6671584a38f7862a732e8 100644
--- a/skais/tests/ais/test_ais_trajectory.py
+++ b/skais/tests/ais/test_ais_trajectory.py
@@ -380,8 +380,10 @@ class TestAISTrajectory(unittest.TestCase):
self.assertListEqual(result, expected)
- def test_generate_array_from_positions(self):
- trajectory = AISTrajectory(
+
+class TestAISTrajectoryImageGeneration(unittest.TestCase):
+ def setUp(self) -> None:
+ self.trajectory = AISTrajectory(
pd.DataFrame(
{
"latitude": [0, 10, 0, -10],
@@ -391,8 +393,9 @@ class TestAISTrajectory(unittest.TestCase):
)
)
- result = trajectory.generate_array_from_positions(height=9, width=9, link=False, bounding_box='fit',
- features=None, node_size=0).reshape((9, 9))
+ def test_generate_array_from_positions(self):
+ result = self.trajectory.generate_array_from_positions(height=9, width=9, link=False, bounding_box='fit',
+ features=None, node_size=0).reshape((9, 9))
expected = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
@@ -406,18 +409,8 @@ class TestAISTrajectory(unittest.TestCase):
np.testing.assert_array_equal(result, expected)
def test_generate_array_from_positions_node_size(self):
- trajectory = AISTrajectory(
- pd.DataFrame(
- {
- "latitude": [0, 10, 0, -10],
- "longitude": [0, 10, 10, -10],
- "ts_sec": [i for i in range(4)]
- }
- )
- )
-
- result = trajectory.generate_array_from_positions(height=9, width=9, link=False, bounding_box='fit',
- features=None, node_size=1).reshape((9, 9))
+ result = self.trajectory.generate_array_from_positions(height=9, width=9, link=False, bounding_box='fit',
+ features=None, node_size=1).reshape((9, 9))
expected = np.array([[0, 0, 0, 0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
@@ -512,7 +505,7 @@ class TestAISTrajectory(unittest.TestCase):
"latitude": [0, 10, 0, 20],
"longitude": [0, 10, 20, 20],
"ts_sec": [i for i in range(4)],
- "sog": [10,10,20,40]
+ "sog": [10, 10, 20, 40]
}
)
)
@@ -538,7 +531,7 @@ class TestAISTrajectory(unittest.TestCase):
"latitude": [0, 10, 0, 20],
"longitude": [0, 10, 20, 20],
"ts_sec": [i for i in range(4)],
- "sog": [10,10,20,40],
+ "sog": [10, 10, 20, 40],
"cog": [40, 20, 10, 10]
}
)
@@ -546,14 +539,80 @@ class TestAISTrajectory(unittest.TestCase):
result = trajectory.generate_array_from_positions(height=9, width=18, link=True, bounding_box='fit',
features=['sog', 'cog'], node_size=0)
- expected = np.array([[[0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0.5,0.25]],
- [[0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0.5,0.25]],
- [[0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0.5,0.25]],
- [[0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0.5,0.25]],
- [[0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0.25,1], [0.25,0.5], [0.25,0.5], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0.5,0.25]],
- [[0,0], [0,0], [0,0], [0,0], [0,0], [0.25,1], [0.25,1], [0,0], [0,0], [0,0], [0.25,0.5], [0.25,0.5], [0,0], [0,0], [0,0], [0,0], [0,0], [0.5,0.25]],
- [[0,0], [0,0], [0,0], [0.25,1], [0.25,1], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0.25,0.5], [0.25,0.5], [0,0], [0,0], [0,0], [0.5,0.25]],
- [[0,0], [0.25,1], [0.25,1], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0.25,0.5], [0.25,0.5], [0,0], [0.5,0.25]],
- [[0.25,1], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0,0], [0.25,0.5], [0.5,0.25]]])
-
- np.testing.assert_array_equal(result, expected)
\ No newline at end of file
+ expected = np.array([[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
+ [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0.5, 0.25]],
+ [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
+ [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0.5, 0.25]],
+ [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
+ [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0.5, 0.25]],
+ [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
+ [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0.5, 0.25]],
+ [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0.25, 1], [0.25, 0.5],
+ [0.25, 0.5], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0.5, 0.25]],
+ [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0.25, 1], [0.25, 1], [0, 0], [0, 0], [0, 0],
+ [0.25, 0.5], [0.25, 0.5], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0.5, 0.25]],
+ [[0, 0], [0, 0], [0, 0], [0.25, 1], [0.25, 1], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
+ [0, 0], [0, 0], [0.25, 0.5], [0.25, 0.5], [0, 0], [0, 0], [0, 0], [0.5, 0.25]],
+ [[0, 0], [0.25, 1], [0.25, 1], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
+ [0, 0], [0, 0], [0, 0], [0, 0], [0.25, 0.5], [0.25, 0.5], [0, 0], [0.5, 0.25]],
+ [[0.25, 1], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
+ [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0.25, 0.5], [0.5, 0.25]]])
+
+ np.testing.assert_array_equal(result, expected)
+
+ def test_generate_array_centered(self):
+ result = self.trajectory.generate_array_from_positions(height=9, width=9, link=False, bounding_box='centered',
+ features=None, node_size=0).reshape((9, 9))
+ expected = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 1],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 1, 0, 1],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 1, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0]])
+
+ np.testing.assert_array_equal(result, expected)
+
+ def test_generate_array_bounding_box(self):
+ result = self.trajectory.generate_array_from_positions(height=9, width=9, link=False,
+ bounding_box=[(0, 0), (10, 10)],
+ features=None, node_size=0).reshape((9, 9))
+ expected = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 1],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [1, 0, 0, 0, 0, 0, 0, 0, 1]])
+
+ np.testing.assert_array_equal(result, expected)
+
+ def test_generate_array_feature_bounds(self):
+ trajectory = AISTrajectory(
+ pd.DataFrame(
+ {
+ "latitude": [0, 10, 0, 20],
+ "longitude": [0, 10, 20, 20],
+ "ts_sec": [i for i in range(4)],
+ "sog": [10, 10, 20, 40]
+ }
+ )
+ )
+
+ result = trajectory.generate_array_from_positions(height=9, width=18, link=True, bounding_box='fit',
+ features={"sog": (0, 80)}, node_size=0).reshape((9, 18))
+ expected = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5],
+ [0, 0, 0, 0, 0, 0, 0, 0.25, 0.25, 0.25, 0, 0, 0, 0, 0, 0, 0, 0.5],
+ [0, 0, 0, 0, 0, 0.25, 0.25, 0, 0, 0, 0.25, 0.25, 0, 0, 0, 0, 0, 0.5],
+ [0, 0, 0, 0.25, 0.25, 0, 0, 0, 0, 0, 0, 0, 0.25, 0.25, 0, 0, 0, 0.5],
+ [0, 0.25, 0.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.25, 0.25, 0, 0.5],
+ [0.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.25, 0.5]]) / 2
+
+ np.testing.assert_array_equal(result, expected)