diff --git a/skais/ais/ais_trajectory.py b/skais/ais/ais_trajectory.py
index db1cd8f7311fbdd1e17cb2e6afc863d1bdc997c6..e6ba885dcafca82f0a023eee0196003472ce28b4 100644
--- a/skais/ais/ais_trajectory.py
+++ b/skais/ais/ais_trajectory.py
@@ -59,6 +59,13 @@ def apply_time_sequence(dat, time, func):
return result
+def __get_image_value__(features, bounds):
+ value = []
+ for f, b in zip(features, bounds):
+ value.append(1 - (b[1] - f) / b[1])
+ return value
+
+
class AISTrajectory(AISPoints):
def __init__(self, df, mmsi=0, interpolation_time=None):
df = df.drop_duplicates(subset=['ts_sec'])
@@ -228,9 +235,7 @@ class AISTrajectory(AISPoints):
def generate_array_from_positions(self, height=256, width=256, link=True, bounding_box='fit', features=None,
node_size=0):
nb_channels = 1
- if features is not None:
- nb_channels = len(features)
- data = np.zeros((height, width, nb_channels), dtype=np.uint8)
+
if bounding_box != 'fit':
raise ValueError("feature not implemented")
positions = self.df[['longitude', 'latitude']].to_numpy()
@@ -243,26 +248,70 @@ class AISTrajectory(AISPoints):
min_lon -= 1
max_lon += 1
- for longitude, latitude in positions:
- x_coord, y_coord = get_coord(latitude, longitude, height, width, min_lat, max_lat, min_lon, max_lon)
+ 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_lower_bound = max(0, x_coord - node_size)
- x_upper_bound = min(height - 1, x_coord + node_size)
+ x_lower_bound = max(0, x_coord - node_size)
+ x_upper_bound = min(height - 1, x_coord + node_size)
- y_lower_bound = max(0, y_coord - node_size)
- y_upper_bound = min(width - 1, y_coord + node_size)
+ y_lower_bound = max(0, y_coord - node_size)
+ y_upper_bound = min(width - 1, y_coord + node_size)
- for x in range(x_lower_bound, x_upper_bound + 1):
- for y in range(y_lower_bound, y_upper_bound + 1):
- data[x, y] = [1]
+ for x in range(x_lower_bound, x_upper_bound + 1):
+ for y in range(y_lower_bound, y_upper_bound + 1):
+ data[x, y] = [1]
+ 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)
- 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)
+ lon, lat = longitude, latitude
+ for x, y in bresenham(x_prv, y_prev, x_nxt, y_nxt):
+ data[x, y] = [1]
- lon, lat = longitude, latitude
- for x, y in bresenham(x_prv, y_prev, x_nxt, y_nxt):
- data[x, y] = [1]
+ else:
+ if type(features) is list:
+ nb_channels = len(features)
+ elif type(features) is str:
+ features = [features]
+ 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)
+ 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)
+
+ y_lower_bound = max(0, y_coord - node_size)
+ y_upper_bound = min(width - 1, y_coord + node_size)
+
+ for x in range(x_lower_bound, x_upper_bound + 1):
+ for y in range(y_lower_bound, y_upper_bound + 1):
+ for i, v in enumerate(value):
+ data[x, y, i] = v
+ if link:
+ lon, lat = positions[0, 0], positions[0, 1]
+
+ value = __get_image_value__(features_vectors[0], bounds)
+ 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)
+ lon, lat = longitude, latitude
+ for x, y in bresenham(x_prv, y_prev, x_nxt, y_nxt):
+ for i, v in enumerate(value):
+ data[x, y, i] = v
+ value = __get_image_value__(f, bounds)
return data
diff --git a/skais/tests/ais/test_ais_trajectory.py b/skais/tests/ais/test_ais_trajectory.py
index 6f2b6fa628eb4f52844fb2e07a869f223222e6ea..d5e64a0e87a67f15f19289c2f1d527f215b178db 100644
--- a/skais/tests/ais/test_ais_trajectory.py
+++ b/skais/tests/ais/test_ais_trajectory.py
@@ -503,4 +503,57 @@ class TestAISTrajectory(unittest.TestCase):
[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_from_positions_with_line_grey_scale(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", 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]])
+
+ np.testing.assert_array_equal(result, expected)
+
+ def test_generate_array_from_positions_with_line_multi_chanel(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],
+ "cog": [40, 20, 10, 10]
+ }
+ )
+ )
+
+ 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