diff --git a/skais/ais/ais_trajectory.py b/skais/ais/ais_trajectory.py
index f4ec956b6930cca43121640fc4e2f93a698ce915..9c8cc4292985e6e51f29a62ad3a426acc46efd29 100644
--- a/skais/ais/ais_trajectory.py
+++ b/skais/ais/ais_trajectory.py
@@ -61,11 +61,54 @@ def apply_time_sequence(dat, time, func):
def __get_image_value__(features, bounds):
+ if len(bounds) < 1:
+ return [1]
value = []
for f, b in zip(features, bounds):
value.append(1 - (b[1] - f - b[0]) / (b[1] - b[0]))
return value
+def __get_bounding_box__(bounding_box, positions, ref_index):
+ 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[ref_index]
+ 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:
+ if type(bounding_box[0]) is not numbers.Number:
+ 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:
+ center_lon, center_lat = positions[ref_index]
+ distance_to_center_lon = bounding_box[0]
+ distance_to_center_lat = bounding_box[1]
+ upper_lat = center_lat + distance_to_center_lat
+ lower_lat = center_lat - distance_to_center_lat
+ upper_lon = center_lon + distance_to_center_lon
+ lower_lon = center_lon - distance_to_center_lon
+ 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
+
+ return lower_lon, upper_lon, lower_lat, upper_lat
class AISTrajectory(AISPoints):
def __init__(self, df, mmsi=0, interpolation_time=None):
@@ -233,52 +276,40 @@ class AISTrajectory(AISPoints):
result.append((row['ts_sec'], current_label))
return result
- def generate_array_from_positions(self, height=256, width=256, link=True, bounding_box='fit', ref_index=-1, features=None,
- node_size=0):
- nb_channels = 1
+
+
+ def generate_array_from_positions(self, height=256, width=256, link=True, bounding_box='fit', ref_index=-1,
+ features=None, node_size=0):
positions = self.df[['longitude', 'latitude']].to_numpy()
- 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[ref_index]
- 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:
- if type(bounding_box[0]) is not numbers.Number:
- 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:
- center_lon, center_lat = positions[ref_index]
- distance_to_center_lon = bounding_box[0]
- distance_to_center_lat = bounding_box[1]
- upper_lat = center_lat + distance_to_center_lat
- lower_lat = center_lat - distance_to_center_lat
- upper_lon = center_lon + distance_to_center_lon
- lower_lon = center_lon - distance_to_center_lon
- 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
+ lower_lon, upper_lon, lower_lat, upper_lat = __get_bounding_box__(bounding_box, positions, ref_index)
+ bounds = []
if features is None:
- data = np.zeros((height, width, nb_channels), dtype=np.uint8)
+ features_vectors = None
+ elif type(features) is list:
+ features_vectors = self.df[features].to_numpy()
+ for c in features_vectors.T:
+ bounds.append((0, max(c)))
+ elif type(features) is str:
+ 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")
+
+ if features_vectors is not None:
+ nb_channels = len(features_vectors.T)
+ else:
+ nb_channels = 1
+ data = np.zeros((height, width, nb_channels), dtype=np.float)
+
+ if features_vectors is None:
+
for longitude, latitude in positions:
x_coord, y_coord = get_coord(latitude, longitude, height, width, lower_lat, upper_lat, lower_lon,
upper_lon)
@@ -302,27 +333,7 @@ class AISTrajectory(AISPoints):
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:
- features_vectors = self.df[features].to_numpy()
- for c in features_vectors.T:
- bounds.append((0, max(c)))
- elif type(features) is str:
- 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")
-
- nb_channels = len(features_vectors.T)
- data = np.zeros((height, width, nb_channels), dtype=np.float)
-
-
for pos, f in zip(positions, features_vectors):
latitude = pos[1]
longitude = pos[0]
@@ -339,19 +350,19 @@ class AISTrajectory(AISPoints):
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, 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):
- data[x, y, i] = v
- value = __get_image_value__(f, bounds)
+ 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, 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):
+ data[x, y, i] = v
+ value = __get_image_value__(f, bounds)
return data