diff --git a/skais/ais/ais_trajectory.py b/skais/ais/ais_trajectory.py
index f4ec956b6930cca43121640fc4e2f93a698ce915..336f6da6d695870ec2f2af9dddd203042fde6d1c 100644
--- a/skais/ais/ais_trajectory.py
+++ b/skais/ais/ais_trajectory.py
@@ -60,13 +60,125 @@ def apply_time_sequence(dat, time, func):
return result
+@jit(nopython=True)
def __get_image_value__(features, bounds):
- value = []
+ value = np.zeros((len(features)))
+ i = 0
for f, b in zip(features, bounds):
- value.append(1 - (b[1] - f - b[0]) / (b[1] - b[0]))
+ value[i] = (1 - (b[1] - f - b[0]) / (b[1] - b[0]))
+ i = i + 1
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
+
+
+@jit(nopython=True)
+def generate_points(data, positions, height, width, node_size, lower_lat, upper_lat, lower_lon, upper_lon):
+ for longitude, latitude in positions:
+ 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)
+
+ 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]
+
+
+@jit(nopython=True)
+def generate_links(data, positions, height, width, lower_lat, upper_lat, lower_lon, upper_lon):
+ lon, lat = positions[0, 0], positions[0, 1]
+ for longitude, latitude in positions[1:]:
+ 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]
+
+
+@jit(nopython=True)
+def generate_points_with_features(data, positions, features_vectors, bounds, node_size, height, width,
+ lower_lat, upper_lat, lower_lon, upper_lon, link):
+ for pos, f in zip(positions, features_vectors):
+ latitude = pos[1]
+ longitude = pos[0]
+ 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)
+
+ 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, 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)
+
+
class AISTrajectory(AISPoints):
def __init__(self, df, mmsi=0, interpolation_time=None):
df = df.drop_duplicates(subset=['ts_sec'])
@@ -233,125 +345,44 @@ 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)
- for longitude, latitude in positions:
- 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)
-
- 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]
- 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, 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]
-
+ 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:
- 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")
+ raise TypeError("Type not supported")
+ if features_vectors is not None:
nb_channels = len(features_vectors.T)
- data = np.zeros((height, width, nb_channels), dtype=np.float)
-
+ else:
+ nb_channels = 1
+ data = np.zeros((height, width, nb_channels), dtype=np.float)
- for pos, f in zip(positions, features_vectors):
- latitude = pos[1]
- longitude = pos[0]
- 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)
+ if features_vectors is None:
- y_lower_bound = max(0, y_coord - node_size)
- y_upper_bound = min(width - 1, y_coord + node_size)
+ generate_points(data, positions, height, width, node_size, lower_lat, upper_lat, lower_lon, upper_lon)
- 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, 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)
+ generate_links(data, positions, height, width, lower_lat, upper_lat, lower_lon, upper_lon)
+
+ else:
+ generate_points_with_features(data, positions, features_vectors, bounds, node_size, height, width,
+ lower_lat, upper_lat, lower_lon, upper_lon, link)
return data
diff --git a/skais/utils/geography.py b/skais/utils/geography.py
index de21a9aed417ba8e959aa0bb5a5780f9b879634f..ae539e1ddac0640c78c1e2b266601db67c2dd251 100644
--- a/skais/utils/geography.py
+++ b/skais/utils/geography.py
@@ -23,6 +23,7 @@ def great_circle(lat1, lat2, long1, long2):
return d
+@jit(nopython=True)
def get_coord(lat, lon, height, width, min_lat, max_lat, min_lon, max_lon):
x_coord = max(min(height - int(height * (lat - min_lat) / (max_lat - min_lat)) - 1, height - 1), 0)
y_coord = max(min(int((width - 1) * (lon - min_lon) / (max_lon - min_lon)), width - 1), 0)
diff --git a/skais/utils/geometry.py b/skais/utils/geometry.py
index 0919af4a5b6dd1501a9b5fcb0f3bf746a336cec3..7a111d0f32f291f673eff5dca58607c80e8fb446 100644
--- a/skais/utils/geometry.py
+++ b/skais/utils/geometry.py
@@ -1,3 +1,7 @@
+from numba import jit
+
+
+@jit(nopython=True)
def bresenham(x1, y1, x2, y2):
dx = int(x2 - x1)
dy = int(y2 - y1)