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Commit abd34a4f authored by Raphael Sturgis's avatar Raphael Sturgis
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set boundaries for image and features

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2 merge requests!17Resolve "Functionality for experiments using CNNs",!13Draft: Develop
This commit is part of merge request !17. Comments created here will be created in the context of that merge request.
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skais/ais/ais_trajectory.py
+ 18
− 9
View file @ abd34a4f
...@@ -62,7 +62,7 @@ def apply_time_sequence(dat, time, func): ...@@ -62,7 +62,7 @@ def apply_time_sequence(dat, time, func):
def __get_image_value__(features, bounds): def __get_image_value__(features, bounds):
value = [] value = []
for f, b in zip(features, bounds): 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 return value
...@@ -236,12 +236,11 @@ class AISTrajectory(AISPoints): ...@@ -236,12 +236,11 @@ class AISTrajectory(AISPoints):
node_size=0): node_size=0):
nb_channels = 1 nb_channels = 1
if bounding_box == 'fit':
positions = self.df[['longitude', 'latitude']].to_numpy() positions = self.df[['longitude', 'latitude']].to_numpy()
if bounding_box == 'fit':
lower_lon, upper_lon = (min(positions[:, 0]), max(positions[:, 0])) lower_lon, upper_lon = (min(positions[:, 0]), max(positions[:, 0]))
lower_lat, upper_lat = (min(positions[:, 1]), max(positions[:, 1])) lower_lat, upper_lat = (min(positions[:, 1]), max(positions[:, 1]))
elif bounding_box == 'centered': elif bounding_box == 'centered':
positions = self.df[['longitude', 'latitude']].to_numpy()
center_lon, center_lat = positions[-1] center_lon, center_lat = positions[-1]
min_lon, max_lon = (min(positions[:, 0]), max(positions[:, 0])) min_lon, max_lon = (min(positions[:, 0]), max(positions[:, 0]))
min_lat, max_lat = (min(positions[:, 1]), max(positions[:, 1])) min_lat, max_lat = (min(positions[:, 1]), max(positions[:, 1]))
...@@ -253,7 +252,11 @@ class AISTrajectory(AISPoints): ...@@ -253,7 +252,11 @@ class AISTrajectory(AISPoints):
lower_lat = center_lat - distance_to_center lower_lat = center_lat - distance_to_center
upper_lon = center_lon + distance_to_center upper_lon = center_lon + distance_to_center
lower_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: else:
raise ValueError(f"Option not supported: {bounding_box}") raise ValueError(f"Option not supported: {bounding_box}")
...@@ -291,17 +294,23 @@ class AISTrajectory(AISPoints): ...@@ -291,17 +294,23 @@ class AISTrajectory(AISPoints):
data[x, y] = [1] data[x, y] = [1]
else: else:
bounds = []
if type(features) is list: if type(features) is list:
nb_channels = len(features) 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: 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: else:
raise TypeError("Type not supported") raise TypeError("Type not supported")
data = np.zeros((height, width, nb_channels), dtype=np.float) 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): for pos, f in zip(positions, features_vectors):
latitude = pos[1] latitude = pos[1]
......
skais/tests/ais/test_ais_trajectory.py
+ 31
− 21
View file @ abd34a4f
...@@ -380,6 +380,7 @@ class TestAISTrajectory(unittest.TestCase): ...@@ -380,6 +380,7 @@ class TestAISTrajectory(unittest.TestCase):
self.assertListEqual(result, expected) self.assertListEqual(result, expected)
class TestAISTrajectoryImageGeneration(unittest.TestCase): class TestAISTrajectoryImageGeneration(unittest.TestCase):
def setUp(self) -> None: def setUp(self) -> None:
self.trajectory = AISTrajectory( self.trajectory = AISTrajectory(
...@@ -408,7 +409,6 @@ class TestAISTrajectoryImageGeneration(unittest.TestCase): ...@@ -408,7 +409,6 @@ class TestAISTrajectoryImageGeneration(unittest.TestCase):
np.testing.assert_array_equal(result, expected) np.testing.assert_array_equal(result, expected)
def test_generate_array_from_positions_node_size(self): def test_generate_array_from_positions_node_size(self):
result = self.trajectory.generate_array_from_positions(height=9, width=9, link=False, bounding_box='fit', result = self.trajectory.generate_array_from_positions(height=9, width=9, link=False, bounding_box='fit',
features=None, node_size=1).reshape((9, 9)) features=None, node_size=1).reshape((9, 9))
expected = np.array([[0, 0, 0, 0, 0, 0, 0, 1, 1], expected = np.array([[0, 0, 0, 0, 0, 0, 0, 1, 1],
...@@ -539,15 +539,24 @@ class TestAISTrajectoryImageGeneration(unittest.TestCase): ...@@ -539,15 +539,24 @@ class TestAISTrajectoryImageGeneration(unittest.TestCase):
result = trajectory.generate_array_from_positions(height=9, width=18, link=True, bounding_box='fit', result = trajectory.generate_array_from_positions(height=9, width=18, link=True, bounding_box='fit',
features=['sog', 'cog'], node_size=0) 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]], 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], [0,0], [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], [0.5, 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], [0,0], [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], [0.5, 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, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[[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, 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, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [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], [0,0], [0,0], [0,0], [0,0], [0.25,0.5], [0.25,0.5], [0,0], [0.5,0.25]], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [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]]]) [[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) np.testing.assert_array_equal(result, expected)
...@@ -567,7 +576,8 @@ class TestAISTrajectoryImageGeneration(unittest.TestCase): ...@@ -567,7 +576,8 @@ class TestAISTrajectoryImageGeneration(unittest.TestCase):
np.testing.assert_array_equal(result, expected) np.testing.assert_array_equal(result, expected)
def test_generate_array_bounding_box(self): 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)], 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)) features=None, node_size=0).reshape((9, 9))
expected = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 1], 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],
...@@ -577,11 +587,11 @@ class TestAISTrajectoryImageGeneration(unittest.TestCase): ...@@ -577,11 +587,11 @@ class TestAISTrajectoryImageGeneration(unittest.TestCase):
[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, 0]]) [1, 0, 0, 0, 0, 0, 0, 0, 1]])
np.testing.assert_array_equal(result, expected) np.testing.assert_array_equal(result, expected)
def test_generate_array_from_positions_with_line_grey_scale(self): def test_generate_array_feature_bounds(self):
trajectory = AISTrajectory( trajectory = AISTrajectory(
pd.DataFrame( pd.DataFrame(
{ {
...@@ -594,7 +604,7 @@ class TestAISTrajectoryImageGeneration(unittest.TestCase): ...@@ -594,7 +604,7 @@ class TestAISTrajectoryImageGeneration(unittest.TestCase):
) )
result = trajectory.generate_array_from_positions(height=9, width=18, link=True, bounding_box='fit', 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)) 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], 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5],
......
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