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Commit 944ad87c authored by Raphael Sturgis's avatar Raphael Sturgis
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Merge branch '17-creation-of-images-with-colored-points-using-features' into 'develop' 

Resolve "Creation of images with colored points using features"

See merge request !16
parents 6481fb26 69ba597d
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2 merge requests!16Resolve "Creation of images with colored points using features",!13Draft: Develop
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skais/ais/ais_trajectory.py
+ 69
− 20
View file @ 944ad87c
......@@ -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()
......@@ -243,6 +248,8 @@ class AISTrajectory(AISPoints):
min_lon -= 1
max_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)
......@@ -255,7 +262,6 @@ class AISTrajectory(AISPoints):
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:]:
......@@ -265,4 +271,47 @@ 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:
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
skais/tests/ais/test_ais_trajectory.py
+ 53
− 0
View file @ 944ad87c
......@@ -504,3 +504,56 @@ class TestAISTrajectory(unittest.TestCase):
[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
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