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Resolve "Improve performances of image generation"

Merged Resolve "Improve performances of image generation"
24-improve-performances-of-image-generation into develop
Merged Raphael Sturgis requested to merge 24-improve-performances-of-image-generation into develop
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skais/ais/ais_trajectory.py
+ 142
− 111
@@ -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