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Resolve "Improve performances of image generation"

Merged Raphael Sturgis requested to merge 24-improve-performances-of-image-generation into develop
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@@ -60,14 +60,16 @@ def apply_time_sequence(dat, time, func):
return result
@jit(nopython=True)
def __get_image_value__(features, bounds):
if len(bounds) < 1:
return [1]
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]))
@@ -110,6 +112,73 @@ def __get_bounding_box__(bounding_box, positions, ref_index):
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'])
@@ -276,8 +345,6 @@ 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):
@@ -310,59 +377,12 @@ class AISTrajectory(AISPoints):
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)
x_lower_bound = max(0, x_coord - node_size)
x_upper_bound = min(height - 1, x_coord + node_size)
generate_points(data, positions, height, width, node_size, lower_lat, upper_lat, lower_lon, upper_lon)
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]
generate_links(data, positions, height, width, lower_lat, upper_lat, lower_lon, upper_lon)
else:
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)
generate_points_with_features(data, positions, features_vectors, bounds, node_size, height, width,
lower_lat, upper_lat, lower_lon, upper_lon, link)
return data
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