Draft: Develop

Improvements:

• Normalization using AIS_Points now raise exception if given a bad dictionary

New features:

• Added choosing fields for generating images for adding channels
• Added centering of image on last point
• Added chosing bounding box of image
• Added selecting range of values for features

skais/learn/data_augmentation.py

+import random
+from copy import deepcopy
+
+import numpy as np
+from skais.utils.geometry import vectorize_angle, local_ref, transpose_matrix, \
+    quaternion_multiply, angleize_vector, rotation_quaternions, spherical_to_carthesian, carthesian_to_spherical, \
+    rotate_vector_by_quaternion
+
+base_ref = np.array(
+    [
+        [1, 0, 0],
+        [0, 1, 0],
+        [0, 0, 1]
+    ]
+)
+
+
+def shift_positions(aisPositions, axis_lat, axis_long, angle):
+    copy = deepcopy(aisPositions)
+    rotation_quaternion, inverse_rotation_quaternion = rotation_quaternions(axis_lat, axis_long, angle)
+
+    latitudes = np.radians(aisPositions.df['latitude'].to_numpy())
+    longitudes = np.radians(aisPositions.df['longitude'].to_numpy())
+    cogs = np.radians(aisPositions.df['cog'].to_numpy())
+    headings = np.radians(aisPositions.df['heading'].to_numpy())
+
+    for i in range(len(latitudes)):
+        lat = latitudes[i]
+        long = longitudes[i]
+        v_cog = vectorize_angle(cogs[i])
+        v_heading = vectorize_angle(headings[i])
+
+        new_position = rotate_vector_by_quaternion(spherical_to_carthesian(lat, long), rotation_quaternion, inverse_rotation_quaternion)
+        new_lat, new_long = carthesian_to_spherical(new_position)
+
+        origin_ref = local_ref(lat, long)
+        target_ref = local_ref(new_lat, new_long)
+
+        matrix_ob = transpose_matrix(origin_ref, base_ref)
+        matrix_bt = transpose_matrix(base_ref, target_ref)
+
+        # transpose vectors to base referential
+        v_cog = np.matmul(matrix_ob, v_cog)
+        v_heading = np.matmul(matrix_ob, v_heading)
+
+        # rotate vector
+        v_cog = rotate_vector_by_quaternion(v_cog, rotation_quaternion, inverse_rotation_quaternion)
+        v_heading = rotate_vector_by_quaternion(v_heading, rotation_quaternion, inverse_rotation_quaternion)
+
+        # transpose vector back to local referential of the new position
+        v_cog = np.matmul(matrix_bt, v_cog)
+        v_heading = np.matmul(matrix_bt, v_heading)
+
+        latitudes[i] = new_lat
+        longitudes[i] = new_long
+        cogs[i] = angleize_vector(v_cog)
+        headings[i] = angleize_vector(v_heading)
+
+    copy.df['latitude'] = np.degrees(latitudes)
+    copy.df['longitude'] = np.degrees(longitudes)
+    copy.df['cog'] = np.degrees(cogs)
+    copy.df['heading'] = np.degrees(headings)
+
+    return copy