Merge branch '27-add-link-interpolation' into 'develop'

Resolve "Add link interpolation"

Closes #27

See merge request !22

skais/ais/ais_trajectory.py

 import numbers
 import random
+from copy import copy
 
 import pandas as pd
 import numpy as np
@@ -8,7 +9,7 @@ from scipy.interpolate import interp1d
 
 from skais.utils.geography import great_circle, position_from_distance, get_coord
 from skais.ais.ais_points import AISPoints
-from skais.utils.geometry import bresenham
+from skais.utils.geometry import bresenham, dist_on_grid
 
 
 @jit(nopython=True)
@@ -130,22 +131,9 @@ def generate_points(data, positions, height, width, node_size, lower_lat, upper_
                 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):
+                                  lower_lat, upper_lat, lower_lon, upper_lon):
     for pos, f in zip(positions, features_vectors):
         latitude = pos[1]
         longitude = pos[0]
@@ -162,21 +150,46 @@ def generate_points_with_features(data, positions, features_vectors, bounds, nod
             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
+
+
+@jit(nopython=True)
+def generate_links(data, positions, height, width, lower_lat, upper_lat, lower_lon, upper_lon, values, interpolate=None):
+    lon, lat = positions[0, 0], positions[0, 1]
+
+    current_value = values[0]
+    for pos, nxt_value in zip(positions[1:], values[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
+        if interpolate is not None and nxt_value != current_value:
+            dist = dist_on_grid(x_prv, y_prev, x_nxt, y_nxt)
             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)
+                dist_prev = dist_on_grid(x_prv, y_prev, x, y)
+                dist_next = dist_on_grid(x, y, x_nxt, y_nxt)
+
+                pixel_color = current_value * (1-dist_prev/dist) + nxt_value * (1-dist_next/dist)
+                for i in range(len(pixel_color)):
+                    data[x, y, i] = pixel_color[i]
+        else:
+            for x, y in bresenham(x_prv, y_prev, x_nxt, y_nxt):
+                for i in range(len(current_value)):
+                    data[x, y, i] = current_value[i]
+        current_value = nxt_value
+
+
+@jit(nopython=True)
+def generate_values(features_vectors, bounds):
+    result = np.zeros(features_vectors.shape)
+    for i in range(len(features_vectors)):
+        features = features_vectors[i]
+        value = __get_image_value__(features, bounds)
+        for j in range(len(value)):
+            v = value[j]
+            result[i, j] = v
+    return result
 
 
 class AISTrajectory(AISPoints):
@@ -346,7 +359,7 @@ class AISTrajectory(AISPoints):
         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):
+                                      features=None, node_size=0, interpolation=None):
 
         positions = self.df[['longitude', 'latitude']].to_numpy()
 
@@ -371,18 +384,23 @@ class AISTrajectory(AISPoints):
 
         if features_vectors is not None:
             nb_channels = len(features_vectors.T)
+            bounds = np.array(bounds)
         else:
             nb_channels = 1
-        data = np.zeros((height, width, nb_channels), dtype=np.float)
+        data = np.zeros((height, width, nb_channels), dtype=float)
 
         if features_vectors is None:
 
             generate_points(data, positions, height, width, node_size, lower_lat, upper_lat, lower_lon, upper_lon)
 
             if link:
-                generate_links(data, positions, height, width, lower_lat, upper_lat, lower_lon, upper_lon)
+                generate_links(data, positions, height, width, lower_lat, upper_lat, lower_lon, upper_lon,
+                               np.ones((len(positions), 1)))
 
         else:
             generate_points_with_features(data, positions, features_vectors, np.array(bounds), node_size, height, width,
-                                          lower_lat, upper_lat, lower_lon, upper_lon, link)
+                                          lower_lat, upper_lat, lower_lon, upper_lon, )
+            if link:
+                generate_links(data, positions, height, width, lower_lat, upper_lat, lower_lon, upper_lon,
+                               generate_values(features_vectors, bounds), interpolation)
         return data

skais/tests/ais/test_ais_trajectory.py

@@ -651,4 +651,32 @@ class TestAISTrajectoryImageGeneration(unittest.TestCase):
                              [[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)
\ No newline at end of file
+
+    def test_generate_array_interpolate_links(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": (0, 80)},  node_size=0,
+                                                          interpolation='linear').reshape((9, 18))
+        print(result)
+        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, 0.46875],
+                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4375],
+                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.40625],
+                             [0, 0, 0, 0, 0, 0, 0, 0.125, 0.125, 0.13526987, 0, 0, 0, 0, 0, 0, 0, 0.375],
+                             [0, 0, 0, 0, 0, 0.125, 0.125, 0, 0, 0, 0.15330406, 0.16458619, 0, 0, 0, 0, 0, 0.34375],
+                             [0, 0, 0, 0.125, 0.125, 0, 0, 0, 0, 0, 0, 0, 0.18154526, 0.19313327, 0, 0, 0, 0.3125],
+                             [0, 0.125, 0.125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.20959047, 0.22158235, 0, 0.28125],
+                             [0.125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.23609719, 0.25]])
+
+        np.testing.assert_array_almost_equal(result, expected)

skais/utils/geometry.py

+import numpy as np
 from numba import jit
 
+@jit(nopython=True)
+def dist_on_grid(x1, y1, x2, y2):
+    dx = int(x2 - x1)
+    dy = int(y2 - y1)
+    return np.sqrt(dx ** 2 + dy ** 2)
+
 
 @jit(nopython=True)
 def bresenham(x1, y1, x2, y2):
@@ -12,9 +19,8 @@ def bresenham(x1, y1, x2, y2):
     if dy < 0:
         sy = -1
 
-
     pixels = []
-    if abs(dx) > abs(dy): # slope < 1
+    if abs(dx) > abs(dy):  # slope < 1
         if x1 > x2:
             tmp = x2
             x2 = x1
@@ -28,7 +34,7 @@ def bresenham(x1, y1, x2, y2):
         y = y1
         p = (2 * abs(dy)) - abs(dx)
         pixels.append((x1, y1))
-        
+
         for x in range(x1 + 1, x2 + 1):
             if p < 0:
                 p += 2 * abs(dy)
@@ -36,7 +42,7 @@ def bresenham(x1, y1, x2, y2):
                 y += sy
                 p += (2 * abs(dy)) - (2 * abs(dx))
             pixels.append((x, y))
-    else: # slope >= 1
+    else:  # slope >= 1
         if y1 > y2:
             tmp = x2
             x2 = x1
@@ -59,4 +65,3 @@ def bresenham(x1, y1, x2, y2):
             pixels.append((x, y))
 
     return pixels
-