feat: change order of sequences and add randomize option for street project

mapswipe_workers/mapswipe_workers/project_types/street/project.py

@@ -51,6 +51,7 @@ def __init__(self, project_draft):
             start_time=project_draft.get("startTimestamp", None),
             end_time=project_draft.get("endTimestamp", None),
             organization_id=project_draft.get("organizationId", None),
+            randomize_order=project_draft.get("randomizeOrder", None),
             sampling_threshold=project_draft.get("samplingThreshold", None),
         )
 

mapswipe_workers/mapswipe_workers/utils/process_mapillary.py

@@ -189,6 +189,7 @@ def filter_results(
     organization_id: str = None,
     start_time: str = None,
     end_time: str = None,
+    randomize_order: bool = None,
 ):
     df = results_df.copy()
     if creator_id is not None:
@@ -221,6 +222,9 @@ def filter_results(
             return None
         df = filter_by_timerange(df, start_time, end_time)
 
+    if randomize_order is not None:
+        df.sample(frac=1).reset_index(drop=True)
+
     return df
 
 
@@ -233,6 +237,7 @@ def get_image_metadata(
     organization_id: str = None,
     start_time: str = None,
     end_time: str = None,
+    randomize_order=False,
     sampling_threshold=None,
 ):
     aoi_polygon = geojson_to_polygon(aoi_geojson)
@@ -245,27 +250,33 @@ def get_image_metadata(
         downloaded_metadata["geometry"].apply(lambda geom: isinstance(geom, Point))
     ]
 
-    filtered_metadata = filter_results(
-        downloaded_metadata, creator_id, is_pano, organization_id, start_time, end_time
+    downloaded_metadata = filter_results(
+        downloaded_metadata,
+        creator_id,
+        is_pano,
+        organization_id,
+        start_time,
+        end_time,
+        randomize_order,
     )
 
     if (
-        filtered_metadata is None
-        or filtered_metadata.empty
-        or filtered_metadata.isna().all().all()
+        downloaded_metadata is None
+        or downloaded_metadata.empty
+        or downloaded_metadata.isna().all().all()
     ):
         raise ValueError("No Mapillary Features in the AoI match the filter criteria.")
 
     if sampling_threshold is not None:
-        filtered_metadata = spatial_sampling(filtered_metadata, sampling_threshold)
+        downloaded_metadata = spatial_sampling(downloaded_metadata, sampling_threshold)
 
-    total_images = len(filtered_metadata)
+    total_images = len(downloaded_metadata)
     if total_images > 100000:
         raise ValueError(
             f"Too many Images with selected filter options for the AoI: {total_images}"
         )
 
     return {
-        "ids": filtered_metadata["id"].tolist(),
-        "geometries": filtered_metadata["geometry"].tolist(),
+        "ids": downloaded_metadata["id"].tolist(),
+        "geometries": downloaded_metadata["geometry"].tolist(),
     }

mapswipe_workers/mapswipe_workers/utils/spatial_sampling.py

@@ -146,4 +146,7 @@ def spatial_sampling(df, interval_length):
             [sampled_sequence_df, filtered_sorted_sub_df], axis=0
         )
 
+    # reverse order such that sequence are in direction of travel
+    sampled_sequence_df = sampled_sequence_df.iloc[::-1]
+
     return sampled_sequence_df

mapswipe_workers/tests/unittests/test_spatial_sampling.py

@@ -1,29 +1,32 @@
 import os
-
 import unittest
+
 import numpy as np
 import pandas as pd
 from shapely import wkt
 from shapely.geometry import Point
 
-from mapswipe_workers.utils.spatial_sampling  import distance_on_sphere, filter_points, spatial_sampling
+from mapswipe_workers.utils.spatial_sampling import (
+    distance_on_sphere,
+    filter_points,
+    spatial_sampling,
+)
 
 
 class TestDistanceCalculations(unittest.TestCase):
-
     @classmethod
     def setUpClass(cls):
         with open(
-                os.path.join(
-                    os.path.dirname(os.path.abspath(__file__)),
-                    "..",
-                    "fixtures",
-                    "mapillary_sequence.csv",
-                ),
-                "r",
+            os.path.join(
+                os.path.dirname(os.path.abspath(__file__)),
+                "..",
+                "fixtures",
+                "mapillary_sequence.csv",
+            ),
+            "r",
         ) as file:
             df = pd.read_csv(file)
-            df['geometry'] = df['geometry'].apply(wkt.loads)
+            df["geometry"] = df["geometry"].apply(wkt.loads)
 
             cls.fixture_df = df
 
@@ -42,41 +45,43 @@ def test_filter_points(self):
                 "POINT (-74.006 40.7128)",
                 "POINT (-75.006 41.7128)",
                 "POINT (-76.006 42.7128)",
-                "POINT (-77.006 43.7128)"
+                "POINT (-77.006 43.7128)",
             ]
         }
         df = pd.DataFrame(data)
 
-        df['geometry'] = df['geometry'].apply(wkt.loads)
+        df["geometry"] = df["geometry"].apply(wkt.loads)
 
-        df['long'] = df['geometry'].apply(lambda geom: geom.x if geom.geom_type == 'Point' else None)
-        df['lat'] = df['geometry'].apply(lambda geom: geom.y if geom.geom_type == 'Point' else None)
+        df["long"] = df["geometry"].apply(
+            lambda geom: geom.x if geom.geom_type == "Point" else None
+        )
+        df["lat"] = df["geometry"].apply(
+            lambda geom: geom.y if geom.geom_type == "Point" else None
+        )
         threshold_distance = 100
         filtered_df = filter_points(df, threshold_distance)
 
         self.assertIsInstance(filtered_df, pd.DataFrame)
         self.assertLessEqual(len(filtered_df), len(df))
 
-
     def test_spatial_sampling_ordering(self):
         data = {
             "geometry": [
                 "POINT (-74.006 40.7128)",
                 "POINT (-75.006 41.7128)",
                 "POINT (-76.006 42.7128)",
-                "POINT (-77.006 43.7128)"
+                "POINT (-77.006 43.7128)",
             ],
-            'captured_at': [1, 2, 3, 4],
-            'sequence_id': ['1', '1', '1', '1']
+            "captured_at": [1, 2, 3, 4],
+            "sequence_id": ["1", "1", "1", "1"],
         }
         df = pd.DataFrame(data)
-        df['geometry'] = df['geometry'].apply(wkt.loads)
+        df["geometry"] = df["geometry"].apply(wkt.loads)
 
         interval_length = 0.1
         filtered_gdf = spatial_sampling(df, interval_length)
 
-        self.assertTrue(filtered_gdf['captured_at'].is_monotonic_increasing)
-
+        self.assertTrue(filtered_gdf["captured_at"].is_monotonic_decreasing)
 
     def test_spatial_sampling_with_sequence(self):
         threshold_distance = 0.01
@@ -86,15 +91,13 @@ def test_spatial_sampling_with_sequence(self):
 
         filtered_df.reset_index(drop=True, inplace=True)
         for i in range(len(filtered_df) - 1):
-            geom1 = filtered_df.loc[i, 'geometry']
-            geom2 = filtered_df.loc[i + 1, 'geometry']
+            geom1 = filtered_df.loc[i, "geometry"]
+            geom2 = filtered_df.loc[i + 1, "geometry"]
 
             distance = geom1.distance(geom2)
 
             self.assertLess(distance, threshold_distance)
 
 
-
-
 if __name__ == "__main__":
     unittest.main()