update: fix type

spatialyze/database.py

@@ -32,8 +32,8 @@
 from .video_processor.types import Float33
 
 if TYPE_CHECKING:
-    from psycopg2 import connection as Connection
-    from psycopg2 import cursor as Cursor
+    from psycopg2._psycopg import connection as Connection
+    from psycopg2._psycopg import cursor as Cursor
 
     from .predicate import PredicateNode
 
@@ -313,6 +313,7 @@ def predicate(self, predicate: "PredicateNode", temporal: bool = True):
     def sql(self, query: str) -> pd.DataFrame:
         results, cursor = self.execute_and_cursor(query)
         description = cursor.description
+        assert description is not None
         cursor.close()
         return pd.DataFrame(results, columns=[d.name for d in description])
 

spatialyze/predicate.py

@@ -91,11 +91,11 @@ def __or__(self, other):
             ],
         )
 
-    def __eq__(self, other):
+    def __eq__(self, other):  # pyright: ignore [reportIncompatibleMethodOverride]
         other = wrap_literal(other)
         return CompOpNode(self, "eq", other)
 
-    def __ne__(self, other):
+    def __ne__(self, other):  # pyright: ignore [reportIncompatibleMethodOverride]
         other = wrap_literal(other)
         return CompOpNode(self, "ne", other)
 

spatialyze/video_processor/stages/decode_frame/parallel_decode_frame.py

@@ -63,4 +63,6 @@ def _r(acc: "tuple[int, list[tuple[int, int]]]", frames: int):
             return None, {self.classname(): metadata}
         except BaseException:
             _, output = DecodeFrame()._run(payload)
-            return None, {self.classname(): DecodeFrame.get(output)}
+            images = DecodeFrame.get(output)
+            assert images is not None
+            return None, {self.classname(): images}

spatialyze/video_processor/stages/depth_estimation.py

@@ -113,7 +113,7 @@ def eval_all(self, input_images: "list[npt.NDArray | None]"):
                 # Load image and preprocess
                 input_image = pil.fromarray(im[:, :, [2, 1, 0]])
                 original_width, original_height = input_image.size
-                input_image = input_image.resize((self.feed_width, self.feed_height), pil.LANCZOS)
+                input_image = input_image.resize((self.feed_width, self.feed_height), pil.Resampling.LANCZOS)
                 input_image = transforms.ToTensor()(input_image).unsqueeze(0)
 
                 # PREDICTION

spatialyze/video_processor/stages/detection_2d/ground_truth.py

@@ -189,7 +189,7 @@ def __init__(self, df_annotations: "pd.DataFrame"):
         self.class_to_id = {c: i for i, c in enumerate(self.id_to_classes)}
 
     def _run(self, payload: "Payload"):
-        metadata: "list[Metadatum | None]" = []
+        metadata: list[Metadatum] = []
         dimension = payload.video.dimension
         for i, cc in enumerate(payload.video._camera_configs):
             fid = cc.frame_id
@@ -222,10 +222,10 @@ def _run(self, payload: "Payload"):
             if len(tensor) == 0:
                 metadata.append(Metadatum(torch.Tensor([]), yolo_classes, []))
             else:
-                metadata.append(
-                    Metadatum(
-                        torch.Tensor(tensor), yolo_classes, [DetectionId(i, _id) for _id in ids]
-                    )
-                )
+                metadata.append(Metadatum(
+                    torch.Tensor(tensor),
+                    yolo_classes,
+                    [DetectionId(i, _id) for _id in ids],
+                ))
 
         return None, {self.classname(): metadata}

spatialyze/video_processor/stages/detection_3d/ground_truth.py

@@ -27,7 +27,7 @@ def __init__(self, df_annotations: "pd.DataFrame"):
         self.class_to_id = {c: i for i, c in enumerate(self.id_to_classes)}
 
     def _run(self, payload: "Payload"):
-        metadata: "list[Metadatum | None]" = []
+        metadata: list[Metadatum] = []
         dimension = payload.video.dimension
         for i, cc in enumerate(payload.video._camera_configs):
             fid = cc.frame_id
@@ -79,10 +79,10 @@ def _run(self, payload: "Payload"):
             if len(tensor) == 0:
                 metadata.append(Metadatum(torch.Tensor([]), yolo_classes, []))
             else:
-                metadata.append(
-                    Metadatum(
-                        torch.Tensor(tensor), yolo_classes, [DetectionId(i, _id) for _id in ids]
-                    )
-                )
+                metadata.append(Metadatum(
+                    torch.Tensor(tensor),
+                    yolo_classes,
+                    [DetectionId(i, _id) for _id in ids],
+                ))
 
         return None, {self.classname(): metadata}

spatialyze/video_processor/stages/stage.py

@@ -90,4 +90,6 @@ def tqdm(cls, iterable: "Iterable[_T2]", *args, **kwargs) -> "Iterable[_T2]":
 def _get_classnames(cls: "type") -> "list[str]":
     if cls == Stage:
         return []
-    return [*_get_classnames(cls.__base__), cls.__name__]
+    base = cls.__base__
+    assert base is not None
+    return [*_get_classnames(base), cls.__name__]