feat(dataset filtering): adding support for VLM based dataset filtering following lerobot annotation pipeline style

tests/scripts/test_lerobot_curate_cameras.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+import torch
+
+# The script imports ``lerobot.datasets`` (via the annotation frame provider),
+# which only ships under the ``dataset`` extra.
+pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
+
+from lerobot.scripts.lerobot_curate_cameras import _to_uint8_frame, _uniform_indices  # noqa: E402
+
+
+@pytest.mark.parametrize(
+    "n,k,expected",
+    [
+        (0, 4, []),
+        (5, 0, []),
+        (3, 5, [0, 1, 2]),  # k >= n -> all frames
+        (1, 4, [0]),
+        (10, 1, [0]),
+        (10, 4, [0, 3, 6, 9]),  # evenly spaced, endpoints included
+    ],
+)
+def test_uniform_indices(n, k, expected):
+    assert _uniform_indices(n, k) == expected
+
+
+def test_to_uint8_frame_scales_floats():
+    frame = torch.ones(3, 4, 4, dtype=torch.float32)  # [0,1] float
+    out = _to_uint8_frame(frame)
+    assert out.dtype == torch.uint8
+    assert int(out.max()) == 255
+
+
+def test_to_uint8_frame_passthrough_uint8():
+    frame = torch.zeros(3, 4, 4, dtype=torch.uint8)
+    out = _to_uint8_frame(frame)
+    assert out is frame  # uint8 passes through untouched