ignore deleted frames when converting tracks to shapes (#8834)

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### Motivation and context
<!-- Why is this change required? What problem does it solve? If it
fixes an open
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When a frame which contains a keyframe of a track is deleted, the
keyframe continues to exist. When a dataset is exported (in all formats
except CVAT for video), tracks are interpolated and keyframe from
deleted frame is not ignored.
Fixing it.

### How has this been tested?
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<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **New Features**
- Enhanced shape management by allowing exclusion of shapes based on
deleted frames in both `AnnotationManager` and `TrackManager`.
- Introduced a method for deleting specified frames from job data and
verifying the integrity of subsequent annotations in tests.

- **Bug Fixes**
- Improved handling of deleted frames by transitioning from a dictionary
to a set for better performance and clarity.

- **Tests**
- Added tests to ensure frame deletion functionality works as intended
without affecting subsequent annotations.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

changelog.d/20241216_211239_dmitrii.lavrukhin_deleted_track_keyframe.md

@@ -0,0 +1,4 @@
+### Fixed
+
+- Fixed handling of tracks keyframes from deleted frames on export
+  (<https://github.com/cvat-ai/cvat/pull/8834>)

cvat/apps/dataset_manager/annotation.py

@@ -460,6 +460,7 @@ def _unite_objects(obj0, obj1):
     def _modify_unmatched_object(self, obj, end_frame):
         pass
 
+
 class TrackManager(ObjectManager):
     def to_shapes(self, end_frame: int, *,
         included_frames: Optional[Sequence[int]] = None,
@@ -929,6 +930,8 @@ def propagate(shape, end_frame, *, included_frames=None):
         prev_shape = None
         for shape in sorted(track["shapes"], key=lambda shape: shape["frame"]):
             curr_frame = shape["frame"]
+            if included_frames is not None and curr_frame not in included_frames:
+                continue
             if prev_shape and end_frame <= curr_frame:
                 # If we exceed the end_frame and there was a previous shape,
                 # we still need to interpolate up to the next keyframe,

cvat/apps/dataset_manager/tests/test_formats.py

@@ -28,6 +28,7 @@
     get_paginated_collection, ForceLogin, generate_image_file, ApiTestBase
 )
 
+
 class _DbTestBase(ApiTestBase):
     def setUp(self):
         super().setUp()
@@ -87,6 +88,7 @@ def _create_task(self, data, image_data):
 
         return task
 
+
 class TaskExportTest(_DbTestBase):
     def _generate_custom_annotations(self, annotations, task):
         self._put_api_v2_task_id_annotations(task["id"], annotations)
@@ -520,6 +522,72 @@ def test_frames_outside_are_not_generated(self):
             self.assertTrue(frame.frame in range(6, 10))
         self.assertEqual(i + 1, 4)
 
+    def _delete_job_frames(self, job_id: int, deleted_frames: list[int]):
+        with ForceLogin(self.user, self.client):
+            response = self.client.patch(
+                f"/api/jobs/{job_id}/data/meta?org=",
+                data=dict(deleted_frames=deleted_frames),
+                format="json"
+            )
+            assert response.status_code == status.HTTP_200_OK, response.status_code
+
+    def test_track_keyframes_on_deleted_frames_do_not_affect_later_frames(self):
+        images = self._generate_task_images(4)
+        task = self._generate_task(images)
+        job = self._get_task_jobs(task["id"])[0]
+
+        annotations = {
+            "version": 0,
+            "tags": [],
+            "shapes": [],
+            "tracks": [
+                {
+                    "frame": 0,
+                    "label_id": task["labels"][0]["id"],
+                    "group": None,
+                    "source": "manual",
+                    "attributes": [],
+                    "shapes": [
+                        {
+                            "frame": 0,
+                            "points": [1, 2, 3, 4],
+                            "type": "rectangle",
+                            "occluded": False,
+                            "outside": False,
+                            "attributes": [],
+                        },
+                        {
+                            "frame": 1,
+                            "points": [5, 6, 7, 8],
+                            "type": "rectangle",
+                            "occluded": False,
+                            "outside": True,
+                            "attributes": [],
+                        },
+                        {
+                            "frame": 2,
+                            "points": [9, 10, 11, 12],
+                            "type": "rectangle",
+                            "occluded": False,
+                            "outside": False,
+                            "attributes": [],
+                        },
+                    ]
+                },
+            ]
+        }
+        self._put_api_v2_job_id_annotations(job["id"], annotations)
+        self._delete_job_frames(job["id"], [2])
+
+        task_ann = TaskAnnotation(task["id"])
+        task_ann.init_from_db()
+        task_data = TaskData(task_ann.ir_data, Task.objects.get(pk=task["id"]))
+        extractor = CvatTaskOrJobDataExtractor(task_data)
+        dm_dataset = Dataset.from_extractors(extractor)
+
+        assert len(dm_dataset.get("image_3").annotations) == 0
+
+
 class FrameMatchingTest(_DbTestBase):
     def _generate_task_images(self, paths): # pylint: disable=no-self-use
         f = BytesIO()
@@ -612,6 +680,7 @@ def test_dataset_root(self):
                 root = find_dataset_root(dataset, task_data)
                 self.assertEqual(expected, root)
 
+
 class TaskAnnotationsImportTest(_DbTestBase):
     def _generate_custom_annotations(self, annotations, task):
         self._put_api_v2_task_id_annotations(task["id"], annotations)