Video Upload API (#221)

* Initial async functionality * Made index an optional arg, switched to items instead of frames * Update some docs * Added tests * Added some docs * Better doc strings * docs * typo fix * more typos * Updates to pyproject.toml and CHANGELOG.md
scaleapi · Feb 18, 2022 · eac724d · eac724d
1 parent b237e6e
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -4,6 +4,11 @@ All notable changes to the [Nucleus Python Client](https://github.com/scaleapi/n
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.6.6](https://github.com/scaleapi/nucleus-python-client/releases/tag/v0.6.6) - 2021-02-18
+
+### Added
+- Video upload support
+
 ## [0.6.5](https://github.com/scaleapi/nucleus-python-client/releases/tag/v0.6.5) - 2021-02-16
 
 ### Fixed

diff --git a/nucleus/__init__.py b/nucleus/__init__.py
@@ -14,9 +14,8 @@
     "DatasetItem",
     "DatasetItemRetrievalError",
     "Frame",
-    "Frame",
-    "LidarScene",
     "LidarScene",
+    "VideoScene",
     "Model",
     "ModelCreationError",
     # "MultiCategoryAnnotation", # coming soon!
@@ -124,7 +123,7 @@
     SegmentationPrediction,
 )
 from .retry_strategy import RetryStrategy
-from .scene import Frame, LidarScene
+from .scene import Frame, LidarScene, VideoScene
 from .slice import Slice
 from .upload_response import UploadResponse
 from .validate import Validate

diff --git a/nucleus/constants.py b/nucleus/constants.py
@@ -44,6 +44,7 @@
 ERROR_CODES = "error_codes"
 ERROR_ITEMS = "upload_errors"
 ERROR_PAYLOAD = "error_payload"
+FRAME_RATE_KEY = "frame_rate"
 FRAMES_KEY = "frames"
 FX_KEY = "fx"
 FY_KEY = "fy"
@@ -101,6 +102,10 @@
 UPLOAD_TO_SCALE_KEY = "upload_to_scale"
 URL_KEY = "url"
 VERTICES_KEY = "vertices"
+VIDEO_FRAME_LOCATION_KEY = "video_frame_location"
+VIDEO_FRAME_URL_KEY = "video_frame_url"
+VIDEO_KEY = "video"
+VIDEO_UPLOAD_TYPE_KEY = "video_upload_type"
 WIDTH_KEY = "width"
 YAW_KEY = "yaw"
 W_KEY = "w"

diff --git a/nucleus/dataset.py b/nucleus/dataset.py
@@ -47,6 +47,7 @@
     REQUEST_ID_KEY,
     SLICE_ID_KEY,
     UPDATE_KEY,
+    VIDEO_UPLOAD_TYPE_KEY,
 )
 from .data_transfer_object.dataset_info import DatasetInfo
 from .data_transfer_object.dataset_size import DatasetSize
@@ -65,7 +66,7 @@
     construct_model_run_creation_payload,
     construct_taxonomy_payload,
 )
-from .scene import LidarScene, Scene, check_all_scene_paths_remote
+from .scene import LidarScene, Scene, VideoScene, check_all_scene_paths_remote
 from .slice import Slice
 from .upload_response import UploadResponse
 
@@ -405,16 +406,17 @@ def ingest_tasks(self, task_ids: List[str]) -> dict:
 
     def append(
         self,
-        items: Union[Sequence[DatasetItem], Sequence[LidarScene]],
+        items: Union[
+            Sequence[DatasetItem], Sequence[LidarScene], Sequence[VideoScene]
+        ],
         update: bool = False,
         batch_size: int = 20,
         asynchronous: bool = False,
     ) -> Union[Dict[Any, Any], AsyncJob, UploadResponse]:
         """Appends items or scenes to a dataset.
 
         .. note::
-            Datasets can only accept one of :class:`DatasetItems <DatasetItem>`
-            or :class:`Scenes <LidarScene>`, never both.
+            Datasets can only accept one of DatasetItems or Scenes, never both.
 
             This behavior is set during Dataset :meth:`creation
             <NucleusClient.create_dataset>` with the ``is_scene`` flag.
@@ -478,13 +480,14 @@ def append(
               Union[ \
                 Sequence[:class:`DatasetItem`], \
                 Sequence[:class:`LidarScene`] \
+                Sequence[:class:`VideoScene`]
               ]): List of items or scenes to upload.
             batch_size: Size of the batch for larger uploads. Default is 20.
             update: Whether or not to overwrite metadata on reference ID collision.
               Default is False.
             asynchronous: Whether or not to process the upload asynchronously (and
-              return an :class:`AsyncJob` object). This is highly encouraged for
-              3D data to drastically increase throughput. Default is False.
+              return an :class:`AsyncJob` object). This is required when uploading
+              scenes. Default is False.
 
         Returns:
             For scenes
@@ -508,17 +511,26 @@ def append(
         dataset_items = [
             item for item in items if isinstance(item, DatasetItem)
         ]
-        scenes = [item for item in items if isinstance(item, LidarScene)]
-        if dataset_items and scenes:
+        lidar_scenes = [item for item in items if isinstance(item, LidarScene)]
+        video_scenes = [item for item in items if isinstance(item, VideoScene)]
+        if dataset_items and (lidar_scenes or video_scenes):
             raise Exception(
                 "You must append either DatasetItems or Scenes to the dataset."
             )
-        if scenes:
+        if lidar_scenes:
             assert (
                 asynchronous
-            ), "In order to avoid timeouts, you must set asynchronous=True when uploading scenes."
+            ), "In order to avoid timeouts, you must set asynchronous=True when uploading 3D scenes."
 
-            return self._append_scenes(scenes, update, asynchronous)
+            return self._append_scenes(lidar_scenes, update, asynchronous)
+        if video_scenes:
+            assert (
+                asynchronous
+            ), "In order to avoid timeouts, you must set asynchronous=True when uploading videos."
+
+            return self._append_video_scenes(
+                video_scenes, update, asynchronous
+            )
 
         check_for_duplicate_reference_ids(dataset_items)
 
@@ -601,6 +613,51 @@ def _append_scenes(
         )
         return response
 
+    def _append_video_scenes(
+        self,
+        scenes: List[VideoScene],
+        update: Optional[bool] = False,
+        asynchronous: Optional[bool] = False,
+    ) -> Union[dict, AsyncJob]:
+        # TODO: make private in favor of Dataset.append invocation
+        if not self.is_scene:
+            raise Exception(
+                "Your dataset is not a scene dataset but only supports single dataset items. "
+                "In order to be able to add scenes, please create another dataset with "
+                "client.create_dataset(<dataset_name>, is_scene=True) or add the scenes to "
+                "an existing scene dataset."
+            )
+
+        for scene in scenes:
+            scene.validate()
+
+        if not asynchronous:
+            print(
+                "WARNING: Processing videos usually takes several seconds. As a result, synchronous video scene upload"
+                "requests are likely to timeout. For large uploads, we recommend using the flag asynchronous=True "
+                "to avoid HTTP timeouts. Please see"
+                "https://dashboard.scale.com/nucleus/docs/api?language=python#guide-for-large-ingestions"
+                " for details."
+            )
+
+        if asynchronous:
+            # TODO check_all_scene_paths_remote(scenes)
+            request_id = serialize_and_write_to_presigned_url(
+                scenes, self.id, self._client
+            )
+            response = self._client.make_request(
+                payload={REQUEST_ID_KEY: request_id, UPDATE_KEY: update},
+                route=f"{self.id}/upload_video_scenes?async=1",
+            )
+            return AsyncJob.from_json(response, self._client)
+
+        payload = construct_append_scenes_payload(scenes, update)
+        response = self._client.make_request(
+            payload=payload,
+            route=f"{self.id}/upload_video_scenes",
+        )
+        return response
+
     def iloc(self, i: int) -> dict:
         """Retrieves dataset item by absolute numerical index.
 
@@ -1082,13 +1139,14 @@ def get_scene(self, reference_id: str) -> Scene:
             :class:`Scene<LidarScene>`: A scene object containing frames, which
             in turn contain pointcloud or image items.
         """
-        return LidarScene.from_json(
-            self._client.make_request(
-                payload=None,
-                route=f"dataset/{self.id}/scene/{reference_id}",
-                requests_command=requests.get,
-            )
+        response = self._client.make_request(
+            payload=None,
+            route=f"dataset/{self.id}/scene/{reference_id}",
+            requests_command=requests.get,
         )
+        if VIDEO_UPLOAD_TYPE_KEY in response:
+            return VideoScene.from_json(response)
+        return LidarScene.from_json(response)
 
     def export_predictions(self, model):
         """Fetches all predictions of a model that were uploaded to the dataset.

diff --git a/nucleus/dataset_item.py b/nucleus/dataset_item.py
@@ -22,6 +22,7 @@
     TYPE_KEY,
     UPLOAD_TO_SCALE_KEY,
     URL_KEY,
+    VIDEO_FRAME_URL_KEY,
     W_KEY,
     X_KEY,
     Y_KEY,
@@ -120,34 +121,42 @@ def to_payload(self) -> dict:
 class DatasetItemType(Enum):
     IMAGE = "image"
     POINTCLOUD = "pointcloud"
+    VIDEO = "video"
 
 
 @dataclass  # pylint: disable=R0902
 class DatasetItem:  # pylint: disable=R0902
-    """A dataset item is an image or pointcloud that has associated metadata.
+    """A dataset item is an image, pointcloud or video frame that has associated metadata.
 
     Note: for 3D data, please include a :class:`CameraParams` object under a key named
     "camera_params" within the metadata dictionary. This will allow for projecting
     3D annotations to any image within a scene.
 
     Args:
-        image_location (Optional[str]): Required if pointcloud_location not present: The
-          location containing the image for the given row of data. This can be a
-          local path, or a remote URL.  Remote formats supported include any URL
-          (``http://`` or ``https://``) or URIs for AWS S3, Azure, or GCS
-          (i.e. ``s3://``, ``gcs://``).
-
-        pointcloud_location (Optional[str]): Required if image_location not
-          present: The remote URL containing the pointcloud JSON. Remote
-          formats supported include any URL (``http://`` or ``https://``) or
-          URIs for AWS S3, Azure, or GCS (i.e. ``s3://``, ``gcs://``).
+        image_location (Optional[str]): Required if pointcloud_location and
+          video_frame_location are not present: The location containing the image for
+          the given row of data. This can be a local path, or a remote URL. Remote
+          formats supported include any URL (``http://`` or ``https://``) or URIs for
+          AWS S3, Azure, or GCS (i.e. ``s3://``, ``gcs://``).
+
+        pointcloud_location (Optional[str]): Required if image_location and
+          video_frame_location are not present: The remote URL containing the
+          pointcloud JSON. Remote formats supported include any URL (``http://``
+          or ``https://``) or URIs for AWS S3, Azure, or GCS (i.e. ``s3://``,
+          ``gcs://``).
+
+        video_frame_location (Optional[str]): Required if image_location and
+          pointcloud_location are not present: The remote URL containing the
+          video frame image. Remote formats supported include any URL (``http://``
+          or ``https://``) or URIs for AWS S3, Azure, or GCS (i.e. ``s3://``,
+          ``gcs://``).
 
         reference_id (Optional[str]): A user-specified identifier to reference the
           item.
 
         metadata (Optional[dict]): Extra information about the particular
           dataset item. ints, floats, string values will be made searchable in
-          the query bar by the key in this dict For example, ``{"animal":
+          the query bar by the key in this dict. For example, ``{"animal":
           "dog"}`` will become searchable via ``metadata.animal = "dog"``.
 
           Categorical data can be passed as a string and will be treated
@@ -190,9 +199,10 @@ class DatasetItem:  # pylint: disable=R0902
         upload_to_scale (Optional[bool]): Set this to false in order to use
           `privacy mode <https://nucleus.scale.com/docs/privacy-mode>`_.
 
-          Setting this to false means the actual data within the item (i.e. the
-          image or pointcloud) will not be uploaded to scale meaning that you can
-          send in links that are only accessible to certain users, and not to Scale.
+          Setting this to false means the actual data within the item will not be
+          uploaded to scale meaning that you can send in links that are only accessible
+          to certain users, and not to Scale. Skipping upload to Scale is currently only
+          implemented for images.
     """
 
     image_location: Optional[str] = None
@@ -202,23 +212,33 @@ class DatasetItem:  # pylint: disable=R0902
     metadata: Optional[dict] = None
     pointcloud_location: Optional[str] = None
     upload_to_scale: Optional[bool] = True
+    video_frame_location: Optional[str] = None
 
     def __post_init__(self):
         assert self.reference_id != "DUMMY_VALUE", "reference_id is required."
-        assert bool(self.image_location) != bool(
-            self.pointcloud_location
-        ), "Must specify exactly one of the image_location, pointcloud_location parameters"
-        if self.pointcloud_location and not self.upload_to_scale:
+        assert (
+            bool(self.image_location)
+            + bool(self.pointcloud_location)
+            + bool(self.video_frame_location)
+            == 1
+        ), "Must specify exactly one of the image_location, pointcloud_location, video_frame_location parameters"
+        if (
+            self.pointcloud_location or self.video_frame_location
+        ) and not self.upload_to_scale:
             raise NotImplementedError(
-                "Skipping upload to Scale is not currently implemented for pointclouds."
+                "Skipping upload to Scale is not currently implemented for pointclouds and videos."
             )
         self.local = (
             is_local_path(self.image_location) if self.image_location else None
         )
         self.type = (
             DatasetItemType.IMAGE
             if self.image_location
-            else DatasetItemType.POINTCLOUD
+            else (
+                DatasetItemType.POINTCLOUD
+                if self.pointcloud_location
+                else DatasetItemType.VIDEO
+            )
         )
         camera_params = (
             self.metadata.get(CAMERA_PARAMS_KEY, None)
@@ -238,6 +258,7 @@ def from_json(cls, payload: dict):
         return cls(
             image_location=image_url,
             pointcloud_location=payload.get(POINTCLOUD_URL_KEY, None),
+            video_frame_location=payload.get(VIDEO_FRAME_URL_KEY, None),
             reference_id=payload.get(REFERENCE_ID_KEY, None),
             metadata=payload.get(METADATA_KEY, {}),
             upload_to_scale=payload.get(UPLOAD_TO_SCALE_KEY, True),
@@ -260,13 +281,15 @@ def to_payload(self, is_scene=False) -> dict:
                 payload[URL_KEY] = self.image_location
             elif self.pointcloud_location:
                 payload[URL_KEY] = self.pointcloud_location
+            elif self.video_frame_location:
+                payload[URL_KEY] = self.video_frame_location
             payload[TYPE_KEY] = self.type.value
             if self.camera_params:
                 payload[CAMERA_PARAMS_KEY] = self.camera_params.to_payload()
         else:
             assert (
                 self.image_location
-            ), "Must specify image_location for DatasetItems not in a LidarScene"
+            ), "Must specify image_location for DatasetItems not in a LidarScene or VideoScene"
             payload[IMAGE_URL_KEY] = self.image_location
             payload[UPLOAD_TO_SCALE_KEY] = self.upload_to_scale
 

diff --git a/nucleus/payload_constructor.py b/nucleus/payload_constructor.py
@@ -32,7 +32,7 @@
     PolygonPrediction,
     SegmentationPrediction,
 )
-from .scene import LidarScene
+from .scene import LidarScene, VideoScene
 
 
 def construct_append_payload(
@@ -50,7 +50,8 @@ def construct_append_payload(
 
 
 def construct_append_scenes_payload(
-    scene_list: List[LidarScene], update: Optional[bool] = False
+    scene_list: Union[List[LidarScene], List[VideoScene]],
+    update: Optional[bool] = False,
 ) -> dict:
     scenes = []
     for scene in scene_list: