VideoDB Documentation

VideoDB Documentation

Visual Search and Indexing

Playground for Scene Extractions

⁠

Playground: Extract Scenes without Indexing

Sometimes, it's important to determine the number of scenes needed to describe a video, as this can vary depending on the type of video. For instance, videos of a podcast with two hosts tend to be less dynamic than sports videos

If you want to extract scenes from the video without indexing them, you can use the video.extract_scenes() function.

Using this pipeline you can experiment with scene extraction and find your suitable configuration.

⁠

extract_scenes()

This function accepts the extraction_type and extraction_config and returns a SceneCollection object, that keeps the information about all the extracted scene lists.

scene_collections = video.extract_scenes(

extraction_type=SceneExtractionType.time_based,

extraction_config={"time": 30, "select_frames": ["middle"]},

)

SceneCollection Viewing, Inspecting, and Deleting Scenes

For every scene extraction pipeline that you run on a video, a SceneCollection object is created.

You can use following functions to View, Inspect and Delete your SceneCollections

list_scene_collection

scene_collections = video.list_scene_collection()

for scene_collection in scene_collections:

print("Scene Collection ID :",scene_collection["scene_collection_id"])

Get SceneCollection by ID

scene_collection = video.get_scene_collection("scene_collection_id")

Inspecting SceneCollection

print("This is scene collection id", scene_collection.id)

print("This is scene collection config", scene_collection.config)

⁠

Playground: Play with Prompt

Before finalizing your prompt, consider experimenting with different ones. This will help you see how the search performs for your use cases. Start by iterating over only a few scenes. Then, experiment with your prompt and test it after indexing

We believe that the right prompt is very helpful in finding information that aligns with your domain knowledge and experience. For this we provide following describe(prompt= ) functions at Frame and Scene level.

Read more about Scene and Frame object⁠

⁠

#describe frame image using vision LLM

frame.describe(

prompt=str,

)

# run vision model on scene level

# primarily for activity detection.

Scene.describe(

prompt=str,

)

Start by iterating over only few scenes and experiment with your prompt and test after indexing.

# get scene from collection

scenes = scene_collection.scenes

# Iterate through only 5 scene

for scene in scenes[:5]:

print(f"Scene Duration {scene.start}-{scene.end}")

# Iterate through each frame in the scene

for frame in scene.frames:

print(f"Frame at {frame.frame_time} {frame.url}")

frame.describe(

prompt=str,

)

Experiment with prompt at scene level

# get scene from collection

scenes = scene_collection.scenes

# Iterate through first 5 scene

for scene in scenes[:5] :

scene.describe(

prompt=str,

)

⁠

Index and search scenes

# Give a name to your index for reference

index_id = video.index_scenes(scenes=scenes, name="")

# search using the index_id

res = video.search(query="religious gathering",

index_type=IndexType.scene,

index_id=index_id)

res.play()

Playground: Extract Scenes without Indexing

extract_scenes()

SceneCollection Viewing, Inspecting, and Deleting Scenes

Playground: Play with Prompt

Experiment with prompt at scene level

Index and search scenes

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