VideoDB Documentation

VideoDB Documentation

Examples and Tutorials

Fun with Keyword Search

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💬 Overview

In this tutorial, let’s explore the powerful functionality of Keyword Search in VideoDB. This feature enables users to efficiently locate any keyword or phrase within their video assets, streamlining the process of content discovery.

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Setup

📦 Installing packages

%pip install videodb

🔑 API Keys

Before proceeding, ensure access to

VideoDB⁠

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Get your API key from

VideoDB Console⁠

. ( Free for first 50 uploads, No credit card required ) 🎉

import os

os.environ["VIDEO_DB_API_KEY"] = ""

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Steps

🌐 Step 1: Connect to VideoDB

Establish a session for uploading videos. Import the necessary modules from VideoDB library to access functionalities.

from videodb import connect

conn = connect()

coll = conn.get_collection()

🎥 Step 2: Upload Video

Upload the video to your VideoDB collection. You can upload the video asset from your local device or from a YouTube URL to upload the video from its source. This works as the base video for all the Keyword Search queries.

video = coll.upload(url="https://www.youtube.com/watch?v=Uvufun6xer8")

video.play()

You can upload from your local file system too by passing file_path in upload()

For this tutorial, we’ll run a Keyword Search on the following video:

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🔊 Step 3: Index Spoken Words

Index the spoken words in your video to enable accurate keyword search.

video.index_spoken_words()

🔎 Step 4: Search for any keyword

Utilize the keyword search by using video.search() method with following parameters.

pass search query in query parameter

pass SearchType.keyword in search_type

Note: You will need to import SearchType first to enable this function

from videodb import SearchType

results = video.search(query='metaverse', search_type=SearchType.keyword)

👀 Step 5: Preview and Share

Preview your video with a compilation of all the clips matching your search query. You can access the stream link alongside the preview to share the Keyword Search result with others.

results.play()

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🔎 Bonus : Refining Keyword Search results by adding padding.

Some keyword search results/ compilations may appear slightly choppy, or the cuts may feel abrupt. We can solve this issue by using VideoDB’s padding controls. Here’s how it works:

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Compiled Timeline_Keyword-with padding.png

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The resulting shots can be made smoother by including a little more context from before and after the matching timestamps. That’s exactly what padding controls enable:

Create a timeline, by using the Timeline() function.

Create VideoAsset of each result shot using VideoAsset() with following parameters

asset_id : Video ID of the Video to which result belongs to [base video]

start: To adjust the beginning of the resulting shot, we subtract the padding duration from the original start timestamp. This ensures the resulting shot starts earlier in relation to the base video.

end: Likewise, we extend the resulting shot's end timestamp by adding the padding duration. This gives the resulting shot additional context from the base video.

Finally, we will add these freshly created assets in our Timeline by using Timeline.add_inline()

from videodb.timeline import Timeline

from videodb.asset import VideoAsset

timeline = Timeline(conn)

# Add padding

padding = 0.4

# Compile Video

for shot in results.shots:

asset = VideoAsset(

asset_id=shot.video_id, start=shot.start-padding, end=shot.end+padding

)

timeline.add_inline(asset)

▶️ Here’s the result for the same video, but improved using padding control.

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🎉 Conclusion

Keyword Search in VideoDB empowers users to extract valuable insights from their video assets with ease. For more information and advanced features, explore the

VideoDB Documentation⁠

and join the VideoDB community on

GitHub⁠

Discord⁠

for support and collaboration.

More Examples

Checkout these fun experiments with Keyword search 👇

So basically it’s “basically”

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2. The untold story of “generative”AI

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