The following explains the workflow of a drone video sensing application: the code orchestrates an end-to-end pipeline for processing drone-captured videos and transforming them into searchable, semantically rich insights using Azure's AI capabilities.

It starts by using Azure Video Indexer to:

• Authenticate via API keys and retrieve an access token.

• Upload a video either from a local file or from a URL.

• Trigger indexing to extract metadata like keyframes, themes, and highlights.

• Optionally, it can reindex a video and monitor the indexing status.

Once indexed, the workflow:

• Parses insights to extract meaningful segments.

• Creates a custom project around those segments.

• Renders a new video that highlights specific themes and keyframes.

• Downloads the rendered output for further processing.

With OpenCV, it:

• Downloads the video into memory.

• Extracts frames one by one.

• Each frame is optionally saved locally or uploaded as an image blob to Azure Storage, structured by frame number and path conventions.

For every uploaded frame:

• Generates vector embeddings using the Azure AI Vision vectorization API.

• Performs semantic analysis to extract:

o Tags

o Captions

o Detected objects

o Smart crops

o Dense captions

o Text reading

o People detection

• The results are packaged into structured descriptions.

If enabled:

• It dives into dense captions from the image analysis.

• Extracts bounding boxes for individual objects.

• Clips regions of the image based on those boxes.

• Vectorizes the clipped image.

• Re-analyzes it semantically.

• Uploads object-level insights as new searchable entities.

Finally, every analyzed frame (and optionally, detected objects) is:

• Packed as a document with its ID, vector, bounding box, and description.

• Uploaded to Azure AI Search, making the insights retrievable via semantic queries.

This code is modular, retry-safe, and production-oriented, with detailed logging and fallback mechanisms. Overall,

def indexing_workflow(source_video_url, account_id = None):

    if not account_id:

        account_id = settings.video_indexer_default_account_id

    video_url = index_and_download_video(account_id = account_id, video_url = source_video_url)

    if not video_url:

        return

    extract_and_upload_frames(video_url)

    vector_descriptions = vectorize_extracted_frames(video_url)

    client = get_search_client()

    frame_number = 0

    source_sas_url = get_image_blob_url(video_sas_url, frame_number)

    for vector, description in vector_descriptions:

        form_and_upload_document(client, account_id, frame_number, vector, description, source_sas_url, deep = False)

        frame_number += 1