Advancing Video Analytics With AI Agents

This project provides a blueprint for building visually perceptive and interactive AI agents for video search and summarization (VSS) using:

• LLaMA (LLM)
• Pinecone (vector DB)
• Flask (web UI)
• Vision-Language Models (VLM)
• Retrieval-Augmented Generation (RAG)

Structure

• app/ — Flask web app
• video_processing/ — Video frame extraction and preprocessing
• vlm/ — Vision-language model integration
• llm/ — LLaMA integration
• db/ — Pinecone vector DB integration

Setup

1. Install dependencies: pip install -r requirements.txt
2. Run the app: python app/main.py

Vision-Language Model (VLM) Usage

The vlm/ module uses BLIP for image captioning, with automatic GPU/CPU device management and robust error handling.

Example: Generating Captions for Video Frames

import cv2
from vlm import generate_captions

# Example: Extract a frame from a video and generate a caption
cap = cv2.VideoCapture('sample.mp4')
frames = []
ret, frame = cap.read()
if ret:
    frames.append(frame)
cap.release()

captions = generate_captions(frames)
print(captions)

• The model will use GPU if available, otherwise CPU.
• Errors during caption generation are caught and reported in the output list.

Requirements

• For best performance, a CUDA-capable GPU is recommended for BLIP and LLaMA models.
• Ensure your environment has the necessary drivers and libraries for GPU support.