Video-Data-Processing-with-Python-and-OpenCV

This project focuses on the automated processing, annotation, and visualization of video data using Python and OpenCV. It demonstrates how pre-labeled object detection data can be overlaid on raw video footage to generate an annotated output video, helping developers and researchers better understand and validate visual datasets.

The system reads a video frame-by-frame, applies bounding boxes and category labels (e.g., car, pedestrian, cyclist) using structured CSV annotation files, and outputs both visual previews and a fully annotated video. Designed for clarity and extensibility, this project is ideal for use in computer vision pipelines, dataset exploration, or as a foundation for AI/ML model validation.

Key Objectives

• Read and manipulate video streams with OpenCV
• Integrate object detection labels (bounding boxes + categories)
• Annotate frames with color-coded bounding boxes and text overlays
• Export the final annotated video in MP4 format
• Visualize frame samples interactively within Jupyter notebooks

✅ Features

• Annotates video frames with category-specific bounding boxes
• Visual distinction using color-coded overlays
• Modular function design for annotation, video generation, and visualization
• Optional: Play annotated video directly inside Jupyter Notebook

📦 Input & Output

• Input: A raw driving scene video (.mp4) and object detection labels in CSV format
• Output: A new annotated video showing each object class with bounding boxes and text labels

🚀 Highlights

• Category-specific colors make visual distinction easy
• Frame-by-frame annotation allows high-resolution analysis
• Jupyter integration enables easy demonstration and validation
• Simple architecture — no deep learning or heavy models used; ready for fast prototyping or integration with future object detection systems

Folder Structures

Technologies Used

• Python
• OpenCV
• Pandas
• Matplotlib
• Jupyter Notebook
• TQDM for progress tracking

🔍 Future Enhancements

• Integration with YOLO or Faster R-CNN for real-time object detection
• Export annotations in COCO/YOLO format
• Real-time webcam stream annotation

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Built with a focus on clarity, modularity, and educational value.