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Dive into the world of computer vision! Our Image Classification from Video project uses advanced techniques to identify faces in images and videos. Explore video processing, face extraction, and deep learning magic. Join the adventure now! πŸ‘©β€πŸ’»πŸ“Έ"

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Face-Recognition-System-in-Python-using-FaceNet

πŸš€ Image Classification from Video using Computer Vision and Deep Learning

Project Overview

Dive into the captivating realm of computer vision and artificial intelligence with our Image Classification from Video project! πŸŽ₯πŸ€– This project is more than just recognizing faces; it's an adventure into the heart of cutting-edge technologies. From extracting frames to training models, this project is your ticket to the future of computer vision.

✨ Key Features

  • Video Processing: Swiftly download and process videos using Python scripts.
  • Face Detection: Harness the power of Haar Cascade Algorithm to extract faces from images and videos.
  • Deep Learning Magic: Enchanting pre-trained FaceNet models and the mystical VGGFace architecture for precise face recognition.
  • Modular Mastery: Explore organized modular code for a seamless understanding and effortless customization experience.
  • Data Visualization: Peer into the world of embeddings and model predictions with mesmerizing visualizations.
  • Google Colab Integration: Experience the magic of Google Colab for efficient and cloud-powered model training.

🌟 Data Description

Our dataset is a collection of frames from the beloved sitcom show, Friends. Characters like Rachel, Chandler, Phoebe, Monica, and Ross are the stars of this dataset. With a total of 35 images (7 per person for training and 15 for testing), this dataset forms the bedrock of our image classification odyssey.

πŸ› οΈ Tech Stack

  • Language: Python (Version 3.6.2)
  • Libraries: OpenCV, scikit-learn, numpy, os, pytube, scikit_image, skimage, keras, tensorflow
    • OpenCV: Your trusty sidekick for image and video processing.
    • Haar Cascade Algorithm: The mystical spell for accurate face extraction.
    • Pre-trained FaceNet Model: Unleashing the power of embeddings for face recognition.
    • VGGFace Architecture: Deep learning sorcery for the most precise identification spells.
    • Google Colab: Your enchanted notebook for cloud-powered, robust models.

🏰 Project Structure

  • input: The treasure chest containing training and validation data, downloaded videos, and extracted frames.
  • src: The spellbook, housing the core modular codebase for video processing, face extraction, and model training.
  • output: The magical repository for predicted frames and model outputs.
  • prebuilt_models: The ancient scrolls containing pre-trained models and your very own enchanted, trained models.
  • lib: The magical library filled with reference Jupyter notebooks.
  • requirements.txt: The potion recipe listing all the magical ingredients. Use pip install -r requirements.txt to concoct your magic potion!

⚑ How to Use

  1. Clone the Repository:
git clone https://github.com/your-username/image-classification-from-video.git
  1. Install Dependencies:
pip install -r requirements.txt
  1. Explore the Jupyter Notebooks: Dive into the src folder and explore the modular Jupyter notebooks for detailed insights.

Contact Information

For questions, collaborations, or further information, feel free to reach out:

Project Reference: Project Pro
LinkedIn: Vidhi Waghela
Email: vidhiwaghela99@gmail.com

About

Dive into the world of computer vision! Our Image Classification from Video project uses advanced techniques to identify faces in images and videos. Explore video processing, face extraction, and deep learning magic. Join the adventure now! πŸ‘©β€πŸ’»πŸ“Έ"

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