ColorRevive: AI-Powered Image and Video Colorization 🎨✨

Overview

ColorRevive explores the application of deep learning to transform grayscale images and videos into realistic, colorized versions. By utilizing pre-trained Convolutional Neural Networks (CNNs) and advanced image-processing techniques, this project demonstrates how AI can bridge the gap between monochrome nostalgia and vibrant modern visuals.

The project features an interactive Streamlit web interface, allowing users to upload, process, and download colorized media effortlessly.

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Purpose of the Project

The goal of ColorRevive is to develop an efficient, user-friendly tool for restoring and enhancing grayscale media. It focuses on applying state-of-the-art deep learning models (specifically the Zhang et al. ECCV 2016 architecture) to automate the colorization process, showcasing the versatility of CNNs in multimedia processing.

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Technologies Used

• Python: Core programming language.
• OpenCV (cv2): For image/video processing and DNN (Deep Neural Network) module integration.
• NumPy: For matrix operations and numerical computations.
• Streamlit: For building the interactive web frontend.
• Caffe Model Components:
  • colorization_release_v2.caffemodel: Pre-trained weights.
  • models_colorization_deploy_v2.prototxt: Network architecture definition.
  • pts_in_hull.npy: Cluster centers for the "ab" channels in LAB space.

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How Deep Learning is Used

The project employs CNNs trained on large datasets (like ImageNet) to predict the chrominance ("ab" channels) of a grayscale image based on its luminance ("L" channel).

The Colorization Process

1. Input: A grayscale image (L channel).
2. Feature Extraction: The CNN analyzes the image to detect objects, textures, and patterns.
3. Prediction: The model predicts the 'a' (Green-Red) and 'b' (Blue-Yellow) channels.
4. Reconstruction: The predicted 'ab' channels are resized and combined with the original 'L' channel.
5. Output: The LAB result is converted to RGB for display.

Analogy: Imagine a black-and-white coloring book. The lines (L channel) tell you where objects are. The AI uses its "memory" of the real world to guess which crayons (a and b channels) to use to fill it in!

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Features

Image Colorization

• Instant Results: Upload and view colorized images in seconds.
• Side-by-Side Comparison: Compare the grayscale input vs. the AI output.
• Download: Save the enhanced image locally.

Video Colorization

• Frame-by-Frame Processing: The model iterates through video frames to colorize them individually.
• Smooth Reconstruction: Frames are stitched back together to form a cohesive color video.

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Installation and Setup

1. Clone the Repository

git clone https://github.com/ajay1214/ColorRevive.git
cd ColorRevive

2. Install Dependencies

pip install -r requirements.txt

3. Download Model Files

Create a folder named models in the root directory and download the following files into it:

• colorization_release_v2.caffemodel 👉 Download: https://www.dropbox.com/s/dx0qvhhp5hbcx7z/colorization_release_v2.caffemodel?dl=1
• models_colorization_deploy_v2.prototxt
• pts_in_hull.npy

4. Run the Streamlit app:

streamlit run app.py

Here is the file structure you should have:

ColorRevive/
├── models/
│   ├── colorization_release_v2.caffemodel
│   ├── models_colorization_deploy_v2.prototxt
│   └── pts_in_hull.npy
├── app.py
├── requirements.txt
└── README.md

HAPPY MACHINE LEARNING

MADE With ❤️ by Ajay