This project is a deep learning application that automatically colorizes grayscale images using a pre-trained neural network model. It leverages OpenCV for image manipulation and a Caffe-based model to generate realistic color predictions.

Input (B&W)	Output (Colorized)

• Converts grayscale input to LAB color space
• Uses a Caffe deep learning model trained on color image datasets
• Predicts the 'a' and 'b' color channels from the L (lightness) channel
• Merges and converts LAB back to BGR for viewing

• Language: Python
• Libraries: OpenCV, NumPy
• Model: Pre-trained Caffe .prototxt and .caffemodel
• Frameworks: Deep Learning with OpenCV’s DNN module

• BWopencv/ |--b2w.py # Main script to colorize images |--images/ # Folder containing input B&W images |--Eagle.jpg |--Lion.jpg |--rma.jpg |--Santiago.jpg |--wizard.jpg |--zidane.jpg |--models/ # Pre-trained model files for colorization | |--colorization_deploy_v2.prototxt | |--colorization_release_v2.caffemodel | |--pts_in_null.py |----README.md # Project documentation

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