Black and white image colorization with OpenCV and Deep Learning

Black and White Image Colorization with OpenCV and Deep Learning

Ever wondered how to bring life to old black-and-white photos by adding color? In this video, we’ll explore how to use OpenCV and deep learning techniques to colorize black-and-white images automatically. This tutorial is perfect for enthusiasts and developers interested in computer vision, deep learning, and image processing.

Introduction to Image Colorization

Image colorization is the process of adding color to grayscale images, making them appear more vibrant and realistic. Traditionally, this task required manual effort by artists, but with advancements in deep learning, we can now automate this process using neural networks. The model learns to predict the colors of objects in a scene based on context, making it possible to colorize images with impressive accuracy.

Why Use OpenCV and Deep Learning for Image Colorization?

Combining OpenCV and deep learning offers several advantages:

• Efficiency: OpenCV provides powerful tools for image processing, enhancing the overall performance of the model.
• Pre-trained Models: We can leverage pre-trained deep learning models that have been trained on large datasets, saving time and resources.
• Automation: Automating the colorization process allows for quick and scalable colorization of large image collections.

Setting Up the Project

To get started, ensure your environment is set up with the following prerequisites:

1. Install Python: Ensure Python is installed on your system.
2. Install OpenCV and NumPy: Use pip to install OpenCV (pip install opencv-python) and NumPy (pip install numpy).
3. Download the Pre-trained Model: We’ll use a pre-trained colorization model available in OpenCV’s DNN module.

Black and White Image Colorization Workflow

We’ll follow a step-by-step approach to colorize black-and-white images using OpenCV and deep learning:

1. Load the Image: Use OpenCV to read the input black-and-white image.
2. Prepare the Model: Load the pre-trained deep learning model for colorization.
3. Preprocess the Image: Resize and normalize the image to match the input requirements of the model.
4. Predict Colors: Use the model to predict colors and apply them to the image.
5. Post-process and Display the Results: Convert the colorized image back to the BGR format and display the results.

Step 1: Load the Image

Use OpenCV’s imread() function to load the black-and-white image:

python

import cv2 # Load the black-and-white image image = cv2.imread('black_and_white.jpg') cv2.imshow('Original Black and White Image', image) cv2.waitKey(0)

Step 2: Prepare the Pre-trained Model

Download the pre-trained model files (colorization_deploy_v2.prototxt and colorization_release_v2.caffemodel) from OpenCV’s GitHub repository. Load the model using OpenCV’s DNN module:

python

# Load the pre-trained Caffe model for colorization net = cv2.dnn.readNetFromCaffe('colorization_deploy_v2.prototxt', 'colorization_release_v2.caffemodel') # Load cluster centers pts_in_hull = np.load('pts_in_hull.npy')  # Download the cluster centers from the OpenCV repository pts_in_hull = pts_in_hull.transpose().reshape(2, 313, 1, 1) # Set cluster centers as the network's input net.getLayer(net.getLayerId('class8_ab')).blobs = [pts_in_hull.astype(np.float32)] net.getLayer(net.getLayerId('conv8_313_rh')).blobs = [np.full([1, 313], 2.606, dtype='float32')]

Step 3: Preprocess the Image

Preprocess the image to match the input size required by the model:

python

# Convert the image to grayscale gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Convert the grayscale image to a 3-channel image gray = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB) # Resize the image to the required size (224x224) resized = cv2.resize(gray, (224, 224)) # Normalize the pixel values and convert to float32 resized = resized.astype('float32') / 255.0 # Subtract mean values as required by the model resized -= [0.485, 0.456, 0.406] resized /= [0.229, 0.224, 0.225] # Transpose the image to fit the input format of the network resized = resized.transpose((2, 0, 1))

Step 4: Predict Colors Using the Model

Use the model to predict colors for the image:

python

# Set the input to the network net.setInput(cv2.dnn.blobFromImage(resized)) # Perform forward pass to get the predicted colorization pred = net.forward() # Post-process the output to apply the predicted colors to the original image pred = pred[0].transpose((1, 2, 0)) # Resize the output to the original image size pred = cv2.resize(pred, (image.shape[1], image.shape[0])) # Combine the grayscale image with the predicted colors colorized = cv2.cvtColor(gray, cv2.COLOR_RGB2Lab) colorized[:, :, 1:] = pred * 128 # Convert back to BGR color space colorized = cv2.cvtColor(colorized, cv2.COLOR_Lab2BGR) cv2.imshow('Colorized Image', colorized) cv2.waitKey(0)

Step 5: Display and Save the Results

Display the final colorized image and save it if desired:

python

# Display the colorized image cv2.imshow('Colorized Image', colorized) cv2.waitKey(0) # Save the colorized image cv2.imwrite('colorized_output.jpg', colorized) # Clean up cv2.destroyAllWindows()

Enhancing the Colorization Process

To improve the results, consider these enhancements:

• Fine-tune the Model: Adjust the model’s weights on specific datasets to improve accuracy for particular types of images.
• Experiment with Different Models: Explore other deep learning models like U-Net or GANs for potentially better results.
• Image Preprocessing: Apply additional preprocessing techniques such as sharpening or contrast adjustment to improve model performance.

Applications of Image Colorization

Image colorization with deep learning can be applied in various fields, including:

• Restoring Old Photos: Reviving historical photographs by adding colors.
• Film and Media: Colorizing black-and-white films for modern audiences.
• Creative Arts: Adding artistic color effects to black-and-white images.

Conclusion

By the end of this video, you’ll have the skills to colorize black-and-white images using OpenCV and deep learning. This project not only enhances your understanding of computer vision and neural networks but also opens up creative possibilities for working with images. Whether you’re restoring old photos or exploring AI-driven art, mastering image colorization is a valuable skill in the field of artificial intelligence.

For a detailed step-by-step guide, check out the full article: https://www.geeksforgeeks.org/black-and-white-image-colorization-with-opencv-and-deep-learning/.