This repository provides a Python-based implementation for generating hybrid images, where the low-frequency details of one image are merged with the high-frequency elements of another, creating a fascinating visual effect. The project utilizes OpenCV for image processing and is optimized for execution in Google Colab.
1 Creating Low-Frequency Images: Applies a Gaussian blur to smooth the image and generate its low-frequency representation.
2 Building Hybrid Images: Combines the low-frequency components of one image with the high-frequency features of another to create a hybrid image.
3 Extracting High-Frequency Details: Isolates the fine details of an image by subtracting the blurred version from the original.
4 Display Functionality: Uses cv2_imshow in Google Colab to display the processed images directly.
5 File Saving: Saves the resulting low-frequency, high-frequency, and hybrid images as .jpg files for later use.
1 Clone the repository or copy the script.
2 Replace the paths to your images in the script:
image1_path = '/path/to/image1.png' # Replace with the first image path
image2_path = '/path/to/image2.png' # Replace with the second image path
_3 Execute the script in Google Colab or any Python environment with OpenCV installed.
4 The following output files will be created:
low_freq_image.jpg
high_freq_image.jpg
hybrid_image.jpg
1 Python 3.x
2 OpenCV
3 NumPy
4 Google Colab (optional but recommended for better visualization)