Advanced Image Processing Laboratory
A comprehensive image processing workbench that provides access to a wide range of computer vision algorithms and techniques. This interactive application allows users to upload images and apply various transformations, filters, and analysis methods with real-time results and parameter adjustments.
The application leverages OpenCV's extensive image processing capabilities combined with Streamlit's interactive interface to create a powerful yet user-friendly laboratory environment for experimenting with digital image processing techniques.
Basic Operations
Resize, rotate, flip, and adjust brightness/contrast of images with intuitive controls and real-time previews.
Advanced Filtering
Apply blur, Gaussian, median, bilateral filters and custom kernels with adjustable parameters for noise reduction and image enhancement.
Color Transformations
Convert between RGB, HSV, LAB, and YCrCb color spaces, with the ability to visualize individual color channels separately.
Thresholding Techniques
Apply binary, adaptive, and Otsu thresholding with adjustable threshold values for image segmentation and feature extraction.
Morphological Operations
Perform erosion, dilation, opening, closing, and other morphological transformations with customizable kernel sizes.
Edge Detection
Detect edges using Canny, Sobel, Laplacian, and Scharr operators with adjustable sensitivity and thresholds.
Feature Detection
Identify corners and interesting points using Harris Corner, Shi-Tomasi, and FAST detectors for computer vision applications.
Histogram Operations
View and manipulate image histograms with equalization and CLAHE techniques for enhanced contrast and visibility.
Technologies
- Python for image processing algorithms
- OpenCV for computer vision operations
- Streamlit for interactive web interface
- NumPy for numerical operations and arrays
- Matplotlib for visualization and plotting
- PIL/Pillow for image manipulation
- Hugging Face Spaces for deployment
Implementation
Key technical aspects of this project:
- Modular architecture with specialized functions for each processing category
- Reactive interface that updates in real-time as parameters change
- Efficient image processing pipeline that minimizes memory usage
- Comprehensive input validation and error handling
- Intuitive parameter controls with informative descriptions
- Optimized algorithms for interactive response times
Key Features
The application includes several processing categories:
- Basic Operations: Resize, rotate, flip, brightness/contrast adjustment, and color quantization
- Filtering: Blur, Gaussian, median, bilateral, and custom kernel filters
- Color Spaces: RGB, HSV, LAB, YCrCb conversions and channel separation
- Thresholding: Binary, inverse, truncate, adaptive, and Otsu methods
- Morphological Operations: Erosion, dilation, opening, closing, gradient, top hat, black hat
- Edge Detection: Canny, Sobel, Laplacian, and Scharr algorithms
- Feature Detection: Harris Corner, Shi-Tomasi, and FAST detection methods
- Histogram Operations: Visualization, equalization, and CLAHE enhancement
- Advanced Effects: Pencil sketch, cartoon, and HDR-like transformations
Users can upload their own images, process them with any of these techniques, and download the resulting processed images.
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