Enhanced Image Processor

A Python library for image enhancement using CLAHE (Contrast Limited Adaptive Histogram Equalization) with support for multiple color spaces, automatic parameter optimization, and multi-threaded batch processing.

Features

• Advanced CLAHE Implementation

  • Support for multiple color spaces (LAB, HSV, YUV, YCrCb, HLS, XYZ, LUV)
  • Automatic parameter optimization based on image characteristics
  • Handles various image formats and bit depths

• Efficient Processing

  • Multi-threaded batch processing
  • Progress tracking with progress bar
  • Comprehensive error handling

• Supported Image Formats

  • Common formats: PNG, JPG, JPEG, TIFF, BMP
  • Web formats: WebP
  • Scientific formats: PPM, PGM, PBM
  • High dynamic range: EXR, HDR, PIC
  • Others: SR, RAS

Installation

1. Clone the repository:

git clone https://github.com/MichailSemoglou/enhanced-image-processor.git
cd enhanced-image-processor

2. Install required packages:

pip install opencv-python numpy tqdm

Usage

Basic Usage with Auto-Optimization

from image_processor import ImageProcessor

# Create processor with auto-optimization
processor = ImageProcessor(auto_optimize=True)

# Process and save image with optimized parameters
enhanced_image = processor.enhance_image("input.jpg")
if enhanced_image is not None:
    processor.save_image(enhanced_image, "enhanced_input.jpg")

Custom Output Directory

# Process and save image in a specific directory
enhanced_image = processor.enhance_image("input.jpg")
if enhanced_image is not None:
    processor.save_image(enhanced_image, "output_folder/enhanced_input.jpg")

Batch Processing

# Process multiple images with progress tracking
results = processor.batch_process(
    input_dir="input_folder",
    output_dir="output_folder",
    max_workers=4,
    show_progress=True
)

# Check results
for filename, status in results:
    print(f"{filename}: {status}")

Milder Enhancement

# Create processor with reduced clip limit for milder enhancement
processor = ImageProcessor(
    clip_limit=1.0,  # Lower clip limit (default is 2.0)
    tile_size=16,    # Larger tile size (default is 8)
    normalize_output=True
)

# Process and save image
enhanced_image = processor.enhance_image("input.jpg")
if enhanced_image is not None:
    processor.save_image(enhanced_image, "mild_enhanced_input.jpg")

The enhancement intensity can be adjusted through two main parameters:

• Decrease clip_limit (try values between 0.5 and 1.5 for milder effects)
• Increase tile_size (try 12 or 16 for more gradual transitions)

Custom Color Space

# Use specific color space
processor = ImageProcessor(
    clip_limit=2.0,
    tile_size=8,
    color_space='HSV',
    normalize_output=True
)

# Process and save image
enhanced_image = processor.enhance_image("input.jpg")
if enhanced_image is not None:
    processor.save_image(enhanced_image, "enhanced_hsv_input.jpg")

Parameters

• clip_limit (float, default=2.0): Threshold for contrast limiting
• tile_size (int, default=8): Size of grid for histogram equalization
• color_space (str, default='LAB'): Color space for enhancement
• normalize_output (bool, default=True): Whether to normalize output values
• auto_optimize (bool, default=False): Enable automatic parameter optimization

Save Image Parameters

• image (np.ndarray): The enhanced image to save
• output_path (str or Path): Where to save the image
• create_dirs (bool, default=True): Automatically create output directories if they don't exist

Requirements

• Python 3.7+
• OpenCV (cv2)
• NumPy
• tqdm

Error Handling

The library includes comprehensive error handling for:

• Invalid file paths
• Unsupported image formats
• Image loading failures
• Processing errors
• File saving issues

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License – see the LICENSE file for details.