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Preprocessing script for pneumonia consolidation enhancement.
This script enhances chest X-ray images to better visualize consolidations,
air bronchograms, and subtle patterns necessary for accurate segmentation.
"""
import cv2
import numpy as np
from pathlib import Path
import argparse
def enhance_consolidation(img_path, output_path=None):
"""
Enhance chest X-ray image to better visualize pneumonia consolidation.
Args:
img_path: Path to the input image (JPG/PNG)
output_path: Path to save the enhanced image (optional)
Returns:
Enhanced image as numpy array
"""
# Read image in grayscale
img = cv2.imread(str(img_path), cv2.IMREAD_GRAYSCALE)
if img is None:
raise ValueError(f"Could not read image from {img_path}")
# 1. CLAHE (Contrast Limited Adaptive Histogram Equalization)
# Enhances local contrast to see subtle consolidations
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
enhanced = clahe.apply(img)
# 2. Sharpening filter to reveal air bronchograms
# Air bronchograms are dark branch-like patterns inside consolidation
kernel = np.array([[-1, -1, -1],
[-1, 9, -1],
[-1, -1, -1]])
sharpened = cv2.filter2D(enhanced, -1, kernel)
# 3. Optional: Edge enhancement to see consolidation boundaries
# Using Laplacian for edge detection
laplacian = cv2.Laplacian(sharpened, cv2.CV_64F)
laplacian = np.uint8(np.absolute(laplacian))
# Combine sharpened image with edge information
alpha = 0.8 # Weight for sharpened image
beta = 0.2 # Weight for edge information
result = cv2.addWeighted(sharpened, alpha, laplacian, beta, 0)
# Save if output path provided
if output_path:
cv2.imwrite(str(output_path), result)
print(f"Enhanced image saved to: {output_path}")
return result
def batch_enhance_consolidation(input_dir, output_dir, image_extension='.jpg'):
"""
Process multiple images in a directory.
Args:
input_dir: Directory containing input images
output_dir: Directory to save enhanced images
image_extension: File extension to process (default: .jpg)
"""
input_path = Path(input_dir)
output_path = Path(output_dir)
output_path.mkdir(parents=True, exist_ok=True)
# Find all images with specified extension
images = list(input_path.rglob(f"*{image_extension}"))
print(f"Found {len(images)} images to process")
for img_path in images:
try:
# Create output path maintaining relative structure
rel_path = img_path.relative_to(input_path)
out_path = output_path / rel_path
out_path.parent.mkdir(parents=True, exist_ok=True)
# Process image
enhance_consolidation(img_path, out_path)
print(f"Processed: {img_path.name}")
except Exception as e:
print(f"Error processing {img_path}: {e}")
print(f"\nProcessing complete! Enhanced images saved to: {output_path}")
def create_visualization_comparison(original_path, enhanced_path, output_path):
"""
Create a side-by-side comparison of original and enhanced images.
Args:
original_path: Path to original image
enhanced_path: Path to enhanced image
output_path: Path to save comparison image
"""
original = cv2.imread(str(original_path), cv2.IMREAD_GRAYSCALE)
enhanced = cv2.imread(str(enhanced_path), cv2.IMREAD_GRAYSCALE)
# Resize if needed to match dimensions
if original.shape != enhanced.shape:
enhanced = cv2.resize(enhanced, (original.shape[1], original.shape[0]))
# Create side-by-side comparison
comparison = np.hstack([original, enhanced])
# Add labels
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(comparison, 'Original', (10, 30), font, 1, (255, 255, 255), 2)
cv2.putText(comparison, 'Enhanced', (original.shape[1] + 10, 30), font, 1, (255, 255, 255), 2)
cv2.imwrite(str(output_path), comparison)
print(f"Comparison saved to: {output_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Enhance chest X-ray images for pneumonia consolidation segmentation"
)
parser.add_argument(
'--input',
type=str,
required=True,
help='Input image file or directory'
)
parser.add_argument(
'--output',
type=str,
required=True,
help='Output file or directory'
)
parser.add_argument(
'--batch',
action='store_true',
help='Process entire directory (batch mode)'
)
parser.add_argument(
'--extension',
type=str,
default='.jpg',
help='Image file extension for batch processing (default: .jpg)'
)
args = parser.parse_args()
if args.batch:
batch_enhance_consolidation(args.input, args.output, args.extension)
else:
enhance_consolidation(args.input, args.output)
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