""" Medical Imaging Dataset - Visualization ======================================== Project: Medical Imaging Dataset Website: https://rskworld.in Contact: help@rskworld.in, support@rskworld.in Phone: +91 93305 39277 Founder: Molla Samser Designer & Tester: Rima Khatun This module provides visualization functions for medical images with annotations and diagnostic labels. """ import numpy as np import matplotlib.pyplot as plt import cv2 from pathlib import Path import json from typing import Optional, Dict, List def visualize_medical_image(image_path: str, label_path: Optional[str] = None, title: Optional[str] = None, save_path: Optional[str] = None) -> None: """ Visualize a medical image with optional annotations. Args: image_path (str): Path to the medical image label_path (str): Path to the label/annotation file (JSON) title (str): Title for the plot save_path (str): Path to save the visualization """ # Load image image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) if image is None: raise ValueError(f"Could not load image from {image_path}") # Load labels if provided labels = None if label_path and Path(label_path).exists(): try: with open(label_path, 'r') as f: labels = json.load(f) except (json.JSONDecodeError, IOError) as e: print(f"Warning: Could not load label file {label_path}: {e}") labels = None # Create figure fig, ax = plt.subplots(1, 1, figsize=(10, 10)) # Display image ax.imshow(image, cmap='gray') ax.axis('off') # Add title if title: ax.set_title(title, fontsize=16, fontweight='bold') elif labels and 'diagnosis' in labels: ax.set_title(f"Diagnosis: {labels['diagnosis']}", fontsize=16, fontweight='bold') else: ax.set_title(Path(image_path).stem, fontsize=16, fontweight='bold') # Add annotations if available if labels: annotation_text = [] if 'diagnosis' in labels: annotation_text.append(f"Diagnosis: {labels['diagnosis']}") if 'confidence' in labels: annotation_text.append(f"Confidence: {labels['confidence']:.2f}") if 'date' in labels: annotation_text.append(f"Date: {labels['date']}") if annotation_text: text = '\n'.join(annotation_text) ax.text(0.02, 0.98, text, transform=ax.transAxes, fontsize=12, verticalalignment='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8)) plt.tight_layout() # Save if path provided if save_path: plt.savefig(save_path, dpi=300, bbox_inches='tight') print(f"Visualization saved to {save_path}") plt.show() def visualize_batch(images: List[np.ndarray], labels: Optional[List[Dict]] = None, titles: Optional[List[str]] = None, cols: int = 3, figsize: tuple = (15, 5)) -> None: """ Visualize a batch of medical images. Args: images (list): List of image arrays labels (list): List of label dictionaries titles (list): List of titles for each image cols (int): Number of columns in the grid figsize (tuple): Figure size """ n_images = len(images) rows = (n_images + cols - 1) // cols fig, axes = plt.subplots(rows, cols, figsize=figsize) if n_images == 1: axes = [axes] else: axes = axes.flatten() for i, image in enumerate(images): ax = axes[i] ax.imshow(image, cmap='gray') ax.axis('off') # Add title if titles and i < len(titles): ax.set_title(titles[i], fontsize=10) elif labels and i < len(labels) and labels[i] and 'diagnosis' in labels[i]: ax.set_title(labels[i]['diagnosis'], fontsize=10) else: ax.set_title(f"Image {i+1}", fontsize=10) # Hide unused subplots for i in range(n_images, len(axes)): axes[i].axis('off') plt.tight_layout() plt.show() def compare_preprocessing(image_path: str, save_path: Optional[str] = None) -> None: """ Compare original and preprocessed versions of a medical image. Args: image_path (str): Path to the medical image save_path (str): Path to save the comparison """ from scripts.preprocess import preprocess_image # Load original image original = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) # Determine image type from path image_type = 'xray' if 'ct' in image_path.lower() or 'ct_scan' in image_path.lower(): image_type = 'ct_scan' elif 'mri' in image_path.lower(): image_type = 'mri' # Preprocess image processed = preprocess_image(image_path, image_type=image_type) # Create comparison plot fig, axes = plt.subplots(1, 2, figsize=(15, 7)) # Original axes[0].imshow(original, cmap='gray') axes[0].set_title('Original Image', fontsize=14, fontweight='bold') axes[0].axis('off') # Processed axes[1].imshow(processed, cmap='gray') axes[1].set_title('Preprocessed Image', fontsize=14, fontweight='bold') axes[1].axis('off') plt.tight_layout() if save_path: plt.savefig(save_path, dpi=300, bbox_inches='tight') print(f"Comparison saved to {save_path}") plt.show() def plot_statistics(dataset_info: Dict) -> None: """ Plot dataset statistics. Args: dataset_info (dict): Dictionary containing dataset statistics """ categories = ['X-ray', 'CT Scan', 'MRI'] counts = [ dataset_info.get('xray_count', 0), dataset_info.get('ct_count', 0), dataset_info.get('mri_count', 0) ] fig, ax = plt.subplots(1, 1, figsize=(10, 6)) bars = ax.bar(categories, counts, color=['#0dcaf0', '#6c757d', '#0d6efd']) # Add value labels on bars for bar, count in zip(bars, counts): height = bar.get_height() ax.text(bar.get_x() + bar.get_width()/2., height, f'{count}', ha='center', va='bottom', fontsize=12, fontweight='bold') ax.set_ylabel('Number of Images', fontsize=12) ax.set_title('Medical Imaging Dataset Statistics', fontsize=14, fontweight='bold') ax.grid(axis='y', alpha=0.3) plt.tight_layout() plt.show() if __name__ == '__main__': # Example usage print("Medical Imaging Dataset - Visualization Module") print("This module provides visualization functions for medical images.") print("\nAvailable functions:") print("- visualize_medical_image(): Visualize a single medical image") print("- visualize_batch(): Visualize multiple images in a grid") print("- compare_preprocessing(): Compare original and preprocessed images") print("- plot_statistics(): Plot dataset statistics")