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import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
from deepfake_detector import DeepfakeDetector
from image_processor import ImageProcessor
from labeling import ThreatLabeler
from heatmap_generator import HeatmapGenerator
from combined_visualization import CombinedVisualizer
def ensure_dir(directory):
"""Ensure directory exists"""
if not os.path.exists(directory):
os.makedirs(directory)
def demo_combined_visualization(image1_path, image2_path, output_dir, threshold=30, min_area=100):
"""
Demonstrate the combined visualization that overlays difference image,
low and medium threat heatmaps, and bounding boxes.
Args:
image1_path: Path to original image
image2_path: Path to modified image
output_dir: Directory to save outputs
threshold: Threshold for difference detection
min_area: Minimum area for region detection
"""
# Initialize components
detector = DeepfakeDetector()
labeler = ThreatLabeler()
heatmap_gen = HeatmapGenerator()
img_processor = ImageProcessor()
visualizer = CombinedVisualizer()
# Create output directory
ensure_dir(output_dir)
# Get base filename for outputs
base_name = os.path.splitext(os.path.basename(image1_path))[0]
print(f"Processing images: {image1_path} and {image2_path}")
# Step 1: Verification Module - Process the image pair
results = detector.process_image_pair(image1_path, image2_path, threshold, min_area)
# Step 2: Labeling System - Label detected regions by threat level
original_image = img_processor.load_image(image1_path)
modified_image = img_processor.load_image(image2_path)
labeled_image, labeled_regions = labeler.label_regions(
original_image, results['difference_image'], results['bounding_boxes'])
# Get threat summary
threat_summary = labeler.get_threat_summary(labeled_regions)
# Step 3: Generate multi-level heatmaps
multi_heatmaps = heatmap_gen.generate_multi_level_heatmap(original_image, labeled_regions)
# Prepare results for combined visualization
image_pair_results = {
'original_image': original_image,
'modified_image': modified_image,
'difference_image': results['difference_image'],
'threshold_image': results['threshold_image'],
'annotated_image': results['annotated_image'],
'labeled_image': labeled_image,
'multi_heatmaps': multi_heatmaps,
'bounding_boxes': results['bounding_boxes'],
'labeled_regions': labeled_regions,
'threat_summary': threat_summary,
'smi_score': results['smi_score']
}
# Create combined visualization
output_path = os.path.join(output_dir, f"{base_name}_combined_overlay.png")
visualizer.create_combined_visualization(
image_pair_results,
output_path,
alpha_diff=0.4, # Transparency for difference image
alpha_low=0.3, # Transparency for low threat heatmap
alpha_medium=0.4 # Transparency for medium threat heatmap
)
# Print summary information
print(f"\nAnalysis Results:")
print(f"SMI Score: {results['smi_score']:.4f} (1.0 = identical, 0.0 = completely different)")
print(f"Total regions detected: {threat_summary['total_regions']}")
print(f"Threat counts: Low={threat_summary['threat_counts']['low']}, "
f"Medium={threat_summary['threat_counts']['medium']}, "
f"High={threat_summary['threat_counts']['high']}")
if threat_summary['max_threat']:
print(f"Maximum threat: {threat_summary['max_threat']['level'].upper()} "
f"({threat_summary['max_threat']['percentage']:.1f}%)")
print(f"Average difference: {threat_summary['average_difference']:.1f}%")
print(f"\nCombined visualization saved to: {output_path}")
return output_path
if __name__ == "__main__":
import sys
if len(sys.argv) < 3:
print("Usage: python demo_combined_visualization.py <original_image> <modified_image> [output_dir]")
sys.exit(1)
image1_path = sys.argv[1]
image2_path = sys.argv[2]
output_dir = sys.argv[3] if len(sys.argv) > 3 else "./output"
demo_combined_visualization(image1_path, image2_path, output_dir) |