Results
Reference image compared with uploaded images
"""
# Extract reference features once
ref_features = classifier.extract_features(ref_path)
# Process each comparison image
for i, comp_image in enumerate(comparison_images):
try:
# Save comparison image
comp_path = os.path.join(temp_dir, f"comparison_{i}.jpg")
try:
# First attempt: Try using PIL
with Image.open(comp_image.name) as img:
img = img.convert('RGB')
img_array = np.array(img)
cv2.imwrite(comp_path, cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR))
except Exception as e1:
print(f"PIL failed: {str(e1)}")
# Second attempt: Try using OpenCV directly
img = cv2.imread(comp_image.name)
if img is not None:
cv2.imwrite(comp_path, img)
else:
raise ValueError(f"Could not read image: {comp_image.name}")
# Calculate SSIM for structural similarity
ssim_score = classifier.calculate_ssim(ref_path, comp_path)
# Extract features for physical feature comparison
comp_features = classifier.extract_features(comp_path)
# Calculate feature differences for physical features
feature_diff = np.abs(ref_features - comp_features)
# Calculate different aspects of similarity
avg_feature_diff = np.mean(feature_diff)
max_feature_diff = np.max(feature_diff)
feature_similarity = np.dot(ref_features.flatten(),
comp_features.flatten()) / (
np.linalg.norm(ref_features) * np.linalg.norm(comp_features))
# Stricter similarity criteria
is_similar = True # Start with assumption of similarity
reason = "Images are similar"
# First check for major physical feature differences (like misplaced eyes)
if max_feature_diff > 0.85 or avg_feature_diff > 0.5:
is_similar = False
reason = "Major physical differences detected (missing or misplaced features)"
# Then check for overall structural similarity
elif ssim_score < 0.4: # Lowered SSIM threshold
is_similar = False
reason = "Overall structure is too different"
# Finally check for feature similarity
elif feature_similarity < 0.5:
is_similar = False
reason = "Features don't match well enough"
# Debug information
print(f"\nDebug for {os.path.basename(comp_image.name)}:")
print(f"SSIM Score: {ssim_score:.3f}")
print(f"Max Feature Difference: {max_feature_diff:.3f}")
print(f"Average Feature Difference: {avg_feature_diff:.3f}")
print(f"Feature Similarity: {feature_similarity:.3f}")
# Create HTML output with improved styling and reason
status_color = "#27ae60" if is_similar else "#c0392b" # Green or Red
status_text = "SIMILAR" if is_similar else "NOT SIMILAR"
status_icon = "✓" if is_similar else "✗"
html_output += f"""