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+myenv
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diff --git a/LICENSE b/LICENSE
index 2071b23..ee5603f 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) <year> <copyright holders>
+Copyright (c) <2025> <Robert Strutts>
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 
diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,3 +1,16 @@
 # dedup
 
-Photo De-Duplication
\ No newline at end of file
+Photo De-Duplication
+
+## Install
+```
+cd dedup
+python3 -m venv myenv
+source myenv/bin/activate
+pip install xxhash opencv-python
+```
+## Useage:
+
+```
+python debup.py 0.jpg 1.jpg
+```
diff --git a/dedup.py b/dedup.py
new file mode 100644
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--- /dev/null
+++ b/dedup.py
@@ -0,0 +1,431 @@
+import sys
+import xxhash
+import cv2
+import numpy as np
+
+"""
+Copyright 2025 - Robert Strutts MIT License
+
+Key Optimizations:
+
+Multi-Scale Processing:
+First alignment at low resolution (faster)
+Final refinement at full resolution (accurate)
+
+Matrix Scaling:
+The translation components of the transformation matrix are scaled up
+Rotation and scaling components remain the same
+
+Smart Downscaling:
+Uses INTER_AREA interpolation which is ideal for size reduction
+Maintains aspect ratio
+
+Performance Benefits:
+Processing time scales with area, so 4x downscale = ~16x faster initial alignment
+Memory usage significantly reduced
+"""
+
+def align_with_downscaling(img1, img2, downscale_factor=4, try_common_rotations=True):
+    """
+    Aligns images using a multi-scale approach with initial downscaling
+    
+    Args:
+        img1: Reference image (numpy array)
+        img2: Image to align (numpy array)
+        downscale_factor: How much to reduce size for initial alignment (e.g., 4 = 1/4 size)
+        try_common_rotations: Whether to test common rotations first
+    
+    Returns:
+        aligned_img: Aligned version of img2
+        transform_matrix: Final transformation matrix
+        rotation_angle: Detected simple rotation (None if not found)
+    """
+    # 1. First alignment at low resolution
+    small1 = downscale_image(img1, downscale_factor)
+    small2 = downscale_image(img2, downscale_factor)
+    
+    # Get initial alignment at low resolution
+    _, init_matrix, rotation_angle = align_with_ecc_and_rotation(
+        small1, small2, try_common_rotations
+    )
+    
+    if init_matrix is None:
+        return img2, None, None  # Alignment failed
+    
+    # 2. Refine alignment at full resolution with initial estimate
+    # Apply the rotation if one was detected
+    if rotation_angle is not None:
+        img2 = rotate_image(img2, rotation_angle)
+    
+    # Scale up the transformation matrix
+    full_matrix = init_matrix.copy()
+    full_matrix[:2, 2] *= downscale_factor  # Scale translation components
+    
+    # Convert images to grayscale for final alignment
+    gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+    gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+    
+    # Set criteria for final alignment
+    criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 500, 1e-6)
+    
+    try:
+        # Run ECC with initial estimate
+        cc, full_matrix = cv2.findTransformECC(
+            gray1, gray2, full_matrix, cv2.MOTION_AFFINE, criteria
+        )
+        
+        # Apply final transformation to color image
+        aligned_img = cv2.warpAffine(
+            img2, full_matrix, (img1.shape[1], img1.shape[0]),
+            flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP
+        )
+        
+        return aligned_img, full_matrix, rotation_angle
+    except:
+        return img2, None, None
+
+def downscale_image(img, factor):
+    """Downscale image by specified factor while preserving aspect ratio"""
+    if factor <= 1:
+        return img.copy()
+    
+    height, width = img.shape[:2]
+    new_size = (int(width/factor), int(height/factor))
+    
+    # Use area interpolation for downscaling (best for reduction)
+    return cv2.resize(img, new_size, interpolation=cv2.INTER_AREA)
+
+def rotate_image(image, angle):
+    """Rotate image by specified angle (0, 90, 180, or 270 degrees)"""
+    if angle == 90:
+        return cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)
+    elif angle == 180:
+        return cv2.rotate(image, cv2.ROTATE_180)
+    elif angle == 270:
+        return cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE)
+    return image
+
+def try_ecc_alignment(target, moving):
+    """Try ECC alignment and return aligned image, matrix, and correlation coefficient"""
+    # Initialize warp matrix
+    warp_matrix = np.eye(2, 3, dtype=np.float32)
+    
+    # Set criteria
+    criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 1000, 1e-6)
+    
+    try:
+        # Run ECC
+        cc, warp_matrix = cv2.findTransformECC(
+            target, moving, warp_matrix, cv2.MOTION_AFFINE, criteria
+        )
+        
+        # Apply the transformation
+        aligned = cv2.warpAffine(
+            moving, warp_matrix, (target.shape[1], target.shape[0]),
+            flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP
+        )
+        
+        return aligned, warp_matrix, cc
+    except:
+        return moving, None, 0
+
+def apply_transform(image, matrix, target_shape):
+    """Apply transformation matrix to color image"""
+    if matrix is None:
+        return image
+        
+    if matrix.shape == (2, 3):  # Affine
+        return cv2.warpAffine(
+            image, matrix, (target_shape[1], target_shape[0]),
+            flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP
+        )
+    elif matrix.shape == (3, 3):  # Homography
+        return cv2.warpPerspective(
+            image, matrix, (target_shape[1], target_shape[0]),
+            flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP
+        )
+    return image
+    
+def align_ecc(img1, img2):
+    # Convert to grayscale
+    gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+    gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+    
+    # Define motion model (affine or homography)
+    warp_mode = cv2.MOTION_AFFINE  # or cv2.MOTION_HOMOGRAPHY
+    
+    if warp_mode == cv2.MOTION_HOMOGRAPHY:
+        warp_matrix = np.eye(3, 3, dtype=np.float32)
+    else:
+        warp_matrix = np.eye(2, 3, dtype=np.float32)
+    
+    # Specify termination criteria
+    criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 1000, 1e-6)
+    
+    # Run ECC
+    try:
+        cc, warp_matrix = cv2.findTransformECC(
+            gray1, gray2, warp_matrix, warp_mode, criteria
+        )
+        
+        if warp_mode == cv2.MOTION_HOMOGRAPHY:
+            aligned_img = cv2.warpPerspective(
+                img2, warp_matrix, (img1.shape[1], img1.shape[0])
+            )
+        else:
+            aligned_img = cv2.warpAffine(
+                img2, warp_matrix, (img1.shape[1], img1.shape[0])
+            )
+        
+        return aligned_img, warp_matrix
+    except:
+        print("Alignment failed")
+        return img2, None
+            
+def align_with_ecc_and_rotation(img1, img2, try_common_rotations=True):
+    """
+    Aligns img2 to img1 using ECC, with optional pre-testing of common rotations
+    
+    Args:
+        img1: Reference image (numpy array)
+        img2: Image to align (numpy array)
+        try_common_rotations: If True, tests common rotations first
+    
+    Returns:
+        aligned_img: Aligned version of img2
+        transform_matrix: Transformation matrix used
+        rotation_angle: Detected rotation angle (None if not a simple rotation)
+    """
+    # Convert to grayscale for alignment
+    gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+    gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+    
+    if try_common_rotations:
+        # Test common rotations first
+        best_cc = -1
+        best_aligned = None
+        best_matrix = None
+        best_angle = None
+        
+        for angle in [0, 90, 180, 270]:
+            # Rotate the image
+            rotated = rotate_image(gray2, angle)
+            
+            # Try ECC alignment
+            aligned, matrix, cc = try_ecc_alignment(gray1, rotated)
+            
+            if cc > best_cc:
+                best_cc = cc
+                best_aligned = aligned
+                best_matrix = matrix
+                best_angle = angle if angle != 0 else None
+        
+        if best_cc > 0.3:  # Good enough alignment found
+            # Apply the same transformation to color image
+            if best_angle is not None:
+                rotated_color = rotate_image(img2, best_angle)
+            else:
+                rotated_color = img2
+                
+            if best_matrix is not None:
+                aligned_color = apply_transform(rotated_color, best_matrix, img1.shape)
+            else:
+                aligned_color = rotated_color
+                
+            return aligned_color, best_matrix, best_angle
+    
+    # If no good rotation found or try_common_rotations=False, do regular ECC
+    aligned_img, transform_matrix = align_ecc(img1, img2)
+    return aligned_img, transform_matrix, None
+
+def matrix_similarity_score(matrix):
+    """
+    Calculate similarity score based on deviation from identity matrix.
+    Returns 1 for perfect match (identity), decreasing towards 0 for large transformations.
+    """
+    if matrix is None:
+        return 0.0  # Alignment failed
+    
+    # For affine matrix (2x3)
+    if matrix.shape == (2, 3):
+        ideal = np.eye(2, 3, dtype=np.float32)
+        # Normalize translation components by image dimensions (assuming 1000px as reference)
+        normalized_matrix = matrix.copy()
+        normalized_matrix[:, 2] /= 1000.0
+    # For homography matrix (3x3)
+    elif matrix.shape == (3, 3):
+        ideal = np.eye(3, dtype=np.float32)
+        normalized_matrix = matrix.copy()
+        normalized_matrix[:, 2] /= 1000.0  # Normalize translation
+    else:
+        return 0.0
+    
+    # Calculate Frobenius norm of difference
+    diff = np.linalg.norm(normalized_matrix - ideal)
+    
+    # Convert to similarity score (0-1)
+    score = np.exp(-diff)  # Exponential decay
+    return float(np.clip(score, 0, 1))
+
+def decomposed_similarity_score(matrix, img_width):
+    """
+    Calculate score by analyzing translation, rotation, and scaling separately.
+    img_width is used to normalize translation to image dimensions.
+    """
+    if matrix is None:
+        return 0.0
+    
+    # Decompose affine matrix
+    if matrix.shape == (2, 3):
+        # Extract rotation and scale
+        a, b, c, d = matrix[0,0], matrix[0,1], matrix[1,0], matrix[1,1]
+        scale_x = np.sqrt(a*a + b*b)
+        scale_y = np.sqrt(c*c + d*d)
+        rotation = np.arctan2(-b, a)
+        
+        # Extract translation (normalized by image width)
+        tx = matrix[0,2] / img_width
+        ty = matrix[1,2] / img_width
+    else:
+        return 0.0
+    
+    # Calculate penalties (adjust weights as needed)
+    translation_penalty = np.sqrt(tx*tx + ty*ty) * 0.5  # Weight translation more
+    scale_penalty = np.abs(scale_x - 1) + np.abs(scale_y - 1)
+    rotation_penalty = np.abs(rotation) / np.pi  # Normalized to 0-1
+    
+    # Combine penalties
+    total_penalty = translation_penalty + scale_penalty + rotation_penalty
+    
+    # Convert to similarity score
+    return max(0, 1 - total_penalty)
+
+def comprehensive_similarity(img1, img2, matrix):
+    """Combine matrix analysis with image comparison"""
+    # 1. Matrix-based score (50% weight)
+    matrix_score = matrix_similarity_score(matrix)
+    
+    # 2. Pixel-based score after alignment (50% weight)
+    if matrix is not None:
+        aligned = cv2.warpAffine(img2, matrix, (img1.shape[1], img1.shape[0]))
+        pixel_score = normalized_cross_correlation(img1, aligned)
+    else:
+        pixel_score = 0.0
+    
+    return 0.5 * matrix_score + 0.5 * pixel_score
+
+def normalized_cross_correlation(img1, img2):
+    """Calculate NCC between two images"""
+    # Convert to grayscale
+    gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY).astype(np.float32)
+    gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY).astype(np.float32)
+    
+    # Normalize
+    gray1 = (gray1 - np.mean(gray1)) / (np.std(gray1) + 1e-8)
+    gray2 = (gray2 - np.mean(gray2)) / (np.std(gray2) + 1e-8)
+    
+    # Calculate correlation
+    return np.mean(gray1 * gray2)
+    
+def find_duplicate_with_rotation(img1, img2):
+    # Initialize ORB detector
+    orb = cv2.ORB_create()
+    
+    # Find keypoints and descriptors
+    kp1, des1 = orb.detectAndCompute(img1, None)
+    kp2, des2 = orb.detectAndCompute(img2, None)
+    
+    # Create BFMatcher object
+    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
+    
+    # Match descriptors
+    matches = bf.match(des1, des2)
+    
+    # Sort matches by distance
+    matches = sorted(matches, key=lambda x: x.distance)
+    
+    # Return similarity score (lower is more similar)
+    return len(matches)
+                    
+"""
+xxhash is about 5–10x faster than SHA256, non-cryptographic.
+If you want an even lighter setup (no installs), we can use zlib.crc32 instead — 
+but xxhash is better if you care about collisions!
+"""
+def quick_file_hash(file_path):
+    hasher = xxhash.xxh64()  # 64-bit very fast hash
+    try:
+        with open(file_path, 'rb') as f:
+            while chunk := f.read(8192):  # Read in 8KB chunks
+                hasher.update(chunk)
+    except Exception as e:
+        print(f"Error hashing file: {e}")
+    return hasher.hexdigest()
+
+# --- Example usage ---
+if __name__ == "__main__":
+    if len(sys.argv) < 3:
+        print("Usage: python3 dedup.py file1.jpg file2.jpg")
+        sys.exit(1)
+
+    file1 = sys.argv[1]
+    file2 = sys.argv[2]
+    # Quick hashes
+    hash1 = quick_file_hash(file1)
+    hash2 = quick_file_hash(file2)
+    
+    if (hash1 == hash2):
+        print("xxHash found duplicates")
+        print("✅ Perfect match - images are identical")
+        print("No transformation needed")	    
+        exit(1)
+
+    # Load large images
+    large_img1 = cv2.imread(file1)  # e.g., 4000x3000 pixels
+    large_img2 = cv2.imread(file2)   # e.g., 4000x3000 pixels
+
+    # Align with downscaling (initially process at 1/4 size)
+    aligned, matrix, angle = align_with_downscaling(
+        large_img1, large_img2, 
+        downscale_factor=4,
+        try_common_rotations=True
+    )
+
+    # Save result
+    # cv2.imwrite('aligned_large.jpg', aligned)
+
+    # Print debug info
+    print(f"Detected rotation: {angle}°")
+    print(f"Final transformation matrix:\n{matrix}")
+
+    score = find_duplicate_with_rotation(large_img1, aligned)
+    print(f"Score: {score}")
+    
+    # Calculate scores
+    matrix_score = matrix_similarity_score(matrix)
+    decomposed_score = decomposed_similarity_score(matrix, large_img1.shape[1])
+    combined_score = comprehensive_similarity(large_img1, aligned, matrix)
+    
+    # Check for perfect alignment
+    if matrix_score == 1.0 and decomposed_score == 1.0 and combined_score == 1.0:
+        print("✅ Perfect match - images are identical")
+        print("No transformation needed")
+        exit_code = 1
+    elif matrix_score > 0.9 and decomposed_score > 0.9 and combined_score > 0.7:
+	    print("⚠️ Near-perfect alignment - minor differences detected")
+	    exit_code = 2
+    else:
+        print("❌ Significant transformation required")
+        exit_code = 0
+    
+    print(f"Matrix deviation score: {matrix_score:.4f}")
+    print(f"Decomposed similarity: {decomposed_score:.4f}")
+    print(f"Combined similarity: {combined_score:.4f}")
+    exit(exit_code)
+
+"""
+Matrix-based scores are fast but don't consider image content
+Decomposed analysis gives more interpretable results (separate rotation/scale/translation)
+Combined approaches with pixel comparison are most accurate but slower
+Normalization is crucial - translation should be relative to image size
+"""