From d74678aec9d853639e5868098d71938895264f62 Mon Sep 17 00:00:00 2001
From: Robert <Robert@TryingToScale.com>
Date: Sat, 26 Apr 2025 17:11:10 -0400
Subject: [PATCH] Added CTRL+C Force Kill.

---
 dedup.py | 27 ++++++++++++++++++++++++++-
 1 file changed, 26 insertions(+), 1 deletion(-)

diff --git a/dedup.py b/dedup.py
index 63e39bb..87b5b6a 100644
--- a/dedup.py
+++ b/dedup.py
@@ -1,7 +1,11 @@
+import signal
+import threading
+import os
 import sys
 import xxhash
 import cv2
 import numpy as np
+import time
 
 """
 Copyright 2025 - Robert Strutts MIT License
@@ -25,6 +29,21 @@ Processing time scales with area, so 4x downscale = ~16x faster initial alignmen
 Memory usage significantly reduced
 """
 
+start = time.perf_counter()
+
+def kill_all():
+    print("KILLING PROCESS")
+    os.kill(os.getpid(), signal.SIGKILL)  # Force kernel-level termination
+
+def exit_handler(signum, frame):
+    threading.Thread(target=kill_all).start()  # Run in separate thread
+# CTRL+C will Exit NOW!!!
+signal.signal(signal.SIGINT, exit_handler)
+
+def timer():
+	end = time.perf_counter()
+	print(f"Execution took {end - start:.4f} seconds")
+
 def align_with_downscaling(img1, img2, downscale_factor=4, try_common_rotations=True):
     """
     Aligns images using a multi-scale approach with initial downscaling
@@ -383,7 +402,8 @@ if __name__ == "__main__":
     if (hash1 == hash2):
         print("xxHash found duplicates")
         print("✅ Perfect match - images are identical")
-        print("No transformation needed")	    
+        print("No transformation needed")
+        timer()
         exit(1)
     else:
         print("Done hashing...")    
@@ -396,10 +416,12 @@ if __name__ == "__main__":
     w2, h2 = get_image_dimensions_cv(large_img2)
     if w != w2 and w != h2:
         print("Diffent Resolutions")
+        timer()
         exit(0)
     
     if h != h2 and h != w2:
         print("Diffent Resolutions")
+        timer()
         exit(0)
 
     print("Done loading images...")
@@ -427,9 +449,11 @@ if __name__ == "__main__":
 			
     if matrix_score == 1.0 and is_score != "scores":
         print("✅ Perfect Matrix score, should be identical")
+        timer()
         exit(1)
     if matrix_score == 0.0 and is_score != "scores":
         print("❌ Significant transformation required")
+        timer()
         exit(0)
     if is_score == "scores":
 	    score = find_duplicate_with_rotation(large_img1, aligned)
@@ -453,6 +477,7 @@ if __name__ == "__main__":
     print(f"Matrix deviation score: {matrix_score:.4f}")
     print(f"Decomposed similarity: {decomposed_score:.4f}")
     print(f"Combined similarity: {combined_score:.4f}")
+    timer()
     exit(exit_code)
 
 """