more profiling

README.md

@@ -16,3 +16,5 @@ cd dedup
 source myenv/bin/activate
 python debup.py 0.jpg 1.jpg
 ```
+
+[![Image of ScreenShot](Screenshot2025-04-26.png)](Screenshot)

dedup.py

@@ -40,9 +40,41 @@ def exit_handler(signum, frame):
 # CTRL+C will Exit NOW!!!
 signal.signal(signal.SIGINT, exit_handler)
 
-def timer():
+def exit_timer(level):
 	end = time.perf_counter()
 	print(f"Execution took {end - start:.4f} seconds")
+	exit(level)
+	
+class Timer:
+    def __init__(self, name=None):
+        self.name = name if name else "Timer"
+        self.start_time = None
+        self.end_time = None
+    
+    def __enter__(self):
+        self.start()
+        return self
+    
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.stop()
+        self.print_result()
+    
+    def start(self):
+        self.start_time = time.perf_counter()
+    
+    def stop(self):
+        self.end_time = time.perf_counter()
+    
+    def elapsed(self):
+        if self.start_time is None:
+            raise ValueError("Timer has not been started")
+        if self.end_time is None:
+            return time.perf_counter() - self.start_time
+        return self.end_time - self.start_time
+    
+    def print_result(self):
+        elapsed = self.elapsed()
+        print(f"{self.name}: {elapsed:.6f} seconds")	
 
 def align_with_downscaling(img1, img2, downscale_factor=4, try_common_rotations=True):
     """
@@ -60,14 +92,17 @@ def align_with_downscaling(img1, img2, downscale_factor=4, try_common_rotations=
         rotation_angle: Detected simple rotation (None if not found)
     """
     # 1. First alignment at low resolution
+    with Timer("1st alignment at Low Res-Downsaling"):
         small1 = downscale_image(img1, downscale_factor)
         small2 = downscale_image(img2, downscale_factor)
-    
+    print("Done downscaling...")
+    print("Please wait...Rotation starting.")
     # Get initial alignment at low resolution
+    with Timer("2nd alignment at Low Res-Rotations"):
         _, init_matrix, rotation_angle = align_with_ecc_and_rotation(
             small1, small2, try_common_rotations
         )
-    
+    print("Done rotating low res image...")
     if init_matrix is None:
         return img2, None, None  # Alignment failed
     
@@ -76,23 +111,27 @@ def align_with_downscaling(img1, img2, downscale_factor=4, try_common_rotations=
     if rotation_angle is not None:
         img2 = rotate_image(img2, rotation_angle)
     
+    with Timer("Scaling translation components"):
         # Scale up the transformation matrix
         full_matrix = init_matrix.copy()
         full_matrix[:2, 2] *= downscale_factor  # Scale translation components
-    
+    print("Done scale-up/transform...")
+    with Timer("Convert images to grayscale"):
         # Convert images to grayscale for final alignment
         gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
         gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
-    
+    print("Done greyscale alignment...")
     # Set criteria for final alignment
     criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 500, 1e-6)
-    
+    print("Please wait...ECC initial estimate.")
     try:
+        with Timer("ECC init"):
             # Run ECC with initial estimate
             cc, full_matrix = cv2.findTransformECC(
                 gray1, gray2, full_matrix, cv2.MOTION_AFFINE, criteria
             )
         
+        with Timer("Apply final transformation to color image"):
             # Apply final transformation to color image
             aligned_img = cv2.warpAffine(
                 img2, full_matrix, (img1.shape[1], img1.shape[0]),
@@ -395,6 +434,8 @@ if __name__ == "__main__":
 
     file1 = sys.argv[1]
     file2 = sys.argv[2]
+    
+    with Timer("Hashing"):
         # Quick hashes
         hash1 = quick_file_hash(file1)
         hash2 = quick_file_hash(file2)
@@ -403,11 +444,11 @@ if __name__ == "__main__":
         print("xxHash found duplicates")
         print("✅ Perfect match - images are identical")
         print("No transformation needed")
-        timer()
-        exit(1)
+        exit_timer(1)
     else:
         print("Done hashing...")    
 
+    with Timer("Loading Images"):
         # Load large images
         large_img1 = cv2.imread(file1)  # e.g., 4000x3000 pixels
         large_img2 = cv2.imread(file2)   # e.g., 4000x3000 pixels
@@ -416,26 +457,24 @@ if __name__ == "__main__":
     w2, h2 = get_image_dimensions_cv(large_img2)
     if w == None or w2 == None or h == None or h2 == None:
         print("Aborting...Invalid Image!")
-        timer()
-        exit(8)
+        exit_timer(8)
     if w != w2 and w != h2:
         print("Diffent Resolutions")
-        timer()
-        exit(0)
+        exit_timer(0)
     
     if h != h2 and h != w2:
         print("Diffent Resolutions")
-        timer()
-        exit(0)
+        exit_timer(0)
 
     print("Done loading images...")
+    with Timer("Aligning with downscaling 1/4 size"):
         # 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
         )
-    print("Done downscaling...")
+    
     # Save result
     # cv2.imwrite('aligned_large.jpg', aligned)
 
@@ -453,12 +492,10 @@ 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":
+        exit_timer(1)
+    if matrix_score < 0.3 and is_score != "scores":
         print("❌ Significant transformation required")
-        timer()
-        exit(0)
+        exit_timer(0)
     if is_score == "scores":
 	    score = find_duplicate_with_rotation(large_img1, aligned)
 	    print(f"Score: {score}")	
@@ -481,8 +518,7 @@ 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)
+    exit_timer(exit_code)
 
 """
 Matrix-based scores are fast but don't consider image content