chore(docs): document ScoreExtractor tiling and refactor debug scripts (#563)

scripts/debug/test_blur_match.py

@@ -0,0 +1,41 @@
+import cv2
+import numpy as np
+import time
+
+img0 = cv2.imread(r"C:\Users\Certes\.gemini\antigravity\brain\975cea00-dd68-4689-9ee3-f1a2408b4ee6\raw_chunk_00.png")
+img1 = cv2.imread(r"C:\Users\Certes\.gemini\antigravity\brain\975cea00-dd68-4689-9ee3-f1a2408b4ee6\raw_chunk_01.png")
+
+gray0 = cv2.cvtColor(img0, cv2.COLOR_BGR2GRAY)
+gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+
+w = gray0.shape[1]
+
+best_ov = 0
+min_mad = float('inf')
+
+start_time = time.time()
+# Downsample by 2 horizontally & vertically for extreme speed
+small0 = cv2.resize(gray0, (w//2, gray0.shape[0]//2))
+small1 = cv2.resize(gray1, (w//2, gray1.shape[0]//2))
+sw = small0.shape[1]
+
+# We are testing overlap pixel widths
+for ov in range(sw-2, 10, -1):
+    diff = cv2.absdiff(small0[:, -ov:], small1[:, :ov])
+    mad = np.mean(diff)
+    
+    if mad < min_mad:
+        min_mad = mad
+        best_ov = ov * 2 # map back to original scale
+        
+    if min_mad < 3.0: # Break early if effectively a perfect match!
+        best_ov = ov * 2
+        break
+
+end_time = time.time()
+print(f"MSE MAD found overlap {best_ov}px with MAD {min_mad:.2f} in {(end_time-start_time)*1000:.1f}ms")
+
+# Verify
+stitched = np.hstack([img0, img1[:, best_ov:]])
+cv2.imwrite(r"C:\Users\Certes\.gemini\antigravity\brain\975cea00-dd68-4689-9ee3-f1a2408b4ee6\test_mse_stitch.png", stitched)
+print("Exported test_mse_stitch.png")