Update Test Solver

app.py

@@ -22,21 +22,28 @@ model = VisionEncoderDecoderModel.from_pretrained(
 model.eval()
 model = torch.compile(model, mode="reduce-overhead")
 
-def advanced_preprocess(cv_image):
+def preprocess_v1(cv_image):
     gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)
     gray = cv2.bilateralFilter(gray, 9, 75, 75)
     kernel = np.ones((3,3), np.uint8)
     gray = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel, iterations=2)
-    
     thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
     thresh = cv2.dilate(thresh, kernel, iterations=1)
     thresh = cv2.erode(thresh, kernel, iterations=1)
-    
     pil = Image.fromarray(thresh).convert("RGB")
     pil = pil.filter(ImageFilter.SHARPEN)
     pil = ImageEnhance.Contrast(pil).enhance(3.0)
     pil = ImageEnhance.Brightness(pil).enhance(1.5)
-    pil = pil.filter(ImageFilter.MedianFilter(size=3))
+    return pil
+
+def preprocess_v2(cv_image):
+    gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)
+    _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+    kernel = np.ones((2,2), np.uint8)
+    thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
+    pil = Image.fromarray(thresh).convert("RGB")
+    pil = pil.filter(ImageFilter.SHARPEN)
+    pil = ImageEnhance.Contrast(pil).enhance(2.8)
     return pil
 
 def run_ocr(pil_image):
@@ -86,16 +93,16 @@ def genRotations(svg):
         img2 = create_rotated_image(disable_anim, a, secondcoords)
         combo = combine_images(img2, img1)
         
-        processed = advanced_preprocess(combo)
-        res = run_ocr(processed)
-        
-        if 4 <= len(res) <= 5 and res.isalnum():
-            score = len(res) * 100 + (10 if res.isalnum() else 0)
-            if score > best_score:
-                best_score = score
-                best_result = res
-            if len(res) in (4, 5):
-                return res
+        for prep_func in [preprocess_v1, preprocess_v2]:
+            processed = prep_func(combo)
+            res = run_ocr(processed)
+            if 4 <= len(res) <= 5 and res.isalnum():
+                score = len(res) * 100 + (20 if res.isalnum() else 0)
+                if score > best_score:
+                    best_score = score
+                    best_result = res
+                if len(res) in (4, 5):
+                    return res
 
     return best_result if best_result else ""