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ocr_diagnostics.py

@@ -1,66 +1,66 @@
-import os
-import sys
-import json
-import cv2
-import numpy as np
-from PIL import Image
-
-# Ensure paths are correct
-PROJECT_ROOT = r"c:\Users\Varshith Dharmaj\Downloads\major\math_verification_mvp"
-if PROJECT_ROOT not in sys.path:
-    sys.path.insert(0, PROJECT_ROOT)
-
-local_ocr_path = os.path.join(PROJECT_ROOT, "services", "local_ocr")
-if local_ocr_path not in sys.path:
-    sys.path.append(local_ocr_path)
-
-from mvm2_ocr_engine import MVM2OCREngine
-
-def run_diagnostics():
-    print("MVM2 OCR DIAGNOSTIC TOOL")
-    print("========================")
-    
-    engine = MVM2OCREngine()
-    print(f"Engine Model Loaded: {engine.model_loaded}")
-    
-    test_images = [
-        "test_math.png",
-        "services/dashboard/test_math.png" # Sometimes it's duplicated
-    ]
-    
-    # Create a synthetic complex math image if others don't exist
-    synth_path = "synth_math.png"
-    img = Image.new('RGB', (800, 200), color = 'white')
-    # Since I can't draw complex LaTeX easily here, I'll just check if existing ones work
-    # But I can generate an image with text via CV2
-    synth_img = np.ones((200, 800, 3), dtype=np.uint8) * 255
-    cv2.putText(synth_img, "f(x) = x^2 + 2x + 1", (50, 100), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 0), 3)
-    cv2.imwrite(synth_path, synth_img)
-    test_images.append(synth_path)
-
-    from services.preprocessing_service.image_enhancing import ImageEnhancer
-    enhancer = ImageEnhancer(sigma=1.2)
-
-    for img_path in test_images:
-        abs_path = os.path.abspath(img_path)
-        if not os.path.exists(abs_path):
-            print(f"\n[SKIP] {img_path} not found.")
-            continue
-            
-        print(f"\n[TESTING RAW] {img_path}")
-        result_raw = engine.process_image(abs_path)
-        print(f"Raw Result: {result_raw.get('latex_output')}")
-        
-        print(f"\n[TESTING ENHANCED] {img_path}")
-        try:
-            enhanced_img, _ = enhancer.enhance(abs_path)
-            enhanced_tmp = f"enhanced_{os.path.basename(img_path)}"
-            cv2.imwrite(enhanced_tmp, enhanced_img)
-            result_enh = engine.process_image(enhanced_tmp)
-            print(f"Enhanced Result: {result_enh.get('latex_output')}")
-            os.remove(enhanced_tmp)
-        except Exception as e:
-            print(f"Enhancement failed: {e}")
-
-if __name__ == "__main__":
-    run_diagnostics()
+import os
+import sys
+import json
+import cv2
+import numpy as np
+from PIL import Image
+
+# Ensure paths are correct
+PROJECT_ROOT = r"c:\Users\Varshith Dharmaj\Downloads\major\math_verification_mvp"
+if PROJECT_ROOT not in sys.path:
+    sys.path.insert(0, PROJECT_ROOT)
+
+local_ocr_path = os.path.join(PROJECT_ROOT, "services", "local_ocr")
+if local_ocr_path not in sys.path:
+    sys.path.append(local_ocr_path)
+
+from mvm2_ocr_engine import MVM2OCREngine
+
+def run_diagnostics():
+    print("MVM2 OCR DIAGNOSTIC TOOL")
+    print("========================")
+    
+    engine = MVM2OCREngine()
+    print(f"Engine Model Loaded: {engine.model_loaded}")
+    
+    test_images = [
+        "test_math.png",
+        "services/dashboard/test_math.png" # Sometimes it's duplicated
+    ]
+    
+    # Create a synthetic complex math image if others don't exist
+    synth_path = "synth_math.png"
+    img = Image.new('RGB', (800, 200), color = 'white')
+    # Since I can't draw complex LaTeX easily here, I'll just check if existing ones work
+    # But I can generate an image with text via CV2
+    synth_img = np.ones((200, 800, 3), dtype=np.uint8) * 255
+    cv2.putText(synth_img, "f(x) = x^2 + 2x + 1", (50, 100), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 0), 3)
+    cv2.imwrite(synth_path, synth_img)
+    test_images.append(synth_path)
+
+    from services.preprocessing_service.image_enhancing import ImageEnhancer
+    enhancer = ImageEnhancer(sigma=1.2)
+
+    for img_path in test_images:
+        abs_path = os.path.abspath(img_path)
+        if not os.path.exists(abs_path):
+            print(f"\n[SKIP] {img_path} not found.")
+            continue
+            
+        print(f"\n[TESTING RAW] {img_path}")
+        result_raw = engine.process_image(abs_path)
+        print(f"Raw Result: {result_raw.get('latex_output')}")
+        
+        print(f"\n[TESTING ENHANCED] {img_path}")
+        try:
+            enhanced_img, _ = enhancer.enhance(abs_path)
+            enhanced_tmp = f"enhanced_{os.path.basename(img_path)}"
+            cv2.imwrite(enhanced_tmp, enhanced_img)
+            result_enh = engine.process_image(enhanced_tmp)
+            print(f"Enhanced Result: {result_enh.get('latex_output')}")
+            os.remove(enhanced_tmp)
+        except Exception as e:
+            print(f"Enhancement failed: {e}")
+
+if __name__ == "__main__":
+    run_diagnostics()