Update mdr_pdf_parser.py

mdr_pdf_parser.py

@@ -2819,20 +2819,20 @@ class MDRExtractionEngine:
         print("  Engine: Analyzing image...")
         # --- START: ADDED CLAHE PREPROCESSING ---
         # Convert PIL Image to OpenCV BGR format
-        ori_im_cv = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
+        #ori_im_cv = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
 
-        gray_cv = cv2.cvtColor(ori_im_cv, cv2.COLOR_BGR2GRAY)
-        clahe_obj = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
-        enhanced_gray_cv = clahe_obj.apply(gray_cv)
+        #gray_cv = cv2.cvtColor(ori_im_cv, cv2.COLOR_BGR2GRAY)
+        #clahe_obj = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
+        #enhanced_gray_cv = clahe_obj.apply(gray_cv)
         # Convert back to BGR for downstream components that might expect 3 channels
         # (even if they only use one, like the detector)
         # And then back to PIL Image for the optimizer
-        processed_cv_bgr = cv2.cvtColor(enhanced_gray_cv, cv2.COLOR_GRAY2BGR)
+        #processed_cv_bgr = cv2.cvtColor(enhanced_gray_cv, cv2.COLOR_GRAY2BGR)
 
         # Convert the processed OpenCV image back to PIL Image for the optimizer
         # The optimizer expects a PIL Image.
         # The image passed to optimizer will now be the CLAHE'd version.
-        processed_pil_image = pil_fromarray(cv2.cvtColor(processed_cv_bgr, cv2.COLOR_BGR2RGB))
+        processed_pil_image = image #pil_fromarray(cv2.cvtColor(processed_cv_bgr, cv2.COLOR_BGR2RGB))
         print("  Engine: CLAHE preprocessing applied to input image.")
         optimizer = MDRImageOptimizer(processed_pil_image, adjust_points)
         print("  Engine: Initial OCR...")