Update ocr_engine.py

ocr_engine.py

@@ -1,34 +1,40 @@
 import cv2
 import easyocr
 import re
+from dblur import Deblurrer
+import numpy as np
+from PIL import Image
 
+# Initialize EasyOCR reader and Restormer deblurrer
 reader = easyocr.Reader(['en'], gpu=False)
+restormer = Deblurrer(model_name="restormer")
 
-def extract_weight_from_image(img_cv):
-    # Resize for clarity
-    img = cv2.resize(img_cv, None, fx=2.0, fy=2.0, interpolation=cv2.INTER_CUBIC)
+def deblur_image(pil_img):
+    img_np = np.array(pil_img)
+    deblurred_np = restormer.deblur(img_np)
+    return Image.fromarray(deblurred_np)
 
-    # Convert to grayscale
-    if len(img.shape) == 3:
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+def extract_weight_from_image(pil_img):
+    # Deblur and convert to OpenCV format
+    pil_img = deblur_image(pil_img)
+    img_cv = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2GRAY)
 
-    # Adaptive threshold to boost contrast
-    img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, 
+    # Resize and apply thresholding
+    img = cv2.resize(img_cv, None, fx=2.0, fy=2.0, interpolation=cv2.INTER_CUBIC)
+    img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
                                 cv2.THRESH_BINARY, 11, 3)
 
-    # Morphological close to unify digits
     kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
     img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
 
-    # OCR detection
+    # OCR
     results = reader.readtext(img)
 
-    # Filter only short, confident numeric candidates
     filtered = [text for _, text, conf in results if len(text.strip()) <= 5 and conf > 0.4]
     raw_text = " ".join(filtered)
-
     matches = re.findall(r"\d+\.\d+|\d+", raw_text)
+
     weight = matches[0] if matches else "0.0"
     confidence = int(results[0][2] * 100) if results else 0
 
-    return weight, confidence, raw_text
+    return weight, confidence, raw_text, pil_img  # return deblurred image too