Working state.

reading_annotations.py

@@ -3,7 +3,7 @@ import os
 import json
 import numpy as np
 import shutil
-from PIL import Image, ImageChops
+from PIL import Image, ImageChops, ImageFilter
 Image.MAX_IMAGE_PIXELS = None
 from pdf2image import convert_from_path
 import annotating # Reuse rendering logic
@@ -36,7 +36,10 @@ def detect_checks_and_notes(output_dir):
     # Warning: If the PDF is huge, pdf2image might split pages or OOM.
     # Assuming user didn't change page dimensions/order.
     try:
-        user_pages = convert_from_path(pdf_path, dpi=DPI)
+        # user_pages = convert_from_path(pdf_path, dpi=DPI)
+        # La version suivante évite les size mismatch
+        # Mais donne plus de bruit
+        user_pages = convert_from_path(pdf_path, dpi=72)
     except Exception as e:
         print(f"Error reading PDF: {e}")
         return [], None
@@ -97,19 +100,21 @@ def detect_checks_and_notes(output_dir):
             # Expand mask slightly to catch sloppy ticks
             mask_draw.rectangle([x1-5, y1-5, x2+5, y2+5], fill=0)
         else:
-            # print("A box, not checked !", density)
-            # Even if not "checked", mask the box area slightly to avoid
-            # artifacts if user hovered over it, though arguably we keep it.
-            # Let's strictly mask only if checked to verify detection?
-            # No, prompt says "not extract the part that are just checking".
-            # If user checked it, we mask it.
-            pass
+            mask_draw.rectangle([x1-2, y1-2, x2+2, y2+2], fill=0)
+
+        if box["type"] == "score" and box["value"] == 0.0:
+            # Mask the whole line
+            mask_draw.rectangle([0, y1-5, ref_img.width, y2+5], fill=0)
 
     # --- Extraction Phase ---
     # Create the "Manual Notes" layer
     # Logic: User - Ref. If Diff is dark -> Note.
     # We want a transparent image with just the pen strokes.
 
+    # Try Gaussian Blur, peut-être inutile.
+    ref_blur = ref_img.filter(ImageFilter.GaussianBlur(5))
+    user_blur = user_img.filter(ImageFilter.GaussianBlur(5))
+
     # 1. Get difference image
     diff_img = ImageChops.difference(ref_img, user_img).convert("L")
 
@@ -117,7 +122,8 @@ def detect_checks_and_notes(output_dir):
     # Pixels that are different enough:
     diff_data = np.array(diff_img)
     # Create alpha channel: 0 where no diff, 255 where diff
-    alpha = np.where(diff_data > 20, 255, 0).astype(np.uint8)
+    # Higher treshold is better
+    alpha = np.where(diff_data > 100, 255, 0).astype(np.uint8)
 
     # 3. Create output image (Black strokes, variable alpha)
     # Or Copy user colors? Better to copy user pixels.