Update app.py

app.py

@@ -82,63 +82,63 @@ def detect_objects(image):
     
     return annotated_image, summary
 
-# def scan_edges(image):
-#     """
-#     Simple edge detection using OpenCV
-#     """
-#     # Convert PIL image to numpy array
-#     if isinstance(image, Image.Image):
-#         image = np.array(image)
+def scan_edges(image):
+    """
+    Simple edge detection using OpenCV
+    """
+    # Convert PIL image to numpy array
+    if isinstance(image, Image.Image):
+        image = np.array(image)
     
-#     # Convert to grayscale
-#     gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    # Convert to grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
     
-#     clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
     
-#     # Apply Gaussian blur
-#     blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+    # Apply Gaussian blur
+    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
 
-#     enhanced = clahe.apply(gray_image)
+    enhanced = clahe.apply(gray_image)
     
-#     # Edge detection using Canny
-#     edges = cv2.Canny(enhanced, 50, 150)
+    # Edge detection using Canny
+    edges = cv2.Canny(enhanced, 50, 150)
     
-#     # Convert back to RGB for display
-#     edges_rgb = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
+    # Convert back to RGB for display
+    edges_rgb = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
     
-#     return edges_rgb
+    return edges_rgb
 
-def scan_edges(image):
+# def scan_edges(image):
 
-    # --- 1. Convert PIL image to numpy array if needed ---
-    if isinstance(image, Image.Image):
-        image = np.array(image)
+#     # --- 1. Convert PIL image to numpy array if needed ---
+#     if isinstance(image, Image.Image):
+#         image = np.array(image)
 
-    # --- 2. Convert to grayscale ---
-    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+#     # --- 2. Convert to grayscale ---
+#     gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
 
-    # --- 3. Gaussian blur BEFORE CLAHE to reduce high-freq noise ---
-    #        that CLAHE would otherwise amplify
-    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+#     # --- 3. Gaussian blur BEFORE CLAHE to reduce high-freq noise ---
+#     #        that CLAHE would otherwise amplify
+#     blurred = cv2.GaussianBlur(gray, (5, 5), 0)
 
-    # --- 4. Apply CLAHE on the blurred image ---
-    #        clipLimit=2.0  → controls noise amplification in flat regions
-    #        tileGridSize   → 8x8 tiles work well for engine block scale features;
-    #                         increase (e.g. 16x16) if bearing saddles are small
-    #                         relative to full image resolution
-    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-    enhanced = clahe.apply(blurred)
-
-    # --- 5. Canny edge detection on CLAHE-enhanced image ---
-    #        Lower threshold (30) helps recover weak edges in shadow regions
-    #        Upper threshold (120) keeps strong structural edges
-    #        Tune these if you get too much noise or missing arcs
-    edges = cv2.Canny(enhanced, 30, 120)
-
-    # --- 6. Convert single-channel edge map back to RGB for display ---
-    edges_rgb = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
+#     # --- 4. Apply CLAHE on the blurred image ---
+#     #        clipLimit=2.0  → controls noise amplification in flat regions
+#     #        tileGridSize   → 8x8 tiles work well for engine block scale features;
+#     #                         increase (e.g. 16x16) if bearing saddles are small
+#     #                         relative to full image resolution
+#     clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+#     enhanced = clahe.apply(blurred)
+
+#     # --- 5. Canny edge detection on CLAHE-enhanced image ---
+#     #        Lower threshold (30) helps recover weak edges in shadow regions
+#     #        Upper threshold (120) keeps strong structural edges
+#     #        Tune these if you get too much noise or missing arcs
+#     edges = cv2.Canny(enhanced, 30, 120)
+
+#     # --- 6. Convert single-channel edge map back to RGB for display ---
+#     edges_rgb = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
 
-    return edges_rgb
+#     return edges_rgb
 
 def process_image(image, mode):
     """