Update app.py

app.py

@@ -97,8 +97,8 @@ def process_image(job_id, image_path, object_type, multiplier):
                 jobs[job_id]['result'] = {"error": "Roboflow model not available."}
                 return
 
-            # Upscale the image if it's small to help detect small boxes.
-            scale_factor = 1  # default: no upscaling
+            # Upscale the image if it is small to help detect small boxes.
+            scale_factor = 1
             if img_width < 1000 or img_height < 1000:
                 scale_factor = 2
 
@@ -149,27 +149,25 @@ def process_image(job_id, image_path, object_type, multiplier):
             # We assume the printed marker is 5 cm x 5 cm.
             marker_real_width_cm = 5.0
             try:
-                # Use a fresh copy for marker detection.
+                # Use a fresh copy for marker detection to avoid any drawn overlays.
                 aruco_image = image.copy()
                 gray = cv2.cvtColor(aruco_image, cv2.COLOR_BGR2GRAY)
-                # Increase contrast using histogram equalization.
+                # Apply histogram equalization to improve contrast.
                 gray = cv2.equalizeHist(gray)
-                # Use DICT_6X6_250; ensure your printed marker is generated with this dictionary and has ID 42.
+                # Use the DICT_6X6_250 dictionary. (Make sure your printed marker was generated accordingly.)
                 aruco_dict = cv2.aruco.getPredefinedDictionary(cv2.aruco.DICT_6X6_250)
                 aruco_params = cv2.aruco.DetectorParameters_create()
-                # Tweak parameters for small markers.
-                aruco_params.adaptiveThreshWinSizeMin = 3
-                aruco_params.adaptiveThreshWinSizeMax = 23
-                aruco_params.adaptiveThreshWinSizeStep = 10
-                # Lower the minimum marker perimeter rate to help detect small markers.
-                aruco_params.minMarkerPerimeterRate = 0.02  
+                # Adjust parameters for better detection of small markers:
+                aruco_params.adaptiveThreshWinSizeMax = 400
+                aruco_params.minDistanceToBorder = 0
+                aruco_params.minMarkerPerimeterRate = 0.02  # Lowered from 0.03
                 aruco_params.cornerRefinementMethod = cv2.aruco.CORNER_REFINE_SUBPIX
 
                 corners, ids, rejected = cv2.aruco.detectMarkers(gray, aruco_dict, parameters=aruco_params)
                 print("DEBUG: Detected candidate IDs:", ids)
                 conversion_factor = None
                 if ids is not None and len(ids) > 0:
-                    # Look for marker with ID 42.
+                    # Look for marker with ID 42 explicitly.
                     selected_index = None
                     for i, marker_id in enumerate(ids):
                         if marker_id[0] == 42:
@@ -179,6 +177,7 @@ def process_image(job_id, image_path, object_type, multiplier):
                         print("DEBUG: Marker with ID 42 not found among detected markers.")
                     else:
                         marker_corners = corners[selected_index].reshape((4, 2))
+                        # Draw only the selected marker.
                         cv2.aruco.drawDetectedMarkers(image, [corners[selected_index]], [ids[selected_index]])
                         side_lengths = []
                         for i in range(4):