Update app.py

app.py

@@ -21,9 +21,9 @@ jobs = {}  # jobs[job_id] = {"progress": int, "result": {...}}
 #########################################
 
 # --- Roboflow Box Detection Model ---
-API_KEY = "wLjPoPYaLmrqCIOFA0RH"  # Your Roboflow API key
+API_KEY = "wLjPoPYaLmrqCIOFA0RH"           # Your Roboflow API key
 PROJECT_ID = "base-model-box-r4suo-8lkk1-6dbqh"  # Your Roboflow project ID
-VERSION_NUMBER = "2"  # Your trained model version number
+VERSION_NUMBER = "2"                        # Your trained model version number
 
 try:
     rf = roboflow.Roboflow(api_key=API_KEY)
@@ -152,21 +152,24 @@ def process_image(job_id, image_path, object_type, multiplier):
                 # Use a fresh copy for marker detection.
                 aruco_image = image.copy()
                 gray = cv2.cvtColor(aruco_image, cv2.COLOR_BGR2GRAY)
-                # Use DICT_6X6_250; your printed marker should be generated with this dictionary.
+                # Increase contrast using histogram equalization.
+                gray = cv2.equalizeHist(gray)
+                # Use DICT_6X6_250; ensure your printed marker is generated with this dictionary and has ID 42.
                 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
-                aruco_params.minMarkerPerimeterRate = 0.03
+                # Lower the minimum marker perimeter rate to help detect small markers.
+                aruco_params.minMarkerPerimeterRate = 0.02  
                 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.
                     selected_index = None
                     for i, marker_id in enumerate(ids):
                         if marker_id[0] == 42: