Update app.py

app.py

@@ -150,30 +150,37 @@ def process_image(job_id, image_path, object_type, multiplier):
             marker_real_width_cm = 5.0
             conversion_factor = None
             try:
-                # Use a fresh copy for marker detection to avoid any drawn overlays.
-                aruco_image = image.copy()
-                gray = cv2.cvtColor(aruco_image, cv2.COLOR_BGR2GRAY)
-                # Apply histogram equalization to improve contrast.
-                gray = cv2.equalizeHist(gray)
-                # Use the DICT_6X6_250 dictionary. Ensure your printed marker was generated accordingly.
-                aruco_dict = cv2.aruco.getPredefinedDictionary(cv2.aruco.DICT_6X6_250)
-                aruco_params = cv2.aruco.DetectorParameters_create()
-                # 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)
+                # Convert to grayscale
+                gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+                # Try with histogram equalization
+                equalized = cv2.equalizeHist(gray)
+                
+                # Use the DICT_6X6_250 dictionary; ensure your marker is generated with this dictionary.
+                aruco_dict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_6X6_250)
+                parameters = cv2.aruco.DetectorParameters_create()
+                # Optionally tweak parameters if needed:
+                parameters.adaptiveThreshWinSizeMax = 400
+                parameters.minDistanceToBorder = 0
+                parameters.minMarkerPerimeterRate = 0.02
+                parameters.cornerRefinementMethod = cv2.aruco.CORNER_REFINE_SUBPIX
+
+                # First attempt detection on the equalized image
+                corners, ids, rejected = cv2.aruco.detectMarkers(equalized, aruco_dict, parameters=parameters)
+                print("DEBUG: Detected marker IDs (equalized):", ids)
+                # If no markers found, try the original grayscale image
+                if ids is None or len(ids) == 0:
+                    print("DEBUG: No markers detected on equalized image. Trying original grayscale.")
+                    corners, ids, rejected = cv2.aruco.detectMarkers(gray, aruco_dict, parameters=parameters)
+                    print("DEBUG: Detected marker IDs (original):", ids)
+                
                 if ids is not None and len(ids) > 0:
                     selected_index = None
-                    # First, try to find marker with ID 42
+                    # Try to select marker with ID 42 first
                     for i, marker_id in enumerate(ids):
                         if marker_id[0] == 42:
                             selected_index = i
                             break
-                    # If marker 42 is not found, use the first detected marker
+                    # If marker 42 is not found, use the first detected marker.
                     if selected_index is None:
                         print("DEBUG: Marker with ID 42 not found. Using first detected marker.")
                         selected_index = 0
@@ -189,8 +196,8 @@ def process_image(job_id, image_path, object_type, multiplier):
                     avg_side_length = np.mean(side_lengths)
                     conversion_factor = marker_real_width_cm / avg_side_length
                     print("DEBUG: Detected marker with ID", ids[selected_index][0],
-                          "avg side length (px):", avg_side_length,
-                          "conversion factor:", conversion_factor)
+                          "Average side length (px):", avg_side_length,
+                          "Conversion factor:", conversion_factor)
                 else:
                     print("DEBUG: No markers detected in ArUco detection.")
             except Exception as e: