Update app.py

app.py

@@ -1,4 +1,4 @@
-import os
+ import os
 import uuid
 import threading
 import time
@@ -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 is small to help detect small boxes.
-            scale_factor = 1
+            # Upscale the image if it's small to help detect small boxes.
+            scale_factor = 1  # default: no upscaling
             if img_width < 1000 or img_height < 1000:
                 scale_factor = 2
 
@@ -141,21 +141,21 @@ def process_image(job_id, image_path, object_type, multiplier):
                 except Exception as e:
                     continue
 
-            # Use a higher NMS threshold to prevent merging valid nearby detections.
+            # Use a higher NMS IoU threshold to avoid merging valid nearby detections.
             box_detections = custom_nms(processed_preds, iou_threshold=0.7)
             jobs[job_id]['progress'] = 60
 
             # --- ArUco Marker Detection for Measurement ---
-            # We assume the marker is printed at 5cm x 5cm.
+            # We assume the printed marker is 5cm x 5cm.
             marker_real_width_cm = 5.0
             try:
-                # Create a fresh copy for ArUco detection to avoid drawn overlays
+                # Use a fresh copy for marker detection.
                 aruco_image = image.copy()
                 gray = cv2.cvtColor(aruco_image, cv2.COLOR_BGR2GRAY)
-                # Use DICT_6X6_250 – ensure your printed marker is generated with this dictionary.
+                # Use the DICT_6X6_250 dictionary.
                 aruco_dict = cv2.aruco.getPredefinedDictionary(cv2.aruco.DICT_6X6_250)
-                # Create and adjust detector parameters for small markers
                 aruco_params = cv2.aruco.DetectorParameters_create()
+                # Adjust parameters to help detect small markers.
                 aruco_params.adaptiveThreshWinSizeMin = 3
                 aruco_params.adaptiveThreshWinSizeMax = 23
                 aruco_params.adaptiveThreshWinSizeStep = 10
@@ -163,23 +163,34 @@ def process_image(job_id, image_path, object_type, multiplier):
                 aruco_params.cornerRefinementMethod = cv2.aruco.CORNER_REFINE_SUBPIX
 
                 corners, ids, rejected = cv2.aruco.detectMarkers(gray, aruco_dict, parameters=aruco_params)
-                print("DEBUG: Detected ArUco ids:", ids)
-                if ids is not None and len(corners) > 0:
-                    marker_corners = corners[0].reshape((4, 2))
-                    cv2.aruco.drawDetectedMarkers(image, corners, ids)
-                    side_lengths = []
-                    for i in range(4):
-                        pt1 = marker_corners[i]
-                        pt2 = marker_corners[(i + 1) % 4]
-                        side_lengths.append(np.linalg.norm(pt1 - pt2))
-                    avg_side_length = np.mean(side_lengths)
-                    conversion_factor = marker_real_width_cm / avg_side_length
-                    print("DEBUG: Marker conversion factor:", conversion_factor)
+                print("DEBUG: Detected candidate IDs:", ids)
+                conversion_factor = None
+                if ids is not None and len(ids) > 0:
+                    # Look for marker with id 42.
+                    selected_index = None
+                    for i, marker_id in enumerate(ids):
+                        if marker_id[0] == 42:
+                            selected_index = i
+                            break
+                    if selected_index is None:
+                        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):
+                            pt1 = marker_corners[i]
+                            pt2 = marker_corners[(i + 1) % 4]
+                            side_lengths.append(np.linalg.norm(pt1 - pt2))
+                        avg_side_length = np.mean(side_lengths)
+                        conversion_factor = marker_real_width_cm / avg_side_length
+                        print("DEBUG: Detected marker ID 42, avg side length (px):", avg_side_length,
+                              "conversion factor:", conversion_factor)
                 else:
-                    print("DEBUG: No ArUco marker detected.")
-                    conversion_factor = None
+                    print("DEBUG: No markers detected in ArUco detection.")
             except Exception as e:
-                print("DEBUG: Exception in ArUco detection:", e)
+                print("DEBUG: Exception during ArUco detection:", e)
                 conversion_factor = None
 
             for pred in box_detections: