ValueError: shapes not aligned (when running deep sort with yolov8 object detector)

[Vishnu15018]

I tried yolov8 object detection , and deep sort object tracking to track vehicles , using the Nicolai Nielsen tutorials.I got this below error.

Trace if the error.

`ValueError Traceback (most recent call last)

in () 69 # Update tracker with bounding boxes 70 print(list1) ---> 71 tracks = object_tracker.update_tracks(list1,frame) 72 73 # Process each tracked object 8 frames /usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deepsort_tracker.py in update_tracks(self, raw_detections, embeds, frame, today, others, instance_masks) 226 # Update tracker. 227 self.tracker.predict() --> 228 self.tracker.update(detections, today=today) 229 230 return self.tracker.tracks /usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/tracker.py in update(self, detections, today) 95 96 # Run matching cascade. ---> 97 matches, unmatched_tracks, unmatched_detections = self._match(detections) 98 99 # Update track set. /usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/tracker.py in _match(self, detections) 149 unmatched_tracks_a, 150 unmatched_detections, --> 151 ) = linear_assignment.matching_cascade( 152 gated_metric, 153 self.metric.matching_threshold, /usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/linear_assignment.py in matching_cascade(distance_metric, max_distance, cascade_depth, tracks, detections, track_indices, detection_indices) 145 continue 146 --> 147 matches_l, _, unmatched_detections = min_cost_matching( 148 distance_metric, 149 max_distance, /usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/linear_assignment.py in min_cost_matching(distance_metric, max_distance, tracks, detections, track_indices, detection_indices) 60 return [], track_indices, detection_indices # Nothing to match. 61 ---> 62 cost_matrix = distance_metric(tracks, detections, track_indices, detection_indices) 63 cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5 64 # indices = linear_assignment(cost_matrix) /usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/tracker.py in gated_metric(tracks, dets, track_indices, detection_indices) 131 features = np.array([dets[i].feature for i in detection_indices]) 132 targets = np.array([tracks[i].track_id for i in track_indices]) --> 133 cost_matrix = self.metric.distance(features, targets) 134 cost_matrix = linear_assignment.gate_cost_matrix( 135 self.kf, cost_matrix, tracks, dets, track_indices, detection_indices, only_position=self.gating_only_position /usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/nn_matching.py in distance(self, features, targets) 172 cost_matrix = np.zeros((len(targets), len(features))) 173 for i, target in enumerate(targets): --> 174 cost_matrix[i, :] = self._metric(self.samples[target], features) 175 return cost_matrix /usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/nn_matching.py in _nn_cosine_distance(x, y) 93 94 """ ---> 95 distances = _cosine_distance(x, y) 96 return distances.min(axis=0) 97 /usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/nn_matching.py in _cosine_distance(a, b, data_is_normalized) 52 a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True) 53 b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True) ---> 54 return 1.0 - np.dot(a, b.T) 55 56 ValueError: shapes (2,1280,3) and (3,1280,2) not aligned: 3 (dim 2) != 1280 (dim 1)` code snippet: while True : ret, frame = cap.read() print("frame:", frame.shape) if not ret: break count += 1 # Perform object detection on the frame (assuming you have the model defined somewhere) results = model.predict(frame, save=True, conf=0.25) annotated=results[0].plot() cv2_imshow(annotated) z=results[0].boxes list1 = [] cords=z.xywhn.cpu().numpy() confs=list(z.conf) cls_ids=(z.cls) for i in range(len(cords)): x1=cords[i][0] y1=cords[i][1] w=cords[i][2] h=cords[i][3] c=(confs[i].cpu()).item() d=str(cls_ids[i].item()) list1.append(([x1,y1,w,h],c,d)) #([left,top,w,h],confidence,class_name) # Update tracker with bounding boxes print(list1) #here I am getting the error tracks = object_tracker.update_tracks(list1,frame) # Process each tracked object print(tracks) for track in tracks: if not track.is_confirmed(): continue tk_id = int(track.track_id) ltrb = track.to_ltrb() print(ltrb) x3,y3,x4,y4=ltrb cx=int(x3+x4)//2 cy=int(y3+y4)//2 Deep sort Initialisation: from deep_sort_realtime.deepsort_tracker import DeepSort object_tracker = DeepSort(max_age=5, n_init=2, nms_max_overlap=0.9, max_cosine_distance=0.3, nn_budget=None, override_track_class = None, embedder="mobilenet", half=True, bgr=True, embedder_gpu=True, embedder_model_name=None, embedder_wts=None, polygon=False, today=None) I am new to object tracking, provide me with the solution or any article related to this error. Input image size is (720, 1280, 3) Thanks in advance.

[irjkf129]

Hello! You have error in str tracks = object_tracker.update_tracks(list1,frame) Need use arghument "frame" tracks = object_tracker.update_tracks(list1,frame=frame)

[mrsahabu]

any solution?

[Vishnu15018]

any solution? No, solution. I copy pasted the code directly from Nicolai Nielsen tutorials GitHub, repo on deepsort.

[Woodthorne]

I found that by adding a row a, b = np.squeeze(a), np.squeeze(b) after row 53 inside of the if not data_is_normalized portion of function _cosine_distance in nn_matching.py allows the program to proceed. I now face issues elsewhere in my program, but I haven't been able to diagnose fully if they are related to this error and fix.

[Woodthorne]

After running the fix for a few days now I can say that the issues that followed were unrelated and squeezing the variables seems to be working.