analyze videos error: ZeroDivisionError: division by zero ( BUCTD) #2989

deeplabcut.analyze_videos(
    config,
    videofile_path,
    shuffle=CTD_SHUFFLE,
    ctd_tracking=True,
    save_as_csv=True,
)


Analyzing videos with /content/drive/My Drive/LDx_v4m-HLH-2025-05-14/dlc-models-pytorch/iteration-1/LDx_v4mMay14-trainset80shuffle3/train/snapshot-best-070.pt
CTD tracking can only be used with batch size 1. Updating it.
Starting to analyze /content/drive/My Drive/LDx_v4m-HLH-2025-05-14/videos/mod3.mkv
Video metadata: 
  Overall # of frames:    969
  Duration of video [s]:  31.98
  fps:                    30.3
  resolution:             w=1920, h=1080

Running pose prediction with batch size 1

  4%|▍         | 37/969 [00:03<01:21, 11.49it/s]

---------------------------------------------------------------------------

ZeroDivisionError                         Traceback (most recent call last)

<ipython-input-10-74060d4d252a> in <cell line: 0>()
----> 1 deeplabcut.analyze_videos(
      2     config,
      3     videofile_path,
      4     shuffle=CTD_SHUFFLE,
      5     ctd_tracking=True,

9 frames

/usr/local/lib/python3.11/dist-packages/deeplabcut/compat.py in analyze_videos(config, videos, videotype, shuffle, trainingsetindex, gputouse, save_as_csv, in_random_order, destfolder, batchsize, cropping, TFGPUinference, dynamic, modelprefix, robust_nframes, allow_growth, use_shelve, auto_track, n_tracks, animal_names, calibrate, identity_only, use_openvino, engine, **torch_kwargs)
    952                 torch_kwargs["batch_size"] = batchsize
    953 
--> 954         return analyze_videos(
    955             config,
    956             videos=videos,

/usr/local/lib/python3.11/dist-packages/deeplabcut/pose_estimation_pytorch/apis/videos.py in analyze_videos(config, videos, videotype, shuffle, trainingsetindex, save_as_csv, in_random_order, snapshot_index, detector_snapshot_index, device, destfolder, batch_size, detector_batch_size, dynamic, ctd_conditions, ctd_tracking, top_down_dynamic, modelprefix, use_shelve, robust_nframes, transform, auto_track, n_tracks, animal_names, calibrate, identity_only, overwrite, cropping, save_as_df)
    543         else:
    544             runtime = [time.time()]
--> 545             predictions = video_inference(
    546                 video=video_iterator,
    547                 pose_runner=pose_runner,

/usr/local/lib/python3.11/dist-packages/deeplabcut/pose_estimation_pytorch/apis/videos.py in video_inference(video, pose_runner, detector_runner, cropping, shelf_writer, robust_nframes)
    202         shelf_writer.open()
    203 
--> 204     predictions = pose_runner.inference(images=tqdm(video), shelf_writer=shelf_writer)
    205     if shelf_writer is not None:
    206         shelf_writer.close()

/usr/local/lib/python3.11/dist-packages/torch/utils/_contextlib.py in decorate_context(*args, **kwargs)
    114     def decorate_context(*args, **kwargs):
    115         with ctx_factory():
--> 116             return func(*args, **kwargs)
    117 
    118     return decorate_context

/usr/local/lib/python3.11/dist-packages/deeplabcut/pose_estimation_pytorch/runners/inference.py in inference(self, images, shelf_writer)
    392         """
    393         if self.tracking:
--> 394             return self._ctd_tracking_inference(images, shelf_writer)
    395 
    396         results = []

/usr/local/lib/python3.11/dist-packages/deeplabcut/pose_estimation_pytorch/runners/inference.py in _ctd_tracking_inference(self, images, shelf_writer)
    487         results = []
    488         for data in images:
--> 489             inputs, context = self._prepare_ctd_inputs(data)
    490             model_kwargs = context.pop("model_kwargs", {})
    491             predictions = self.predict(inputs, **model_kwargs)

/usr/local/lib/python3.11/dist-packages/deeplabcut/pose_estimation_pytorch/runners/inference.py in _prepare_ctd_inputs(self, data)
    545             return torch.as_tensor(inputs), context
    546 
--> 547         inputs, context = self.preprocessor(inputs, context)
    548         return inputs, context
    549 

/usr/local/lib/python3.11/dist-packages/deeplabcut/pose_estimation_pytorch/data/preprocessor.py in __call__(self, image, context)
    176     def __call__(self, image: Image, context: Context) -> tuple[Image, Context]:
    177         for preprocessor in self.components:
--> 178             image, context = preprocessor(image, context)
    179         return image, context
    180 

/usr/local/lib/python3.11/dist-packages/deeplabcut/pose_estimation_pytorch/data/preprocessor.py in __call__(self, image, context)
    389         images, offsets, scales = [], [], []
    390         for bbox in context["bboxes"]:
--> 391             crop, offset, scale = top_down_crop(
    392                 image,
    393                 bbox,

/usr/local/lib/python3.11/dist-packages/deeplabcut/pose_estimation_pytorch/data/image.py in top_down_crop(image, bbox, output_size, margin, center_padding, crop_with_context)
    273     w, h = x2 - x1, y2 - y1
    274     if not crop_with_context:
--> 275         input_ratio = w / h
    276         output_ratio = out_w / out_h
    277         if input_ratio > output_ratio:  # h/w < h0/w0 => h' = w * h0/w0

ZeroDivisionError: division by zero