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When I use my own picture to run the image_demo.py, it occurs /OMG-Seg/demo/image_demo.py:68 in │ │ │ │ 65 │ result = results[0] │ │ 66 │ print(result) │ │ 67 │ img = mmcv.imread(result.img_path, channel_order='rgb') │ │ ❱ 68 │ visualizer.add_datasample( │ │ 69 │ │ 'test_img', │ │ 70 │ │ img, │ │ 71 │ │ data_sample=result, │ │ │ │ /usr/local/lib/python3.8/dist-packages/mmengine/dist/utils.py:401 in wrapper │ │ │ │ 398 │ @functools.wraps(func) │ │ 399 │ def wrapper(*args, **kwargs): │ │ 400 │ │ if is_main_process(): │ │ ❱ 401 │ │ │ return func(*args, **kwargs) │ │ 402 │ │ │ 403 │ return wrapper │ │ 404 │ │ │ │ /OMG-Seg/seg/evaluation/hooks/visual_hook.py:294 in add_datasample │ │ │ │ 291 │ │ │ │ │ │ │ │ │ │ │ 'not provided when ' \ │ │ 292 │ │ │ │ │ │ │ │ │ │ │ 'visualizing panoptic ' \ │ │ 293 │ │ │ │ │ │ │ │ │ │ │ 'segmentation results.' │ │ ❱ 294 │ │ │ │ pred_img_data = self._draw_panoptic_seg( │ │ 295 │ │ │ │ │ pred_img_data, data_sample.pred_panoptic_seg.numpy(), │ │ 296 │ │ │ │ │ classes, palette) │ │ 297 │ │ │ │ /mmdetection-3.1.0/mmdet/visualization/local_visualizer.py:300 in _draw_panoptic_seg │ │ │ │ 297 │ │ │ max_id = np.argmax(stats[1:, -1]) + 1 │ │ 298 │ │ │ positions.append(centroids[max_id]) │ │ 299 │ │ │ areas.append(stats[max_id, -1]) │ │ ❱ 300 │ │ areas = np.stack(areas, axis=0) │ │ 301 │ │ scales = _get_adaptive_scales(areas) │ │ 302 │ │ │ │ 303 │ │ text_palette = get_palette(self.text_color, max_label + 1) │ │ in stack:200 │ │ │ │ /usr/local/lib/python3.8/dist-packages/numpy/core/shape_base.py:460 in stack │ │ │ │ 457 │ │ │ 458 │ arrays = [asanyarray(arr) for arr in arrays] │ │ 459 │ if not arrays: │ │ ❱ 460 │ │ raise ValueError('need at least one array to stack') │ │ 461 │ │ │ 462 │ shapes = {arr.shape for arr in arrays} │ │ 463 │ if len(shapes) != 1: ValueError: need at least one array to stack sem_seg in image_demo.py is all 240. sem_seg: tensor([[[240, 240, 240, ..., 240, 240, 240], [240, 240, 240, ..., 240, 240, 240], [240, 240, 240, ..., 240, 240, 240], ..., [240, 240, 240, ..., 240, 240, 240], [240, 240, 240, ..., 240, 240, 240], [240, 240, 240, ..., 240, 240, 240]]] how to solve it ?
The text was updated successfully, but these errors were encountered:
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When I use my own picture to run the image_demo.py, it occurs
/OMG-Seg/demo/image_demo.py:68 in │
│ │
│ 65 │ result = results[0] │
│ 66 │ print(result) │
│ 67 │ img = mmcv.imread(result.img_path, channel_order='rgb') │
│ ❱ 68 │ visualizer.add_datasample( │
│ 69 │ │ 'test_img', │
│ 70 │ │ img, │
│ 71 │ │ data_sample=result, │
│ │
│ /usr/local/lib/python3.8/dist-packages/mmengine/dist/utils.py:401 in wrapper │
│ │
│ 398 │ @functools.wraps(func) │
│ 399 │ def wrapper(*args, **kwargs): │
│ 400 │ │ if is_main_process(): │
│ ❱ 401 │ │ │ return func(*args, **kwargs) │
│ 402 │ │
│ 403 │ return wrapper │
│ 404 │
│ │
│ /OMG-Seg/seg/evaluation/hooks/visual_hook.py:294 in add_datasample │
│ │
│ 291 │ │ │ │ │ │ │ │ │ │ │ 'not provided when ' \ │
│ 292 │ │ │ │ │ │ │ │ │ │ │ 'visualizing panoptic ' \ │
│ 293 │ │ │ │ │ │ │ │ │ │ │ 'segmentation results.' │
│ ❱ 294 │ │ │ │ pred_img_data = self._draw_panoptic_seg( │
│ 295 │ │ │ │ │ pred_img_data, data_sample.pred_panoptic_seg.numpy(), │
│ 296 │ │ │ │ │ classes, palette) │
│ 297 │
│ │
│ /mmdetection-3.1.0/mmdet/visualization/local_visualizer.py:300 in _draw_panoptic_seg │
│ │
│ 297 │ │ │ max_id = np.argmax(stats[1:, -1]) + 1 │
│ 298 │ │ │ positions.append(centroids[max_id]) │
│ 299 │ │ │ areas.append(stats[max_id, -1]) │
│ ❱ 300 │ │ areas = np.stack(areas, axis=0) │
│ 301 │ │ scales = _get_adaptive_scales(areas) │
│ 302 │ │ │
│ 303 │ │ text_palette = get_palette(self.text_color, max_label + 1) │
│ in stack:200 │
│ │
│ /usr/local/lib/python3.8/dist-packages/numpy/core/shape_base.py:460 in stack │
│ │
│ 457 │ │
│ 458 │ arrays = [asanyarray(arr) for arr in arrays] │
│ 459 │ if not arrays: │
│ ❱ 460 │ │ raise ValueError('need at least one array to stack') │
│ 461 │ │
│ 462 │ shapes = {arr.shape for arr in arrays} │
│ 463 │ if len(shapes) != 1:
ValueError: need at least one array to stack
sem_seg in image_demo.py is all 240.
sem_seg: tensor([[[240, 240, 240, ..., 240, 240, 240],
[240, 240, 240, ..., 240, 240, 240],
[240, 240, 240, ..., 240, 240, 240],
...,
[240, 240, 240, ..., 240, 240, 240],
[240, 240, 240, ..., 240, 240, 240],
[240, 240, 240, ..., 240, 240, 240]]]
how to solve it ?
The text was updated successfully, but these errors were encountered: