About visualization on val

[ymlzOvO]

Hi, I'm trying to reproduce your codes, and it does run successfully with:
python test.py "configs/frnet/frnet-semantickitti_seg.py" "pretrained/frnet-semantickitti_seg.pth"
but when I want to visualize the results using follow command:
python test.py "configs/frnet/frnet-semantickitti_seg.py" "pretrained/frnet-semantickitti_seg.pth" --show --show-dir "show_dirs" --task "lidar_seg"
it turns to be
AssertionError: 'data_sample' must contain 'img_path' or 'lidar_path'
So how do you do to visualize just like what is showed in the project page, thank you! I am not familiar with mmcv, and just tried the command in its document.

[Xiangxu-0103]

Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along BEV and range image, which has not been fully supported in mmdet3d.

Moreover, we will provide a script to visualize the results in the future (due to busy recently).

[ymlzOvO]

Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along BEV and range image, which has not been fully supported in mmdet3d.

Moreover, we will provide a script to visualize the results in the future (due to busy recently).

@Xiangxu-0103 Thank you! I've finished saving the results as .label just like ground truth. Also, I use Det3DLocalVisualizer() to display in a static way. Could you tell me more details about realizing your demo video using BEV and range image...what library, documents, or codebase should I refer to? Thanks again!

[Wansit99]

Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along BEV and range image, which has not been fully supported in mmdet3d.
Moreover, we will provide a script to visualize the results in the future (due to busy recently).

@Xiangxu-0103 Thank you! I've finished saving the results as .label just like ground truth. Also, I use Det3DLocalVisualizer() to display in a static way. Could you tell me more details about realizing your demo video using BEV and range image...what library, documents, or codebase should I refer to? Thanks again!

Hello! I've also been working on visualizing results recently. May I share your code for saving the results? Thank you!

[ymlzOvO]

Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along BEV and range image, which has not been fully supported in mmdet3d.
Moreover, we will provide a script to visualize the results in the future (due to busy recently).

@Xiangxu-0103 Thank you! I've finished saving the results as .label just like ground truth. Also, I use Det3DLocalVisualizer() to display in a static way. Could you tell me more details about realizing your demo video using BEV and range image...what library, documents, or codebase should I refer to? Thanks again!

Hello! I've also been working on visualizing results recently. May I share your code for saving the results? Thank you!

Hi, I just take inference from #13 , there's the codes you need. For a static display, I use the template given in mmdet3d, but i don't how to make it be dynamic...

import numpy as np

from mmdet3d.visualization import Det3DLocalVisualizer

points = np.fromfile('demo/data/sunrgbd/000017.bin', dtype=np.float32)
points = points.reshape(-1, 3)
visualizer = Det3DLocalVisualizer()
mask = np.random.rand(points.shape[0], 3)
points_with_mask = np.concatenate((points, mask), axis=-1)
visualizer.set_points(points, pcd_mode=2, vis_mode='add')
visualizer.draw_seg_mask(points_with_mask)
visualizer.show()

[Wansit99]

Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along BEV and range image, which has not been fully supported in mmdet3d.
Moreover, we will provide a script to visualize the results in the future (due to busy recently).

@Xiangxu-0103 Thank you! I've finished saving the results as .label just like ground truth. Also, I use Det3DLocalVisualizer() to display in a static way. Could you tell me more details about realizing your demo video using BEV and range image...what library, documents, or codebase should I refer to? Thanks again!

Hello! I've also been working on visualizing results recently. May I share your code for saving the results? Thank you!

Hi, I just take inference from #13 , there's the codes you need. For a static display, I use the template given in mmdet3d, but i don't how to make it be dynamic...

import numpy as np

from mmdet3d.visualization import Det3DLocalVisualizer

points = np.fromfile('demo/data/sunrgbd/000017.bin', dtype=np.float32)
points = points.reshape(-1, 3)
visualizer = Det3DLocalVisualizer()
mask = np.random.rand(points.shape[0], 3)
points_with_mask = np.concatenate((points, mask), axis=-1)
visualizer.set_points(points, pcd_mode=2, vis_mode='add')
visualizer.draw_seg_mask(points_with_mask)
visualizer.show()

Thank you for your response. However, I am still not quite clear on how to save the results from a network as a .label file. Could you provide more detailed information? Thank you!

[ymlzOvO]

Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along BEV and range image, which has not been fully supported in mmdet3d.
Moreover, we will provide a script to visualize the results in the future (due to busy recently).

@Xiangxu-0103 Thank you! I've finished saving the results as .label just like ground truth. Also, I use Det3DLocalVisualizer() to display in a static way. Could you tell me more details about realizing your demo video using BEV and range image...what library, documents, or codebase should I refer to? Thanks again!

Hello! I've also been working on visualizing results recently. May I share your code for saving the results? Thank you!

Hi, I just take inference from #13 , there's the codes you need. For a static display, I use the template given in mmdet3d, but i don't how to make it be dynamic...

import numpy as np

from mmdet3d.visualization import Det3DLocalVisualizer

points = np.fromfile('demo/data/sunrgbd/000017.bin', dtype=np.float32)
points = points.reshape(-1, 3)
visualizer = Det3DLocalVisualizer()
mask = np.random.rand(points.shape[0], 3)
points_with_mask = np.concatenate((points, mask), axis=-1)
visualizer.set_points(points, pcd_mode=2, vis_mode='add')
visualizer.draw_seg_mask(points_with_mask)
visualizer.show()

Thank you for your response. However, I am still not quite clear on how to save the results from a network as a .label file. Could you provide more detailed information? Thank you!

In the config file frnet-semantickitti_seg.py, there's a line to assign test_evaluator = dict(type='SegMetric'), you should register a new one like in #13 , and use it instead of the default one.

[Wansit99]

Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along BEV and range image, which has not been fully supported in mmdet3d.
Moreover, we will provide a script to visualize the results in the future (due to busy recently).

@Xiangxu-0103 Thank you! I've finished saving the results as .label just like ground truth. Also, I use Det3DLocalVisualizer() to display in a static way. Could you tell me more details about realizing your demo video using BEV and range image...what library, documents, or codebase should I refer to? Thanks again!

Hello! I've also been working on visualizing results recently. May I share your code for saving the results? Thank you!

Hi, I just take inference from #13 , there's the codes you need. For a static display, I use the template given in mmdet3d, but i don't how to make it be dynamic...

import numpy as np

from mmdet3d.visualization import Det3DLocalVisualizer

points = np.fromfile('demo/data/sunrgbd/000017.bin', dtype=np.float32)
points = points.reshape(-1, 3)
visualizer = Det3DLocalVisualizer()
mask = np.random.rand(points.shape[0], 3)
points_with_mask = np.concatenate((points, mask), axis=-1)
visualizer.set_points(points, pcd_mode=2, vis_mode='add')
visualizer.draw_seg_mask(points_with_mask)
visualizer.show()

Thank you for your response. However, I am still not quite clear on how to save the results from a network as a .label file. Could you provide more detailed information? Thank you!

In the config file frnet-semantickitti_seg.py, there's a line to assign test_evaluator = dict(type='SegMetric'), you should register a new one like in #13 , and use it instead of the default one.

Thanks！I will try it

[Wansit99]

Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along BEV and range image, which has not been fully supported in mmdet3d.
Moreover, we will provide a script to visualize the results in the future (due to busy recently).

@Xiangxu-0103 Thank you! I've finished saving the results as .label just like ground truth. Also, I use Det3DLocalVisualizer() to display in a static way. Could you tell me more details about realizing your demo video using BEV and range image...what library, documents, or codebase should I refer to? Thanks again!

Hello! I've also been working on visualizing results recently. May I share your code for saving the results? Thank you!

Hi, I just take inference from #13 , there's the codes you need. For a static display, I use the template given in mmdet3d, but i don't how to make it be dynamic...

import numpy as np

from mmdet3d.visualization import Det3DLocalVisualizer

points = np.fromfile('demo/data/sunrgbd/000017.bin', dtype=np.float32)
points = points.reshape(-1, 3)
visualizer = Det3DLocalVisualizer()
mask = np.random.rand(points.shape[0], 3)
points_with_mask = np.concatenate((points, mask), axis=-1)
visualizer.set_points(points, pcd_mode=2, vis_mode='add')
visualizer.draw_seg_mask(points_with_mask)
visualizer.show()

Thank you for your response. However, I am still not quite clear on how to save the results from a network as a .label file. Could you provide more detailed information? Thank you!

In the config file frnet-semantickitti_seg.py, there's a line to assign test_evaluator = dict(type='SegMetric'), you should register a new one like in #13 , and use it instead of the default one.

But when I try it, it looks that, how can i solve it? Thanks!

#map_inv = self.dataset_meta['learning_map_inv'] #inv mapping
#KeyError: 'learning_map_inv'

[ymlzOvO]

Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along BEV and range image, which has not been fully supported in mmdet3d.
Moreover, we will provide a script to visualize the results in the future (due to busy recently).

@Xiangxu-0103 Thank you! I've finished saving the results as .label just like ground truth. Also, I use Det3DLocalVisualizer() to display in a static way. Could you tell me more details about realizing your demo video using BEV and range image...what library, documents, or codebase should I refer to? Thanks again!

Hello! I've also been working on visualizing results recently. May I share your code for saving the results? Thank you!

Hi, I just take inference from #13 , there's the codes you need. For a static display, I use the template given in mmdet3d, but i don't how to make it be dynamic...

import numpy as np

from mmdet3d.visualization import Det3DLocalVisualizer

points = np.fromfile('demo/data/sunrgbd/000017.bin', dtype=np.float32)
points = points.reshape(-1, 3)
visualizer = Det3DLocalVisualizer()
mask = np.random.rand(points.shape[0], 3)
points_with_mask = np.concatenate((points, mask), axis=-1)
visualizer.set_points(points, pcd_mode=2, vis_mode='add')
visualizer.draw_seg_mask(points_with_mask)
visualizer.show()

Thank you for your response. However, I am still not quite clear on how to save the results from a network as a .label file. Could you provide more detailed information? Thank you!

In the config file frnet-semantickitti_seg.py, there's a line to assign test_evaluator = dict(type='SegMetric'), you should register a new one like in #13 , and use it instead of the default one.

But when I try it, it looks that, how can i solve it? Thanks!

#map_inv = self.dataset_meta['learning_map_inv'] #inv mapping #KeyError: 'learning_map_inv'

use 'label_mapping' instead

[Wansit99]

Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along BEV and range image, which has not been fully supported in mmdet3d.
Moreover, we will provide a script to visualize the results in the future (due to busy recently).

@Xiangxu-0103 Thank you! I've finished saving the results as .label just like ground truth. Also, I use Det3DLocalVisualizer() to display in a static way. Could you tell me more details about realizing your demo video using BEV and range image...what library, documents, or codebase should I refer to? Thanks again!

Hello! I've also been working on visualizing results recently. May I share your code for saving the results? Thank you!

Hi, I just take inference from #13 , there's the codes you need. For a static display, I use the template given in mmdet3d, but i don't how to make it be dynamic...

import numpy as np

from mmdet3d.visualization import Det3DLocalVisualizer

points = np.fromfile('demo/data/sunrgbd/000017.bin', dtype=np.float32)
points = points.reshape(-1, 3)
visualizer = Det3DLocalVisualizer()
mask = np.random.rand(points.shape[0], 3)
points_with_mask = np.concatenate((points, mask), axis=-1)
visualizer.set_points(points, pcd_mode=2, vis_mode='add')
visualizer.draw_seg_mask(points_with_mask)
visualizer.show()

Thank you for your response. However, I am still not quite clear on how to save the results from a network as a .label file. Could you provide more detailed information? Thank you!

In the config file frnet-semantickitti_seg.py, there's a line to assign test_evaluator = dict(type='SegMetric'), you should register a new one like in #13 , and use it instead of the default one.

But when I try it, it looks that, how can i solve it? Thanks!
#map_inv = self.dataset_meta['learning_map_inv'] #inv mapping #KeyError: 'learning_map_inv'

use 'label_mapping' instead

Thank you for sharing, I have successfully saved the results. May I add your personal contact information? I am also currently researching FRNet, can we communicate and discuss together!

[xiaosa269]

Hello,

Thank you very much for your contribution to the visualization of Val. I have now saved the .label files generated by the test. However, when I use the semantic-kitti-api（https://github.com/PRBonn/semantic-kitti-api） for visualization,

 python  ./visualize.py --sequence 11 --dataset /data/semantickitti_frnet/dataset --predictions /data/semantickitti_frnet/dataset

it shows that the number of labels does not match the number of points.

Have you encountered such an issue? If so, how did you resolve it?