Comments (11)
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).
from frnet.
Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along
BEV
andrange image
, which has not been fully supported inmmdet3d
.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!
from frnet.
Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along
BEV
andrange image
, which has not been fully supported inmmdet3d
.
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!
from frnet.
Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along
BEV
andrange image
, which has not been fully supported inmmdet3d
.
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()
from frnet.
Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along
BEV
andrange image
, which has not been fully supported inmmdet3d
.
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!
from frnet.
Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along
BEV
andrange image
, which has not been fully supported inmmdet3d
.
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.
from frnet.
Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along
BEV
andrange image
, which has not been fully supported inmmdet3d
.
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 assigntest_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
from frnet.
Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along
BEV
andrange image
, which has not been fully supported inmmdet3d
.
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 assigntest_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'
from frnet.
Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along
BEV
andrange image
, which has not been fully supported inmmdet3d
.
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 assigntest_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
from frnet.
Hi @ymlzOvO, you can save the predictions offline for visualization. We visualize the results along
BEV
andrange image
, which has not been fully supported inmmdet3d
.
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 assigntest_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!
from frnet.
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?
from frnet.
Related Issues (20)
- About how to test HOT 2
- Testing issues:OSError: Caught OSError in DataLoader worker process 0. And OSError: [Errno 5] Input/output error HOT 3
- About the result of test HOT 6
- freeze model HOT 2
- param and fps HOT 4
- run on customized dataset HOT 8
- About update HOT 10
- About export prediction results HOT 2
- Question about .onnx model HOT 1
- Inference on different dataset HOT 2
- How to visualize HOT 2
- About FPS HOT 2
- Question About Replacing SemanticKITTI Dataset with Custom Dataset HOT 1
- Test Time Augment
- fast-FRNet HOT 2
- about infer time HOT 1
- add some points, original point's label will be change HOT 2
- How can we perform infering without ground truth?
- About 4090
- What about the losses? Did you use the supervised loss proposed in your paper or the one used in the LaserMix paper?
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from frnet.