52 lines
2.3 KiB
Markdown
52 lines
2.3 KiB
Markdown
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# Quick Demo
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Here we provide a quick demo to test a pretrained model on the custom point cloud data and visualize the predicted results.
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We suppose you already followed the [INSTALL.md](INSTALL.md) to install the `OpenPCDet` repo successfully.
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1. Download the provided pretrained models as shown in the [README.md](../README.md).
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2. Make sure you have already installed the [`Open3D`](https://github.com/isl-org/Open3D) (faster) or `mayavi` visualization tools.
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If not, you could install it as follows:
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```
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pip install open3d
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# or
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pip install mayavi
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```
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3. Prepare your custom point cloud data (skip this step if you use the original KITTI data).
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* You need to transform the coordinate of your custom point cloud to
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the unified normative coordinate of `OpenPCDet`, that is, x-axis points towards to front direction,
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y-axis points towards to the left direction, and z-axis points towards to the top direction.
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* (Optional) the z-axis origin of your point cloud coordinate should be about 1.6m above the ground surface,
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since currently the provided models are trained on the KITTI dataset.
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* Set the intensity information, and save your transformed custom data to `numpy file`:
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```python
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# Transform your point cloud data
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...
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# Save it to the file.
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# The shape of points should be (num_points, 4), that is [x, y, z, intensity] (Only for KITTI dataset).
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# If you doesn't have the intensity information, just set them to zeros.
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# If you have the intensity information, you should normalize them to [0, 1].
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points[:, 3] = 0
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np.save(`my_data.npy`, points)
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```
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4. Run the demo with a pretrained model (e.g. PV-RCNN) and your custom point cloud data as follows:
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```shell
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python demo.py --cfg_file cfgs/kitti_models/pv_rcnn.yaml \
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--ckpt pv_rcnn_8369.pth \
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--data_path ${POINT_CLOUD_DATA}
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```
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Here `${POINT_CLOUD_DATA}` could be in any of the following format:
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* Your transformed custom data with a single numpy file like `my_data.npy`.
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* Your transformed custom data with a directory to test with multiple point cloud data.
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* The original KITTI `.bin` data within `data/kitti`, like `data/kitti/training/velodyne/000008.bin`.
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Then you could see the predicted results with visualized point cloud as follows:
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<p align="center">
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<img src="demo.png" width="99%">
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</p>
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