Comments (4)
Actually you could use the following function to load ground truth sparse depth online during training/validation phase. And there will be no need to generate.
I don't know if this answers to your issue.
def loadSparseDepth(nusc, sample):
cam_token = sample['camera_token']
lidar_token = sample['lidar_token']
points, coloring, im = nusc.explorer.map_pointcloud_to_image(pointsensor_token=lidar_token,camera_token=cam_token)
sparse_depth = np.zeros((450,800))
sparse_depth[(points[1][:]/2).astype(np.int), (points[0][:]/2).astype(np.int)] = coloring
return sparse_depth
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Actually you could use the following function to load ground truth sparse depth online during training/validation phase. And there will be no need to generate. I don't know if this answers to your issue.
def loadSparseDepth(nusc, sample): cam_token = sample['camera_token'] lidar_token = sample['lidar_token'] points, coloring, im = nusc.explorer.map_pointcloud_to_image(pointsensor_token=lidar_token,camera_token=cam_token) sparse_depth = np.zeros((450,800)) sparse_depth[(points[1][:]/2).astype(np.int), (points[0][:]/2).astype(np.int)] = coloring return sparse_depth
where can put this function?
And where to call it?
Thx!
from rcdpt.
Actually you could use the following function to load ground truth sparse depth online during training/validation phase. And there will be no need to generate. I don't know if this answers to your issue.
def loadSparseDepth(nusc, sample): cam_token = sample['camera_token'] lidar_token = sample['lidar_token'] points, coloring, im = nusc.explorer.map_pointcloud_to_image(pointsensor_token=lidar_token,camera_token=cam_token) sparse_depth = np.zeros((450,800)) sparse_depth[(points[1][:]/2).astype(np.int), (points[0][:]/2).astype(np.int)] = coloring return sparse_depth
Sorry,maybe really need your help,i don't know where to put it and call it,i tried many times but still error : (
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If you still have the same issue, you could let me know it.
Otherwise, I would recommend you to go through again the official tutorial jupyter notebook file.
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Related Issues (4)
- open the code HOT 1
- Train my own dataset HOT 1
- trained model
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