Comments (2)
May be you can do like this:
semantic_mask = semantic_mask.numpy() * 255
# semantic_mask = semantic_mask.astype('uint8') * 255
semantic_mask = np.moveaxis(semantic_mask, 0, -1)
Image.fromarray(np.uint8(semantic_mask)).save(semantic_path)
# instance_mask = instance_mask.sum(0).astype('uint8')
# instance_color_mask = color_map[instance_mask].astype('uint8')
instance_mask = np.uint8(instance_mask.sum(0).numpy())
instance_color_mask = color_map[instance_mask]
Image.fromarray(np.uint8(instance_color_mask)).save(instance_path)
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@xiaomouse2004 Appreciated!
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Related Issues (20)
- Inconsistent Intrinsic Matrix in data fetching, probably leading to worse IPMNet performance HOT 2
- Confusion about VPN, Lift splat, and IPM Net
- How to Temporal Fusion HOT 1
- About the threshold setting equals [2, 4, 6], different from [0.2, 0.5, 1] HOT 2
- About the IOU HOT 1
- Can someone reproduce the results in the paper and provide some simple guidance? HOT 3
- gpu used in training HOT 2
- About post-process time problem
- 运行vis_pred.py 时报错 HOT 9
- lost intrins in IPM transformation HOT 2
- Is there anyway to get the World Coordinates from predicted BEV?
- XIO: fatal IO error 0 (Success) on X server "localhost:10.0"
- Is there any wrong with eval_iou()? HOT 6
- 'instance_seg' and 'direction_pred' both use 'self.up1_embedded.' is it intended?
- Are the HDMaPNet results from the validation set or the nuscene test set?
- The vectorized visualization results are very bad, but the training accuracy is close to the accuracy proposed in the paper。 HOT 3
- problem while evaluating
- How to predict new Data set HOT 2
- result visualization HOT 2
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