Comments (2)
我使用的视差图着色代码在https://github.com/gangweiX/IGEV/blob/main/IGEV-Stereo/demo_imgs.py中的第52行,即
plt.imsave(output_directory / f"{file_stem}.png", disp.squeeze(), cmap='jet'),如果是cv2.imwrite的话,则是
cv2.imwrite(filename, cv2.applyColorMap(cv2.convertScaleAbs(disp.squeeze(), alpha=0.01),cv2.COLORMAP_JET), [int(cv2.IMWRITE_PNG_COMPRESSION), 0])。方便的话,可以把我的几篇论文都引用一下,应该是和你的论文主题一致的,如果不一致就算了
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我使用的视差图着色代码在https://github.com/gangweiX/IGEV/blob/main/IGEV-Stereo/demo_imgs.py中的第52行,即 plt.imsave(output_directory / f"{file_stem}.png", disp.squeeze(), cmap='jet'),如果是cv2.imwrite的话,则是 cv2.imwrite(filename, cv2.applyColorMap(cv2.convertScaleAbs(disp.squeeze(), alpha=0.01),cv2.COLORMAP_JET), [int(cv2.IMWRITE_PNG_COMPRESSION), 0])。方便的话,可以把我的几篇论文都引用一下,应该是和你的论文主题一致的,如果不一致就算了
ACVNet和IGEV都引用了,感谢您的帮助
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Related Issues (20)
- Two way matching (left->right, right->left)
- 【MVS】推理代码 HOT 1
- 请问相机的参数是否会对结果造成一定影响?测试中的摩托照片是用什么设备拍摄的? HOT 1
- 请问合并kitti2012和kitti2015数据集进行微调后,如何选出最优的kitti2012的模型呢?与kitti2015最优的模型是同一个吗? HOT 2
- 测试时相同输入推理结果不一致
- 多卡训练问题
- Considerations with Colmap data
- epe evaluation metric is different from RAFT-stereo
- Runtime is not matching as mentioned in the paper HOT 4
- 【slow_fast_gru options】
- 关于kitti验证的问题
- shared_backbone支持
- onnx模型 HOT 4
- 为什么一个场景,对应一个pth模型?
- kitti训练问题
- Q HOT 5
- 请教如何在KITTI Benchmark上评估自己的模型
- RuntimeError: CUDA out of memory
- 如何获取视差值 HOT 2
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