Comments (5)
你可以修改num_control_points(越少变形越明显,不建议低于5)或者max_displacement(越大变形越明显,不得超过1)
from medvision.
你可以修改num_control_points(越少变形越明显,不建议低于5)或者max_displacement(越大变形越明显,不得超过1)
按照您的建议我进行了修改,图像的标签确实产生了大的变形,但是我发现,图像并没有产生相应的变形,只是在标签上产生了变形。如图所示:
原图像中图像和标签是可以很好的重合的。如下图:
.
此外,我在将方法应用到我自己的数据集的时候产生了如下结果,不知道是为什么,希望您能帮我解答一下。
非常感谢!
from medvision.
非常抱歉,我在版本升级时忘了修改test脚本示例,你需要指定
result = {
'img_dim': 2,
'img_spacing': (1.0, 1.0),
'filename': img_filename,
'img': img,
'img_fields': ['img'],
'gt_det': det,
'det_fields': ['gt_det'],
'gt_seg': seg,
'seg_fields': ['gt_seg']
}
中的'img_fields'或det_fields或seg_fields
from medvision.
此外,我在将方法应用到我自己的数据集的时候产生了如下结果,不知道是为什么,希望您能帮我解答一下。
关于这个你需要注意的是我使用了均值为-400,标准差为700的归一化操作,具体参见test.load_utils.load_3d_image_with_seg
from medvision.
此外,我在将方法应用到我自己的数据集的时候产生了如下结果,不知道是为什么,希望您能帮我解答一下。
关于这个你需要注意的是我使用了均值为-400,标准差为700的归一化操作,具体参见
test.load_utils.load_3d_image_with_seg
根据您的建议我已经得到了正确的结果。非常感谢!
from medvision.
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from medvision.