# registration fixed_image = sitk.VectorIndexSelectionCast(fixed_rgb, 1) moving_image = sitk.VectorIndexSelectionCast(moving_rgb, 1) fixed_image = sitk.Cast(fixed_image, sitk.sitkFloat32) moving_image = sitk.Cast(moving_image, sitk.sitkFloat32) def command_iteration(method): if (method.GetOptimizerIteration() == 0): print("Estimated Scales: ", method.GetOptimizerScales()) print(f"{method.GetOptimizerIteration():3} = {method.GetMetricValue():7.5f} : {method.GetOptimizerPosition()}") pixelType = sitk.sitkFloat32 R = sitk.ImageRegistrationMethod() R.SetMetricAsCorrelation()#Use negative normalized cross correlation image metric. R.SetOptimizerAsRegularStepGradientDescent(learningRate=4.0, minStep=0.1, numberOfIterations=5000, gradientMagnitudeTolerance=1e-8)#Regular Step Gradient descent optimizer. R.SetOptimizerScalesFromIndexShift()#Estimate scales from maximum voxel shift in index space cause by parameter change. tx = sitk.CenteredTransformInitializer(fixed_image, moving_image, sitk.Similarity2DTransform()) R.SetInitialTransform(tx) R.SetInterpolator(sitk.sitkLinear) R.AddCommand(sitk.sitkIterationEvent, lambda: command_iteration(R)) outTx = R.Execute(fixed_image, moving_image) print("-------") print(outTx) print(f"Optimizer stop condition: {R.GetOptimizerStopConditionDescription()}") print(f" Iteration: {R.GetOptimizerIteration()}") print(f" Metric value: {R.GetMetricValue()}") resampler = sitk.ResampleImageFilter() resampler.SetReferenceImage(fixed_image) resampler.SetInterpolator(sitk.sitkLinear) resampler.SetDefaultPixelValue(1) resampler.SetTransform(outTx) out = resampler.Execute(moving_image) simg1 = sitk.Cast(sitk.RescaleIntensity(fixed_image), sitk.sitkUInt8) simg2 = sitk.Cast(sitk.RescaleIntensity(out), sitk.sitkUInt8) cimg = sitk.Compose(simg1, simg2, simg1 // 2. + simg2 // 2.) myshow(cimg)在这段代码中找到调整步长的地方
时间: 2024-03-22 16:37:25 浏览: 14
在这段代码中,调整步长的地方是在设置优化器时,使用了`SetOptimizerAsRegularStepGradientDescent`函数,并设置了`learningRate`参数为4.0,`minStep`参数为0.1。可以看出,这里使用的是固定步长的梯度下降优化器。具体代码如下:
```
R.SetOptimizerAsRegularStepGradientDescent(learningRate=4.0, minStep=0.1, numberOfIterations=5000, gradientMagnitudeTolerance=1e-8)
```
相关问题
pose_graph = registration.PoseGraph() AttributeError: module 'registration' has no attribute 'PoseGraph'
根据引用和引用的内容,根据您提供的错误信息来看,可能是由于open3d的版本更新所致。为了解决这个问题,您可以按照以下步骤进行调整:
1. 在Open3D/examples/Python/ReconstructionSystem/目录下的所有.py文件中,将出现的o3d.pipelines.xxx改为o3d.xxx。
2. 对于AttributeError: 'bool' object has no attribute 'lower'的错误,您可以打开refine_registration.py文件,并将if config["python_multi_threading"].lower() == "true"的部分修改为if str(config["python_multi_threading"]).lower() == "true"。
关于您提到的pose_graph = registration.PoseGraph()错误,根据错误信息"AttributeError: module 'registration' has no attribute 'PoseGraph'"可知,registration模块中没有PoseGraph属性。为了解决这个问题,您可以尝试使用以下代码进行修改:
pose_graph = open3d.registration.PoseGraph()
long registration_time = System.currentTimeMillis();请将这个定义修改一下,使它能转换成smalldatetime类型的数据
在Java中,没有smalldatetime类型,它是SQL Server中的一种日期时间类型。如果要将Java中的long类型时间戳转换成SQL Server中的smalldatetime类型,可以使用以下代码:
```
long registration_time = System.currentTimeMillis();
java.sql.Timestamp timestamp = new java.sql.Timestamp(registration_time);
java.sql.Date date = new java.sql.Date(timestamp.getTime());
java.sql.Time time = new java.sql.Time(timestamp.getTime());
java.sql.Timestamp smalldatetime = java.sql.Timestamp.valueOf(date.toString() + " " + time.toString().substring(0, 8));
```
这里首先将long类型时间戳转换成Java的Timestamp类型,然后再将其转换成Date类型和Time类型。最后通过将Date和Time类型的字符串拼接成一个完整的时间戳字符串,并将其转换成SQL Server的Timestamp类型,从而实现了long类型时间戳到smalldatetime类型的转换。