unravel.image_tools.spatial_averaging module#
Use img_spatial_avg (spatial_avg) from UNRAVEL to load an image and apply 3D spatial averaging.
- Input image types:
.czi, .nii.gz, .ome.tif series, .tif series, .h5, .zarr
- Outputs:
.nii.gz, .tif series, or .zarr depending on the output path extension.
- 3D spatial averaging:
Apply a 3D spatial averaging filter to a 3D numpy array.
Default kernel size is 3x3x3, for the current voxel and its 26 neighbors.
The output array is the same size as the input array.
The edges of the output array are padded with zeros.
The output array is the same data type as the input array.
The input array must be 3D.
The xy and z resolutions are required for saving the output as .nii.gz.
The output is saved as .nii.gz, .tif series, or .zarr.
- 2D spatial averaging:
Apply a 2D spatial averaging filter to each slice of a 3D numpy array.
Default kernel size is 3x3, for the current pixel and its 8 neighbors.
The output array is the same size as the input array.
The edges of the output array are padded with zeros.
The output array is the same data type as the input array.
The input array must be 3D.
The xy and z resolutions are required for saving the output as .nii.gz.
The output is saved as .nii.gz, .tif series, or .zarr.
Usage:#
img_spatial_avg -i <tif_dir> -o spatial_avg.zarr -d 2 [-k 3] [-c 0] [-x 3.5232] [-z 6] [-dt uint16] [-r metadata_referenece.nii.gz] [-ao xyz] [-v]
- unravel.image_tools.spatial_averaging.spatial_average_3D(arr, kernel_size=3)[source]#
Apply a 3D spatial averaging filter to a 3D numpy array.
Parameters: - arr (np.ndarray): The input 3D array. - kernel_size (int): The size of the cubic kernel. Default is 3, for the current voxel and its 26 neighbors.
Returns: - np.ndarray: The array after applying the spatial averaging.
- unravel.image_tools.spatial_averaging.apply_2D_mean_filter(slice, kernel_size=(3, 3))[source]#
Apply a 2D mean filter to a single slice.
- unravel.image_tools.spatial_averaging.spatial_average_2D(volume, filter_func, kernel_size=(3, 3), threads=8)[source]#
Apply a specified 2D filter function to each slice of a 3D volume in parallel.
Parameters: - volume (np.ndarray): The input 3D array. - filter_func (callable): The filter function to apply to each slice. - kernel_size (tuple): The dimensions of the kernel to be used in the filter. - threads (int): The number of parallel threads to use.
Returns: - np.ndarray: The volume processed with the filter applied to each slice.