⚠️ 已重命名为 multiscale-spatial-image

spatial-image-multiscale

生成一个多尺度、分块的多维空间图像数据结构，可以序列化为 OME-NGFF。

每个尺度都是一个科学Python Xarray spatial-image Dataset，组织成Xarray Datatree的节点。

安装

pip install spatial_image_multiscale

使用

import numpy as np
from spatial_image import to_spatial_image
from spatial_image_multiscale import to_multiscale
import zarr

# Image pixels
array = np.random.randint(0, 256, size=(128,128), dtype=np.uint8)

image = to_spatial_image(array)
print(image)

一个Xarray spatial-image DataArray。也可以在构建时传递空间元数据。

<xarray.SpatialImage 'image' (y: 128, x: 128)>
array([[114,  47, 215, ..., 245,  14, 175],
       [ 94, 186, 112, ...,  42,  96,  30],
       [133, 170, 193, ..., 176,  47,   8],
       ...,
       [202, 218, 237, ...,  19, 108, 135],
       [ 99,  94, 207, ..., 233,  83, 112],
       [157, 110, 186, ..., 142, 153,  42]], dtype=uint8)
Coordinates:
  * y        (y) float64 0.0 1.0 2.0 3.0 4.0 ... 123.0 124.0 125.0 126.0 127.0
  * x        (x) float64 0.0 1.0 2.0 3.0 4.0 ... 123.0 124.0 125.0 126.0 127.0

# Create multiscale pyramid, downscaling by a factor of 2, then 4
multiscale = to_multiscale(image, [2, 4])
print(multiscale)

一个分块的多尺度空间图像Dask Array MultiscaleSpatialImage Xarray Datatree。

DataTree('multiscales', parent=None)
├── DataTree('scale0')
│   Dimensions:  (y: 128, x: 128)
│   Coordinates:
│     * y        (y) float64 0.0 1.0 2.0 3.0 4.0 ... 123.0 124.0 125.0 126.0 127.0
│     * x        (x) float64 0.0 1.0 2.0 3.0 4.0 ... 123.0 124.0 125.0 126.0 127.0
│   Data variables:
│       image    (y, x) uint8 dask.array<chunksize=(128, 128), meta=np.ndarray>
├── DataTree('scale1')
│   Dimensions:  (y: 64, x: 64)
│   Coordinates:
│     * y        (y) float64 0.5 2.5 4.5 6.5 8.5 ... 118.5 120.5 122.5 124.5 126.5
│     * x        (x) float64 0.5 2.5 4.5 6.5 8.5 ... 118.5 120.5 122.5 124.5 126.5
│   Data variables:
│       image    (y, x) uint8 dask.array<chunksize=(64, 64), meta=np.ndarray>
└── DataTree('scale2')
    Dimensions:  (y: 16, x: 16)
    Coordinates:
      * y        (y) float64 3.5 11.5 19.5 27.5 35.5 ... 91.5 99.5 107.5 115.5 123.5
      * x        (x) float64 3.5 11.5 19.5 27.5 35.5 ... 91.5 99.5 107.5 115.5 123.5
    Data variables:
        image    (y, x) uint8 dask.array<chunksize=(16, 16), meta=np.ndarray>

存储为Open Microscopy Environment-Next Generation File Format (OME-NGFF) / netCDF Zarr 存储库。

强烈建议在构建Zarr存储库时使用 dimension_separator='/'。

store = zarr.storage.DirectoryStore('multiscale.zarr', dimension_separator='/')
multiscale.to_zarr(store)

示例

• Hello 多尺度空间图像世界！
• 转换itk.Image
• 转换imageio ImageResource

开发

欢迎并感谢贡献。

运行测试套件

git clone https://github.com/spatial-image/spatial-image-multiscale
cd spatial-image-multiscale
pip install -e ".[test]"
cid=$(grep 'IPFS_CID =' test/test_spatial_image_multiscale.py | cut -d ' ' -f 3 | tr -d '"')
# Needs ipfs, e.g. https://docs.ipfs.io/install/ipfs-desktop/
ipfs get -o ./test/data -- $cid
pytest
# Notebook tests
pytest --nbmake --nbmake-timeout=3000 examples/*ipynb