Expose data type of CuImage object for interoperability with NumPy

[gigony]

• Expose typestr property on CuImage object
• Expose DLDataType and DLDataTypeCode under cucim.clara

Without this patch,
DLDataType and DLDataTypecode can be accessible with the below workaround.

>>> from cucim import CuImage
>>> a = CuImage("notebooks/input/image.tif")
>>> b = a.read_region((0,0), (10,10))
>>> import numpy as np
>>> np.dtype(b.__array_interface__["typestr"]) # b would expose `__cuda_array_interface__` if memory is in GPU.
dtype('uint8')

With this patch, we can convert data type to NumPy's dtype easily, and also can access cuCIM's DLDataType.

>>> from cucim import CuImage
>>> a = CuImage("notebooks/input/image.tif")
>>> b = a.read_region((0,0), (10,10))
>>> import numpy as np
>>> b.typestr
'|u1'
>>> np.dtype(b.typestr) == np.uint8
True
>>> from cucim.clara import DLDataType, DLDataTypeCode
>>> b.dtype == DLDataType(DLDataTypeCode.DLUInt, 8, 1)
True

Fixes #243

[gigony]

It seems that there is an infra issue.

Even if libnvjpeg-dev-11-6_11.6.0.55-1_arm64.deb is listed https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/sbsa/, cannot download https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/sbsa/libnvjpeg-dev-11-6_11.6.0.55-1_arm64.deb

[jakirkham]

Generally this seems reasonable. Would be good to have a test. Are we thinking about other endianness?

[gigony]

Generally this seems reasonable. Would be good to have a test. Are we thinking about other endianness?

Since our major platform (x86-64 and aarch64 on Jetson) uses little-endian only and most TIFF files are using little-endian, supporting big-endian is lower-priority.

Will add tests later today when I am able to access my machine.

[jakirkham]

@gpucibot merge