how to save an array representing an image with 40 band to a .tif file

Question

I have an array with 600×600×40 dimension that each band(from 40 band) represent a 600×600 image I want to save it to a multiple band .tif image. I have tried this functions

Answer 1

Mark's clever answer is making a multi-page TIFF. Unfortunately, imagemagick and PIL are really MONO / RGB / RGBA / CMYK libraries and they don't have direct support for multiband images.

pyvips has true multiband support. For example:

import sys
import pyvips
import numpy as np

# make a (100, 100, 40) numpy image
array = np.zeros((100, 100, 40), dtype=sys.argv[2])

# convert to vips and save
image = numpy2vips(array)
image.write_to_file(sys.argv[1])

# read it back, convert to numpy, and show info
image2 = pyvips.Image.new_from_file(sys.argv[1])
array = vips2numpy(image2)

print("shape =", array.shape)
print("format =", array.dtype)

I can run it like this:

$ ./try284.py x.tif uint8
shape = (100, 100, 40)
format = uint8
$ vipsheader x.tif
x.tif: 100x100 uchar, 40 bands, srgb, tiffload
$ identify x.tif
x.tif TIFF 100x100 100x100+0+0 8-bit sRGB 400KB 0.000u 0:00.000

It supports other dtypes as well:

$ ./try284.py x.tif uint32
shape = (100, 100, 40)
format = uint32
$ ./try284.py x.tif float32
shape = (100, 100, 40)
format = float32

etc. etc.

You can load these TIFFs in gdal. I guess gdal can be used to write them as well, though I've not tried. Annoyingly, it moves the 40 to the outermost dimension.

$ python3
Python 3.6.7 (default, Oct 22 2018, 11:32:17) 
[GCC 8.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from osgeo import gdal
>>> x = gdal.Open("x.tif")
>>> a = x.ReadAsArray()
>>> a.shape
(40, 100, 100)

vips2numpy() and numpy2vips() are defined here:

https://github.com/libvips/pyvips/blob/master/examples/pil-numpy-pyvips.py

Copy-pasted for reference:

# map vips formats to np dtypes
format_to_dtype = {
    'uchar': np.uint8,
    'char': np.int8,
    'ushort': np.uint16,
    'short': np.int16,
    'uint': np.uint32,
    'int': np.int32,
    'float': np.float32,
    'double': np.float64,
    'complex': np.complex64,
    'dpcomplex': np.complex128,
}

# map np dtypes to vips
dtype_to_format = {
    'uint8': 'uchar',
    'int8': 'char',
    'uint16': 'ushort',
    'int16': 'short',
    'uint32': 'uint',
    'int32': 'int',
    'float32': 'float',
    'float64': 'double',
    'complex64': 'complex',
    'complex128': 'dpcomplex',
}

# numpy array to vips image
def numpy2vips(a):
    height, width, bands = a.shape
    linear = a.reshape(width * height * bands)
    vi = pyvips.Image.new_from_memory(linear.data, width, height, bands,
                                      dtype_to_format[str(a.dtype)])
    return vi

# vips image to numpy array
def vips2numpy(vi):
    return np.ndarray(buffer=vi.write_to_memory(),
                      dtype=format_to_dtype[vi.format],
    shape=[vi.height, vi.width, vi.bands])