Question: Any way to get pyvips to distinguish between time and depth for tiff images?

[petoor]

Hello.
This question is inspired by : libvips/ruby-vips#91
Is there a way to make pyvips.Image.new_from_file() return the dimension of bands and the dimensions of z?

Using your multipage tiff file as example : http://downloads.openmicroscopy.org/images/OME-TIFF/2016-06/bioformats-artificial/multi-channel-z-series.ome.tif

pyvips.Image.new_from_file("multi-channel-z-series.ome.tif", access="sequential")
yields : <pyvips.Image 439x167 char, 1 bands, b-w>

And we can then loop over the bands with the pages argument. However, it seems like the colorchannel and the z-channel are multiplied together...
That is, pyvips.Image.new_from_file("multi-channel-z-series.ome.tif", access="sequential", pages=13) yields the 3rd channel at the 4th time.

Compare this to bio formats, the metadata is.
Axes: 4
Axis 'x' size: 439
Axis 'y' size: 167
Axis 'c' size: 3
Axis 'z' size: 5
Pixel Datatype: i1

So the question is, is there a way to make pyvips return the metadata of (t,x,y,z,c)?
(Time, Height, Width, Depth, Color)

This would make it easier to structure data into a database.

Best regards.

[jcupitt]

Hello @petoor,

Yes, you can get the OME XML from image-description, eg.:

$ vipsheader -f image-description multi-channel-z-series.ome.tif

Or in Python:

xml = image.get("image-description")

You can set new xml as well, though it's up to you to ensure conformance.

[petoor]

Cheers, this gives exactly what i'm looking for!

When i try to do the same with a .ndpi / .bmp / .png file i get a vips_image_get: field "image-description" not found
i guess due to the lack of the files not having a xlm header.

Is there any way to get a standardized output from pyvips containing the metadata of the images?

Also, is there a way to see which key words image.get accepts? I can't find much about it here : https://libvips.github.io/pyvips/vimage.html#pyvips.Image.get

[jcupitt]

That's right, image-description is part of libtiff:

https://www.awaresystems.be/imaging/tiff/tifftags/imagedescription.html

And OME uses it to carry the extra XML metadata it needs around.

You can check for the existence of a field with get_typeof(), eg.:

>>> x = pyvips.Image.new_from_file("k2.jpg")
>>> x.get("banana")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/john/.local/lib/python3.7/site-packages/pyvips/vimage.py", line 771, in get
    raise Error('unable to get {0}'.format(name))
pyvips.error.Error: unable to get banana
  vips_image_get: field "banana" not found

>>> x.get_typeof("banana")
0
>>> x.get_typeof("width")
24

The int returned by get_typeof is a GType, 0 means unknown. Have a look at:

https://github.com/libvips/pyvips/blob/master/pyvips/gvalue.py#L18

vipsheader -a somefile is a handy way to see what metadata an image has.

[jcupitt]

libvips is really a colour 2D image processing library, so width / height / bands / format is the standard metadata.

It has some support for things like volumetric, time-series and multi-res images, but it's a bit ad-hoc and it depends on the format to a degree. DICOM volumetric images are loaded as multi-page "toilet roll" images, for example, like PDFs and GIFs. OpenSlide images are opened a layer at a time and use the standard openslide metadata to select subimages at open time, just like pyramidal TIFFs.

[jcupitt]

... I meant to say, you'll need to check that image-description contains OME XML. Most TIFF files will have image-description, but it'll usually be something dull like "created with photoshop 12".

[petoor]

Thank you for the elaboration John.
running vipsheader -a "image" from the terminal gives me all the information i'm looking for.
Is there a way to call vipsheader -a inside a pyton file and save the metadata as a dict or list?

[jcupitt]

.get() returns the same information. .get_fields() gives you the names of all fields.

metadata = [image.get(field) for field in image.get_fields()]

[petoor]

metadata = [image.get(field) for field in image.get_fields()]
returns [] for both the .ndpi file and the ome.tif file
vipsheader -a works for both.

[petoor]

using your multi-channel-z-series.ome.tif file as test

[jcupitt]

Oh, strange, it works for me:

>>> x = pyvips.Image.new_from_file("multi-channel-z-series.ome.tif")
>>> x.get_fields()
['width', 'height', 'bands', 'format', 'coding', 'interpretation', 'xoffset', 'yoffset', 'xres', 'yres', 'filename', 'vips-loader', 'n-pages', 'image-description', 'resolution-unit', 'orientation']
>>> [x.get(field) for field in x.get_fields()]
[439, 167, 1, 'char', 'none', 'b-w', 0, 0, 0.0, 0.0, ...

Could something else be wrong?

A hash might be more useful, of course:

>>> {field: x.get(field) for field in x.get_fields()}
{'width': 439, 'height': 167, 'bands': 1, 'format': 'char', ...

[petoor]

Actually, the vipsheader -a multi-channel-z-series.ome.tif returns :
width: 439
height: 167
bands: 1
format: 1 - char
coding: 0 - none
interpretation: 1 - b-w

Shouldn't this be 3 bands? Since the image contains 3 color channels.
Or is the interpretation bands /= color channels?

[petoor]

Output from terminal :
Python 3.6.9 (default, Nov 7 2019, 10:44:02) [GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
import pyvips
img_path = '/home/peter/Downloads/multi-channel-z-series.ome.tif'
image = pyvips.Image.new_from_file(img_path)
image.get_fields()
[]
{field: image.get(field) for field in image.get_fields()}
{}

Version :
Name: pyvips
Version: 2.1.11
Summary: binding for the libvips image processing library, API mode
Home-page: https://github.com/libvips/pyvips
Author: John Cupitt
Author-email: jcupitt@gmail.com
License: MIT
Location: /home/peter/.local/lib/python3.6/site-packages
Requires: cffi, pkgconfig
Required-by:

[jcupitt]

Do you have an old libvips? I see:

>>> pyvips.version(0)
8
>>> pyvips.version(1)
9
>>> pyvips.version(2)
1

ie. my pyvips is using libvips version 8.9.1.

[jcupitt]

OME puts the colour into separate planes, so you need to extract and recombine layers to get a recognisable colour image (I think).