Multi tool use

Working with TIFFs (import, export) in Python using numpy

I need a python routine that can open and import TIFF images into numpy arrays, so I can analyze and modify the contained data and afterwards save them as TIFFs again. (They are basically light intensity maps in greyscale, representing the respective values per pixel)

I tried to find something, but there is no documentation on PIL methods concerning TIFF. I tried to figure it out, but only got bad mode/ file type not supported errors.

What do I need to use here?

6 Answers
6

First, I downloaded a test TIFF image from this page called a_image.tif. Then I opened with PIL like this:

a_image.tif

>>> from PIL import Image
>>> im = Image.open('a_image.tif')
>>> im.show()


This showed the rainbow image. To convert to a numpy array, it's as simple as:

>>> import numpy
>>> imarray = numpy.array(im)


We can see that the size of the image and the shape of the array match up:

>>> imarray.shape
(44, 330)
>>> im.size
(330, 44)


And the array contains uint8 values:

uint8

>>> imarray
array([[ 0, 1, 2, ..., 244, 245, 246],
[ 0, 1, 2, ..., 244, 245, 246],
[ 0, 1, 2, ..., 244, 245, 246],
...,
[ 0, 1, 2, ..., 244, 245, 246],
[ 0, 1, 2, ..., 244, 245, 246],
[ 0, 1, 2, ..., 244, 245, 246]], dtype=uint8)


Once you're done modifying the array, you can turn it back into a PIL image like this:

>>> Image.fromarray(imarray)
<Image.Image image mode=L size=330x44 at 0x2786518>


I use matplotlib for reading TIFF files:

import matplotlib.pyplot as plt
I = plt.imread(tiff_file)


and I will be of type ndarray.

I

ndarray

According to the documentation though it is actually PIL that works behind the scenes when handling TIFFs as matplotlib only reads PNGs natively, but this has been working fine for me.

There's also a plt.imsave function for saving.

plt.imsave

You could also use GDAL to do this. I realize that it is a geospatial toolkit, but nothing requires you to have a cartographic product.

Link to precompiled GDAL binaries for windows (assuming windows here)
http://www.gisinternals.com/sdk/

To access the array:

from osgeo import gdal

dataset = gdal.Open("path/to/dataset.tiff", gdal.GA_ReadOnly)
for x in range(1, dataset.RasterCount + 1):
band = dataset.GetRasterBand(x)
array = band.ReadAsArray()


.astype(sometype)

ReadAsArray()

xrange

xrange

range

pylibtiff worked better for me than PIL (which doesn't support color images with more than 8 bits per color).

from libtiff import TIFF

tif = TIFF.open('filename.tif', mode='r')
# read an image in the currect TIFF directory as a numpy array
image = tif.read_image()

# read all images in a TIFF file:
for image in tif.iter_images():
pass

tif = TIFF.open('filename.tif', mode='w')
tif.write_image(image)


If you're on python3 you can't pip3 install libtiff. Instead, manually install with

pip3 install libtiff

git clone git@github.com:pearu/pylibtiff.git
python3 setup.py install


the readme of pylibtiff also mentions tifffile.py, but I haven't tried it.

You can also use pytiff of which I'm the author.

import pytiff

with pytiff.Tiff("filename.tif") as handle:
part = handle[100:200, 200:400]

# multipage tif
with pytiff.Tiff("multipage.tif") as handle:
for page in handle:
part = page[100:200, 200:400]


It's a fairly small module and may not have as many features as other modules, but it supports tiled tiffs and bigtiff, so you can read parts of large images.

In case of image stacks, I find it easier to use scikit-image to read, and matplotlib to show or save. I have handled 16-bit TIFF image stacks with the following code.

scikit-image

matplotlib

from skimage import io
import matplotlib.pyplot as plt

# read the image stack
img = io.imread('a_image.tif')
# show the image
plt.imshow(mol,cmap='gray')
plt.axis('off')
# save the image
plt.savefig('output.tif', transparent=True, dpi=300, bbox_inches="tight", pad_inches=0.0)


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