Speed up image analysis in Python with efficient reading, batch-processing, viewing functions and easily extend your own function for batch processing.
Project description
impy
is All You Need in Image Analysis
impy
is an all-in-one image analysis library, equipped with parallel processing, GPU support, GUI based tools and so on.
The core array, ImgArray
, is a subclass of numpy.ndarray
, tagged with information such as
- image axes
- scale of each axis
- directory of the original image
- history of image processing
- and other image metadata
By making full use of them, impy
provides super efficient tools of image analysis for you.
I'm also working on documentation for tutorials and API. Please take a look if you're interested in.
:warning: Image processing algorithms in ImgArray
are almost stable so that their behavior will not change a lot. However, since napari
is under development and I'm groping for better UI right now, any functions that currently implemented in impy
viewer may change or no longer work in the future. Make sure keeping napari
and impy
updated when you use.
Installation
- use pip
pip install impy-array
- from source
git clone https://github.com/hanjinliu/impy
Code as fast as you speak
Almost all the functions, such as filtering, deconvolution, labeling, single molecule detection, and even those pure numpy
functions, are aware of image metadata. They "know" which dimension corresponds to "z"
axis, which axes they should iterate along or where to save the image. As a result, your code will be very concise:
import impy as ip
import numpy as np
img = ip.imread("path/to/image") # Read images with metadata.
img["z=3;t=0"].imshow() # Plot image slice at z=3 and t=0.
img_fil = img.gaussian_filter(sigma=2) # Paralell batch denoising. No more for loop!
img_prj = np.max(img_fil, axis="z") # Z-projection (numpy is aware of image axes!).
img_prj.imsave(f"Max-{img.name}") # Save in the same place. Don't spend time on searching for the directory!
Seamless interface between napari
napari is an interactive viewer for multi-dimensional images. impy
has a simple and efficient interface with it, via the object ip.gui
. Since ImgArray
is tagged with image metadata, you don't have to care about axes or scales. Just run
ip.gui.add(img)
impy
's viewer also provides many useful widgets and functions such as
- Excel-like table for data analysis, layer property editing etc.
- Compact file explorer
- interactive
matplotlib
figure canvas - cropping, duplication, measurement, filtering tools
Extend your function for batch processing
Already have a function for numpy
and scipy
? Decorate it with @ip.bind
@ip.bind
def imfilter(img, param=None):
# Your function here.
# Do something on a 2D or 3D image and return image, scalar or labels
return out
and it's ready for batch processing!
img.imfilter(param=1.0)
Commaind line usage
impy
also supports command line based image analysis. All method of ImgArray
is available
from commad line, such as
impy path/to/image.tif ./output.tif --method gaussian_filter --sigma 2.0
which is equivalent to
import impy as ip
img = ip.imread("path/to/image.tif")
out = img.gaussian_filter(sigma=2.0)
out.imsave("./output.tif")
For more complex procedure, it is possible to send image directly to IPython
impy path/to/image.tif -i
thr = img.gaussian_filter().threshold()
or to napari
impy path/to/image.tif -n
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