Test deconvolution imports

Before testing the dependencies make sure you are in the new devbio-napari based environment you created in the last section. Type the following.

mamba activate decon-dl-env

Now test whether the dependencies have been installed properly

try:
    import imnotthere as int
    imnothtere_found=True
    print('ImNotThere found')
except ImportError:
    imnotthere_found=False
    print('!ImNotThere NOT found')

try:
    from clij2fft.richardson_lucy import richardson_lucy_nc
    clij2_rl_found=True
    print('clij2fft found')
except ImportError:
    clij2_rl_found=False
    print('!clij2fft NOT found')
    
try:
    import RedLionfishDeconv as rl
    redlionfish_rl_found=True
    print('RLF found')
except ImportError:
    redlionfish_rl_found=False
    print('!RLF NOT found')
    
try:
    import cupy as cp
    from tnia.deconvolution.richardson_lucy import richardson_lucy_cp
    cupy_found=True
    print('cupy found')
except ImportError:
    cupy_found=False
    print('!cupy NOT found')

import numpy as np
    

!ImNotThere NOT found
clij2fft found
RLF found
cupy found

Get test data

1. Get images and models from this link https://www.dropbox.com/scl/fo/yc5jaoj3ap936dqqy8urc/h?rlkey=ho8vp2nbvprzyltytryj9demp&dl=0

2. Place both the data and models folders beside the docs folder.

3. Use imread to verify you can open the image, then print the size of the image and verify it is (128,256,256)

from skimage.io import imread
from pathlib import Path
# local path to the data folder
image_path = Path('../../data/deconvolution/')

image_name='Bars-G10-P30-stack.tif'
psf_name='PSF-Bars-stack.tif'
truth_name='Bars-stack.tif'

im=imread(image_path / image_name)
psf=imread(image_path / psf_name)
truth=imread(image_path / truth_name)
im=im.astype('float32')
psf=psf.astype('float32')
psf=psf/psf.sum()
print(im.shape, psf.shape, truth.shape)

(128, 256, 256) (128, 256, 256) (128, 256, 256)

View 3D Data in notebook

View the data using the xy, xz, and zy max projections from tnia-python.

from tnia.plotting.projections import show_xyz_slice_center 

fig = show_xyz_slice_center(im, figsize=(7,7))
fig.suptitle('Image')

Text(0.5, 0.98, 'Image')

Use stackview to explore data

Alternatively we can use the stackview library to explore the data.

import stackview
stackview.orthogonal(psf, continuous_update=True)
stackview.orthogonal(truth, continuous_update=True)
stackview.orthogonal(im, continuous_update=True)

import helper and show image

import sys
sys.path.append('../')

import decon_helper as dh

dh.show_xyz_slice(im, 'tnia')

tnia available
stackview available

Import deconvolution libraries

Try importing the clij2 and redlionfish implementations of Richardson Lucy. If they are found perform a deconvolution with each library

try:
    from clij2fft.richardson_lucy import richardson_lucy_nc
    clij2_rl_found=True
except ImportError:
    clij2_rl_found=False
try:
    import RedLionfishDeconv as rl
    redlionfish_rl_found=True
except ImportError:
    redlionfish_rl_found=False

try:
    from tnia.deconvolution.richardson_lucy import richardson_lucy_cp
    tnia_rl_cupy_found=True
except ImportError:
    tnia_rl_cupy_found=False
    
print('clij2 rl found',clij2_rl_found)
print('redlionfish rl found',redlionfish_rl_found)    
print('tnia rl cupy found',tnia_rl_cupy_found)

clij2 rl found True
redlionfish rl found True
tnia rl cupy found True

Deconvolve with clij2

if clij2_rl_found==True:
    decon_clij2=richardson_lucy_nc(im,psf,100,0)
    show_xyz_slice_center(decon_clij2,figsize=(7,7))
else:
    print('clij2 not found')
    decon_clij2=np.zeros_like(im)

get lib
calling convcorr 0 0

Deconvolve with tnia-cupy

if tnia_rl_cupy_found:
    decon_tnia=richardson_lucy_cp(im,psf,100,True)
    show_xyz_slice_center(decon_tnia,figsize=(7,7))

0 10 20 30 40 50 60 70 80 90 

Look at the deconvolution result with stackview

stackview.orthogonal(decon_clij2, continuous_update=True)

Deconvolve with redlionfish

if redlionfish_rl_found==True:
    decon_rlf=rl.doRLDeconvolutionFromNpArrays(im, psf, niter=100, method='gpu', resAsUint8=False )
else:
    print('redlionfish not found')
    decon_rlf=np.zeros_like(im)

fig=show_xyz_slice_center(decon_rlf, figsize=(7,7))

View data in Napari and ask questions

View the data in Napari, switch to a 3D view and rotate and explore the dataset? How good is the reconstruction? Is it a truer representation of the data as compared to the image?

Excercise: Deconvolve the data for 1000 iterations, or 10,000 if you have a fast GPU (or even 100,000 if you have a fast GPU and a bit of time). Does the reconstruction continue to improve with more iterations?

import napari
viewer=napari.Viewer()
viewer.add_image(im)
viewer.add_image(decon_clij2)
viewer.add_image(decon_rlf)
viewer.add_image(truth)

<Image layer 'truth' at 0x1d228e14bb0>