This notebook demonstrates applying a CARE model for a 3D denoising task, assuming that training was already completed via 2_training.ipynb.
The trained model is assumed to be located in the folder
models with the name
More documentation is available at http://csbdeep.bioimagecomputing.com/doc/.
from __future__ import print_function, unicode_literals, absolute_import, division import numpy as np import matplotlib.pyplot as plt %matplotlib inline %config InlineBackend.figure_format = 'retina' from tifffile import imread from csbdeep.utils import Path, download_and_extract_zip_file, plot_some from csbdeep.io import save_tiff_imagej_compatible from csbdeep.models import CARE
Using TensorFlow backend.
download_and_extract_zip_file ( url = 'http://csbdeep.bioimagecomputing.com/example_data/tribolium.zip', targetdir = 'data', )
Files found, nothing to download. data: - tribolium - tribolium/test - tribolium/test/GT - tribolium/test/GT/nGFP_0.1_0.2_0.5_20_14_late.tif - tribolium/test/low - tribolium/test/low/nGFP_0.1_0.2_0.5_20_14_late.tif - tribolium/train - tribolium/train/GT - tribolium/train/GT/nGFP_0.1_0.2_0.5_20_13_late.tif - tribolium/train/low - tribolium/train/low/nGFP_0.1_0.2_0.5_20_13_late.tif
Plot the test stack pair and define its image axes, which will be needed later for CARE prediction.
y = imread('data/tribolium/test/GT/nGFP_0.1_0.2_0.5_20_14_late.tif') x = imread('data/tribolium/test/low/nGFP_0.1_0.2_0.5_20_14_late.tif') axes = 'ZYX' print('image size =', x.shape) print('image axes =', axes) plt.figure(figsize=(16,10)) plot_some(np.stack([x,y]), title_list=[['low (maximum projection)','GT (maximum projection)']], pmin=2,pmax=99.8);
image size = (45, 954, 486) image axes = ZYX