maweigert

/* QuPath-Script to export annotations to label tif images (e.g. to be used for stardist) 

 Use "Run for project" to export annotations for all images within a QuPath project

 Afterwards both images and mask tiffs will be stored in the project subfolder 
 
 ground_truth
 ├── images
 └── masks
 

maweigert

import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage import label, zoom

from stardist.matching import matching
from stardist.plot import render_label, render_label_pred 
np.random.seed(42)

def create_example():
    rand_gt = np.random.uniform(0,1,(8,8))

maweigert

#@ ImagePlus imp
#@ OUTPUT ImagePlus result

from ij import IJ
from ij.plugin.frame import RoiManager

result = IJ.createImage("Labeling", "16-bit black", imp.getWidth(), imp.getHeight(), 1)
ip = result.getProcessor()
rm = RoiManager.getInstance()

maweigert

#!/usr/bin/env bash

source ~/.bashrc

git clone https://github.com/mpicbg-csbd/stardist

my_venvs_create stardist_env
my_venvs_activate stardist_env
pip install tifffile==2019.7.2   # as newer versions somehow wont install on noto
pip install stardist

maweigert

import numpy as np
from colorsys import rgb_to_hls, hls_to_rgb

def _rgb2hls(im): 
  return np.stack(np.vectorize(rgb_to_hls)(*im.transpose(2,0,1)/256), axis=-1) 

def _hls2rgb(im): 
  return (255*np.stack(np.vectorize(hls_to_rgb)(*im.transpose(2,0,1)), axis=-1)).astype(np.uint8)

def invert(img): 

maweigert

import numpy as np
from csbdeep.utils import normalize

def norm_minmse(gt, x, normalize_gt=True):
    """
    normalizes and affinely scales an image pair such that the MSE is minimized  
     
    Parameters
    ----------
    gt: ndarray