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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

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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): 

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#!/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

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#@ 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()

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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))

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/* 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
 

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import tensorflow as tf
import numpy as np
import psutil
import os 
from stardist.models import Config3D, StarDist3D

os.environ['CUDA_VISIBLE_DEVICES']= "0"# '1,0'

from tensorflow.keras.utils import Sequence
import stardist

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# Average-Downsampling example in OpenCL (via gputools)
# please install it first via
# pip install gputools

import numpy as np
from gputools import OCLProgram, OCLArray, get_device
from timeit import default_timer
from skimage.transform import downscale_local_mean

# opencl kernel

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"""
The default momentum of MobileNet (for running average calculation) in tf seems to be very high (0.99?), resulting in huge (and suprising) differences in train vs evaluate loss

https://github.com/keras-team/keras/issues/6977

Setting it to something sensible (e.g. 0.9) fixes this 

https://stackoverflow.com/questions/65415799/fit-works-as-expected-but-then-during-evaluate-model-performs-at-chance/66692755#66692755
"""

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name: dl-image-mw
channels:
  - defaults
  - nvidia
dependencies:
  - python=3.9
  - cudatoolkit=11.0.*
  - cudnn=8.0.*
  - jupyter
  - numpy