dnlcrl

dnlcrl

version: 2
updates:
  # Maintain dependencies for GitHub Actions
  - package-ecosystem: "github-actions"
    directory: "/"
    schedule:
      interval: "daily"
    commit-message:
      prefix: "chore:"
      include: "scope"

dnlcrl

import torch
import sys
import os

print("Warning: Do not use this for BatchNorm-using models!")

model_names = sys.argv[1:]

if len(model_names) < 2:
    print("need at least two models to average")

dnlcrl

#!/usr/bin/env python
import SimpleITK as sitk
import sys, os
if len ( sys.argv ) < 3:
    print( "Usage: DicomSeriesReader <input_directory> <output_file>" )
    sys.exit ( 1 )
print( "Reading Dicom directory:", sys.argv[1] )
reader = sitk.ImageSeriesReader()
dicom_names = reader.GetGDCMSeriesFileNames( sys.argv[1] )
reader.SetFileNames(dicom_names)

dnlcrl

import torch
import torch.nn as nn

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear = nn.Linear(1,1)

    def forward(self, x):
        y = self.linear(x)

dnlcrl

dnlcrl

dnlcrl

from __future__ import print_function
from PIL import Image
from xtermcolor import colorize
from skimage.exposure import rescale_intensity
import argparse
import numpy as np
import matplotlib.pyplot as plt
import scipy.misc

PIXEL = '  '

dnlcrl

 for f in  `find . -name "fake*.png"`; do convert real_samples.png $f +append $f; done
 

dnlcrl

def bbox(img):
    rows = np.any(img, axis=1)
    cols = np.any(img, axis=0)
    rmin, rmax = np.where(rows)[0][[0, -1]]
    cmin, cmax = np.where(cols)[0][[0, -1]]

    return slice(rmin, rmax), slice(cmin, cmax)