nimpy

DateTime=`date "+%Y-%m-%d %H:%M"`
echo $DateTime

nimpy

# credit: https://stackoverflow.com/a/44228436/4031135

from pathlib import Path

p = Path('.')

# all subdirectories in the current directory, not recursive
list_subdirs = [f.name for f in p.iterdir() if f.is_dir()]
list_subdirs = sorted(list_subdirs, reverse=True)
for subdir in list_subdirs:

nimpy

# credit: https://stackoverflow.com/a/44228436/4031135

from pathlib import Path

p = Path('.')

# all subdirectories in the current directory, not recursive
list_subdirs = [f.name for f in p.iterdir() if f.is_dir()]
list_subdirs.sort()
for subdir in list_subdirs:

nimpy

#!/usr/bin/env python

import argparse
import imageio


parser = argparse.ArgumentParser()
parser.add_argument('input', default='image.png',
                    help="Path to the input image to be cropped")
parser.add_argument('--output',

nimpy

import numpy as np

def rgb_to_greyscale(image):
    return np.dot(image[...,:3], [0.2989, 0.5870, 0.1140])

nimpy

def rmse(a, b):
    # Root MSE (Mean Squared Error)
    return np.sqrt(np.mean(np.square(np.subtract(a, b, dtype=np.float32))))

nimpy

file = open('path/to/file', 'r') 
count = 0

while True:
    line = file.readline()
    if not line: 
        break
    
    # do something here
    

nimpy

import pickle
import datetime

def pickle_vars(var1, var2):
    pickle_file_path = 'zimnica/pickled_vars_' + datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + '.pickle'
    try:
        pickle.dump((var1, var2), open(pickle_file_path, "wb"))
    except Exception as e:
        print("Problem while trying to pickle: ", str(e))
        

nimpy

def max_pool(image, pool_size=8):
    """
    Fast implementation of max pooling, given the following is true:
    - the length of image shape is 3
    - the image is channels-last
    - pooling height, width, and stride are all equal (pool_size)
    - height % pool_size == 0
    - width % pool_size == 0
    
    Code taken and adjusted from: max_pool_forward_reshape() in https://github.com/mratsim/Arraymancer/issues/174

nimpy

import numpy as np

image_3ch = np.repeat(image_1ch, 3, axis=1).reshape((image_1ch.shape[0], image_1ch.shape[1], 3))