nimpy

import numpy as np
def calculate_ssd(img1, img2):
    """Computing the sum of squared differences (SSD) between two images."""
    if img1.shape != img2.shape:
        print("Images don't have the same shape.")
        return
    return np.sum((np.array(img1, dtype=np.float32) - np.array(img2, dtype=np.float32))**2)

nimpy

import numpy as np
def add_gaussian_noise(image, mean=0, sigma=20):
    """Add Gaussian noise to an image of type np.uint8."""
    gaussian_noise = np.random.normal(mean, sigma, image.shape)
    gaussian_noise = gaussian_noise.reshape(image.shape)
    noisy_image = image + gaussian_noise
    noisy_image = np.clip(noisy_image, 0, 255)
    noisy_image = noisy_image.astype(np.uint8)
    return noisy_image

nimpy

import numpy as np
def calculate_psnr(img1, img2, max_value=255):
    """"Calculating peak signal-to-noise ratio (PSNR) between two images."""
    mse = np.mean((np.array(img1, dtype=np.float32) - np.array(img2, dtype=np.float32)) ** 2)
    if mse == 0:
        return 100
    return 20 * np.log10(max_value / (np.sqrt(mse)))

nimpy

import numpy as np

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

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 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

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

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

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

import numpy as np

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

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',