BilHim

# Weighted average grayscale
for x in range(width):
        for y in range(height):
            r, g, b = image.getpixel((x, y))

            r_ = g_ = b_ = 0.299*r + 0.587*g + 0.114*b

            new_pixel = (int(r_), int(g_), int(b_))
            new_image.putpixel((x, y), new_pixel)

BilHim

# Grayscale filter
for x in range(width):
  for y in range(height):
    # Get original pixel colors
    r, g, b = image.getpixel((x, y))
    
    # New pixel colors
    r_ = g_ = b_ = (r+g+b)/3

    # Change new pixel

BilHim

from PIL import Image

# Open image
image = Image.open('image.jpg')

# Filter
def filter(image):
    # Get image dimesions
    width, height = image.size

BilHim

from PIL import Image

# Open image
image = Image.open('image.jpg')

# Show image
image.show()

BilHim

np.einsum("ik,kj->ij", A, B)

BilHim

# Returns matrix product of A and B
def prod(A, B):
  # Dimensions
  m = A.shape[0]
  n = A.shape[1] # same as B.shape[0]
  p = B.shape[1]
  # Initialize an empty (m,p) matrix
  C = np.zeros((m, p))
  # Fill it
  for i in range(m):

BilHim

import bpy
from math import *

# Settings
width = 800
height = 800
z_height = 0.25 # Displace modifier strength
tex_res = 1     # Texture resolution (1:1)
mesh_res = 4    # Mesh resolution (8:1)

BilHim

from PIL import Image

width = 800
height = 600

def getpixel(x, y):
  n = 0.5  # Get value here
  return int(255*n)

# Create a new image. L for grayscale

BilHim

scale = 75
octaves = 8
persistence = 0.5
lacunarity = 2

land = (133, 222, 70)
water = (21, 60, 177)

def getColor(x, y):
    a = pnoise2(x/scale,

BilHim

def getColor(x, y):
    a = pnoise2(x/scale,
                y/scale,
                octaves = octaves, 
                persistence = persistence, 
                lacunarity = lacunarity)
    a = int(127*(a+1))  # a is between -1 and 1, this gives us values between 0 and 255
    return (a,)*3