143 lines
4.1 KiB
Python
143 lines
4.1 KiB
Python
import random
|
|
from io import BytesIO
|
|
|
|
import numpy as np
|
|
import multiprocessing
|
|
|
|
import requests
|
|
from mpi4py import MPI
|
|
from PIL import Image
|
|
|
|
|
|
def count_randomized_hits(iterations):
|
|
counter = 0
|
|
for i in range(iterations):
|
|
x = random.uniform(0, 1)
|
|
y = random.uniform(0, 1)
|
|
if ((x ** 2) + (y ** 2)) ** (1 / 2) <= 1:
|
|
counter += 1
|
|
return counter
|
|
|
|
|
|
FLAG_default = 0
|
|
FLAG_threaded = 1
|
|
FLAG_network = 2
|
|
|
|
|
|
def monte_carlo_methode(n, mode=0):
|
|
if mode == 1: # Multithreading mode
|
|
num_threads = 16
|
|
iterations_per_thread = n // num_threads
|
|
with multiprocessing.Pool(num_threads) as pool:
|
|
hits = pool.map(count_randomized_hits, [iterations_per_thread] * num_threads)
|
|
hit = sum(hits)
|
|
|
|
elif mode == 2: # MPI parallel mode
|
|
comm = MPI.COMM_WORLD
|
|
rank = comm.Get_rank()
|
|
size = comm.Get_size()
|
|
local_hit = count_randomized_hits(n // size)
|
|
hit = comm.reduce(local_hit, op=MPI.SUM, root=0)
|
|
|
|
else: # Default mode
|
|
hit = count_randomized_hits(n)
|
|
|
|
pi_approx = (hit / n) * 4
|
|
pi_diff = abs(np.pi - pi_approx)
|
|
|
|
return pi_approx, pi_diff
|
|
|
|
|
|
def uniform_kernel(n):
|
|
if n % 2 == 0:
|
|
print("Size needs to be odd")
|
|
exit(1)
|
|
K = 1 / n / n * np.ones([n, n])
|
|
return K
|
|
|
|
|
|
def gauss_kernel(s):
|
|
n = 3 * s
|
|
pos = np.arange(-n, n + 1)
|
|
x = np.meshgrid(pos, pos)
|
|
K = 1.0 / (2.0 * np.pi * s * s) * np.exp(-(x[0] ** 2 + x[1] ** 2) / (2.0 * s * s))
|
|
K = K / sum(sum(K))
|
|
return K
|
|
|
|
|
|
FLAG_gauss = 0
|
|
FLAG_uniform = 1
|
|
|
|
|
|
def process_image_part(data_part, kernel):
|
|
y_part_size, x_part_size, _ = data_part.shape
|
|
data_part_new = np.zeros_like(data_part)
|
|
for i in range((kernel.shape[0] - 1) // 2, y_part_size - (kernel.shape[0] - 1) // 2):
|
|
for j in range((kernel.shape[1] - 1) // 2, x_part_size - (kernel.shape[1] - 1) // 2):
|
|
for k in range(3):
|
|
new_value = 0.0
|
|
for ii in range(kernel.shape[0]):
|
|
for jj in range(kernel.shape[1]):
|
|
iii = ii - (kernel.shape[0] - 1) // 2
|
|
jjj = jj - (kernel.shape[1] - 1) // 2
|
|
new_value += kernel[ii, jj] * data_part[i + iii, j + jjj, k]
|
|
data_part_new[i, j, k] = new_value
|
|
return data_part_new
|
|
|
|
|
|
def process_image(img, func=0, mode=0):
|
|
if isinstance(img, str):
|
|
img = Image.open(img)
|
|
|
|
if img.mode == "P":
|
|
img = img.convert(mode="RGB")
|
|
|
|
data = np.asarray(img, dtype=np.float64) / 255.0
|
|
|
|
if func == 1:
|
|
kernel = uniform_kernel(3)
|
|
else:
|
|
kernel = gauss_kernel(3)
|
|
|
|
if mode == 1: # Multithreading mode
|
|
num_threads = 16
|
|
data_parts = np.array_split(data, num_threads, axis=0)
|
|
with multiprocessing.Pool(num_threads) as pool:
|
|
data_new_parts = pool.starmap(process_image_part, zip(data_parts, [kernel]*num_threads))
|
|
data_new = np.concatenate(data_new_parts, axis=0)
|
|
|
|
elif mode == 2: # MPI parallel mode
|
|
comm = MPI.COMM_WORLD
|
|
rank = comm.Get_rank()
|
|
size = comm.Get_size()
|
|
data_part = np.array_split(data, size, axis=0)[rank]
|
|
data_new_part = process_image_part(data_part, kernel)
|
|
data_new_parts = comm.gather(data_new_part, root=0)
|
|
if rank == 0:
|
|
data_new = np.concatenate(data_new_parts, axis=0)
|
|
else:
|
|
data_new = None
|
|
data_new = comm.bcast(data_new, root=0)
|
|
|
|
else: # Default mode
|
|
data_new = process_image_part(data, kernel)
|
|
|
|
data_new = data_new * 255.0
|
|
data_new = np.uint8(data_new)
|
|
|
|
return Image.fromarray(data_new, mode="RGB")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
print(monte_carlo_methode(1000, FLAG_default))
|
|
print(monte_carlo_methode(1000, FLAG_threaded))
|
|
print(monte_carlo_methode(1000, FLAG_network))
|
|
|
|
url = "https://i.wfcdn.de/teaser/660/27020.jpg"
|
|
response = requests.get(url)
|
|
|
|
if response.status_code == 200:
|
|
image = Image.open(BytesIO(response.content))
|
|
image = process_image(image, FLAG_uniform, FLAG_threaded)
|
|
image.show()
|