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