UNI_Python/ha_09/loosen_janniclas_1540907_10.py

import random
from concurrent.futures import ThreadPoolExecutor
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


def monte_carlo_methode(n, mode=0):
    func_comm = MPI.COMM_WORLD
    func_rank = func_comm.Get_rank()
    func_size = func_comm.Get_size()

    if mode == 1:  # Multithreading mode
        if func_rank == 0:
            num_threads = 16
            iterations_per_thread = n // num_threads

            with ThreadPoolExecutor(max_workers=num_threads) as executor:
                hits = list(executor.map(count_randomized_hits, [iterations_per_thread] * num_threads))
            hit = sum(hits)

    elif mode == 2:  # MPI parallel mode
        func_comm = MPI.COMM_WORLD
        func_rank = func_comm.Get_rank()
        func_size = func_comm.Get_size()

        n = func_comm.bcast(n, root=0)

        local_hit = count_randomized_hits(n // func_size)
        hit = func_comm.reduce(local_hit, op=MPI.SUM, root=0)
        hit = int(func_comm.bcast(hit, root=0))

    else:  # Default mode
        if func_rank == 0:
            hit = count_randomized_hits(n)

    if func_rank == 0:
        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


def process_image_part(data_part, kernel, padding):
    y_part_size, x_part_size, _ = data_part.shape
    pad_y, pad_x = padding
    data_part_new = np.zeros((y_part_size - 2 * pad_y, x_part_size - 2 * pad_x, 3))
    # DO NOT CHANGE THIS LOOP
    for i in range(pad_y, y_part_size - pad_y):
        for j in range(pad_x, x_part_size - pad_x):
            for k in range(3):
                new_value = 0.0
                for ii in range(kernel.shape[1]):
                    for jj in range(kernel.shape[0]):
                        iii = ii - (kernel.shape[1] - 1) // 2
                        jjj = jj - (kernel.shape[0] - 1) // 2
                        new_value += kernel[ii, jj] * data_part[i + iii, j + jjj, k]
                data_part_new[i - pad_y, j - pad_x, k] = new_value
    return data_part_new


def overlapping_submatrices(arr, n, overlap):
    sub_array_length = len(arr) // n
    # Zugegeben, den Loop hab ich auch nur durch ChatGPT hinbekommen :O
    sub_arrays = [
        arr[i * sub_array_length - min(i * overlap, overlap): (i + 1) * sub_array_length + min((n - i - 1) * overlap,
                                                                                               overlap)]
        for i in range(n)
    ]
    return np.array(sub_arrays, dtype=object)


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
    padding = 9

    print(f"Before: {data.shape}")

    if func == 1:
        kernel = uniform_kernel(padding)
    else:
        kernel = gauss_kernel(padding)

    padding = (padding // 2, padding // 2)

    if mode == 1:  # Multithreading mode
        num_threads = 5
        data_parts = overlapping_submatrices(data, num_threads, padding[0])
        with ThreadPoolExecutor(max_workers=num_threads) as executor:
            data_new_parts = list(
                executor.map(process_image_part, data_parts, [kernel] * num_threads, [padding] * 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()

        if rank == 0:
            data_parts = overlapping_submatrices(data, size, padding[0])
        else:
            data_parts = None

        data_part = comm.scatter(data_parts, root=0)
        data_part_new = process_image_part(data_part, kernel, padding)

        if rank == 0:
            data_new_parts = comm.gather(data_part_new, root=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, padding)

    data_new = data_new * 255.0
    data_new = np.uint8(data_new)
    print(f"After: {data_new.shape}")
    return Image.fromarray(data_new, mode="RGB")


if __name__ == '__main__':
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    size = comm.Get_size()

    FLAG_gauss = 0
    FLAG_uniform = 1
    FLAG_default = 0
    FLAG_threaded = 1
    FLAG_network = 2

    if rank == 0:
        print(monte_carlo_methode(1000, mode=FLAG_default))
        print(monte_carlo_methode(1000, mode=FLAG_threaded))
        print(monte_carlo_methode(1000, mode=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_network)
        image.show()