2024-heraklion-parallel-python/exercises/exerciseC/process_images.py

import os
import sys
from multiprocessing import Pool as ProcessPool
import time

def process_image(input_tuple):

    fname, A = input_tuple
    n_threads = os.getenv('OMP_NUM_THREADS', '(unset)')
    print(f"Worker {fname=} OMP_NUM_THREADS={n_threads}", flush=True)

    # Decompose image
    U, S, Vh = np.linalg.svd(A)

    # Remove first singular value
    S[0] = 0
    smat = np.zeros(A.shape, dtype=complex)
    smat[:min(A.shape), :min(A.shape)] = np.diag(S)

    # Re-compose image
    A = np.dot(U, np.dot(smat, Vh)).real
    A = (256*(A - A.min())/A.max()).astype('uint8')

    return A
    

if __name__ == '__main__':
    n_processes = int(sys.argv[1])
    n_threads = int(sys.argv[2])
    fnames = sys.argv[3:]

    # Check that the output folders exist, or create them if needed
    if not os.path.isdir('timings'): os.mkdir('timings')
    if not os.path.isdir('processed_images'): os.mkdir('processed_images')

    print(f"Controller with {n_processes} processes and {n_threads} threads / worker", flush=True)

    # The environment that is set in the parent is inherited by child workers,
    # we need to set the variable before numpy is imported!
    os.environ['OMP_NUM_THREADS'] = str(n_threads)

    # We delay the import of numpy because we have to set OMP_NUM_THREADS before import.
    # We delay the import of PIL in case it uses numpy internally.
    import numpy as np
    from PIL import Image

    # I/O Load the images
    image_arrays = []
    for fname in fnames:
        im = Image.open(fname)
        A = np.array(im)
        image_arrays.append((fname, A))

    # Time the execution of the pool map
    start_time = time.time()
    with ProcessPool(n_processes) as p:
        new_images = p.map(process_image, image_arrays)
    elapsed_time = time.time() - start_time

    # I/O save the processed images
    for im, fname in zip(new_images, fnames):
        im = Image.fromarray(im)
        im.save(fname.replace('images', 'processed_images'))

    print(f'{n_processes} processes and {n_threads} threads and {len(fnames)} jobs: {elapsed_time}\n',
          flush=True)

    # I/O: Save the timing to a unique txt file
    filename = f'timings/{n_processes:02}_processes_{n_threads:02}_threads.txt'
    with open(filename, 'w') as file:
        file.write(f'{n_processes} {n_threads} {elapsed_time:.6f}')