use multiprocessing to speed up

multiprocessing.py

# you have run this code in .py file (cannot be in .ipynb)
# you have to add the head `if __name__ == '__main__':`

if __name__ == '__main__':
    start = time.time()
    
    
    img_ids = df.image_id.values # a list
    pool = Pool(processes=multiprocessing.cpu_count())
    pool.map(crop_all_img, img_ids) # (function, list)

    delta = time.time() - start
    print(f'Used time: {delta}')