使用多处理来处理图像，但使用8 cpus不能获得性能提升

Question

I got a function called cartoonize(image_path) , which takes 'path/to/image' as an argument. The script is a bit hefty takes a couple of minutes to process a 1920x1080 sized image. I tried to use all my 8 cores using multiprocessing module , but there is no performance gain I see with the following code. Another problem is how to save the image. It returns a CV2 object. Normally, I save the image to the disk by see below code , but with multiprocessing it is giving error "img is not a numpy array, neither a scalar." I also want a performance gain, which I can't figure out how to do it efficiently.

out_final = cartoonize('path/to/image'))
cv2.imwrite('cartoon.png', out_final)



import multiprocessing

if __name__ == '__main__':
    # mark the start time
    startTime = time.time()

    print "cartoonizing please wait ..."
    pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())
    pool_outputs = pool.apply_async(cartoonize, args=(image_path,))
    pool.close()
    pool.join()
    print ('Pool:', pool_outputs)

    # mark the end time
    endTime = time.time()
    print('Took %.3f seconds' % (startTime - endTime)) 

Answer 1

Question : multiprocessing it is giving error "img is not a numpy array, neither a scalar."

pool_outputs = pool.apply_async(cartoonize, args=(image_path,))

Your code returns AsyncResult

Python » 3.6.1 Documentation » multiprocessing.pool.AsyncResult
class multiprocessing.pool.AsyncResult The class of the result returned by Pool.apply_async() and Pool.map_async().