使用多处理返回一个 numpy 数组

Question

我有以下功能，

def detect_face(image, return_val):
 frame = Image.open('input/' + image + '.jpg')
 face_boxes = face_recognition.face_locations(np.array(frame), model='cnn')

 if len(face_boxes) > 1:
    print("-----2 faces detected in {} image-----".format(image))
    return_val.append(None)
    return

 elif len(face_boxes) == 0:
    print("-----No face detected in {} image-----".format(image))
    return_val.append(None)
    return

 top, right, bottom, left = np.squeeze(face_boxes)
 frameCropped = frame.crop(box=(left, top, right, bottom + 15))

 frame_resized = np.array(frameCropped.resize((224, 224)))
 Image.fromarray(frame_resized).save('input/cropped_' + image + '.jpg')

 preprocess_image = np.expand_dims(np.array(frame_resized, dtype=np.float64), 0)
 preprocess_image = preprocess_input(np.array(preprocess_image), version=1)

 fig,ax = plt.subplots(nrows=1, ncols=3, figsize=(5,12))
 ax[0].imshow(np.array(frame))
 ax[0].axis('off')
 ax[1].imshow(np.array(frame_resized))
 ax[1].axis('off')
 ax[2].imshow(np.array(preprocess_image[0]))
 ax[2].axis('off')

 return_val.append(preprocess_image)
 return preprocess_image

manager = multiprocessing.Manager()
return_val = manager.list()

preprocess_image = Process(target=detect_face, args=('ammar', return_val))
preprocess_image.start()
preprocess_image.join()

print(return_val)

return_val 总是一个空列表 如何使用多处理模块返回我的 numpy 数组 我试图寻找答案，但似乎我做错了什么

Answer 1

import numpy as np
import multiprocessing as mp

n_elements = 1000 # how many elements your numpy should have

def myProc( shared_var ):
    '''
    here you convert your shared variable from mp.RawArray to numpy
    then treat it as it is numpy array e.g. fill it in with some 
    random numbers for demonstration purpose
    '''
    var = np.reshape( np.frombuffer( shared_var, dtype=np.uint32 ), -1 )
    for i in range( n_elements ):    
        var[i] = np.random.randint( 0, 2**16, 1 )
    print( 'myProc var.mean() = ', var.mean() )               
                                  
#buffer that contains the memory
mp_var = mp.RawArray( 'i', n_elements )
p = mp.Process( target=myProc, args=(mp_var,) )
p.start()                                      
p.join()
#after the process has ended, you convert the buffer that was passed to it
var = np.reshape( np.frombuffer( mp_var, dtype=np.uint32 ), -1)
#and again, you can treat it like a numpy array
print( '   out var.mean() = ',var.mean() )