如何在多个线程之间共享一个 numpy 数组 python？

Question

我实际上是在尝试修改一些 yolov5 脚本。这里我试图在线程之间传递一个数组。

def detection(out_q):
    while(cam.isOpened()):
        ref, img = cam.read()
        img = cv2.resize(img, (640, 320))

        result = model(img)
        yoloBbox = result.xywh[0].numpy() # yolo format
        bbox = result.xyxy[0].numpy() # pascal format
        for i in bbox:
            out_q.put(i) # 'i' is the List of length 6
        
def resultant(in_q):

    while(cam.isOpened()):
        ref, img =cam.read()
        img = cv2.resize(img, (640, 320))
        qbbox = in_q.get()
        print(qbbox)
if __name__=='__main__':
    q = Queue(maxsize = 10)

    t1 = threading.Thread(target= detection, args = (q, ))

    t2 = threading.Thread(target= resultant, args = (q, ))

    t1.start()
    t2.start()

    t1.join()
    t2.join()

我试过这个，但它给我这样的错误：

Assertion fctx->async_lock failed at libavcodec/pthread_frame.c:155

那么有没有其他方法可以传数组呢？任何类型的教程/解决方案表示赞赏。如果对我的问题有任何误解，请告诉我。非常感谢！！

更新:::

我正在尝试这样..

def detection(ns, event):#
##    a = np.array([1, 2, 3])   -
####    a= list(a)               | #This is working
##    ns.value = a               |
##    event.set()               -
    while(cam.isOpened()):
        ref, img = cam.read()
        img = cv2.resize(img, (640, 320))
    
        result = model(img)
        yoloBbox = result.xywh[0].numpy() # yolo format
        bbox = result.xyxy[0].numpy() # pascal format
        for i in bbox:
            arr = np.squeeze(np.array(i))
            print("bef: ", arr)      -
            ns.value = arr            |  # This is not working
            event.set()              -

def transfer(ns, event):
    event.wait()
    print(ns.value)

if __name__=='__main__':
    ##    detection()

    manager = multiprocessing.Manager()
    namespace = manager.Namespace()
    event=multiprocessing.Event()

    p1 = multiprocessing.Process(target=detection, args= 
(namespace, event),)
    p2= multiprocessing.Process(target=transfer, args=(namespace, 
 event),)
    p1.start()
    p2.start()
    p1.join()
    p2.join()

The output from the above "arr" = [          0      1.8232      
407.98      316.46     0.92648           0]

但我得到的只是空白。没有错误，没有警告，只有空白。我测试过 arr 是有价值的。我测试了列表，np 数组都在共享标记为工作的数据。但是为什么“arr”数组中的数据是空白的（共享后）那我该怎么办？

Answer 1

so is there any other method to pass the array?

是的，您可以使用 multiprocessing.shared_memory, it is part of standard library since python3.8, and PyPI has backport 允许在 python3.6 和 python3.7 中使用它。请参阅链接文档中的示例以了解如何将 multiprocessing.shared_memory 与 numpy.ndarray

一起使用

Answer 2

@Daweo 提供的建议使用共享内存的答案是正确的。

但是，也值得考虑使用锁来 'protect' 访问 numpy 数组（不是 thread-safe）。

参见：- this

Answer 3

好的，谢谢你的帮助。我使用多处理队列来共享数据。然后我将我的程序多处理转移到线程。

def capture(q):
    cap = 
        cv2.VideoCapture(0)
    while True:
        ref, frame = cap.read()
        frame = cv2.resize(frame, (640, 480))
        q.put(frame)

def det(q):
    model = torch.hub.load('ultralytics/yolov5','yolov5s',device='cpu') 
    model.conf = 0.30  # model confidence level
    model.classes = [0]  # model classes (where 0 = person, 2 = car)
    model.iou = 0.55 # bounding box accuracy
    while True:
        mat = q.get()
        det = model(mat)
        bbox = det.xyxy[0].numpy()
        for i in bbox:
            print(i)

如何在多个线程之间共享一个 numpy 数组 python？

How to share a numpy array between multiple threads python?

python

multithreading

python-3.x

更新:::