Многопроцессный Python возвращает видеопоток в реальном времени

Question

Я застрял на том, как определить возвращаемое значение для моего многопроцессорного.Я не могу запустить свою программу в одном потоке или процессе, потому что у меня есть 2 бесконечных цикла while.Один предназначен для видеосъемки 2 видеопотоков с моей веб-камеры и малиновой пи-камеры, а второй - для распознавания лиц в этом сшитом потоке.Поэтому я пытаюсь сделать многопоточность или многопроцессорность, но я не знаю, как вернуть видеопоток из потока.Я подумал об использовании очереди или потоков позже, если это не сработает.

Есть идеи для этого?Функции выполняются, если они выполняются отдельно.

#Process #1
def VideoStitching()
    # initialize the video streams and allow them to warmup
    print("[INFO] starting cameras for video stitching...")
    leftStream = VideoStream(src=0).start()
    rightStream = VideoStream(usePiCamera=True).start()
    time.sleep(2.0)

    # initialize the image stitcher
    # number of frames read
    stitcher = Stitcher()


    # loop over frames from the video streams
    while True:
        # grab the frames from their respective video streams
        left = leftStream.read()
        right = rightStream.read()

        # resize the frames
        left = imutils.resize(left, width=400)
        right = imutils.resize(right, width=400)

        # stitch the frames together to form the panorama
        # IMPORTANT: you might have to change this line of code
        # depending on how your cameras are oriented; frames
        # should be supplied in left-to-right order
        result = stitcher.stitch([left, right])

        # no homograpy could be computed
        if result is None:
            print("[INFO] homography could not be computed")
            break

    # show the output images
    #cv2.imshow("Result", result)
    return result
    key = cv2.waitKey(1) & 0xFF

    # if the `q` key was pressed, break from the loop
    if key == ord("q"):
        break

    # do a bit of cleanup
    leftStream.stop()
    rightStream.stop()


#Process #2
def FaceRecog(vs)
    # start the FPS counter
    fps = FPS().start()

    # loop over frames from the video file stream
    while True:
        # grab the frame from the threaded video stream and resize it
        # to 500px (to speedup processing)
        #frame = result.read()
        #frame = imutils.resize(frame, width=800)

        frame = vs  

        # convert the input frame from (1) BGR to grayscale (for face
        # detection) and (2) from BGR to RGB (for face recognition)
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

        # detect faces in the grayscale frame
        rects = detector.detectMultiScale(gray, scaleFactor=1.1, 
            minNeighbors=5, minSize=(30, 30),
            flags=cv2.CASCADE_SCALE_IMAGE)

        # OpenCV returns bounding box coordinates in (x, y, w, h) order
        # but we need them in (top, right, bottom, left) order, so we
        # need to do a bit of reordering
        boxes = [(y, x + w, y + h, x) for (x, y, w, h) in rects]

        # compute the facial embeddings for each face bounding box
        encodings = face_recognition.face_encodings(rgb, boxes)
        names = []

        # loop over the facial embeddings
        for encoding in encodings:
            # attempt to match each face in the input image to our known
            # encodings
            matches = face_recognition.compare_faces(data["encodings"],
                encoding)
            name = "Unknown"

            # check to see if we have found a match
            if True in matches:
                # find the indexes of all matched faces then initialize a
                # dictionary to count the total number of times each face
                # was matched
                matchedIdxs = [i for (i, b) in enumerate(matches) if b]
                counts = {}

                # loop over the matched indexes and maintain a count for
                # each recognized face face
                for i in matchedIdxs:
                    name = data["names"][i]
                    counts[name] = counts.get(name, 0) + 1

                # determine the recognized face with the largest number
                # of votes (note: in the event of an unlikely tie Python
                # will select first entry in the dictionary)
                name = max(counts, key=counts.get)

            # update the list of names
            names.append(name)

        # loop over the recognized faces
        for ((top, right, bottom, left), name) in zip(boxes, names):
            # draw the predicted face name on the image
            cv2.rectangle(frame, (left, top), (right, bottom),
                (0, 255, 0), 2)
            y = top - 15 if top - 15 > 15 else top + 15
            cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
                0.75, (0, 255, 0), 2)

        # display the image to our screen
        cv2.imshow("Frame", frame)
        key = cv2.waitKey(1) & 0xFF

    # if the `q` key was pressed, break from the loop
    if key == ord("q"):
        break

    # do a bit of cleanup
    cv2.destroyAllWindows()
    vs.stop()

if __name__ == '__main__':
    p = Pool(processes=2)
    vs = p.map(VideoStitching)
    frame = p.map(FaceRecog, vs)
    p.close() 
    p.join()