Python OpenCV与joblib或多处理池并行使用

Question

我试图与Python 3和joblib并行计算图像对的单应性。我们在左图像中有对象的边界框，并希望在右图中找到相应的边界框。但是，使用下面显示的方法，似乎只使用了我的一个机器核心。

#THIS CORRESPONDS TO ONE LOOP ITERATION.
def processOneImage(params):
    #Unpack params
    leftFname, leftImgDir, rightImgDir, leftDetectionDir, rightDetectionDir = params

    #Load detections. Early out if none.
    objectDetections = []
    detFname = leftDetectionDir + leftFname[:-4] + '.txt'
    with open(detFname, 'r') as f:
        for line in f:
            objectDetections.append(line.split())
        f.close()
    if len(objectDetections) == 0:
        return None

    #Load images.
    leftImg = cv2.imread(leftImgDir + leftFname)
    rightImg = cv2.imread(rightImgDir + leftFname)

    #Compute homography.
    H = computeHomography(leftImg, rightImg)
    if H is None:
        return None

    #Compute Bounding boxes and write out.
    outFname = rightDetectionDir + leftFname[:-4] + '.txt'
    outFile = open(outFname, 'w')
    processLabelBoundingBoxes(leftImg, rightImg, H, objectDetections, outFile)
    outFile.flush()
    outFile.close()

#HERE WE RUN THE "LOOP" IN PARALLEL
def processDataset(leftImgFnames, leftImgDir, rightImgDir, leftDetectionDir, rightDetectionDir):
    numItems = len(leftImgFnames)
    params = zip(leftImgFnames, [leftImgDir]*numItems, [rightImgDir]*numItems, [leftDetectionDir]*numItems, [rightDetectionDir]*numItems)
    res = joblib.Parallel(n_jobs=4, backend="multiprocessing")(map(joblib.delayed(processOneImage), params))

是否可以以并行方式使用OpenCV？如果是这样，我是否必须以某种方式指定OpenCV运行哪个线程？

编辑：我也尝试过python多处理库中的Pools，但结果是一样的。

def processDataset(leftImgFnames, leftImgDir, rightImgDir, leftDetectionDir, rightDetectionDir):
    numItems = len(leftImgFnames)
    params = zip(leftImgFnames, [leftImgDir]*numItems, [rightImgDir]*numItems, [leftDetectionDir]*numItems, [rightDetectionDir]*numItems)
    p = multiprocessing.Pool(4)#I have 4 cores/threads.
    p.map(processOneImage, params)

为完整起见，这里有辅助函数

#THIS USES OPENCV
def computeHomography(leftImg, rightImg):
    #Compute ORB keypoints and descriptors for both images.
    orb = cv2.ORB_create()
    keypointsLeft, descriptorsLeft = orb.detectAndCompute(leftImg, None)
    keypointsRight, descriptorsRight = orb.detectAndCompute(rightImg, None)

    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
    matches = bf.match(descriptorsLeft, descriptorsRight)

    #Verify match quality.
    goodMatches = []
    for m in matches:
        goodMatches.append(m)

    if len(goodMatches) > 10:
        src_pts = np.float32([keypointsLeft[m.queryIdx].pt for m in goodMatches]).reshape(-1,1,2)
        dst_pts = np.float32([keypointsRight[m.trainIdx].pt for m in goodMatches]).reshape(-1,1,2)

        M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
        return M
    return None

#THIS USES OPENCV    
def processLabelBoundingBoxes(leftImg, rightImg, H, objectDetections, outFile):
    instanceCounts = dict()
    for detection in objectDetections:
        objID = detection[0]
        objConf = float(detection[5])

        bb_x = int(detection[1])
        bb_y = int(detection[2])
        bb_w = int(detection[3]) - int(detection[1])
        bb_h = int(detection[4]) - int(detection[2])
        rect = (bb_x, bb_y, bb_w, bb_h)
        maskLeft = np.zeros(leftImg.shape[:2], np.uint8)

        #Replace this with the code below for graphcuts. This is bounding box.
        for x in range(bb_x, bb_x + bb_w):
            for y in range(bb_y, bb_y + bb_h):
                if x >= 0 and x < leftImg.shape[1] and y >= 0 and y < leftImg.shape[0]:
                    maskLeft[y][x] = 255

        maskRight = cv2.warpPerspective(maskLeft, H, (leftImg.shape[1], leftImg.shape[0]))

        #TO-DO: Find a way to do this automatically, with cv2 maybe...
        #Currently too lazy, it's late...
        xmin = rightImg.shape[1]
        xmax = 0
        ymin = rightImg.shape[0]
        ymax = 0
        for y in range(0, rightImg.shape[0]):
            for x in range(0, rightImg.shape[1]):
                if maskRight[y][x] > 0:
                    if y < ymin:
                        ymin = y
                    if y > ymax:
                        ymax = y
                    if x < xmin:
                        xmin = x
                    if x > xmax:
                        xmax = x
        bbox_width = xmax - xmin
        bbox_height = ymax - ymin

        outFile.write("%s %d %d %d %d %f\n" % (objID, xmin, ymin, bbox_width, bbox_height, objConf))