def _locateAll_opencv(needleImage, haystackImage, grayscale=None, limit=10000, region=None, step=1,
                      confidence=0.999):
    """ faster but more memory-intensive than pure python
        step 2 skips every other row and column = ~3x faster but prone to miss;
            to compensate, the algorithm automatically reduces the confidence
            threshold by 5% (which helps but will not avoid all misses).
        limitations:
          - OpenCV 3.x & python 3.x not tested
          - RGBA images are treated as RBG (ignores alpha channel)
    """
    if grayscale is None:
        grayscale = GRAYSCALE_DEFAULT

    confidence = float(confidence)

    needleImage = _load_cv2(needleImage, grayscale)
    needleHeight, needleWidth = needleImage.shape[:2]
    haystackImage = _load_cv2(haystackImage, grayscale)

    if region:
        haystackImage = haystackImage[region[1]:region[1]+region[3],
                                      region[0]:region[0]+region[2]]
    else:
        region = (0, 0)  # full image; these values used in the yield statement
    if (haystackImage.shape[0] < needleImage.shape[0] or
        haystackImage.shape[1] < needleImage.shape[1]):
        # avoid semi-cryptic OpenCV error below if bad size
        raise ValueError('needle dimension(s) exceed the haystack image or region dimensions')

    if step == 2:
        confidence *= 0.95
        needleImage = needleImage[::step, ::step]
        haystackImage = haystackImage[::step, ::step]
    else:
        step = 1

    # get all matches at once, credit: https://stackoverflow.com/questions/7670112/finding-a-subimage-inside-a-numpy-image/9253805#9253805
    result = cv2.matchTemplate(haystackImage, needleImage, cv2.TM_CCOEFF_NORMED)
    match_indices = numpy.arange(result.size)[(result > confidence).flatten()]
    matches = numpy.unravel_index(match_indices[:limit], result.shape)

    if len(matches[0]) == 0:
        if USE_IMAGE_NOT_FOUND_EXCEPTION:
            raise ImageNotFoundException('Could not locate the image (highest confidence = %.3f)' % result.max())
        else:
            return None

    # use a generator for API consistency:
    matchx = matches[1] * step + region[0]  # vectorized
    matchy = matches[0] * step + region[1]
    for x, y in zip(matchx, matchy):
        yield Box(x, y, needleWidth, needleHeight)
faster but more memory-intensive than pure python
        step 2 skips every other row and column = ~3x faster but prone to miss;
            to compensate, the algorithm automatically reduces the confidence
            threshold by 5% (which helps but will not avoid all misses).
        limitations:
          - OpenCV 3.x & python 3.x not tested
          - RGBA images are treated as RBG (ignores alpha channel)