问aws-lambda和opencv -过早退出，不会出现错误

EN

START RequestId: e8001171-fe9e-41f0-aacc-66bd27626ff5 Version: $LATEST
Downloading file...
Checking result file exists in result bucket
File does not exist in bucket
Loading data from disk
Opening results csv file
Calling algorithm with screenshot
matching...
END RequestId: e8001171-fe9e-41f0-aacc-66bd27626ff5
REPORT RequestId: e8001171-fe9e-41f0-aacc-66bd27626ff5 Duration: 9934.52 ms Billed 
Duration: 10000 ms Memory Size: 128 MB Max Memory Used: 129 MB
It then seems to immediatly try to do it again!
RequestId: e8001171-fe9e-41f0-aacc-66bd27626ff5 Process exited before completing request
OpenBLAS WARNING - could not determine the L2 cache size on this system, assuming 256k

def search_for_image_in_image(screenshot_path, detectable_path, epsilon):
    """
    Uses template matching algorithm to detect an image within an image
    :param screenshot_path: (str) Path to the screenshot to search
    :param detectable_path: (str) Path to the detectable to search for
    :param epsilon: (float) 1 - the maximum value the algorithm found must be less than this value
        in order to be considered a match
    :return: (tuple) containing:
        (bool) - Whether or not the detectable was found within the screenshot, using the given
            epsilon
        (float) - Maximum value the algorithm found
        (list) - x and y position in the screenshot where the maximum value is located. Or in
            other words, where the algorithm thinks the top left of the detectable is most likely to
            be (if it is there at all)
    """
    screenshot = cv2.imread(screenshot_path, cv2.IMREAD_COLOR)
    screenshot = cv2.cvtColor(screenshot, cv2.COLOR_BGR2RGB)

    template = cv2.imread(detectable_path, cv2.IMREAD_COLOR)
    template = cv2.cvtColor(template, cv2.COLOR_BGR2RGB)

    height = template.shape[0]
    width = template.shape[1]

    # Note that there are several algorithms available.
    # Some might not work as well in some cases.
    # TODO - Perhaps we should go through each and evaluate the successes and failures
    #        Like "if 4 out of the 6 agree, then the answer is x"
    #        But note that would be more computation. Let's look at how long one takes first.
    methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR', 'cv2.TM_CCORR_NORMED',
               'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
    method = eval(methods[1])

    print("matching...")
    result = cv2.matchTemplate(screenshot, template, method)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)

    print("done matching...")
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc
    bottom_right = (top_left[0] + width, top_left[1] + height)

    result_img = screenshot.copy()
    cv2.rectangle(result_img, top_left, bottom_right, 255, 2)

    print("returning")
    return 1.0 - max_val < epsilon, max_val, max_loc