def _removeSmallerContours(self, image):
        cv2.rectangle(image, (0, 0), (640, 480), (0, 0, 0), thickness=1)
        contours, _ = cv2.findContours(image.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
        bestContour = max(contours, key=cv2.contourArea)

        for contour in contours:
            if not numpy.array_equal(contour, bestContour):
                leftmostX = findLeftmostPoint(contour)[0]
                topmostY = findTopmostPoint(contour)[1]
                rightmostX = findRightmostPoint(contour)[0]
                bottommostY = findBottommostPoint(contour)[1]
                cv2.rectangle(image, (leftmostX, topmostY), (rightmostX, bottommostY), (255, 255, 255), thickness=-1)

        contours, _ = cv2.findContours(image.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
        bestContour = max(contours, key=cv2.contourArea)

        for contour in contours:
            if not numpy.array_equal(contour, bestContour):
                leftmostX = findLeftmostPoint(contour)[0]
                topmostY = findTopmostPoint(contour)[1]
                rightmostX = findRightmostPoint(contour)[0]
                bottommostY = findBottommostPoint(contour)[1]
                cv2.rectangle(image, (leftmostX, topmostY), (rightmostX, bottommostY), (0, 0, 0), thickness=-1)

        cv2.rectangle(image, (0, 0), (640, 480), (0, 0, 0), thickness=169)

        return image
    def discriminateContours(self, contours, rgbImage):
        bestContours = []

        for contour in contours:
            bottomPointX, bottomPointY = findBottommostPoint(contour)
            pointsBelow = [(bottomPointX, bottomPointY + j * 5) for j in range(1, 5)]
            pointBelowContourX, pointBelowContourY = bottomPointX, bottomPointY + 10

            for x, y in pointsBelow:
                if not all(coordinate < self.BLACK_COLOR_THRESHOLD for coordinate in rgbImage[y, x]):
                    break
            else:
                bestContours.append(contour)

        return bestContours