image = cv2.imread(args['image'])
accumEdged = np.zeros(image.Shape[:2], dtype='uint8')
for chan in cv2.splIT(image):
    chan = cv2.medianBlur(chan, 11)
    edged = cv2.Canny(chan, 50, 200)
    accumEdged = cv2.bitwise_or(accumEdged, edged)
cv2.imshow('edge map', accumEdged)

cnts = cv2.findContours(accumEdged.copy(), cv2.RETR_EXTERNAL,  cv2.CHaiN_APPROX_SIMPLE)
cnts = grab_contours(cnts)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
orig = image.copy()
# unsorted
for (i, c) in enumerate(cnts):
    orig = draw_contour(orig, c, i)
cv2.imshow('Unsorted', orig)
cv2.imwrite("./Unsorted.jpg", orig)

def grab_contours(cnts):
    # if the length the contours tuple returned by cv2.findContours
    # is '2' then we are using either OpenCV v2.4, v4-beta, or
    # v4-official
    if len(cnts) == 2:
        cnts = cnts[0]

    # if the length of the contours tuple is '3' then we are using
    # either OpenCV v3, v4-pre, or v4-alpha
    elif len(cnts) == 3:
        cnts = cnts[1]

    return cnts

def sort_contours(cnts, method='left-to-right'):
    # initialize the reverse flag and sort index
    reverse = False
    i = 0
    # handle if sort in reverse
    if method == 'right-to-left' or method == 'bottom-to-top':
        reverse = True
    # handle if sort against y rather than x of the bounding box
    if method == 'bottom-to-top' or method == 'top-to-bottom':
        i = 1

    boundingBoxes = [cv2.boundingRect(c) for c in cnts]
    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes), key=lambda b: b[1][i], reverse=reverse))
    return (cnts, boundingBoxes)

# sorted
(cnts, boundingboxes) = sort_contours(cnts, method=args['method'])
for (i, c) in enumerate(cnts):
    image = draw_contour(image, c, i)
cv2.imshow('Sorted', image)
cv2.waitKey(0)