How to use the pyflow.coarse2fine_flow function in pyflow

for i in range(n):
        im[i] = imresize(imSeq[i], (flowSz, flowSz))

    # compute Motion Saliency per frame
    salImSeq = np.zeros((n, flowSz, flowSz))
    numDomFrames = 0
    for i in range(n):
        isFrameDominant = 0
        for j in range(-flowF, flowF + 1):
            if j == 0 or i + j < 0 or i + j >= n:
                continue
            # flow = calcOpticalFlowFarneback(
            #     color.rgb2gray(im[i]), color.rgb2gray(im[i + j]), 0.5, 4,
            #     flowWinSz, 10, 5, 1.1, OPTFLOW_FARNEBACK_GAUSSIAN)
            # pyflow needs im: float in [0,1]
            u, v, _ = pyflow.coarse2fine_flow(
                im[i].astype(float) / 255., im[i + j].astype(float) / 255.,
                alpha, ratio, minWidth, nOuterFPIterations, nInnerFPIterations,
                nSORIterations, 0)
            flow = np.concatenate((u[..., None], v[..., None]), axis=2)

            dominant, _, target, salIm = isDominant(
                flow, flowMagTh, flowDirTh, dirBins=flowDirBins)

            if False:
                odir = '/home/dpathak/local/data/trash/my_nlc/nlc_out/'
                np.save(odir + '/np/outFlow_%d_%d.npy' % (i, i + j), flow)
                import cv2
                hsv = np.zeros((100, 100, 3), dtype=np.uint8)
                hsv[:, :, 0] = 255
                hsv[:, :, 1] = 255
                mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])

def get_flow(im1, im2):
    im1 = np.array(im1)
    im2 = np.array(im2)
    im1 = im1.astype(float) / 255.
    im2 = im2.astype(float) / 255.
    
    # Flow Options:
    alpha = 0.012
    ratio = 0.75
    minWidth = 20
    nOuterFPIterations = 7
    nInnerFPIterations = 1
    nSORIterations = 30
    colType = 0  # 0 or default:RGB, 1:GRAY (but pass gray image with shape (h,w,1))
    
    u, v, im2W = pyflow.coarse2fine_flow(im1, im2, alpha, ratio, minWidth, nOuterFPIterations, nInnerFPIterations,nSORIterations, colType)
    flow = np.concatenate((u[..., None], v[..., None]), axis=2)
    #flow = rescale_flow(flow,0,1)
    return flow

def get_flow(im1, im2):
    im1 = np.array(im1)
    im2 = np.array(im2)
    im1 = im1.astype(float) / 255.
    im2 = im2.astype(float) / 255.
    
    # Flow Options:
    alpha = 0.012
    ratio = 0.75
    minWidth = 20
    nOuterFPIterations = 7
    nInnerFPIterations = 1
    nSORIterations = 30
    colType = 0  # 0 or default:RGB, 1:GRAY (but pass gray image with shape (h,w,1))
    
    u, v, im2W = pyflow.coarse2fine_flow(im1, im2, alpha, ratio, minWidth, nOuterFPIterations, nInnerFPIterations,nSORIterations, colType)
    flow = np.concatenate((u[..., None], v[..., None]), axis=2)
    #flow = rescale_flow(flow,0,1)
    return flow

im1 = np.array(Image.open('examples/car1.jpg'))
im2 = np.array(Image.open('examples/car2.jpg'))
im1 = im1.astype(float) / 255.
im2 = im2.astype(float) / 255.

# Flow Options:
alpha = 0.012
ratio = 0.75
minWidth = 20
nOuterFPIterations = 7
nInnerFPIterations = 1
nSORIterations = 30
colType = 0  # 0 or default:RGB, 1:GRAY (but pass gray image with shape (h,w,1))

s = time.time()
u, v, im2W = pyflow.coarse2fine_flow(
    im1, im2, alpha, ratio, minWidth, nOuterFPIterations, nInnerFPIterations,
    nSORIterations, colType)
e = time.time()
print('Time Taken: %.2f seconds for image of size (%d, %d, %d)' % (
    e - s, im1.shape[0], im1.shape[1], im1.shape[2]))
flow = np.concatenate((u[..., None], v[..., None]), axis=2)
np.save('examples/outFlow.npy', flow)

if args.viz:
    import cv2
    hsv = np.zeros(im1.shape, dtype=np.uint8)
    hsv[:, :, 0] = 255
    hsv[:, :, 1] = 255
    mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
    hsv[..., 0] = ang * 180 / np.pi / 2
    hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)