discrete wavelet transform and inverse discrete wavelet transform TypeError in python

I am currently doing a project in image processing. I am new to python. Any help will be appreciated. I am decomposing image into bands using discrete wavelet transform and modify the coefficients. So below code gives me an error. here is the code

`import cv2
import numpy as np
from PIL import Image
import numpy
from pywt import dwt2, idwt2
from random import Random
import random
import pywt


img = cv2.imread('xyy.png')

def im2double(im):
    min_val = np.min(im.ravel())
    max_val = np.max(im.ravel())
    out = (im.astype('float') - min_val) / (max_val - min_val)
    return out
k=2
cover_object=im2double(img)

Mc=np.shape(cover_object)
Nc=np.shape(cover_object)
print(Mc[0],Nc[1])

#watermark image
water = cv2.imread('1.png')
gray_image = cv2.cvtColor(water, cv2.COLOR_BGR2GRAY)
xy=cv2.imwrite('grayim.png',gray_image) 
messagee = cv2.imread('grayim.png')
file_name1=im2double(messagee)
print(file_name1)
a=np.shape(gray_image)
b=a[0]*a[1]

images_rss = gray_image.reshape([b, 1])/256
print(images_rss)

np.random.seed(0)
key=round(100*numpy.random.rand(1))
print(key)

cA, (cH, cV, cD) = dwt2(img, 'haar')
leng=len(images_rss)
#print(leng)

for kk in range(1,leng):
    q = 2*(random.randint(512/2,512/2)-0.5)
    pn_sequence_h=round(q,0)
    w = 2*(random.randint(512/2,512/2)-0.5)
    pn_sequence_v=round(w,0)
    #print(pn_sequence_h)
    if (file_name1(kk) == 0):
        cH=cH+k*pn_sequence_h
        cV=cV+k*pn_sequence_v
idwt2(cA,cH,cV,cD,'haar')[:Mc,:Nc]

Below is error

Traceback (most recent call last):
  File "stack.py", line 53, in <module>
    if (file_name1(kk) == 0):
TypeError: 'numpy.ndarray' object is not callable

How can I get rid of this error? Also please tell if DWT and IDWT syntax is correct?

    import cv2
    import numpy as np
    from PIL import Image
    import numpy
    from pywt import dwt2, idwt2
    from random import Random
    import random
    import pywt


    img = cv2.imread('xyy.png')

    def im2double(im):
        min_val = np.min(im.ravel())
        max_val = np.max(im.ravel())
        out = (im.astype('float') - min_val) / (max_val - min_val)
        return out
    k=2
    cover_object=im2double(img)

    Mc=np.shape(cover_object)
    Nc=np.shape(cover_object)
    print(Mc[0],Nc[1])

    #watermark image
    water = cv2.imread('1.png')
    gray_image = cv2.cvtColor(water, cv2.COLOR_BGR2GRAY)
    xy=cv2.imwrite('grayim.png',gray_image) 
    messagee = cv2.imread('grayim.png')
    file_name1=im2double(messagee)
    print(file_name1)
    a=np.shape(gray_image)
    b=a[0]*a[1]

    images_rss = gray_image.reshape([b, 1])/256
    print(images_rss)

    np.random.seed(0)
    key=round(100*numpy.random.rand(1))
    print(key)

    cA, (cH, cV, cD) = dwt2(img, 'haar')
    leng=len(images_rss)
    #print(leng)

    for kk in range(1,leng):
        q = 2*(random.randint(512/2,512/2)-0.5)
        pn_sequence_h=round(q,0)
        w = 2*(random.randint(512/2,512/2)-0.5)
        pn_sequence_v=round(w,0)
        #print(pn_sequence_h)
        if (file_name1[kk] == 0.0):
            cH=cH+k*pn_sequence_h
            cV=cV+k*pn_sequence_v

coeffs1 = (cA, (cH, cV, cD) )           
watermarked_image = np.array(pywt.idwt2(coeffs1, 'haar'),np.uint8);