plot.i += 1

plt.subplot(2,3,plot.i)

plt.imshow(data)

plt.gray()

#####------Adding Gaussian noise with mean=20 and sigma=40------#####

if noise_type == "gauss":

row,col= image.shape

img=np.zeros((row,col))

for i in range(row):

for j in range(col):

img[i][j]=image[i][j]

mean = 20

gauss = np.random.normal(mean,40,(row,col))

gauss = gauss.reshape(row,col)

noisy = img + gauss

return noisy

#####------Adding 20% Salt and Pepper noise------#####

elif noise_type == "s&p":

row,col = image.shape

img=np.zeros((row,col))

for i in range(row):

for j in range(col):

img[i][j]=image[i][j]

s_vs_p = 0.5

amount = 0.040

out = img

# Salt mode

num_salt = np.ceil(amount * img.size * s_vs_p)

coords = [np.random.randint(0, i - 1, int(num_salt))

for i in img.shape]

out[coords] = 255

# Pepper mode

num_pepper = np.ceil(amount* img.size * (1. - s_vs_p))

coords = [np.random.randint(0, i - 1, int(num_pepper))

for i in img.shape]

out[coords] = 0

coeffs = pywt.dwt2(image, wavelet)

cA, (cH, cV, cD) = coeffs

#####------For db2 trimming down the image to maintain size consistancy------#####

if wavelet=='db2':

cA=cA[1:len(cA),1:len(cA)]

cH=cH[1:len(cH),1:len(cH)]

cV=cV[1:len(cV),1:len(cV)]

cD=cD[1:len(cD),1:len(cD)]

print 'len(cA) '+str(len(cA))

print 'len(cH) '+str(len(cH))

print 'len(cV) '+str(len(cV))

print 'len(cD) '+str(len(cD))

#####------Scaling the transformed image by 2------#####

cA=cv2.pyrUp(cA)

cH=cv2.pyrUp(cH)

cV=cv2.pyrUp(cV)

cD=cv2.pyrUp(cD)

print 'len(cA)up '+str(len(cA))

print 'len(cH)up '+str(len(cH))

print 'len(cV)up '+str(len(cV))

print 'len(cD)up '+str(len(cD))

print '1'

cA1,cH1,cV1,cD1= WaveletTransform(image,wavelet)

print '2'

cA2,cH2,cV2,cD2= WaveletTransform(cA1,wavelet)

print '3'

cA3,cH3,cV3,cD3= WaveletTransform(cA2,wavelet)

print '4'

cA4,cH4,cV4,cD4= WaveletTransform(cA3,wavelet)

cHH1=cv2.multiply(cH1,cH2)

cVV1=cv2.multiply(cV1,cV2)

cDD1=cv2.multiply(cD1,cD2)

cHH2=cv2.multiply(cH2,cH3)

cVV2=cv2.multiply(cV2,cV3)

cDD2=cv2.multiply(cD2,cD3)

cHH3=cv2.multiply(cH3,cH4)

cVV3=cv2.multiply(cV3,cV4)

cDD3=cv2.multiply(cD3,cD4)

#####------Adding the horizontal, vertical and diagonal details to form a combined edge map------#####

final1=cHH1+cVV1+cDD1

final2=cHH2+cVV2+cDD2

final3=cHH3+cVV3+cDD3

imsave('results/'+img+'level_1and2.png',final1)

imsave('results/'+img+'level_2and3.png',final2)

imsave('results/'+img+'level_3and4.png',final3)

#plot(final1,img+'level 1&2')

#plot(final2,img+'level 2&3')