Why don't you use imshow instead?

You can plot a 2D image by doing:

plt.imshow(Image1, cmap='gray') # I would add interpolation='none'

Afterwards, you can easily overlay the segmentation by doing:

plt.imshow(Image2_mask, cmap='jet', alpha=0.5) # interpolation='none'

Changing the alpha will change the opacity of the overlay.

Additionaly, why do you create 2 masks? Only one should be enough, you can do:

Image2_mask = ma.masked_array(Image2 > 0, Image2)

Practical example:

import numpy as np
mask = np.zeros((10,10))
mask[3:-3, 3:-3] = 1 # white square in black background
im = mask + np.random.randn(10,10) * 0.01 # random image
masked = np.ma.masked_where(mask == 0, mask)

import matplotlib.pyplot as plt
plt.figure()
plt.subplot(1,2,1)
plt.imshow(im, 'gray', interpolation='none')
plt.subplot(1,2,2)
plt.imshow(im, 'gray', interpolation='none')
plt.imshow(masked, 'jet', interpolation='none', alpha=0.7)
plt.show()

you could overlay two images plotting them with plt.imshow in the same cell.

from matplotlib import pyplot as plt

plt.figure(figsize = (10,10))
plt.imshow(img_1)
plt.imshow(img_2, alpha=0.5)

I tested "alpha" as a 5x5 array, but the "imshow" simply ignores that array. So I joined "B" with an "alpha" column, so I got an (5 x 5 x 4) RGBA matrix, called "C".

So you don't need to pass a "alpha" array parameter in "imshow".

import matplotlib as m
import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(2, 3, figsize=(6, 5))
ax1 = ax[0, 0]
ax2 = ax[0, 1]
ax3 = ax[1, 0]
ax4 = ax[1, 1]
ax5 = ax[0, 2]
ax6 = ax[1, 2]

A = np.arange(5*5*3).reshape((5, 5, 3)) / (5*5*3)
ax1.imshow(A)
ax1.set_title("A")

B = np.zeros(5*5*3).reshape((5, 3, 5))
B[2:4, 2:4] = 1
#B.shape = (5, 5, 3) # do some shifting so its not grayscale
B = B.reshape((5, 5, 3))
ax2.imshow(B)
ax2.set_title("B")

alphas = np.zeros(5*5).reshape((5, 5))
alphas[B[:, :, 1] > 0] = 0.66
ax3.imshow(alphas)
ax3.set_title("Alpha mask")

alphas_2 = alphas.reshape(5, 5, 1)
C = np.dstack((B, alphas_2))

ax4.imshow(A)
ax4.imshow(B)
ax4.set_title("A and B")

ax5.imshow(C)
ax5.set_title("C")

ax6.imshow(A)
ax6.imshow(C)
ax6.set_title("A and C")

plt.show()

You have to match their extent:

import numpy as np
import matplotlib.pyplot as plt

from scipy.misc import face

x = face(gray=False) # shape (768, 1024, 3)
y = np.random.random((100, 133)) # shape (100, 133)
fig, ax = plt.subplots()
img0 = ax.imshow(x)
img1 = ax.imshow(y, cmap="jet", alpha=0.3, extent=img0.get_extent())
plt.show()