Overlay an image segmentation with numpy and matplotlib

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 npmask = np.zeros((10,10))mask[3:-3, 3:-3] = 1 # white square in black backgroundim = mask + np.random.randn(10,10) * 0.01 # random imagemasked = np.ma.masked_where(mask == 0, mask)import matplotlib.pyplot as pltplt.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()

I can give you my function two overlap a picture and a mask of dataset :

def get_overlapped_img(filename, img_folder, mask_folder):# Import orginal imgimg = cv2.imread(img_folder+"/"+filename+".jpg")# Import and convert the mask from binary to RGBmask = Image.open(mask_folder+"/"+filename+".png").convert('RGB')width, height = mask.size# Convert the white color (for blobs) to magentamask_colored = change_color(mask, width, height, (255, 255, 255), (186,85,211))# Convert the black (for background) to white --> important to make a good overlappingmask_colored = change_color(mask_colored, width, height, (0, 0, 0), (255,255,255))return cv2.addWeighted(np.array(img),0.4,np.array(mask_colored),0.3,0)

Function to change color of each pixel in a picture :

def change_color(picture, width, height, ex_color, new_color):# Process every pixelfor x in range(width):    for y in range(height):        current_color = picture.getpixel( (x,y) )        if current_color == ex_color:            picture.putpixel( (x,y), new_color)return picture