Overlay an image segmentation with numpy and matplotlib

Question

I am trying to overlay two images. The first one is a 512x512 NumPy array (from a CT image). The second one is also a 512x512 NumPy array but I am just interested in the pixels where the value is larger than 0 (a functional image).

To do that I am trying to create a masked array.

import numpy as np  import numpy.ma as ma import matplotlib.pyplot as plt  # Both images are loaded from a dicom. Both are numpy arrays of (512,512)  Image1 = readimage(path) Image2 = readimage(path) # Create image 2 mask mask = ma.masked_where(Image2>0, Image2) Image2_mask = ma.masked_array(Image2,mask)  # Plot images plt.figure(dpi=300) y, x = np.mgrid[1:513,1:513] plt.axes().set_aspect('equal', 'datalim') plt.set_cmap(plt.gray()) plt.pcolormesh(x, y, Image1,cmap='gray') plt.pcolormesh(x, y, Image2_mask,cmap='jet') plt.axis([x.min(), x.max(), y.min(), y.max()]) plt.colorbar() plt.show() 

This code does not show any overlay. What I am doing wrong? Is there any straight way? I am coming from a Matlab environment and I am quite new to Python.

Answer 1

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) 

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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() 

Answer 2

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 img img = cv2.imread(img_folder+"/"+filename+".jpg")  # Import and convert the mask from binary to RGB mask = Image.open(mask_folder+"/"+filename+".png").convert('RGB') width, height = mask.size  # Convert the white color (for blobs) to magenta mask_colored = change_color(mask, width, height, (255, 255, 255), (186,85,211)) # Convert the black (for background) to white --> important to make a good overlapping mask_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 pixel for 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