how to get matplotlib plot data as numpy array

Question

I am plotting figure as:

plt.imshow(image, cmap='gray', interpolation='none')
plt.imshow(masked_contour, cmap='cool', interpolation='none', alpha=0.7)
plt.show()

The figure shows in greyscale with a blue contour inside it.

Now I want to get this figure as a numpy array (also not as a masked array). One way can be, save the plot as an image, then read it from there. Is there any better approach?

@soothy Thanks for the comment. The link works, but the problem is that it takes the image's input as a small-windowed image. If I try to make it a full-windowed image as mentioned here: link. However, it still saves as in small-windowed image. — Dr.PB
– Dr.PB, Commented Jun 13, 2021 at 7:27

Dr.PB · Accepted Answer · 2021-06-13 07:50:16Z

0

fig = plt.figure(figsize=(20, 20)) # this is imp for sizing
# plot
plt.imshow(image, cmap='gray', interpolation='none')
plt.imshow(masked_contour, cmap='cool', interpolation='none', alpha=0.7)
# get image as np.array
canvas = plt.gca().figure.canvas
canvas.draw()
data = np.frombuffer(canvas.tostring_rgb(), dtype=np.uint8)
image = data.reshape(canvas.get_width_height()[::-1] + (3,))
# (Optional) show image
plt.imshow(image)
plt.show()

answered Jun 13, 2021 at 7:50

Dr.PB

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1 Comment

Pranjal Sahu Over a year ago

Thank you very much for sharing complete code snippet.

bedbad · Accepted Answer · 2024-01-21 07:07:40Z

I found that all matplotlib approaches need frombuffer canvas tostring_rgb()/buffer_rgba() hoopla and sometimes end up displaying unneeded plots or have surprising behavior when same code run on a different os(matplotlib code behaves differently depending on backend/os); They also very slow

I had to write my own plotter, justpyplot, and you can control the figure parameters, color, size of points/thickness of lines in vectorized way all in numpy:

import numpy as np 
import cv2
import time
import justpyplot as jplt

xs, ys = [], []
while(cv2.waitKey(1) != 27):
    xt = time.perf_counter() - t0
    yx = np.sin(xt)
    xs.append(xt)
    ys.append(yx)
    
    frame = np.full((500,470,3), (255,255,255), dtype=np.uint8)
    
    vals = np.array(ys)

    plotted_in_array = jplt.just_plot(frame, vals,title="sin() from Clock")
    
    cv2.imshow('np array plot', plotted_in_array)

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