Numpy array is showing strange color values when using pyplot.imshow()

Question

Im trying to show a simple RGB array using pyplot.imshow. However it behaves unexpectedly, for example for a pixel value (1,1,1) is shown as completely white, (0,0,0) as completely black and also value close to (255,255,255) are also shown as black. Whats going on here. Is the input matrix getting somehow scaled pixel-wise?

@ThomasKühn wrt #1 I think imshow works fine with color images, just make sure you're dealing with an array that contains color data. stackoverflow.com/a/12117605/7486933 - this comment looks crazy since the one it's referencing has been deleted. I'll leave it since the question asks about RGB arrays, and this is one example of loading them in imshow. — aorr
– aorr, Commented Jul 31, 2017 at 20:31
Could be a duplicate of this one: stackoverflow.com/questions/45259493/… — ImportanceOfBeingErnest
– ImportanceOfBeingErnest, Commented Jul 31, 2017 at 20:39

unutbu · Accepted Answer · 2017-07-31 20:34:30Z

2

It sounds like your array has floating-point dtype. Change it to unsigned 8-bit ints (uint8):

arr = arr.astype('uint8')

import numpy as np
import matplotlib.pyplot as plt

float_arr = np.array([[(1,1,1),(0,0,0)], [(0,0,0),(255,255,255)]], dtype='float32')
int_arr = np.array([[(1,1,1),(0,0,0)], [(0,0,0),(255,255,255)]], dtype='uint8')

fig, ax = plt.subplots(ncols=2)
ax[0].imshow(float_arr)
ax[0].set_title('float32')
ax[1].imshow(int_arr)
ax[1].set_title('uint8')
plt.legend()
plt.show()

The image on the left replicates the behavior you are seeing. The image on the right is what you'd get if your array had uint8 dtype.

Per the docs, imshow can accept an MxNx3 RGB array of either floating-point or uint8 dtype.

However, if the array has floating-point dtype, the values are expected to be between 0.0 and 1.0.

If the array has uint8 dtype, then the values are expected to be between 0 and 255.

edited Jul 31, 2017 at 20:34

answered Jul 31, 2017 at 20:17

unutbu

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1 Comment

Lugi Over a year ago

Yeah, this is what's going on, but what got me confused is that the one that expects values to be in range <0.0, 1.0> is neither clipping the 0-255 values, nor scalling them down to fit the range.

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