How to convert a python numpy array to an RGB image with Opencv 2.4?

Question

I have searched for similar questions, but haven't found anything helpful as most solutions use older versions of OpenCV.

I have a 3D numpy array, and I would like to display and/or save it as a BGR image using OpenCV (cv2).

As a short example, suppose I had:

import numpy, cv2
b = numpy.zeros([5,5,3])

b[:,:,0] = numpy.ones([5,5])*64
b[:,:,1] = numpy.ones([5,5])*128
b[:,:,2] = numpy.ones([5,5])*192

What I would like to do is save and display b as a color image similar to:

cv2.imwrite('color_img.jpg', b)
cv2.imshow('Color image', b)
cv2.waitKey(0)
cv2.destroyAllWindows()

This doesn't work, presumably because the data type of b isn't correct, but after substantial searching, I can't figure out how to change it to the correct one. If you can offer any pointers, it would be greatly appreciated!

Thanks for the comment @beaker! However, the issue in that question is that the poster forgot an argument in `imshow()`. My issue is that `imshow`/ `imwrite` won't take the numpy array as created above. — Gillespie, Oct 31 '14 at 20:08
Do you want to save it as `BGR` or `RGB`? The title and question have both. — jmunsch, Oct 31 '14 at 20:27
@jmanring220 Yes, but the answers still show how to convert a numpy array to a mat. — beaker, Oct 31 '14 at 21:02

score 55 · Answer 1 · edited Apr 15 '20 at 06:36

55

You don't need to convert NumPy array to Mat because OpenCV cv2 module can accept NumPyarray. The only thing you need to care for is that {0,1} is mapped to {0,255} and any value bigger than 1 in NumPy array is equal to 255. So you should divide by 255 in your code, as shown below.

img = numpy.zeros([5,5,3])

img[:,:,0] = numpy.ones([5,5])*64/255.0
img[:,:,1] = numpy.ones([5,5])*128/255.0
img[:,:,2] = numpy.ones([5,5])*192/255.0

cv2.imwrite('color_img.jpg', img)
cv2.imshow("image", img)
cv2.waitKey()

edited Apr 15 '20 at 06:36

Dipanshu Mahla

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answered Nov 01 '14 at 07:15

bikz05

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can you please explain why do we multiply by 64, 128 and 192 – Shubh Patni Jun 14 '20 at 19:56
2

oh I got it, I think that are just the values that should be in the rgb positions. – Shubh Patni Jun 14 '20 at 20:01
If someone might need to invert the color channels: cv2.imshow("color_img.jpg", input[..., ::-1] * 255) – LC117 Jan 24 '22 at 19:13
so clear! it is helpful for me – lam vu Nguyen Feb 06 '23 at 07:05

Martin Thoma · Answer 2 · 2019-08-02T05:16:09.263

25

You are looking for scipy.misc.toimage:

import scipy.misc
rgb = scipy.misc.toimage(np_array)

It seems to be also in scipy 1.0, but has a deprecation warning. Instead, you can use pillow and PIL.Image.fromarray

edited Aug 02 '19 at 05:16

answered Jun 15 '16 at 19:22

Martin Thoma

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8

This doesn't seem to exist in scipy 0.18.0 – Alex Kreimer Aug 16 '16 at 08:22
@AlexKreimer: http://docs.scipy.org/doc/scipy-0.18.0/reference/generated/scipy.misc.toimage.html – Martin Thoma Aug 16 '16 at 08:51
5

The question is asking about approach using OPEN-CV. – M. Doosti Lakhani Aug 03 '19 at 12:34
2

Yes. And still my answer is useful for 9 people. – Martin Thoma Aug 03 '19 at 16:03
This was actually what I was looking for, I have a numpy array from opencv and I want to show it in the pysimpleui, where it requires a proper image. – kirhgoff Jul 25 '20 at 04:38

jmunsch · Answer 3 · 2014-10-31T21:24:19.120

The images c, d, e , and f in the following show colorspace conversion they also happen to be numpy arrays <type 'numpy.ndarray'>:

import numpy, cv2
def show_pic(p):
        ''' use esc to see the results'''
        print(type(p))
        cv2.imshow('Color image', p)
        while True:
            k = cv2.waitKey(0) & 0xFF
            if k == 27: break 
        return
        cv2.destroyAllWindows()

b = numpy.zeros([200,200,3])

b[:,:,0] = numpy.ones([200,200])*255
b[:,:,1] = numpy.ones([200,200])*255
b[:,:,2] = numpy.ones([200,200])*0
cv2.imwrite('color_img.jpg', b)


c = cv2.imread('color_img.jpg', 1)
c = cv2.cvtColor(c, cv2.COLOR_BGR2RGB)

d = cv2.imread('color_img.jpg', 1)
d = cv2.cvtColor(c, cv2.COLOR_RGB2BGR)

e = cv2.imread('color_img.jpg', -1)
e = cv2.cvtColor(c, cv2.COLOR_BGR2RGB)

f = cv2.imread('color_img.jpg', -1)
f = cv2.cvtColor(c, cv2.COLOR_RGB2BGR)


pictures = [d, c, f, e]

for p in pictures:
        show_pic(p)
# show the matrix
print(c)
print(c.shape)

See here for more info: http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html#cvtcolor

OR you could:

img = numpy.zeros([200,200,3])

img[:,:,0] = numpy.ones([200,200])*255
img[:,:,1] = numpy.ones([200,200])*255
img[:,:,2] = numpy.ones([200,200])*0

r,g,b = cv2.split(img)
img_bgr = cv2.merge([b,g,r])

score 8 · Answer 4 · edited Aug 11 '20 at 03:53

8

This is due to the fact that cv2 uses the type "uint8" from numpy. Therefore, you should define the type when creating the array.

Something like the following:

import numpy
import cv2

b = numpy.zeros([5,5,3], dtype=numpy.uint8)
b[:,:,0] = numpy.ones([5,5])*64
b[:,:,1] = numpy.ones([5,5])*128
b[:,:,2] = numpy.ones([5,5])*192

edited Aug 11 '20 at 03:53

Saad

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answered Aug 11 '20 at 02:27

leonardo leao barros

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score 6 · Answer 5 · answered Aug 14 '18 at 13:09

6

If anyone else simply wants to display a black image as a background, here e.g. for 500x500 px:

import cv2
import numpy as np

black_screen  = np.zeros([500,500,3])
cv2.imshow("Simple_black", black_screen)
cv2.waitKey(0)

answered Aug 14 '18 at 13:09

AlexeyGy

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score 4 · Answer 6 · answered Dec 02 '19 at 10:47

The size of your image is not sufficient to see in a naked eye. So please try to use atleast 50x50

import cv2 as cv
import numpy as np

black_screen = np.zeros([50,50,3])

black_screen[:, :, 2] = np.ones([50,50])*64/255.0
cv.imshow("Simple_black", black_screen)
cv.waitKey(0)
cv.displayAllWindows()

score 0 · Answer 7 · edited Aug 09 '23 at 13:31

0

img = np.ones((1080, 720, 3), dtype=np.uint8) * 255

creates white image, the dtype you have to use is np.uint8.

edited Aug 09 '23 at 13:31

toyota Supra

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answered Aug 09 '23 at 12:37

Somnath Roy

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How to convert a python numpy array to an RGB image with Opencv 2.4?

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