How to convert numpy matrix to OpenCV image [python]

Question

I have a numpy 2d matrix which represents a colored image. This matrix has some negative and floating point numbers but of course I can display the image using imshow(my_matrix).

my_matrix_screenshot

I need to perform histogram equalization to this colored image so I found a code here in stackoverflow using OpenCV (OpenCV Python equalizeHist colored image) but the problem is I am unable to convert the 2d matrix to OpenCV matrix which takes three channels for RGB.

I was searching again but all I found is to convert regular 3d numpy matrix to OpenCV matrix so how can numpy 2d matrix be converted to OpenCV matrix which has 3 channels?

"I have a numpy 2d matrix which represents a colored image" If you know the original shape of this color image, you can use np.reshape() to convert this to a 3D array — akilat90
– akilat90, Commented Oct 14, 2017 at 17:41
It gives an error: ValueError: cannot reshape array of size 9000 into shape (90,33,3) in this line: new_img = np.reshape(new_img_tmp,(int(new_img_tmp.shape[0]), int(new_img_tmp.shape[1]/3),3)) — user2266175
– user2266175, Commented Oct 14, 2017 at 19:18

Jeru Luke · Accepted Answer · 2022-07-25 20:26:39Z

10

because the numpy.ndarray is the base of OpenCV, so you just write the code as usual,like

img_np = np.ones([100,100])
img_cv = cv2.resize(img_np,(200,200))

you can try

edited Jul 25, 2022 at 20:26

Jeru Luke

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answered Feb 13, 2018 at 3:43

shouhuxianjian

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1 Comment

Rafael_Espericueta Over a year ago

This answer seems to be answering an entirely different question. The question didn't ask about resizing an array.

Prasad · Accepted Answer · 2017-10-14 18:54:13Z

-3

It is better to stack the existing numpy array one above the other of its own copy than to reshape it and add the third axis. Check this code:

import numpy as np
import matplotlib.pyplot as plt

a = np.random.rand(90, 100) # Replace this line with your 90x100 numpy array.
a = np.expand_dims(a, axis = 2)
a = np.concatenate((a, a, a), axis = 2)
print(a.shape)
# (90, 100, 3)
plt.imshow(a)
plt.show()

You will get a gray colored image.

edited Oct 14, 2017 at 18:54

answered Oct 14, 2017 at 18:02

Prasad

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3 Comments

user2266175 Over a year ago

Thank you! but I get totally distracted image or you can say different image when I used the code above!

Prasad Over a year ago

Hope you have not used np.random.rand line while running. You have to replace that line with your already existing 90x100 numpy array. I generated a random array just for the code completion sake.

user2266175 Over a year ago

of course not. I just plugged my matrix over there ;) but I got the weird result. I don't know if it is possible to send you the original image and the resulted image. here is what I am doing: a = new_img_tmp a = np.expand_dims(a, axis = 2) a = np.concatenate((a, a, a), axis = 2) print(a.shape) # (90, 100, 3) plt.imshow(a) plt.show()

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