CODEWITHSUNDEEP
python-3.xnumpypython-imaging-library
Haobo Zhang
Haobo Zhang

Reputation: 141

Change Dimensions of ndarray and Multiply Contents

I have an MxN ndarray that contains True and False values inside those arrays and want to draw those as an image. The goal is to convert the array to a pillow image with each True value as a constant color. I was able to get it working by looping through each pixel and changing them individually by a comparison and drawing the pixel on a blank image, but that method is way too slow. 

# img is a PIL image result
# image is the MxN ndarray

pix = img.load()
for x in range(image.shape[0]):
  for y in range(image.shape[1]):
     if image[x, y]:
       pix[y, x] = (255, 0, 0)

Is there a way to change the ndarray to a MxNx3 by replacing the tuples directly to the True values?

Upvotes: 3

Views: 194

Answers (3)

Mark Setchell
Mark Setchell

Reputation: 207445

I think you can do this quite simply and fast like this:

# Make a 2 row by 3 column image of True/False values
im = np.random.choice((True,False),(2,3))

Mine looks like this:

array([[False, False,  True],
       [ True,  True,  True]])

Now add a new axis it make it 3 channel and multiply the truth values by your new "colour":

result = im[..., np.newaxis]*[255,255,255]

which gives you this:

array([[[  0,   0,   0],
        [  0,   0,   0],
        [255, 255, 255]],

       [[255, 255, 255],
        [255, 255, 255],
        [255, 255, 255]]])

Upvotes: 0

Haobo Zhang
Haobo Zhang

Reputation: 141

Did find another solution, converted to an image first, then converted to RGB, then converted back to separate to 3 channels. When I was trying to combine multiple boolean arrays together, this way was a lot faster. 

img = Image.fromarray(image * 1, 'L').convert('RGB')
data = np.array(img)
red, green, blue = data.T
area = (red == 1)
data[...][area.T] = (255, 255, 255)
img = Image.fromarray(data)

Upvotes: 1

BCJuan
BCJuan

Reputation: 825

If you have your True/False 2D array and the label for the color, for example [255,255,255], the following will work:

colored = np.expand_dims(bool_array_2d,axis=-1)*np.array([255,255,255])

To illustrate it with a dummy example: in the following code I have created a random matrix of 0s and 1s and then have turned the 1s to white ([255,255,255]).

import numpy as np
import matplotlib.pyplot as plt

array = np.random.randint(0,2, (100,100))
colors = np.array([255,255,255])
colored = np.expand_dims(array, axis=-1)*colors

plt.imshow(colored)

Hope this has helped

Upvotes: 1

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