How to Convert a Numpy Array to Pil Image Applying Matplotlib Colormap

How to convert a NumPy array to PIL image applying matplotlib colormap

Quite a busy one-liner, but here it is:

1. First ensure your NumPy array, myarray, is normalised with the max value at 1.0.
2. Apply the colormap directly to myarray.
3. Rescale to the 0-255 range.
4. Convert to integers, using np.uint8().
5. Use Image.fromarray().

And you're done:

from PIL import Image
from matplotlib import cm
im = Image.fromarray(np.uint8(cm.gist_earth(myarray)*255))

with plt.savefig():

with im.save():

Using colormap to create a PIL Image from the luminosity map of an image

PIL works with pixel data, so each of RGBA is a value from 0 to 255. A colormap default generates its RGBA values in the range 0-1. To convert them, you could multiply those by 255 and convert to 8 bit unsigned integers (uint8), like so:

greyscale = np.uint8(cmap(faces[index].reshape(64,64)) * 255)

But, matplotlib's colormaps also support a parameter to directly generate those bytes:

greyscale = cmap(faces[index].reshape(64,64), bytes=True)

You could reshape your arrays afterwards to (64,64,4), but it is easier and more readable to do the conversion before applying the colormap.

There is a choice of several sequential colormaps for this type of images. Appending an _r to the name gives the reverse colormap (so dark and light reversed).

Here is some code to get you started:

import PIL.Image as pilim
import matplotlib.cm as cm
import numpy as np
from matplotlib import pyplot as plt

cmap = cm.get_cmap('copper_r') # 'bone_r', 'Greys', 'copper_r', 'Purple', ...
num_images = 1

faces = np.tile(np.linspace(0,1,4096), 698).reshape(698, 4096)

num_faces = faces.shape[0]
sample_images = np.random.choice(num_faces, num_images, replace=False)
print(sample_images)
for index in sample_images:
     greyscale = cmap(faces[index].reshape(64,64), bytes=True)
     im = pilim.fromarray(greyscale, mode='RGBA')
     im.save(f'test{index}.png')

PS: There is also an imsave function in matplotlib, which would further simplify the code:

for index in sample_images:
     plt.imsave(f'test{index}.png', faces[index].reshape(64,64), cmap=cmap)

If the image would show up upside down, adding origin='lower' to imsave would reverse it.

Saving a Numpy array as an image

You can use PyPNG. It's a pure Python (no dependencies) open source PNG encoder/decoder and it supports writing NumPy arrays as images.

Need help convert numpy array image to image with full color

cv2 uses BGR, Image uses RGB. So reverting the channel order would work

Image.fromarray(numpy_array[:,:,::-1].astype('uint8'))

Numpy array to PIL image format

In my comments I meant that you do something like this:

import numpy as np
from PIL import Image

img = np.array(image)
mean = 0
# var = 0.1
# sigma = var**0.5
gauss = np.random.normal(mean, 1, img.shape)

# normalize image to range [0,255]
noisy = img + gauss
minv = np.amin(noisy)
maxv = np.amax(noisy)
noisy = (255 * (noisy - minv) / (maxv - minv)).astype(np.uint8)

im = Image.fromarray(noisy)

matplotlib plot numpy array of images as markers

You need to set the limits of the axis accordingly, or they would default to just (0,1):

from matplotlib.offsetbox import OffsetImage, AnnotationBbox
fig, ax = plt.subplots()
for x0, y0, img in zip(x, y, images):
    ab = AnnotationBbox(OffsetImage(img, zoom=5, cmap='gray'), (x0, y0), frameon=False)
    ax.add_artist(ab)
plt.xlim(x.min(), x.max()+1)
plt.ylim(y.min(), y.max()+1)
plt.show()

Output:

---

Related Topics