I have created an array thusly:
import numpy as np
data = np.zeros( (512,512,3), dtype=np.uint8)
data[256,256] = [255,0,0]
What I want this to do is display a single red dot in the center of a 512x512 image. (At least to begin with... I think I can figure out the rest from there)
This question is related to
python
arrays
image
numpy
data-visualization
Shortest path is to use scipy
, like this:
from scipy.misc import toimage
toimage(data).show()
This requires PIL or Pillow to be installed as well.
A similar approach also requiring PIL or Pillow but which may invoke a different viewer is:
from scipy.misc import imshow
imshow(data)
The Python Imaging Library can display images using Numpy arrays. Take a look at this page for sample code:
EDIT: As the note on the bottom of that page says, you should check the latest release notes which make this much simpler:
I know there are simpler answers but this one will give you understanding of how images are actually drawn from a numpy array.
Load example
from sklearn.datasets import load_digits
digits = load_digits()
digits.images.shape #this will give you (1797, 8, 8). 1797 images, each 8 x 8 in size
Display array of one image
digits.images[0]
array([[ 0., 0., 5., 13., 9., 1., 0., 0.],
[ 0., 0., 13., 15., 10., 15., 5., 0.],
[ 0., 3., 15., 2., 0., 11., 8., 0.],
[ 0., 4., 12., 0., 0., 8., 8., 0.],
[ 0., 5., 8., 0., 0., 9., 8., 0.],
[ 0., 4., 11., 0., 1., 12., 7., 0.],
[ 0., 2., 14., 5., 10., 12., 0., 0.],
[ 0., 0., 6., 13., 10., 0., 0., 0.]])
Create empty 10 x 10 subplots for visualizing 100 images
import matplotlib.pyplot as plt
fig, axes = plt.subplots(10,10, figsize=(8,8))
Plotting 100 images
for i,ax in enumerate(axes.flat):
ax.imshow(digits.images[i])
Result:
What does axes.flat
do?
It creates a numpy enumerator so you can iterate over axis in order to draw objects on them.
Example:
import numpy as np
x = np.arange(6).reshape(2,3)
x.flat
for item in (x.flat):
print (item, end=' ')
import numpy as np
from keras.preprocessing.image import array_to_img
img = np.zeros([525,525,3], np.uint8)
b=array_to_img(img)
b
Using pygame, you can open a window, get the surface as an array of pixels, and manipulate as you want from there. You'll need to copy your numpy array into the surface array, however, which will be much slower than doing actual graphics operations on the pygame surfaces themselves.
Using pillow's fromarray, for example:
from PIL import Image
from numpy import *
im = array(Image.open('image.jpg'))
Image.fromarray(im).show()
The following should work:
from matplotlib import pyplot as plt
plt.imshow(data, interpolation='nearest')
plt.show()
If you are using Jupyter notebook/lab, use this inline command before importing matplotlib:
%matplotlib inline
Supplement for doing so with matplotlib. I found it handy doing computer vision tasks. Let's say you got data with dtype = int32
from matplotlib import pyplot as plot
import numpy as np
fig = plot.figure()
ax = fig.add_subplot(1, 1, 1)
# make sure your data is in H W C, otherwise you can change it by
# data = data.transpose((_, _, _))
data = np.zeros((512,512,3), dtype=np.int32)
data[256,256] = [255,0,0]
ax.imshow(data.astype(np.uint8))
this could be a possible code solution:
from skimage import io
import numpy as np
data=np.random.randn(5,2)
io.imshow(data)
Source: Stackoverflow.com