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.

If you have matplotlib, you can do:

import matplotlib.pyplot as plt
plt.imshow(matrix) #Needs to be in row,col order
plt.savefig(filename)

This will save the plot (not the images itself).

for saving a numpy array as image, U have several choices:

1) best of other: OpenCV

 import cv2   
 cv2.imwrite('file name with extension(like .jpg)', numpy_array)

2) Matplotlib

  from matplotlib import pyplot as plt
  plt.imsave('file name with extension(like .jpg)', numpy_array)

3) PIL

  from PIL import Image
  image = Image.fromarray(numpy_array)
  image.save('file name with extension(like .jpg)')

4) ...

scipy.misc gives deprecation warning about imsave function and suggests usage of imageio instead.

import imageio
imageio.imwrite('image_name.png', img)

For those looking for a direct fully working example:

from PIL import Image
import numpy

w,h = 200,100
img = numpy.zeros((h,w,3),dtype=numpy.uint8) # has to be unsigned bytes

img[:] = (0,0,255) # fill blue

x,y = 40,20
img[y:y+30, x:x+50] = (255,0,0) # 50x30 red box

Image.fromarray(img).convert("RGB").save("art.png") # don't need to convert

also, if you want high quality jpeg's
.save(file, subsampling=0, quality=100)

matplotlib svn has a new function to save images as just an image -- no axes etc. it's a very simple function to backport too, if you don't want to install svn (copied straight from image.py in matplotlib svn, removed the docstring for brevity):

def imsave(fname, arr, vmin=None, vmax=None, cmap=None, format=None, origin=None):
    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
    from matplotlib.figure import Figure

    fig = Figure(figsize=arr.shape[::-1], dpi=1, frameon=False)
    canvas = FigureCanvas(fig)
    fig.figimage(arr, cmap=cmap, vmin=vmin, vmax=vmax, origin=origin)
    fig.savefig(fname, dpi=1, format=format)

Imageio is a Python library that provides an easy interface to read and write a wide range of image data, including animated images, video, volumetric data, and scientific formats. It is cross-platform, runs on Python 2.7 and 3.4+, and is easy to install.

This is example for grayscale image:

import numpy as np
import imageio

# data is numpy array with grayscale value for each pixel.
data = np.array([70,80,82,72,58,58,60,63,54,58,60,48,89,115,121,119])

# 16 pixels can be converted into square of 4x4 or 2x8 or 8x2
data = data.reshape((4, 4)).astype('uint8')

# save image
imageio.imwrite('pic.jpg', data)

If you are working in python environment Spyder, then it cannot get more easier than to just right click the array in variable explorer, and then choose Show Image option.

This will ask you to save image to dsik, mostly in PNG format.

PIL library will not be needed in this case.

Use cv2.imwrite.

import cv2
assert mat.shape[2] == 1 or mat.shape[2] == 3, 'the third dim should be channel'
cv2.imwrite(path, mat) # note the form of data should be height - width - channel  

With pygame

so this should work as I tested (you have to have pygame installed if you do not have pygame install it by using pip -> pip install pygame (that sometimes does not work so in that case you will have to download the wheel or sth but that you can look up on google)):

import pygame


pygame.init()
win = pygame.display.set_mode((128, 128))
pygame.surfarray.blit_array(win, yourarray)
pygame.display.update()
pygame.image.save(win, 'yourfilename.png')

just remember to change display width and height according to your array

here is an example, run this code:

import pygame
from numpy import zeros


pygame.init()
win = pygame.display.set_mode((128, 128))
striped = zeros((128, 128, 3))
striped[:] = (255, 0, 0)
striped[:, ::3] = (0, 255, 255)
pygame.surfarray.blit_array(win, striped)
pygame.display.update()
pygame.image.save(win, 'yourfilename.png')

I attach an simple routine to convert a npy to an image. Works 100% and it is a piece of cake!

from PIL import Image import matplotlib

img = np.load('flair1_slice75.npy')

matplotlib.image.imsave("G1_flair_75.jpeg", img)

In the folowing answer has the methods as proposed by @Nima Farhadi in time measurement.

The fastest is CV2 , but it's important to change colors order from RGB to BGR. The simples is matplotlib.

It's important to assure, that the array have unsigned integer format uint8/16/32.

Code:

#Matplotlib
from matplotlib import pyplot as plt
plt.imsave('c_plt.png', c.astype(np.uint8))

#PIL
from PIL import Image
image = Image.fromarray(c.astype(np.uint8))
image.save('c_pil.png')


#CV2, OpenCV
import cv2
cv2.imwrite('c_cv2.png', cv2.cvtColor(c, cv2.COLOR_RGB2BGR))