PIL image to array (numpy array to array) - Python

Question

I have a .jpg image that I would like to convert to Python array, because I implemented treatment routines handling plain Python arrays.

It seems that PIL images support conversion to numpy array, and according to the documentation I have written this:

from PIL import Image
im = Image.open("D:\Prototype\Bikesgray.jpg")
im.show()

print(list(np.asarray(im)))

This is returning a list of numpy arrays. Also, I tried with

list([list(x) for x in np.asarray(im)])

which is returning nothing at all since it is failing.

How can I convert from PIL to array, or simply from numpy array to Python array?

Solution 1

I think what you are looking for is:

list(im.getdata())

or, if the image is too big to load entirely into memory, so something like that:

for pixel in iter(im.getdata()):
    print pixel

from PIL documentation:

getdata

im.getdata() => sequence

Returns the contents of an image as a sequence object containing pixel values. The sequence object is flattened, so that values for line one follow directly after the values of line zero, and so on.

Note that the sequence object returned by this method is an internal PIL data type, which only supports certain sequence operations, including iteration and basic sequence access. To convert it to an ordinary sequence (e.g. for printing), use list(im.getdata()).

Solution 2

I highly recommend you use the tobytes function of the Image object. After some timing checks this is much more efficient.

def jpg_image_to_array(image_path):
  """
  Loads JPEG image into 3D Numpy array of shape 
  (width, height, channels)
  """
  with Image.open(image_path) as image:         
    im_arr = np.fromstring(image.tobytes(), dtype=np.uint8)
    im_arr = im_arr.reshape((image.size[1], image.size[0], 3))                                   
  return im_arr

The timings I ran on my laptop show

In [76]: %timeit np.fromstring(im.tobytes(), dtype=np.uint8)
1000 loops, best of 3: 230 µs per loop

In [77]: %timeit np.array(im.getdata(), dtype=np.uint8)
10 loops, best of 3: 114 ms per loop

```

Solution 3

Based on zenpoy's answer:

import Image
import numpy

def image2pixelarray(filepath):
    """
    Parameters
    ----------
    filepath : str
        Path to an image file

    Returns
    -------
    list
        A list of lists which make it simple to access the greyscale value by
        im[y][x]
    """
    im = Image.open(filepath).convert('L')
    (width, height) = im.size
    greyscale_map = list(im.getdata())
    greyscale_map = numpy.array(greyscale_map)
    greyscale_map = greyscale_map.reshape((height, width))
    return greyscale_map

Solution 4

I use numpy.fromiter to invert a 8-greyscale bitmap, yet no signs of side-effects

import Image
import numpy as np

im = Image.load('foo.jpg')
im = im.convert('L')

arr = np.fromiter(iter(im.getdata()), np.uint8)
arr.resize(im.height, im.width)

arr ^= 0xFF  # invert
inverted_im = Image.fromarray(arr, mode='L')
inverted_im.show()

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