Using Pillow (which works with Python 3.X as well as Python 2.7+), you can do the following:

from PIL import Image
im = Image.open('image.jpg', 'r')
width, height = im.size
pixel_values = list(im.getdata())

Now you have all pixel values. If it is RGB or another mode can be read by im.mode. Then you can get pixel (x, y) by:

pixel_values[width*y+x]

Alternatively, you can use Numpy and reshape the array:

>>> pixel_values = numpy.array(pixel_values).reshape((width, height, 3))
>>> x, y = 0, 1
>>> pixel_values[x][y]
[ 18  18  12]

A complete, simple to use solution is

# Third party modules
import numpy
from PIL import Image


def get_image(image_path):
    """Get a numpy array of an image so that one can access values[x][y]."""
    image = Image.open(image_path, "r")
    width, height = image.size
    pixel_values = list(image.getdata())
    if image.mode == "RGB":
        channels = 3
    elif image.mode == "L":
        channels = 1
    else:
        print("Unknown mode: %s" % image.mode)
        return None
    pixel_values = numpy.array(pixel_values).reshape((width, height, channels))
    return pixel_values


image = get_image("gradient.png")

print(image[0])
print(image.shape)

Smoke testing the code

You might be uncertain about the order of width / height / channel. For this reason I've created this gradient:

The image has a width of 100px and a height of 26px. It has a color gradient going from #ffaa00 (yellow) to #ffffff (white). The output is:

[[255 172   5]
 [255 172   5]
 [255 172   5]
 [255 171   5]
 [255 172   5]
 [255 172   5]
 [255 171   5]
 [255 171   5]
 [255 171   5]
 [255 172   5]
 [255 172   5]
 [255 171   5]
 [255 171   5]
 [255 172   5]
 [255 172   5]
 [255 172   5]
 [255 171   5]
 [255 172   5]
 [255 172   5]
 [255 171   5]
 [255 171   5]
 [255 172   4]
 [255 172   5]
 [255 171   5]
 [255 171   5]
 [255 172   5]]
(100, 26, 3)

Things to note:

• The shape is (width, height, channels)
• The image[0], hence the first row, has 26 triples of the same color