Image Class in Python Image Library (PIL)

Using the Image Class

The most important class in the Python Imaging Library is the Image class, defined in the module with the same name. You can create instances of this class in several ways; either by loading images from files, processing other images, or creating images from scratch.

To load an image from a file, use the open function in the Image module.

>>> import Image
>>> im = Image.open("lena.ppm")

If successful, this function returns an Image object. You can now use instance attributes to examine the file contents.

>>> print im.format, im.size, im.mode
PPM (512, 512) RGB

The format attribute identifies the source of an image. If the image was not read from a file, it is set to None. The size attribute is a 2-tuple containing width and height (in pixels). The mode attribute defines the number and names of the bands in the image, and also the pixel type and depth. Common modes are “L” (luminance) for greyscale images, “RGB” for true colour images, and “CMYK” for pre-press images.

If the file cannot be opened, an IOError exception is raised.

Once you have an instance of the Image class, you can use the methods defined by this class to process and manipulate the image. For example, let’s display the image we just loaded:

    >>> im.show()

(The standard version of show is not very efficient, since it saves the image to a temporary file and calls the xv utility to display the image. If you don’t have xv installed, it won’t even work. When it does work though, it is very handy for debugging and tests.)

The following sections provide an overview of the different functions provided in this library.

Reading and Writing Images

The Python Imaging Library supports a wide variety of image file formats. To read files from disk, use the open function in the Image module. You don’t have to know the file format to open a file. The library automatically determines the format based on the contents of the file.

To save a file, use the save method of the Image class. When saving files, the name becomes important. Unless you specify the format, the library uses the filename extension to discover which file storage format to use.

Convert files to JPEG

import os, sys
import Image

for infile in sys.argv[1:]:
    f, e = os.path.splitext(infile)
    outfile = f + ".jpg"
    if infile != outfile:
        try:
            Image.open(infile).save(outfile)
        except IOError:
            print "cannot convert", infile

A second argument can be supplied to the save method which explicitly specifies a file format. If you use a non-standard extension, you must always specify the format this way:

Create JPEG Thumbnails

import os, sys
import Image

size = 128, 128

for infile in sys.argv[1:]:
    outfile = os.path.splitext(infile)[0] + ".thumbnail"
    if infile != outfile:
        try:
            im = Image.open(infile)
            im.thumbnail(size)
            im.save(outfile, "JPEG")
        except IOError:
            print "cannot create thumbnail for", infile

It is important to note that the library doesn’t decode or load the raster data unless it really has to. When you open a file, the file header is read to determine the file format and extract things like mode, size, and other properties required to decode the file, but the rest of the file is not processed until later.

This means that opening an image file is a fast operation, which is independent of the file size and compression type. Here’s a simple script to quickly identify a set of image files:

Identify Image Files

import sys
import Image

for infile in sys.argv[1:]:
    try:
        im = Image.open(infile)
        print infile, im.format, "%dx%d" % im.size, im.mode
    except IOError:
        pass

Cutting, Pasting and Merging Images

The Image class contains methods allowing you to manipulate regions within an image. To extract a sub-rectangle from an image, use the crop method.

Copying a subrectangle from an image

    box = (100, 100, 400, 400)
    region = im.crop(box)

The region is defined by a 4-tuple, where coordinates are (left, upper, right, lower). The Python Imaging Library uses a coordinate system with (0, 0) in the upper left corner. Also note that coordinates refer to positions between the pixels, so the region in the above example is exactly 300×300 pixels.

The region could now be processed in a certain manner and pasted back.

Processing a subrectangle, and pasting it back

    region = region.transpose(Image.ROTATE_180)
    im.paste(region, box)

When pasting regions back, the size of the region must match the given region exactly. In addition, the region cannot extend outside the image. However, the modes of the original image and the region do not need to match. If they don’t, the region is automatically converted before being pasted (see the section on Colour Transforms below for details).

Here’s an additional example:

Rolling an image

def roll(image, delta):
    "Roll an image sideways"

    xsize, ysize = image.size

    delta = delta % xsize
    if delta == 0: return image

    part1 = image.crop((0, 0, delta, ysize))
    part2 = image.crop((delta, 0, xsize, ysize))
    image.paste(part2, (0, 0, xsize-delta, ysize))
    image.paste(part1, (xsize-delta, 0, xsize, ysize))

    return image

For more advanced tricks, the paste method can also take a transparency mask as an optional argument. In this mask, the value 255 indicates that the pasted image is opaque in that position (that is, the pasted image should be used as is). The value 0 means that the pasted image is completely transparent. Values in-between indicate different levels of transparency.

The Python Imaging Library also allows you to work with the individual bands of an multi-band image, such as an RGB image. The split method creates a set of new images, each containing one band from the original multi-band image. The merge function takes a mode and a tuple of images, and combines them into a new image. The following sample swaps the three bands of an RGB image:

Splitting and merging bands

r, g, b = im.split()
im = Image.merge("RGB", (b, g, r))

Note that for a single-band image, split returns the image itself. To work with individual colour bands, you may want to convert the image to “RGB” first.

Geometrical Transforms

The Image class contains methods to resize and rotate an image. The former takes a tuple giving the new size, the latter the angle in degrees counter-clockwise.

Simple geometry transforms

out = im.resize((128, 128))
out = im.rotate(45) # degrees counter-clockwise

To rotate the image in 90 degree steps, you can either use the rotate method or the transpose method. The latter can also be used to flip an image around its horizontal or vertical axis.

Transposing an image

out = im.transpose(Image.FLIP_LEFT_RIGHT)
out = im.transpose(Image.FLIP_TOP_BOTTOM)
out = im.transpose(Image.ROTATE_90)
out = im.transpose(Image.ROTATE_180)
out = im.transpose(Image.ROTATE_270)

There’s no difference in performance or result between transpose(ROTATE) and corresponding rotate operations.

A more general form of image transformations can be carried out via the transform method. See the reference section for details.

Colour Transforms

The Python Imaging Library allows you to convert images between different pixel representations using the convert function.

Converting between modes

    im = Image.open("lena.ppm").convert("L")

The library supports transformations between each supported mode and the “L” and “RGB” modes. To convert between other modes, you may have to use an intermediate image (typically an “RGB” image).

Image Enhancement

The Python Imaging Library provides a number of methods and modules that can be used to enhance images.

Filters #

The ImageFilter module contains a number of pre-defined enhancement filters that can be used with the filter method.

Applying filters

import ImageFilter
out = im.filter(ImageFilter.DETAIL)

Point Operations

The point method can be used to translate the pixel values of an image (e.g. image contrast manipulation). In most cases, a function object expecting one argument can be passed to this method. Each pixel is processed according to that function:

Applying point transforms

# multiply each pixel by 1.2
out = im.point(lambda i: i * 1.2)

Using the above technique, you can quickly apply any simple expression to an image. You can also combine the point and paste methods to selectively modify an image:

Processing individual bands

# split the image into individual bands
source = im.split()

R, G, B = 0, 1, 2

# select regions where red is less than 100
mask = source[R].point(lambda i: i < 100 and 255)

# process the green band
out = source[G].point(lambda i: i * 0.7)

# paste the processed band back, but only where red was < 100
source[G].paste(out, None, mask)

# build a new multiband image
im = Image.merge(im.mode, source)

Note the syntax used to create the mask:

    imout = im.point(lambda i: expression and 255)

Python only evaluates the portion of a logical expression as is necessary to determine the outcome, and returns the last value examined as the result of the expression. So if the expression above is false (0), Python does not look at the second operand, and thus returns 0. Otherwise, it returns 255.

Enhancement #

For more advanced image enhancement, you can use the classes in the ImageEnhance module. Once created from an image, an enhancement object can be used to quickly try out different settings.

You can adjust contrast, brightness, colour balance and sharpness in this way.

Enhancing images

import ImageEnhance

enh = ImageEnhance.Contrast(im)
enh.enhance(1.3).show("30% more contrast")

Image Sequences

The Python Imaging Library contains some basic support for image sequences (also called animation formats). Supported sequence formats include FLI/FLC, GIF, and a few experimental formats. TIFF files can also contain more than one frame.

When you open a sequence file, PIL automatically loads the first frame in the sequence. You can use the seek and tell methods to move between different frames:

Reading sequences

import Image

im = Image.open("animation.gif")
im.seek(1) # skip to the second frame

try:
    while 1:
        im.seek(im.tell()+1)
        # do something to im
except EOFError:
    pass # end of sequence

As seen in this example, you’ll get an EOFError exception when the sequence ends.

Note that most drivers in the current version of the library only allow you to seek to the next frame (as in the above example). To rewind the file, you may have to reopen it.

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The following iterator class lets you to use the for-statement to loop over the sequence:

A sequence iterator class

class ImageSequence:
    def __init__(self, im):
        self.im = im
    def __getitem__(self, ix):
        try:
            if ix:
                self.im.seek(ix)
            return self.im
        except EOFError:
            raise IndexError # end of sequence

for frame in ImageSequence(im):
    # ...do something to frame...

Postscript Printing

The Python Imaging Library includes functions to print images, text and graphics on Postscript printers. Here’s a simple example:

Drawing Postscript

import Image
import PSDraw

im = Image.open("lena.ppm")
title = "lena"
box = (1*72, 2*72, 7*72, 10*72) # in points

ps = PSDraw.PSDraw() # default is sys.stdout
ps.begin_document(title)

# draw the image (75 dpi)
ps.image(box, im, 75)
ps.rectangle(box)

# draw centered title
ps.setfont("HelveticaNarrow-Bold", 36)
w, h, b = ps.textsize(title)
ps.text((4*72-w/2, 1*72-h), title)

ps.end_document()