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.
>>> from PIL import Image >>> im = Image.open("lena.ppm")
If successful, this function returns an
You can now use instance attributes to examine the file contents:
>>> from __future__ import print_function >>> print(im.format, im.size, im.mode) PPM (512, 512) RGB
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 color images, and “CMYK” for
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:
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
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¶
from __future__ import print_function import os, sys from PIL 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
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¶
from __future__ import print_function import os, sys from PIL import Image size = (128, 128) for infile in sys.argv[1:]: outfile = os.path.splitext(infile) + ".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¶
from __future__ import print_function import sys from PIL import Image for infile in sys.argv[1:]: try: with Image.open(infile) as im: print(infile, im.format, "%dx%d" % im.size, im.mode) except IOError: pass
Cutting, pasting, and merging images¶
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 300x300 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 Color 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. For example, pasting an RGBA image and also using it as the mask would paste the opaque portion of the image but not its transparent background.
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:
Simple geometry transforms¶
out = im.resize((128, 128)) out = im.rotate(45) # degrees counter-clockwise
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
A more general form of image transformations can be carried out via the
The Python Imaging Library allows you to convert images between different pixel
representations using the
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).
The Python Imaging Library provides a number of methods and modules that can be used to enhance images.
from PIL import ImageFilter out = im.filter(ImageFilter.DETAIL)
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)
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.
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, color balance and sharpness in this way.
from PIL import ImageEnhance enh = ImageEnhance.Contrast(im) enh.enhance(1.3).show("30% more contrast")
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:
from PIL 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
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.
The following iterator class lets you 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...
The Python Imaging Library includes functions to print images, text and graphics on Postscript printers. Here’s a simple example:
from PIL import Image from PIL 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 title ps.setfont("HelveticaNarrow-Bold", 36) ps.text((3*72, 4*72), title) ps.end_document()
More on reading images¶
im = Image.open("lena.ppm")
If everything goes well, the result is an
IOError exception is raised.
You can use a file-like object instead of the filename. The object must
tell() methods, and be opened in binary mode.
Reading from an open file¶
fp = open("lena.ppm", "rb") im = Image.open(fp)
To read an image from string data, use the
Reading from a string¶
import StringIO im = Image.open(StringIO.StringIO(buffer))
Note that the library rewinds the file (using
seek(0)) before reading the
image header. In addition, seek will also be used when the image data is read
(by the load method). If the image file is embedded in a larger file, such as a
tar file, you can use the
TarIO modules to access it.
Reading from a tar archive¶
from PIL import TarIO fp = TarIO.TarIO("Imaging.tar", "Imaging/test/lena.ppm") im = Image.open(fp)
Controlling the decoder¶
Some decoders allow you to manipulate the image while reading it from a file. This can often be used to speed up decoding when creating thumbnails (when speed is usually more important than quality) and printing to a monochrome laser printer (when only a greyscale version of the image is needed).
draft() method manipulates an opened but not yet
loaded image so it as closely as possible matches the given mode and size. This
is done by reconfiguring the image decoder.
Reading in draft mode¶
from __future__ import print_function im = Image.open(file) print("original =", im.mode, im.size) im.draft("L", (100, 100)) print("draft =", im.mode, im.size)
This prints something like:
original = RGB (512, 512) draft = L (128, 128)
Note that the resulting image may not exactly match the requested mode and size. To make sure that the image is not larger than the given size, use the thumbnail method instead.