# Tutorial

## 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:

    >>> from PIL import Image
    >>> im = Image.open("hopper.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 color images, and “CMYK” for pre-press images.

If the file cannot be opened, an `OSError` 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()

Note

The standard version of `show()` is not very efficient, since it saves the image to a temporary file and calls a utility to display the image. If you don’t have an appropriate utility 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
    from PIL import Image

    for infile in sys.argv[1:]:
        f, e = os.path.splitext(infile)
        outfile = f + ".jpg"
        if infile != outfile:
            try:
                with Image.open(infile) as im:
                    im.save(outfile)
            except OSError:
                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
    from PIL import Image

    size = (128, 128)

    for infile in sys.argv[1:]:
        outfile = os.path.splitext(infile)[0] + ".thumbnail"
        if infile != outfile:
            try:
                with Image.open(infile) as im:
                    im.thumbnail(size)
                    im.save(outfile, "JPEG")
            except OSError:
                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
    from PIL import Image

    for infile in sys.argv[1:]:
        try:
            with Image.open(infile) as im:
                print(infile, im.format, f"{im.size}x{im.mode}")
        except OSError:
            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 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(part1, (xsize - delta, 0, xsize, ysize))
        image.paste(part2, (0, 0, xsize - delta, 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:

### 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 color bands, you may want to convert the image to “RGB” first.

## Geometrical transforms

The `PIL.Image.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)

`transpose(ROTATE)` operations can also be performed identically with `rotate()` operations, provided the `expand` flag is true, to provide for the same changes to the image’s size.

A more general form of image transformations can be carried out via the `transform()` method.

## Color transforms

The Python Imaging Library allows you to convert images between different pixel representations using the `convert()` method.

### Converting between modes

    from PIL import Image

    with Image.open("hopper.ppm") as im:
        im = im.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

    from PIL 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, color balance and sharpness in this way.

#### Enhancing images

    from PIL 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

    from PIL import Image

    with Image.open("animation.gif") as im:
        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.

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

### Using the ImageSequence Iterator class

    from PIL import ImageSequence
    for frame in ImageSequence.Iterator(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

    from PIL import Image
    from PIL import PSDraw

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

        ps = PSDraw.PSDraw()  # default is sys.stdout or sys.stdout.buffer
        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

As described earlier, the `open()` function of the `Image` module is used to open an image file. In most cases, you simply pass it the filename as an argument. `Image.open()` can be used as a context manager:

    from PIL import Image
    with Image.open("hopper.ppm") as im:
        ...

If everything goes well, the result is an `PIL.Image.Image` object. Otherwise, an `OSError` exception is raised.

You can use a file-like object instead of the filename. The object must implement `file.read`, `file.seek` and `file.tell` methods, and be opened in binary mode.

### Reading from an open file

    from PIL import Image

    with open("hopper.ppm", "rb") as fp:
        im = Image.open(fp)

To read an image from binary data, use the `BytesIO` class:

### Reading from binary data

    from PIL import Image
    import io

    im = Image.open(io.BytesIO(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 `ContainerIO` or `TarIO` modules to access it.

### Reading from a tar archive

    from PIL import Image, TarIO

    fp = TarIO.TarIO("Tests/images/hopper.tar", "hopper.jpg")
    im = Image.open(fp)

### Batch processing

Operations can be applied to multiple image files. For example, all PNG images in the current directory can be saved as JPEGs at reduced quality.

    import glob
    from PIL import Image

    def compress_image(source_path, dest_path):
        with Image.open(source_path) as img:
            if img.mode != "RGB":
                img = img.convert("RGB")
            img.save(dest_path, "JPEG", optimize=True, quality=80)

    paths = glob.glob("*.png")
    for path in paths:
        compress_image(path, path[:-4] + ".jpg")

Since images can also be opened from a `Path` from the `pathlib` module, the example could be modified to use `pathlib` instead of the `glob` module.

    from pathlib import Path

    paths = Path(".").glob("*.png")
    for path in paths:
        compress_image(path, path.stem + ".jpg")

## 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).

The `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

This is only available for JPEG and MPO files.

    from PIL import Image

    with Image.open(file) as im:
        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.