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《PIL Handbook》是Python Imaging Library（PIL）的一个详细指南，主要针对使用Python进行图像处理的开发者。PIL是Python中的一个强大的图像处理库，它提供了丰富的功能，包括读取、操作和保存多种图像文件格式。这个压缩包里的"pil-handbook.pdf"很可能是该手册的电子版，包含了PIL的全面介绍和使用教程。在Python编程中，PIL库允许开发者进行像素级别的图像操作，如调整大小、裁剪、旋转、色彩转换等。此外，它还支持多种图像文件格式，如JPEG、PNG、BMP、GIF以及一些位图和矢量图形格式。PIL的核心功能包括： 1. **图像读取和写入**：使用`Image.open()`函数可以打开图像文件，`Image.save()`则用于将图像保存到指定文件。 2. **图像处理**：PIL提供了众多方法来处理图像，例如`resize()`用于调整图像大小，`rotate()`实现图像旋转，`crop()`用于裁剪图像，`thumbnail()`则可以生成图像的缩略图。 3. **颜色空间转换**：PIL支持从RGB到灰度、CMYK等各种颜色空间的转换。 4. **滤镜和效果**：可以应用模糊、锐化、边缘检测等滤镜，增强图像效果。 5. **图像分析**：提供像素级别的访问，可用于图像分析和计算，比如颜色直方图的生成。 6. **文字添加**：通过`ImageDraw`模块，可以在图像上添加文本，这对于创建动态图片或水印非常有用。 7. **分块操作**：对于大图像，可以按块进行处理，提高效率。 8. **支持透明度**：PIL能够处理透明度信息，适用于处理PNG等支持透明的图像格式。 9. **图像模式**：PIL支持多种图像模式，如L（灰度）、RGB（红绿蓝三通道）和RGBA（增加了透明度通道）。 10. **图像操作函数**：如`paste()`可以将一个图像粘贴到另一个图像上，`convert()`可以改变图像的颜色模式。 11. **动画处理**：虽然PIL主要用于静态图像，但通过组合多帧图像，也可以实现简单的动画效果。学习和掌握PIL，不仅能够提升Python在图像处理领域的应用能力，还能为数据可视化、网页设计、游戏开发等领域提供强大的工具。《PIL Handbook》是深入理解和使用PIL的重要参考资料，建议开发者们仔细阅读并实践其中的示例，以充分利用这个强大的库。

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Python Imaging Library Overview

PIL 1.1.3 | March 12, 2002 | Fredrik Lundh, Matthew Ellis

Introduction

The Python Imaging Library adds image processing capabilities to your Python

interpreter.

This library provides extensive file format support, an efficient internal representation,

and fairly powerful image processing capabilities.

The core image library is designed for fast access to data stored in a few basic pixel

formats. It should provide a solid foundation for a general image processing tool.

Let's look at a few possible uses of this library:

Image Archives

The Python Imaging Library is ideal for for image archival and batch processing

applications. You can use the library to create thumbnails, convert between file formats,

print images, etc.

The current version identifies and reads a large number of formats. Write support is

intentionally restricted to the most commonly used interchange and presentation formats.

Image Display

The current release includes Tk PhotoImage and BitmapImage interfaces, as well as a

Windows DIB interface that can be used with PythonWin. For X and Mac displays, you can

use Jack Jansen's img library.

For debugging, there's also a show method in the Unix version which calls xv to display the

image.

Image Processing

The library contains some basic image processing functionality, including point operations,

filtering with a set of built-in convolution kernels, and colour space conversions.

The library also supports image resizing, rotation and arbitrary affine transforms.

There's a histogram method allowing you to pull some statistics out of an image. This can

be used for automatic contrast enhancement, and for global statistical analysis.

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.

>>> 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.

Example: Convert files to JPEG

import os, sys

import Image

for infile in sys.argv[1:]:

outfile = os.path.splitext(infile)[0] + ".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:

Example: Create JPEG Thumbnails

import os, sys

import Image

for infile in sys.argv[1:]:

outfile = os.path.splitext(infile)[0] + ".thumbnail"

if infile != outfile:

try:

im = Image.open(infile)

im.thumbnail((128, 128))

im.save(outfile, "JPEG")

except IOError:

print "cannot create thumbnail for", infile

It is important to note is 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:

Example: 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.

Example: 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.

Example: 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:

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:

Example: Splitting and merging bands

r, g, b = im.split()

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

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.

Example: 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.

Example: 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.

Example: 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.

Example: Applying filters

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