Pillow 6.1.0官方文档PDF版：离线图像处理指南

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Pillow文档（PIL Fork）是针对Python图像处理库的官方指南，适用于版本6.1.0。该PDF文件提供了一个详细的安装、手册、参考和各个模块的概述，旨在帮助用户理解和使用Pillow进行图像操作。 1. **安装** 部分介绍了可能遇到的警告和注意事项，以及基本的安装方法，包括通过包管理器（如pip）自动安装和从源代码构建。这有助于新用户快速入门并确保兼容性和稳定性。 2. **手册** 部分涵盖了Pillow的核心概念，包括一个简要的概述，逐步教程，以及对图像处理基础概念的深入解释。这部分内容适合初学者学习图像处理的基础操作，如图像打开、保存、裁剪、缩放、颜色转换等。 3. **参考** 是文档的核心部分，详细介绍了Pillow提供的众多模块功能。这些模块包括： - `ImageModule`: 图像文件操作，如读取、写入和显示。 - `ImageChops`: 实现图像通道运算，如叠加、差分等。 - `ImageColor`: 处理颜色空间和颜色理论，如颜色转换、混合等。 - `ImageCms`: CMYK色彩空间支持，用于色彩管理。 - `ImageDraw`: 绘制工具，用于在图像上绘制线条、形状和文本。 - `ImageEnhance`: 提供图像增强功能，如对比度、亮度调整。 - `ImageFile`: 文件I/O接口，支持多种图像格式。 - `ImageFilter`: 图像滤波器，如模糊、锐化等效果。 - `ImageFont`: 字体处理，用于添加文本到图像。 - `ImageGrab` (仅限macOS和Windows): 截取屏幕功能。 - `ImageMath`: 数学运算在像素级别应用。 - `ImageMorph`: 形态学操作，如膨胀、腐蚀等。 - `ImageOps`: 提供各种图像操作，如旋转、翻转等。 - `ImagePalette`: 色彩映射和调色板操作。 - `ImagePath`: 处理图像路径和文件系统操作。 - `ImageQt` (Python Qt绑定): 与Qt图形库集成。 - `ImageSequence`: 处理图像序列，如GIF和视频帧。 - `ImageStat`: 图像统计信息，如直方图和平均值。 - `ImageTk` (Tkinter绑定): 与Tk GUI工具包集成。 - `ImageWin` (Windows-only): 原始Windows API图像处理支持。 - `ExifTags`: EXIF元数据处理。 - `TiffTags`: TIFF标签处理。 - `PSDraw`: PostScript绘图模块。 - `PixelAccessClass`: 低级像素访问类。 - `PyAccessModule`: 用于访问PIL内部数据结构的模块。 - `PILPackage`: 自动化的PIL模块访问。通过阅读这个详细的文档，用户可以深入理解如何利用Pillow的强大功能进行复杂的图像处理和分析任务。无论是开发图像处理应用程序、网站优化还是简单的图像编辑，这份文档都是不可或缺的参考资料。

Pillow (PIL Fork) Documentation, Release 6.1.0

2.1.3 Image Processing

The library contains 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.

2.2 Tutorial

2.2.1 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:

>>> from __future__ import print_function

>>> 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 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()

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.

2.2.2 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.

12 Chapter 2. Handbook

Pillow (PIL Fork) Documentation, Release 6.1.0

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.

2.2.4 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 ex-

pand 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.

2.2.5 Color transforms

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

convert() method.

2.2. Tutorial 15

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Pillow 6.1.0官方文档PDF版：离线图像处理指南

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