python图形处理库PIL(Python Image Library)的基本概念

python图形处理库PIL(Python Image Library)的基本概念

4/25/2009 2:24:59 PM

Concepts
The Python Imaging Library handles raster images, that is, rectangles of pixel data.
Bands
An image can consist of one or more bands of data. The Python Imaging Library allows you
to store several bands in a single image, provided they all have the same dimensions and
depth.
To get the number and names of bands in an image, use the getbands method.
Mode
The mode of an image defines the type and depth of a pixel in the image. The current
release supports the following standard modes:
· 1 (1-bit pixels, black and white, stored as 8-bit pixels)
· L (8-bit pixels, black and white)
· P (8-bit pixels, mapped to any other mode using a colour palette)
· RGB (3x8-bit pixels, true colour)
· RGBA (4x8-bit pixels, true colour with transparency mask)
· CMYK (4x8-bit pixels, colour separation)
· YCbCr (3x8-bit pixels, colour video format)
· I (32-bit integer pixels)
· F (32-bit floating point pixels)
PIL also supports a few special modes, including RGBX (true colour with padding) and RGBa
(true colour with premultiplied alpha).
You can read the mode of an image through the mode attribute. This is a string containing
one of the above values.
Size
You can read the image size through the size attribute. This is a 2-tuple, containing the
horizontal and vertical size in pixels.
Coordinate System
The Python Imaging Library uses a Cartesian pixel coordinate system, with (0,0) in the
upper left corner. Note that the coordinates refer to the implied pixel corners; the centre
of a pixel addressed as (0, 0) actually lies at (0.5, 0.5):
(0, 0)
(1, 1)
Coordinates are usually passed to the library as 2-tuples (x, y). Rectangles are represented
as 4-tuples, with the upper left corner given first. For example, a rectangle covering all of
an 800x600 pixel image is written as (0, 0, 800, 600).
Palette
The palette mode ("P") uses a colour palette to define the actual colour for each pixel.
Info
You can attach auxiliary information to an image using the info attribute. This is a
dictionary object.
How such information is handled when loading and saving image files is up to the file
format handler (see the chapter on Image File Formats).
Filters
For geometry operations that may map multiple input pixels to a single output pixel, the
Python Imaging Library provides four different resampling filters.
NEAREST. Pick the nearest pixel from the input image. Ignore all other input
pixels.
·
BILINEAR. Use linear interpolation over a 2x2 environment in the input image.
Note that in the current version of PIL, this filter uses a fixed input environment
when downsampling.
·
BICUBIC. Use cubic interpolation over a 4x4 environment in the input image.
Note that in the current version of PIL, this filter uses a fixed input environment
when downsampling.
·
ANTIALIAS. (New in PIL 1.1.3). Calculate the output pixel value using a
high-quality resampling filter (a truncated sinc) on all pixels that may contribute
to the output value. In the current version of PIL, this filter can only be used
with the resize and thumbnail methods.
·
Note that in the current version of PIL, the ANTIALIAS filter is the only filter that behaves
properly when downsampling (that is, when converting a large image to a small one). The
BILINEAR and BICUBIC filters use a fixed input environment, and are best used for
scale-preserving geometric transforms and upsamping.