Image Processing with QccPack and Python

How to use QccPack to manipulate images with Python in code and from the Python prompt.
The pildriver Utility

The pildriver tool gives you access to most PIL functions from your operating system's command-line interface. When called as a script, the command-line arguments are passed to a PILDriver instance. If there are no command-line arguments, the module runs an interactive interpreter, each line of which is split into space-separated tokens and passed to the execute method. The pildriver tool was contributed by Eric S. Raymond. The following commands are from the Python prompt:

>>> pildriver program
>>> pildriver show crop 0 0 200 300 open test.png
>>> pildriver save rotated.png rotate 30 open test.tiff

The PILDriver Class

The pildriver module provides a single class called PILDriver. An instance of the PILDriver class is essentially a software stack machine (Polish-notation interpreter) for sequencing PIL image transformations. The state of the instance is the interpreter stack. The only method one normally will invoke after initialization is the execute method. This takes an argument list of tokens, pushes them onto the instance's stack, and then tries to clear the stack by successive evaluation of PILdriver operators. Any part of the stack not cleaned off persists and is part of the evaluation context for the next call of the execute method. PILDriver doesn't catch any exceptions on the theory that these actually contain diagnostic information that should be interpreted by the calling code.

The pilconvert Utility

The pilconvert tool converts an image from one format to another. The output format is determined by the target extension, unless explicitly specified with the -c option:

>>> pilconvert lenna.tif lena.png
>>> pilconvert -c JPEG lenna.tif lena.tmp

SDC Morphology Toolbox

The SDC Morphology Toolbox for Python is software used for image analysis and signal processing. It is based on the principle of discrete nonlinear filters followed by lattice operations. These filters are called morphological operators. Morphological operators are useful for restoration, segmentation and quantitative analysis of images and signals. SDC Morphology is effectively useful for machine vision, medical imaging, desktop publishing, document processing, and food industry and agriculture needs.

Grayscale images generally work fine with 8 or 16 bits to represent each pixel. Elementary operators on the images are used in a hierarchical manner. There are two types of elementary operators: dilation and erosion. Operators other than these are distance transform, watershed, reconstruction, labeling and area-opening. The SDC Morphology Toolbox is supported on various platforms, such as Win95/98/NT, Linux and Solaris.

Some common conventions are used in this toolbox. All operators of the SDC Morphology Toolbox start with mm. These return a single data structure, and parameters passed are position- and type-dependent. Most functions in the SDC Morphology Toolbox operate in 3-D.


Special thanks to James Fowler for his contribution in QccPack. Thanks also to W. Pearlman of RPI and L. Granda of PrimaComp for their QccPackSPIHT module. And, last but not least, thanks to the Python SIG group for PIL.

Suhas A. Desai works with Tech Mahindra Ltd. He writes on open source and security. In his free time, he volunteers for social causes.



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image processing with scripting language

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Python Imaging Library have great features and your blog is like a teacher where every information is very vital and informative for me.thanks a lot providing me such kind of information.

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