Image Processing with QccPack and Python
Python, xv and the PIL package are essential for Python image processing programming. Run these commands to build PIL in Linux:
python setup.py build_ext -i python selftest.py
The most important class in the Python Imaging Library is the Image class, defined in the module with the same name. We create instances of this class in several ways: 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 ("lenna.ppm")
The Python Imaging Library supports a wide variety of image file formats. The library automatically determines the format based on the contents of the file or the extension.
Listing 1. Convert Files to JPEG
import os, sys import Image for infile in sys.argv[1:]: outfile = os.path.splitext(infile) + ".jpg" if infile != outfile: try: Image.open(infile).save(outfile) except IOError: print "cannot convert", infile
The next example (Listing 2) shows how the Image class contains methods to resize and rotate an image.
Listing 2. Simple Geometry Transforms
out = im.resize((128, 128)) out = im.rotate(45) out = im.transpose(Image.ROTATE_90)
The Python Imaging Library allows you to convert images between different pixel representations using the convert function—for example, converting between modes:
im = Image.open("lenna.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.
The ImageFilter module contains a number of predefined enhancement filters that can be used with the filter method. For example, from the Python prompt, do the following:
>>> import ImageFilter >>> out = im.filter(ImageFilter.DETAIL)
Once you have imported the module, you can use any of these filters:
Some decoders allow you to manipulate an image while reading it from a file. This often can be used to speed up decoding when creating thumbnails and printing to a monochrome laser printer. The draft method manipulates an opened but not yet loaded image so it matches the given mode and size as closely as possible. Reconfiguring the image decoder does this. See Listing 3 for an example of how to read an image in draft mode.
Listing 3. Reading in Draft Mode
im = Image.open (file) 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)
Listing 4 shows how the ImageDraw module provides basic graphics support for Image objects.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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