compile opencv 3 with ffmpeg in OS X

Some framework not set correctly.

cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local \

-D PYTHON2_PACKAGES_PATH=~/Library/Python/2.7/lib/python/site-packages/ \

-D PYTHON2_LIBRARY=~/Library/Python/2.7/bin/ \

-D PYTHON2_INCLUDE_DIR=/System/Library/Frameworks/Python.framework/Headers \

-D INSTALL_C_EXAMPLES=ON -D INSTALL_PYTHON_EXAMPLES=ON \

-D BUILD_EXAMPLES=ON \

-D OPENCV_EXTRA_MODULES_PATH=/Users/jeromy/work/personal/opencv_contrib/modules ..

change /Users/jeromy/work/personal/opencv/modules/videoio/CMakeLists.txt line 150 to:

     list(APPEND VIDEOIO_LIBRARIES "-framework Security" "-framework CoreMedia" "-framework CoreVideo" "-framework AudioToolbox" "-framework VideoToolbox" "-fra    mework VideoDecodeAcceleration" bz2)

正则表达式Lookahead

Say I want to retrieve from a text document all the words that are immediately followed by a comma. We’ll use this example string:

What then, said I, shall I do?  You shan't, he replied, do anything.

As a first attempt, I could use this regular expression to get one or more word parts followed by a comma:

[A-Za-z']+,

This yields four results over the string:

1. then,
2. I,
3. shan't,
4. replied,

Notice that this gets me the comma too, though, which I would then have to remove.  Wouldn’t it be better if we could express that we want to match a word that is followed by a comma without also matching the comma?

We can do that by modifying our regex as follows:

[A-Za-z']+(?=,)

This matches groups of word characters that are followed by a comma, but because of the use of lookahead the comma is not part of the matched text (just as we want it not to be).  The modified regex results in these matches:

1. then
2. I
3. shan't
4. replied

A Positive Negative Example

What if I wanted to match all the words not followed by a comma?  I would use negative lookahead:

(?>[A-Za-z']+)(?!,)

(Okay, negative lookahead and atomic grouping)

…to get these matches:

1. What
2. said
3. shall
4. I
5. do
6. You
7. he
8. do
9. anything

Huh? Atomic Grouping?

Yep.  Otherwise you’ll get the following (unintended matches highlighted):

1. What
2. the
3. said
4. shall
5. I
6. do
7. You
8. shan’
9. he
10. replie
11. do
12. anything

Without atomic grouping (the (?>…) in the regex), when the regex engine sees that a match-in-progress comes up against a disqualifying comma, it simply backs off one letter to complete the match: the + in the regex gives the engine that flexibility.  Applying atomic grouping disallows this and says, don’t give up characters you’ve matched.

When Lookahead Does You No Good

Lookahead doesn’t really help if you only care whether or not there was a match (that is, you don’t care what text was matched).  If  all I care about is whether or not the string contains any words followed by a comma, I would dump lookahead and use the simpler regex:

[A-Za-z']+,

Acknowledgments

Thanks to Jeffrey Friedl for writing Mastering Regular Expressions, 3rd ed., before reading which I had not even heard of regex lookahead.

Also, thanks to Sergey Evdokimov for his online Regular Expression Editor.  Handy!

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Related

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regex

This entry was posted on March 25, 2009, 12:20 pm and is filed under Technical Stuff. You can follow any responses to this entry through RSS 2.0. You can leave a response, or trackback from your own site.

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1. Ascertaining how subtract works with Strings in Groovy « All things Grails and RIA
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ruby

http://www.railstips.org/blog/archives/2006/11/18/class-and-instance-variables-in-ruby/
http://thoughts.codegram.com/understanding-class-instance-variables-in-ruby/
http://www.devalot.com/articles/2008/09/ruby-singleton
http://rubymonk.com/learning/books/4-ruby-primer-ascent/chapters/45-more-classes/lessons/110-instance-variables

https://ruby-china.org/wiki/coding-style
http://www.alanmacdougall.com/blog/2012/06/08/interactive-debugging-with-pry/

Mobile TA

http://www.vogella.com/tutorials/Robotium/article.html

https://code.google.com/p/robotium/

Histogram

http://toyoizumilab.brain.riken.jp/hideaki/res/histogram.html

http://pubs.research.avayalabs.com/pdfs/ALR-2007-003-paper.pdf

http://stats.stackexchange.com/questions/6753/when-to-use-equal-frequency-histograms

Running median

http://stats.stackexchange.com/questions/7959/algorithm-to-dynamically-monitor-quantiles
http://stats.stackexchange.com/questions/103258/online-estimation-of-quartiles-without-storing-observations

http://en.wikipedia.org/wiki/Reservoir_sampling

https://dcc.ligo.org/T030168/public

http://web.ipac.caltech.edu/staff/fmasci/home/statistics_refs/Remedian.pdf

http://stats.stackexchange.com/questions/346/what-is-a-good-algorithm-for-estimating-the-median-of-a-huge-read-once-data-set

http://stackoverflow.com/questions/1309263/rolling-median-algorithm-in-c

http://stackoverflow.com/questions/10930732/c-efficiently-calculating-a-running-median

of the Best Free and Open Source Data Mining

5 of the Best Free and Open Source Data Mining Software | TechSource

Gerald Lushington takes a look at the current status of open source data mining

I’ve recently received some questions about the best open source data mining tools out there.  Good excuse to peak my head up out of the sand and see what’s out there!

Let me say up front that I use Weka quite religiously, and I would be quite happy to promote it to anyone as an efficient and reasonably flexible playground for anyone who needs to make sense of a lot of numbers.  I do, however, get the sense that it is a bit static in its methodological offerings, and that its graphics are marginal.  So what else is available?

The title link to this post is itself a blog post on this very question.  The only deficiency that post has is that it is now 2.5 years old, which might be considered somewhat ‘mature’ in the fast paced world of open source development.  So it does suggest Weka as one of it’s top five, as well asRapidMiner, which may well be the king of the data mining platforms period (open source or not).  I was surprised but pleased to see Knime listed:  Knime is much beloved in the chemical informatics community, and is a wonderful tool, but I personally had always thought of it as much more of a data integration platform than as a data mining engine.  I guess I need to open my mind a bit wider sometimes.  The remaining two codes profiled by Auza, include Orange and JHepWork, neither of which I had heard of, but both described in compelling and attractive terms:  Orange as a powerful but user-friendly platform that sounds suitable for beginners, and JHepWork as perhaps the most graphically endowed of the bunch.

New recommendations

What are some up and coming programs that Auza missed, or didn’t include in his top five?  Definitely Waffles, which provides a fine general environment within which to carry out a broad range of data analyses.  On a somewhat more narrow (but still broadly applicable level) I would definitely suggest ELKIfor its sophisticated techniques for model validation and outlier detection. Finally, for those people with more specialized interests, the MLoss list should be useful — it includes both the general data mining packages as well as a reasonable number that are highly tuned to specific disciplines or types of computation.

Finally, I would mention that if any of you have personal favorites that have not been mentioned here and may be of relevance to chemistry or biology, I would greatly appreciate hearing about them!

Posts in Gerald Lushington’s open source software blog represent the honest opinions of an open source software fan who has not been influenced by commercial interests or other inducements.