trying some new algorithm for the template match

The numbers the matchTemplate function gives us were not reliable for
large needles, so I tried something else and I'm now calculating the MSE
of the area and the scene part found matching.

The MSE I map to a comparable percentage number, following
http://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio example

I also extended .travis.yml to run the tests and added a dozen more
test cases found during deployment
two more test cases
imroot: check if we're really root
Merge pull request #142 from DimStar77/imroot-check

imroot: check if we're really root
Merge pull request #140 from os-autoinst/trying_opencv

trying some new algorithm for the template match
remove direct support for crop.py

- it makes the code much more complicated and makes it much harder to add
  new features
- it's not used by our current work flows
- the tool can still be used with interactive_mode
mark STDOUT and STDERR unbuffered
when an assert_screen fails, save the 20 biggest changes as screens

this makes it (hopefully) possible to create needles for screens that have
a timeout (like the bootloader or the screen saver)