StiiCeva

Choosing the Right Data Access Pattern in TypeScript

Audience: Developers

---

Introduction

StiiCeva

status = error

appender.console.type = Console
appender.console.name = console
appender.console.layout.type = PatternLayout
appender.console.layout.pattern = [%d{ISO8601}][%-5p][%-25c{1.}] [%node_name]%marker %m%n

rootLogger.level = info
rootLogger.appenderRef.console.ref = console

StiiCeva

## JVM configuration

################################################################
## IMPORTANT: JVM heap size
################################################################
##
## You should always set the min and max JVM heap
## size to the same value. For example, to set
## the heap to 4 GB, set:
##

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class ActionBase:
    """
        class ActionBase : fn is mandatory and should be a callable.
        You can pass arguments and keyword arguments at instantiation, but you can also pass "named parameters" ( with
        'set_params_at_runtime' method) at runtime from an object ( from example a.c will return 2 at the moment of
        execution, but at instance creation is None ).
        By default the object will store the returned value of the executed function in self.result, but you can also
        store data in another object by using 'set_store_result_in' method (example a.set_store_result_in(b,"some_attr")
        after a() will save data in b.some_attr)
        .

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class Job(threading.Thread):
    def __init__(self, interval, execute, *args, **kwargs):
        '''
        Start class with .start()
        :param interval: timedelta
        :param execute: a function
        :param args: list of args
        :param kwargs: dict of args
        '''
        threading.Thread.__init__(self)

StiiCeva

This is a note on how to cross compile opencv for pretty much any ARM device(HardFP supported in this case) and deploy. Native
compiling in ARM devices can be painfully slow and they seem to hang often during build(mine got stuck at 43%). So if you happen
to have a desktop/laptop/server running ubuntu or similar linux distro, u can build opencv in fractionth of the time taken for 
native compiling without any issues.
Building opencv3 with TBB and NEON and VFP support can boost opencv performance. Thanks to Adrian at pyimagesearch for pointing 
that out.
Both my PC and target machine aka orange pi zero are running ubuntu 16.04 with python2.7 and python 3.5.
Let us use the term "build machine" for your PC where you are building opencv and "target machine" for the ARM single board computer.

1.Run the following commands in both machines(I think installing these in target machine only would do) to install the necessary libraries etc.(mine worked with them,so they should be enough