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Commit edff3cd

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Update README.md
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‎9_part grab screen multiprocessing/README.md‎

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@@ -57,8 +57,8 @@ if __name__=="__main__":
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q = multiprocessing.JoinableQueue()
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# creating new processes
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p1 = multiprocessing.Process(target=GRABMSS_screeb, args=(q, ))
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p2 = multiprocessing.Process(target=SHOWMSS_screeb, args=(q, ))
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p1 = multiprocessing.Process(target=GRABMSS_screen, args=(q, ))
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p2 = multiprocessing.Process(target=SHOWMSS_screen, args=(q, ))
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# starting our processes
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p1.start()
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# Queue
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q = multiprocessing.JoinableQueue()
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```
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With following lines we are creating p1 and p2 processes which will run in background. p1 function will call GRABMSS_screeb() function and p2 will call SHOWMSS_screeb() function. As an argument for these functions we must give arguments, we give q there.
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With following lines we are creating p1 and p2 processes which will run in background. p1 function will call GRABMSS_screen() function and p2 will call SHOWMSS_screen() function. As an argument for these functions we must give arguments, we give q there.
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```
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# creating new processes
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p1 = multiprocessing.Process(target=GRABMSS_screen, args=(q, ))
@@ -83,3 +83,16 @@ Final step is to start our processes, after these commands our grab screen funct
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p1.start()
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p2.start()
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```
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For comparison I ran old code without multiprocessing and with multiprocessing. Here is results without multiprocessing:
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<p align="center">
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<img src="https://github.com/pythonlessons/TensorFlow-object-detection-tutorial/blob/master/1_part%20images/09_FPS_slow.JPG"
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</p>
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We can see that average is about 19-20 FPS.
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Here is results with multiprocessing:
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<p align="center">
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<img src="https://github.com/pythonlessons/TensorFlow-object-detection-tutorial/blob/master/1_part%20images/09_FPS_fast.JPG"
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</p>
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We can see that average is about 32 FPS. So our final result is that our grab screen improoved in around 50%. I would like like to impoove it more, but for now I don't have ideas how to do that. Anyway reults are much better than before !
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### Original text version of tutorial you can visit [here](http://pylessons.com/).

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