[CentralOH] python blur detection

Eric Miller miller.eric.t at gmail.com
Wed Sep 9 02:57:35 CEST 2015


the "amount of change" problem seems to be the lowest hanging fruit.
Something like:

- identify an x/y mask that defines the largest rectangular area that is
100% sky.  This prevents unwanted changes in light over time on non sky
objects (trees/buildings/etc) from contributing noise to the amount of
change calculation.

- using PIL or similar, iterate over every pixel in the mask, avg them out,
and build a dict of frame #'s to RGB avgs: { '0001' : [10,20,30], '0002' :
[20,30,40] }.

- compare avg RGB vals for first and last frames to establish start and end
RGB values. (or skip this and use absolute 0,0,0 = 0%,  255,255,255 = 100%)

- compare avg RGB vals for each frame to the one previous, to establish %
change (relative to total determined in previous step)

wait a minute...didn't you do this already? Like exactly this, lol?




On Tue, Sep 8, 2015 at 8:16 PM, Eric Floehr <eric at intellovations.com> wrote:

> Hi Eric,
>
> Thanks for sharing! I do enjoy that :-) ! There are a few things I would
> love to do with my timelapse photos that deal with opencv or machine
> learning, and if anyone has any ideas for how to do it, I'd love to hear
> them.
>
> First, I'd love to be able to classify the images into certain categories:
> solid overcast, heavy clouds, puffy clouds, high wispy clouds, clear skies,
> etc. Basically be able to segregate and identify cloud types.
>
> Second, I'd like to quantify some measure of "amount of change" between
> images. Similarly, I'd like to quantify the "velocity" of things moving,
> namely clouds.
>
> Cheers,
> Eric
>
>
>
> On Tue, Sep 8, 2015 at 2:28 PM, Eric Miller <miller.eric.t at gmail.com>
> wrote:
>
>> Seems like something Mr. Floehr might enjoy:
>>
>> http://www.pyimagesearch.com/2015/09/07/blur-detection-with-opencv/
>>
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