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Today's Topics:
1. Re: Python object-detection post-hook repo (Greg Thomas)
----------------------------------------------------------------------
Message: 1
Date: Mon, 16 Aug 2021 00:02:19 +1200
From: Greg Thomas <[email protected]>
To: Motion discussion list <[email protected]>
Subject: Re: [Motion-user] Python object-detection post-hook repo
Message-ID:
<cage5y2cdhu5q1ptwnxg3mamkh4j-w75b5e5mnrz9xxjyoka...@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"
Hi Yash,
Happy to share, but honestly after looking at Peter's coffee, I'm
somewhat embarrassed with my code. I'm not a phyton developer so I've
probably taken an excruciatingly painful route and made it way more complex
that it needs to be, but with that caveat, I'll post a link in a day or so.
Regards
Greg
On Sun, 15 Aug 2021, 9:55 pm Yash Sondhi, <[email protected]> wrote:
> Hi Greg,
> That sounds super useful, would you be able to share the code for that?
> Cheers
>
>
> On Sun, 15 Aug 2021, 03:59 Greg Thomas, <[email protected]> wrote:
>
>> Hi Peter,
>> I did something similar, I'm running a Pi4 with the coral TPU. I send
>> the images rather than the movies (publishing the filename) to the Pi via
>> MQTT, and I built a small server application to handle multiple cameras,
>> using the Coco SSDMobilenet model from the Google TPU example site. I also
>> limited the objects detected to those in interested in so only a subset of
>> the standard 90 in the COCO set.
>>
>> Works pretty well generally speaking, but I'm having difficulty with
>> night time images - does you setup handle night time images/movies reliably?
>>
>> Regards
>>
>>
>>
>> On Fri, 13 Aug 2021, 8:13 am Peter Torelli, <[email protected]>
>> wrote:
>>
>>> A few months ago I asked this list if there was a way to natively
>>> perform object detection inside the main motion code. Since that isn't
>>> feasible, and since I didn't get a response with existing projects, I wrote
>>> a quick post-processor that uses SSD Mobilenet trained on COCO running on
>>> the Google Coral USB Accelerator. I got tired of wrangling the DeepStream
>>> SDK additions to GStreamer on my Xavier AGX, so this is a nice compromise.
>>>
>>> https://github.com/petertorelli/motion-nnet
>>>
>>> Basically it is a server that analyzes the component pictures of an
>>> event movie, and if it detects one of the 90 classes, it moves that MP4 to
>>> another folder (or you could push it to the cloud, or whatever). It cuts
>>> back on having to look at lots of shadows during the day, or bugs and rain
>>> at night. The server is triggered by a call from motion after a movie write
>>> event to the client python script that takes %f as the input parameter. The
>>> files must start with %v for the event so that the server knows which
>>> pictures to scan.
>>>
>>> Hopefully someone finds this useful, as I'm glad to not have to sort
>>> through hundreds of bogus movies (Caveat: SSDMobileNet isn't as good as a
>>> big ResNet with classical feature extraction, so you won't get
>>> everything... this is just a first shot at cutting back on noise).
>>>
>>> Peter
>>>
>>>
>>>
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>>
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