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#apertus IRC Channel Logs

2019/02/02

Timezone: UTC


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seku_
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seku_
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01:55
Bertl
off to bed now ... have a good one everyone!
01:55
Bertl
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BAndiT1983|away
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08:58
Bertl_zZ
changed nick to: Bertl
08:58
Bertl
morning folks!
09:15
se6astian|away
changed nick to: se6astian
09:42
siddhantsahu
Good Morning!
09:43
se6astian
hi siddhantsahu
09:43
siddhantsahu
se6astian: how can task T987 : Try running Tensorflow DeepLab-v3+ inside AXIOM Beta help?
09:44
siddhantsahu
I mean what is the need of semantic segmentation in AXIOM Beta?
09:45
BAndiT1983
to recognize object and beautify them, have you never used instagram filters? ;)
09:45
BAndiT1983
*objects
09:49
RexOrCine
changed nick to: RexOrCine|away
09:51
se6astian
siddhantsahu: there is no need currently, its purley a research and experiment centered task
09:54
siddhantsahu
se6astian: ok. Let me give it try. Which processor does AXIOM has and how much main memory is available?
09:55
BAndiT1983
zynq
09:55
se6astian
2 arm cores @ 800mhz
09:55
BAndiT1983
not much memory around 1gb, if i remember correctly
09:55
se6astian
microzed has 1gb ram in total, shared with fpga
09:55
BAndiT1983
plus the rest of linux system which requires its share
09:58
siddhantsahu
These deep learning models are pretty big not sure if the DeepLab-v3+ model fit in the available memory
09:59
BAndiT1983
tensorflow lite maybe
09:59
BAndiT1983
but this is task really not of importance at the moment, as firmware is priority 1
09:59
siddhantsahu
BAndiT1983: Yes
10:00
siddhantsahu
I know a bit of deep learning, that is why I was asking about this task
10:02
BAndiT1983
can't think of any usage cases at the moment, but i'm a developer and not camera user
10:15
Bertl
siddhantsahu: what amount of memory are we talking here?
10:16
siddhantsahu
Bertl: Let me check the model details
10:21
vup2
i think without any acceleration from the fpga side semantic segmentation would be nearly impossible
10:21
vup2
but a fun application would be for example to automatically blur any faces to increase anonymity
10:39
siddhantsahu
The MobileNetV2 (focused towards mobile devices)model can be used as a feature extractor with DeepLab-v3 which has less precision and number of parameters suited for mobile devices.
10:39
Bertl
vup2: yeah, movie makers will love that! :)
10:42
siddhantsahu
Refer Table 3 for the MobileNetV2 paper for model size and memory requirements. http://openaccess.thecvf.com/content_cvpr_2018/papers/Sandler_MobileNetV2_Inverted_Residuals_CVPR_2018_paper.pdf
11:11
vup2
It would certainly be a unique feature
11:40
BAndiT1983
se6astian, the menu of axiom remote is now moving when using the virtual knob, but i need some info on how it works for the real one
11:41
BAndiT1983
according to info from Bertl, the knob is using values from 0 to 255 and does also overflow or underflow for this ones
11:42
BAndiT1983
but if i start the visualizer and turn from default state 0 to 1, the menu entries are iterated fast till the end
11:42
BAndiT1983
if i turn back, then back to the top, probably i'm missing something which handles detents of the knob
11:42
se6astian
let me dig out the corresponding code lines
11:42
BAndiT1983
ah, found it in main.c, you are subtracting new and last state
11:43
BAndiT1983
int8_t diff = data[0] - qe[0];
11:43
BAndiT1983
knob_event_handler(E1_ROT, diff);
11:43
se6astian
yeah thats it
11:44
BAndiT1983
ok, will add corresponding code and upload working version
11:54
BAndiT1983
it works, will commit now
11:57
se6astian
great
11:57
BAndiT1983
done, bassic version for now, soem code will be moved to other areas, like init of white balance options, just added it to visualizer for quicker results
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13:12
Bertl
off for now ... bbl
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Bertl
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BAndiT1983
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anjan
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slikdigit
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19:24
se6astian
off to bed
19:24
se6astian
good night
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se6astian
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BAndiT1983|away
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