Can't load an ONNX model in SSD Detector

[tomasz-lewicki]

Can't load an ONNX model in SSD Detector

[tomasz-lewicki]

Hi @rjv-vila ,

Exp layer was added in OpenCV 4.5.2. by this PR: opencv/opencv#19545

You will need to upgrade from OpenCV 4.4. All AI Thermometer functionality should still work under OpenCV 4.5.2.

[rjv-vila]

Thank you for your reply @tomek-l , should I upgrade it by building like in your instructions

wget https://raw.githubusercontent.com/mdegans/nano_build_opencv/master/build_opencv.sh
chmod +x build_opencv.sh
./build_opencv.sh

or pip install will do?

[tomasz-lewicki]

You will need to build from source to have CUDA acceleration (so build_opencv.sh rather than pip install).

[rjv-vila]

Alright, thanks again. I will edit the version of the opencv written in the build_opencv.sh then build it. Is it the proper way of doing that? I saw that the default version of opencv written in build_opencv.sh is 4.4.0 so I though I just need to edit it.

I'll comment here an update if things goes fine or not. Thanks! :))

[rjv-vila]

@tomek-l here is what I get after upgrading to OpenCV 4.5.2

`
Loading SSD weights from file...
Weights loaded!
Running first net inference...
Detector initialized!
GST_ARGUS: Creating output stream
CONSUMER: Waiting until producer is connected...
GST_ARGUS: Available Sensor modes :
GST_ARGUS: 3264 x 2464 FR = 21.000000 fps Duration = 47619048 ; Analog Gain range min 1.000000, max 10.625000; Exposure Range min 13000, max 683709000;

GST_ARGUS: 3264 x 1848 FR = 28.000001 fps Duration = 35714284 ; Analog Gain range min 1.000000, max 10.625000; Exposure Range min 13000, max 683709000;

GST_ARGUS: 1920 x 1080 FR = 29.999999 fps Duration = 33333334 ; Analog Gain range min 1.000000, max 10.625000; Exposure Range min 13000, max 683709000;

GST_ARGUS: 1640 x 1232 FR = 29.999999 fps Duration = 33333334 ; Analog Gain range min 1.000000, max 10.625000; Exposure Range min 13000, max 683709000;

GST_ARGUS: 1280 x 720 FR = 59.999999 fps Duration = 16666667 ; Analog Gain range min 1.000000, max 10.625000; Exposure Range min 13000, max 683709000;

GST_ARGUS: 1280 x 720 FR = 120.000005 fps Duration = 8333333 ; Analog Gain range min 1.000000, max 10.625000; Exposure Range min 13000, max 683709000;

GST_ARGUS: Running with following settings:
Camera index = 0
Camera mode = 0
Output Stream W = 3264 H = 2464
seconds to Run = 0
Frame Rate = 21.000000
GST_ARGUS: Setup Complete, Starting captures for 0 seconds
GST_ARGUS: Starting repeat capture requests.
CONSUMER: Producer has connected; continuing.
[ WARN:0] global /tmp/build_opencv/opencv/modules/videoio/src/cap_gstreamer.cpp (1081) open OpenCV | GStreamer warning: Cannot query video position: status=0, value=-1, duration=-1
device opened!
Version gpp: 3.3.26 dsp: 3.3.26
FLIR part #: b'500-0771-01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
FLIR serial #: b'\x02\xf1<\x01\x00\x00\x00\x00'
format: b'UYVY'
frame 160x120 @ 9fps
format: b'Y16 '
frame 160x120 @ 9fps
frame 160x122 @ 9fps
format: b'Y8 '
frame 160x120 @ 9fps
format: b'RGBP'
frame 160x120 @ 9fps
format: b'}\xeb6\xe4'
frame 160x120 @ 9fps
Estimated / selected altsetting bandwith : 18 / 642.
INFO: Using $DISPLAY from environment, not from config
(960, 0)
exit handler called
releasing
CONSUMER: Done Success
GST_ARGUS: Cleaning up
GST_ARGUS: Done Success
Traceback (most recent call last):
File "main.py", line 244, in
mainloop()
File "main.py", line 142, in mainloop
boxes_ir = transform_boxes(boxes, 1.1, 1.1, 0, 0)
File "/home/g1-facemask/ai-thermometer/utils/transforms.py", line 49, in transform_boxes
x1, y1, x2, y2 = bbox
ValueError: not enough values to unpack (expected 4, got 1)
`

Here is a copy of my onnx model if you want to try. Thanks!
https://dropmb.com/EUixz
Here is the label if needed.
https://dropmb.com/DFwQP

[tomasz-lewicki]

Hi @rjv-vila !

Your Neural Net likely has a different output format than the network I used, so the existing code won't work.
To make it work, you you'd have to parse the output of your neural network to extract the bounding boxes from the output tensor.

[rjv-vila]

Hello @tomek-l thank you again for replying.

Do you have any idea on how to do that? I'm kinda clueless on how I will do that process.