5.5.8 - June 13, 2022 - Code Project’s SenseAI Version 1 - See V2 here https://ipcamtalk.com/threads/codeproject-ai-version-2-0.68030/

[MikeLud1]

@MikeLud1 any news on v2 release yet?

Hopefully today they will have the prerelease out for testing and if all testing goes well they will do the release.

 

[Jamie Ficken]

I am completely failing at adding the Custom Models from here - GitHub - MikeLud/CodeProject.AI-Custom-IPcam-Models

I have the Docker instance installed and it's working well, however, I'd like to ignore certain animals like cats but I can't figure out the path to use in Blue Iris.

I put the IPcam-combined.pt file into the C:\CustomModels\ folder on my Blue Iris machine, however, I then get this message repeated over and over in the CodeProject.AI log:

16:49:59: Object Detection (YOLOv5 6.2): /app/AnalysisLayer/ObjectDetectionYolo/custom-models/IPcam-combined.pt does not exist
16:49:59: Object Detection (YOLOv5 6.2): Unable to create YOLO detector for model IPcam-combined


Any help would be greatly appreciated.

The machine that the Docker image is running on does NOT have a GPU which is why I installed the CPU only version of CodeProject.AI when I started messing with it today.

 

[Manu man]

Hi,I finally decided to give codeproject AI a shot. but it does not seems to be detecting people.I am not seeing any events being logged on blueiris, even though when people walk in front of camera object detection rectangles appear over them. Codeproject AI explorer logs indicate it is working as expected. What am i doing wrong?



4:20:02 AM: Latest version available is 1.6.7-Beta
4:20:05 AM: Latest version available is 1.6.7-Beta
4:20:12 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'detect' took 204ms
4:20:12 AM: Object Detection (YOLO): Detecting using ipcam-combined
4:20:12 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'custom' took 117ms
4:21:06 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'detect' took 178ms
4:21:06 AM: Object Detection (YOLO): Detecting using ipcam-combined
4:21:06 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'custom' took 98ms
4:22:13 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'detect' took 224ms
4:22:13 AM: Object Detection (YOLO): Detecting using ipcam-combined
4:22:13 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'custom' took 117ms
4:23:06 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'detect' took 229ms
4:23:06 AM: Object Detection (YOLO): Detecting using ipcam-combined
4:23:06 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'custom' took 113ms

 

[Tinman]

Hi,I finally decided to give codeproject AI a shot. but it does not seems to be detecting people.I am not seeing any events being logged on blueiris, even though when people walk in front of camera object detection rectangles appear over them. Codeproject AI explorer logs indicate it is working as expected. What am i doing wrong?



4:20:02 AM: Latest version available is 1.6.7-Beta
4:20:05 AM: Latest version available is 1.6.7-Beta
4:20:12 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'detect' took 204ms
4:20:12 AM: Object Detection (YOLO): Detecting using ipcam-combined
4:20:12 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'custom' took 117ms
4:21:06 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'detect' took 178ms
4:21:06 AM: Object Detection (YOLO): Detecting using ipcam-combined
4:21:06 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'custom' took 98ms
4:22:13 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'detect' took 224ms
4:22:13 AM: Object Detection (YOLO): Detecting using ipcam-combined
4:22:13 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'custom' took 117ms
4:23:06 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'detect' took 229ms
4:23:06 AM: Object Detection (YOLO): Detecting using ipcam-combined
4:23:06 AM: Object Detection (YOLO): Queue and Processing Object Detection (YOLO) command 'custom' took 113ms

Looks like you have AI working. Show us a screen shot of the BI camera's AI settings.

 

[Manu man]

I do not know like lucas did ,I added yolov5I but not sure if i have that in my custome models folder.

 

[Tinman]

I do not know like lucas did ,I added yolov5I but not sure if i have that in my custome models folder.

in the camera AI settings, in the custom models field, just use ipcam-combined You also might have a zone issue. Crossing zones can be flakey. I just use normal motion detection and let the AI work.

 

[Manu man]

Thanks Tinman!
I removed zone crossing and then use of zones for detection.Attached screen shots.lets see what happens next..............

 

[wittaj]

Hopefully today they will have the prerelease out for testing and if all testing goes well they will do the release.

Snowmageddon is coming end of the week- hopefully 2.X is released by then since everyone in the northern US will be snowed in and can give us something to do LOL

 

[nhs128]

Snowmageddon is coming end of the week- hopefully 2.X is released by then since everyone in the northern US will be snowed in and can give us something to do LOL

Right. I've got local PD asking for my setup right now too due to mail, package, and vehicle item theft. I'm waiting on this release to test before sending them the info. This would definitely keep me busy during all the snow

 

[Manu man]

It seems like working ,but detection is not accurate. I am using it in india and possibly detection not working due to different attaire worn by people. Is there anyway to train based on custom images.Or does the accuracy improve over time?

 

[MikeLud1]

It seems like working ,but detection is not accurate. I am using it in india and possibly detection not working due to different attaire worn by people. Is there anyway to train based on custom images.Or does the accuracy improve over time?

You can train your own model using custom image, see the below links on how to do this with YOLOv5.

Train Custom Data · ultralytics/yolov5 Wiki

Tips for Best Training Results

YOLOv5 in PyTorch > ONNX > CoreML > TFLite. Contribute to ultralytics/yolov5 development by creating an account on GitHub.

github.com

 

[Manu man]

how many images I need to have for a usefull model Mike?

 

[MikeLud1]

how many images I need to have for a usefull model Mike?

At a minimum 1,000 images the more images you have the better the model will be

 

[_lucas_]

I do not know like lucas did ,I added yolov5I but not sure if i have that in my custome models folder.

you have to download it manually and put it in the folder with the other models.....then you can use it

 

[Manu man]

you have to download it manually and put it in the folder with the other models.....then you can use it

Lucas where did you download it from and what adavntage it have over Mikes?

 

[Nunofya]

Hopefully today they will have the prerelease out for testing and if all testing goes well they will do the release.

The new update to BI today doesn't have the AI in it yet, does it?

 

[CrazyAsYou]

At a minimum 1,000 images the more images you have the better the model will be

@MikeLud1, putting the time and effort this would require to one side, do you think there would be any advantage in people training their own custom models based on images purely taken from our own CCTV recordings, of course I’m thinking that you’d need to use all your cameras and also have a good selection of everything you’d want to identify from each one at day and night but it’s something I’ve been thinking about for a while now. My logic is that it would include how people and animals actually look for our setups, camera angles, lighting etc

 

[wittaj]

@MikeLud1, putting the time and effort this would require to one side, do you think there would be any advantage in people training their own custom models based on images purely taken from our own CCTV recordings, of course I’m thinking that you’d need to use all your cameras and also have a good selection of everything you’d want to identify from each one at day and night but it’s something I’ve been thinking about for a while now. My logic is that it would include how people and animals actually look for our setups, camera angles, lighting etc

One will absolutely have better results using their own images from their own CCTV.

I don't think someone has to do that as their first course of action, but if the available models are not successful or the accuracy is not there for a particular user or someone wants to identify something not available in a model, then training your own model with your own images makes the most sense, especially once you get beyond human or vehicle objects.

I think the biggest mistake people make is trying to do too much with one field of view, so if a model has say a squirrel in it and all the trained images are up close images but someone is trying to use it on a camera that is 20 feet high, the object isn't going to look the same and the detection and accuracy will suffer.

 

[digger11]

I think the biggest mistake people make is trying to do too much with one field of view, so if a model has say a squirrel in it and all the trained images are up close images but someone is trying to use it on a camera that is 20 feet high, the object isn't going to look the same and the detection and accuracy will suffer.

One thing I've wondered about is if BI can draw a box around the area in which it sees motion, why can't it send just what is in that box to the AI for analysis? I have a camera that looks down our driveway to a cul-de-sac.
BI often triggers motion on vehicles in the cul de sac, but when CPAI or DS processes the entire image rarely is a vehicle identified. I just took a still from a cancelled motion capture (nothing found) and cropped it down to just the cul-de-sac and had CPAI explorer scan that cropped image it came back with a 93% match as a vehicle.

 

[AlphaBlueIris]

One of the new modules will support some of the older Nvidia GPUs. The developer is looking for someone to do some testing, if you have one of the below GPUs and are willing to do some testing let me know.

GeForce	Quadro, NVS	Tesla/Datacenter
GeForce GTX 770, GeForce GTX 760, GeForce GT 740, GeForce GTX 690, GeForce GTX 680, GeForce GTX 670, GeForce GTX 660 Ti, GeForce GTX 660, GeForce GTX 650 Ti BOOST, GeForce GTX 650 Ti, GeForce GTX 650, GeForce GTX 880M, GeForce GTX 870M, GeForce GTX 780M, GeForce GTX 770M, GeForce GTX 765M, GeForce GTX 760M, GeForce GTX 680MX, GeForce GTX 680M, GeForce GTX 675MX, GeForce GTX 670MX, GeForce GTX 660M, GeForce GT 750M, GeForce GT 650M, GeForce GT 745M, GeForce GT 645M, GeForce GT 740M, GeForce GT 730M, GeForce GT 640M, GeForce GT 640M LE, GeForce GT 735M, GeForce GT 730M GeForce GTX Titan Z, GeForce GTX Titan Black, GeForce GTX Titan, GeForce GTX 780 Ti, GeForce GTX 780, GeForce GT 640 (GDDR5), GeForce GT 630 v2, GeForce GT 730, GeForce GT 720, GeForce GT 710, GeForce GT 740M (64-bit, DDR3), GeForce GT 920M	Quadro K5000, Quadro K4200, Quadro K4000, Quadro K2000, Quadro K2000D, Quadro K600, Quadro K420, Quadro K500M, Quadro K510M, Quadro K610M, Quadro K1000M, Quadro K2000M, Quadro K1100M, Quadro K2100M, Quadro K3000M, Quadro K3100M, Quadro K4000M, Quadro K5000M, Quadro K4100M, Quadro K5100M, NVS 510, Quadro 410 Quadro K6000, Quadro K5200	Tesla K10, GRID K340, GRID K520, GRID K2 Tesla K40, Tesla K20x, Tesla K20

I have GeForce GT 710 and willing to test.