codingmantras / yolov8-streamlit-detection-tracking Goto Github PK

hi first thank you for your great work, i used your code its great but when choosing YouTube errors appear to me
the error
"
Error loading video: ERROR: Unable to extract uploader id; please report this issue on https://yt-dl.org/bug . Make sure you are using the latest version; see https://yt-dl.org/update on how to update. Be sure to call youtube-dl with the --verbose flag and include its complete output.
"
i did use "pip install --upgrade youtube-dl"
and also "youtube-dl --verbose https://www.youtube.com/path...."

but error remains
thank you

hi i have a project that i need to combine 3 yolov8 models using ensemble learning
and i tried to do it inside the code
but i failed , simply everything worked but not in real time, please can you help me

Hello. I was interested in your project, read 3 articles on the medium and now I’m planning to install a copy for myself.

I have a task: I need to track the number of people in a frame in real time. I must have at least 4 cameras on the screen. At the same time, I need to display an image from them and some statistics.

Will it be possible to expand the current webapp to display multiple cameras at the same time?
Can I write directly on the image the number of people in the frame?
Is it possible to write data somewhere for later processing?
For example, display a histogram with the number of people in the frame by hour. Or, for example, if there are less than 5 people in the frame, issue some kind of trigger.

I’m just starting to work with computer vision and streamlit in particular and I would like to understand whether the implementation of my tasks is possible in principle?

hi again sorry for bothering you

when choosing video from pc, it works but the detection is too slow, how to make the detection in real-time?
thank you

Hello Sir,
Im new to ML and trying to train a model(object detedtion) on Google Colab and make a streamlit web app of it in colab bcz my local machine has not the GPU.
My question is:
After training i need to load the model.Im confused which file should i load the best.pt or the yolov8s.pt?
And after that i can make a function to upload and detect images with bounding boxes and labels on it.
I will be very grateful to you.

Hi, its possible to convert this aplication in a .exe ? could you help ?

Hi, this is great! The Yolo object detection and tracker on a webcam feed could be very useful on live microscopy camera video feeds.
Would it be however be possible to add a live counter on the live video feed? For example, if I only wanted to detect one class, on a webcam, could I add a counter to the video feed or in a separate description field below the video feed that counts the number of detected objects in real-time?
And if this is possible would I be able to generate and display a ratio between the number of two classes in real time?

Thank you!

Dear Sir @CodingMantras,
I have modified several YOLOv5 models and want to apply them to your app.
How and where can I change the code?
I am looking forward to hearing from you soon.
Thank you very much.
Best regards.

Hi. The detection works fine for YouTube vids but when trying rtsp feed it freezes after about 2 sec. I am watching the feed simultaneously on another pc so I know the feed is up and running. Any ideas?

I am using a Mac M1.

I try to run app with pytorch cuda, but crush :

Error loading video: Could not run 'torchvision::nms' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'torchvision::nms' is only available for these backends: [CPU, QuantizedCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMeta, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].

CPU: registered at C:\actions-runner_work\vision\vision\pytorch\vision\torchvision\csrc\ops\cpu\nms_kernel.cpp:112 [kernel] QuantizedCPU: registered at C:\actions-runner_work\vision\vision\pytorch\vision\torchvision\csrc\ops\quantized\cpu\qnms_kernel.cpp:124 [kernel] BackendSelect: fallthrough registered at ..\aten\src\ATen\core\BackendSelectFallbackKernel.cpp:3 [backend fallback] Python: registered at ..\aten\src\ATen\core\PythonFallbackKernel.cpp:153 [backend fallback] FuncTorchDynamicLayerBackMode: registered at ..\aten\src\ATen\functorch\DynamicLayer.cpp:498 [backend fallback] Functionalize: registered at ..\aten\src\ATen\FunctionalizeFallbackKernel.cpp:290 [backend fallback] Named: registered at ..\aten\src\ATen\core\NamedRegistrations.cpp:7 [backend fallback] Conjugate: registered at ..\aten\src\ATen\ConjugateFallback.cpp:17 [backend fallback] Negative: registered at ..\aten\src\ATen\native\NegateFallback.cpp:19 [backend fallback] ZeroTensor: registered at ..\aten\src\ATen\ZeroTensorFallback.cpp:86 [backend fallback] ADInplaceOrView: fallthrough registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:86 [backend fallback] AutogradOther: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:53 [backend fallback] AutogradCPU: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:57 [backend fallback] AutogradCUDA: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:65 [backend fallback] AutogradXLA: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:69 [backend fallback] AutogradMPS: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:77 [backend fallback] AutogradXPU: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:61 [backend fallback] AutogradHPU: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:90 [backend fallback] AutogradLazy: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:73 [backend fallback] AutogradMeta: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:81 [backend fallback] Tracer: registered at ..\torch\csrc\autograd\TraceTypeManual.cpp:296 [backend fallback] AutocastCPU: fallthrough registered at ..\aten\src\ATen\autocast_mode.cpp:382 [backend fallback] AutocastCUDA: fallthrough registered at ..\aten\src\ATen\autocast_mode.cpp:249 [backend fallback] FuncTorchBatched: registered at ..\aten\src\ATen\functorch\LegacyBatchingRegistrations.cpp:710 [backend fallback] FuncTorchVmapMode: fallthrough registered at ..\aten\src\ATen\functorch\VmapModeRegistrations.cpp:28 [backend fallback] Batched: registered at ..\aten\src\ATen\LegacyBatchingRegistrations.cpp:1075 [backend fallback] VmapMode: fallthrough registered at ..\aten\src\ATen\VmapModeRegistrations.cpp:33 [backend fallback] FuncTorchGradWrapper: registered at ..\aten\src\ATen\functorch\TensorWrapper.cpp:203 [backend fallback] PythonTLSSnapshot: registered at ..\aten\src\ATen\core\PythonFallbackKernel.cpp:161 [backend fallback] FuncTorchDynamicLayerFrontMode: registered at ..\aten\src\ATen\functorch\DynamicLayer.cpp:494 [backend fallback] PreDispatch: registered at ..\aten\src\ATen\core\PythonFallbackKernel.cpp:165 [backend fallback] PythonDispatcher: registered at ..\aten\src\ATen\core\PythonFallbackKernel.cpp:157 [backend fallback]