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IF YOU ARE HAVING A PROBLEM
- Take a look at the logs in
C:\Program Files\CodeProject\AI\logs and see if there's anything in there that screams 'something broke'.
- Check the FAQs in the CodeProject.AI Server documentation
- Make sure you've tested the server using the Explorer (blue link, top middle of the dashboard) to ensure it's a server issue rather than something else such as Blue Iris or another app using CodeProject.AI server.
- If there's no obvious answer, then copy and paste into a message the contents of the System Info tab, describe what you are doing, and what you see, and what you would expect.
Always include a copy and paste from the System Info tab of the dashboard. It gives us a ton of info on your setup. If an individual module is failing, click the 'Info' button to the right of the module's name in the status list and copy and paste that info too.
How to reinstall a module
Option 1. Go to the install modules tab on the dashboard and try re-installing the package. Make sure you have enough disk space and a reliable internet connection.
Option 2: (Option 1 with a vengeance): If that fails, head to the module's folder ([app root]\modules\module-id), open a terminal in admin mode, and run ..\..\setup. This will force a manual reinstall using the install script.
Docker: In Docker you will need to open a terminal into the docker container. You can do this using Docker Desktop, or Visual Studio Code with the Docker remote extension, or on the command using using docker attach. Then do a cd /app/modules/module-id where module-id is the id of the module you need to resinstall. Next, run sudo bash ../../setup.sh --verbosity info to force a manual reinstall of that module. (Set verbosity as quiet, info or loud to get less or more info)
cheers
Chris Maunder
modified 18-Feb-24 15:48pm.
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If you are a Blue Iris user and you are using custom models, then you would notice that the option, in Blue Iris, to set the custom model location is greyed out. This is because Blue Iris does not currently make changes to CodeProject.AI Server's settings. It can be done by manually starting CodeProject.AI with command line parameters (not a great solution), or editing the module settings files (a little messy), or setting system-wide environment variables (way easier). For version 1.6 we added an API to allow any app to change our settings programmatically, and we take care of stopping/restarting things and persisting the changes.
So: Blue Iris doesn't currently change CodeProject.AI Server's settings, so it doesn't provide you a way to change the custom model folder location from within Blue Iris.
Blue Iris will still use the contents of this folder to determine the calls it makes. If you don't specify a model to use in the Custom Models textbox, then Blue Iris will use all models in the custom models folder that it knows about.
Here we've specified a specific model to use. The Blue Iris help file explains more about how this works, including inclusive and exclusive filters on the models it finds.
CodeProject.AI Server doesn't know about Blue Iris' folder, so it can't tell what models it may be expected to use, nor can it tell Blue Iris about what models CodeProject.AI server has available. Our API allows Blue Iris to get a list of the AI models installed with CodeProject.AI Server, and also to set the folder where these models reside. But Blue Iris doesn't, yet, use that API.
So we do a hack.
At install time we sniff the registry to find where Blue Iris thinks the custom models should be. We then make empty copies of the models that we have, and copy them into that folder. If the folder doesn't exist (eg you were using C:\Program Files\CodeProject\AI\AnalysisLayer\CustomObjectDetection\assets, which no longer exists) then we create that folder, and then copy over the empty files.
When Blue Iris looks in that folder to decide what custom calls it can make, it sees the models, notes their names, and uses those names in the calls. CodeProject.AI Server has those models, so when the calls come through we can process them.
Blue Iris doesn't use the models. It uses the list of model names.
If you have your own models in the Blue Iris folder
You will need to copy them to the CodeProject.AI server's custom model folder (by default this is C:\Program Files\CodeProject\AI\AnalysisLayer\ObjectDetectionYolo\custom-models)
If you've modified the registry and have your own custom models
If you were using a folder in C:\Program Files\CodeProject\AI\AnalysisLayer\CustomObjectDetection\ (which no longer existed after the upgrade, but was recreated by our hack) you'll need to re-copy your custom model into that folder.
The simplest solutions are:
- Modify the registry (Computer\HKEY_LOCAL_MACHINE\SOFTWARE\Perspective Software\Blue Iris\Options\AI, key 'deepstack_custompath') so Blue Iris looks in
C:\Program Files\CodeProject\AI\AnalysisLayer\ObjectDetectionYolo\custom-models for custom models, and copy your models into there.
or
- Modify
C:\Program Files\CodeProject\AI\AnalysisLayer\ObjectDetectionYolo\modulesettings.json file and set CUSTOM_MODELS_DIR to be whatever Blue Iris thinks the custom model folder is.
cheers
Chris Maunder
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My use case is that I want to see more details about the detection requests - parameters submitted, origin, etc. I got into CPAI because of Blue Iris but it's working far from how I expect it to work and I would like to understand more where the various requests are coming from, especially related to static object detection which is at best confusing.
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Try the docs
Is there anything specific we can help with?
cheers
Chris Maunder
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I get this when trying to install the Sound Classifier.
it does appear to work, but also have this in log:
08:47:54:SoundClassifierTF: C:\Program Files\CodeProject\AI\modules\SoundClassifierTF\bin\windows\python39\venv\lib\site-packages\scipy\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.4
08:47:54:SoundClassifierTF: warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
08:47:55:SoundClassifierTF: 2024-05-15 08:47:54.989982: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
08:47:57:SoundClassifierTF: WARNING:tensorflow:From C:\Program Files\CodeProject\AI\modules\SoundClassifierTF\bin\windows\python39\venv\lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.
08:47:58:SoundClassifierTF: WARNING:tensorflow:From C:\Program Files\CodeProject\AI\modules\SoundClassifierTF\sound_classification.py:15: The name tf.disable_v2_behavior is deprecated. Please use tf.compat.v1.disable_v2_behavior instead.
08:47:58:SoundClassifierTF: WARNING:tensorflow:From C:\Program Files\CodeProject\AI\modules\SoundClassifierTF\bin\windows\python39\venv\lib\site-packages\tensorflow\python\compat\v2_compat.py:108: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
08:47:58:SoundClassifierTF: Instructions for updating:
08:47:58:SoundClassifierTF: non-resource variables are not supported in the long term
08:47:58:SoundClassifierTF: WARNING:tensorflow:From C:\Program Files\CodeProject\AI\modules\SoundClassifierTF\sound_classification.py:16: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.
08:48:01:SoundClassifierTF: 2024-05-15 08:48:01.022855: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
08:48:01:SoundClassifierTF: To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
08:48:01:SoundClassifierTF: C:\Program Files\CodeProject\AI\modules\SoundClassifierTF\bin\windows\python39\venv\lib\site-packages\tensorflow\python\keras\engine\base_layer_v1.py:1697: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.
08:48:01:SoundClassifierTF: warnings.warn('`layer.apply` is deprecated and '
08:48:01:SoundClassifierTF: C:\Program Files\CodeProject\AI\modules\SoundClassifierTF\bin\windows\python39\venv\lib\site-packages\tensorflow\python\keras\legacy_tf_layers\core.py:318: UserWarning: `tf.layers.flatten` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Flatten` instead.
08:48:01:SoundClassifierTF: warnings.warn('`tf.layers.flatten` is deprecated and '
08:48:01:SoundClassifierTF: 2024-05-15 08:48:01.364607: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
08:48:02:SoundClassifierTF: C:\Program Files\CodeProject\AI\modules\SoundClassifierTF\bin\windows\python39\venv\lib\site-packages\tensorflow\python\keras\engine\base_layer_v1.py:1697: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.
08:48:02:SoundClassifierTF: warnings.warn('`layer.apply` is deprecated and '
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The sound classifier is a Tensorflow 1 project ported (semi-ported!) to Tensorflow 2. Those warnings are just warnings and can be safely ignored. It needs a proper rewriting.
cheers
Chris Maunder
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Hello all, everytime i try to install any of the modules i get a Error in install:404
thought maybe it was a issue with my firewall that im using UFW. im running ubuntu server 24.04
not sure if there is more ports i need to open other then the port that code project uses 32168
Server version: 2.6.4
System: Linux
Operating System: Linux (Ubuntu 24.04)
CPUs: 12th Gen Intel(R) Core(TM) i7-12700K (Intel)
1 CPU x 12 cores. 20 logical processors (x64)
GPU (Primary): NVIDIA GeForce RTX 2060 (6 GiB) (NVIDIA)
Driver: 535.161.08, CUDA: 12.2 (up to: 12.2), Compute: 7.5, cuDNN:
System RAM: 31 GiB
Platform: Linux
BuildConfig: Release
Execution Env: Native
Runtime Env: Production
Runtimes installed:
.NET runtime: 7.0.18
.NET SDK: 7.0.118
Default Python: 3.12.3
Go: Not found
NodeJS: Not found
Rust: Not found
Video adapter info:
TU104 [GeForce RTX 2060] (rev a1):
Driver Version
Video Processor
System GPU info:
GPU 3D Usage 10%
GPU RAM Usage 1.5 GiB
Global Environment variables:
CPAI_APPROOTPATH = <root>
CPAI_PORT = 32168
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Try starting the server directly
sudo bash /usr/bin/codeproject.ai-server-2.6.4/start.sh
cheers
Chris Maunder
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tryed starting codeproject from terminal and still get the same 404 errors
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Ok, so i got
Training for YoloV5 6.2 to install via Agent DVR, but on the codeproject side i got a bunch of python errors
14:06:07:Started Training for YoloV5 6.2 module
14:06:10:training_objectdetection_YOLOv5_adapter.py: Initializing YoloV5DatasetCreator. Retries left: 3
14:06:10:training_objectdetection_YOLOv5_adapter.py: Traceback (most recent call last):
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/odm/database.py", line 200, in establish_db_conn
14:06:10:training_objectdetection_YOLOv5_adapter.py: _db_service = fos.DatabaseService()
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 80, in __init__
14:06:10:training_objectdetection_YOLOv5_adapter.py: self.start()
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 209, in start
14:06:10:training_objectdetection_YOLOv5_adapter.py: super().start()
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 118, in start
14:06:10:training_objectdetection_YOLOv5_adapter.py: + self.command,
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 238, in command
14:06:10:training_objectdetection_YOLOv5_adapter.py: DatabaseService.find_mongod(),
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 286, in find_mongod
14:06:10:training_objectdetection_YOLOv5_adapter.py: raise ServiceExecutableNotFound("Could not find `mongod`")
14:06:10:training_objectdetection_YOLOv5_adapter.py: fiftyone.core.service.ServiceExecutableNotFound: Could not find `mongod`
14:06:10:training_objectdetection_YOLOv5_adapter.py: During handling of the above exception, another exception occurred:
14:06:10:training_objectdetection_YOLOv5_adapter.py: Traceback (most recent call last):
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/fiftyone_dataset_creator.py", line 51, in init_fiftyone
14:06:10:training_objectdetection_YOLOv5_adapter.py: import fiftyone.zoo as foz
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/__init__.py", line 25, in
14:06:10:training_objectdetection_YOLOv5_adapter.py: from fiftyone.__public__ import *
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/__public__.py", line 16, in
14:06:10:training_objectdetection_YOLOv5_adapter.py: _foo.establish_db_conn(config)
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/odm/database.py", line 206, in establish_db_conn
14:06:10:training_objectdetection_YOLOv5_adapter.py: raise FiftyOneConfigError(
14:06:10:training_objectdetection_YOLOv5_adapter.py: fiftyone.core.config.FiftyOneConfigError: MongoDB could not be installed on your system. Please define a `database_uri` in your `fiftyone.core.config.FiftyOneConfig` to connect to yourown MongoDB instance or cluster
14:06:10:training_objectdetection_YOLOv5_adapter.py: During handling of the above exception, another exception occurred:
14:06:10:training_objectdetection_YOLOv5_adapter.py: Traceback (most recent call last):
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/training_objectdetection_YOLOv5_adapter.py", line 92, in initialise
14:06:10:training_objectdetection_YOLOv5_adapter.py: self.dataset_creator = YoloV5DatasetCreator(self,
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/fiftyone_dataset_creator.py", line 30, in __init__
14:06:10:training_objectdetection_YOLOv5_adapter.py: self.init_fiftyone()
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/fiftyone_dataset_creator.py", line 54, in init_fiftyone
14:06:10:training_objectdetection_YOLOv5_adapter.py: shutil.rmtree(fiftyone_path)
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/lib/python3.8/shutil.py", line 709, in rmtree
14:06:10:training_objectdetection_YOLOv5_adapter.py: onerror(os.lstat, path, sys.exc_info())
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/lib/python3.8/shutil.py", line 707, in rmtree
14:06:10:training_objectdetection_YOLOv5_adapter.py: orig_st = os.lstat(path)
14:06:10:training_objectdetection_YOLOv5_adapter.py: FileNotFoundError: [Errno 2] No such file or directory: '/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/fiftyone'
14:06:10:training_objectdetection_YOLOv5_adapter.py: Initializing YoloV5DatasetCreator. Retries left: 2
14:06:10:training_objectdetection_YOLOv5_adapter.py: Traceback (most recent call last):
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/odm/database.py", line 200, in establish_db_conn
14:06:10:training_objectdetection_YOLOv5_adapter.py: _db_service = fos.DatabaseService()
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 80, in __init__
14:06:10:training_objectdetection_YOLOv5_adapter.py: self.start()
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 209, in start
14:06:10:training_objectdetection_YOLOv5_adapter.py: super().start()
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 118, in start
14:06:10:training_objectdetection_YOLOv5_adapter.py: + self.command,
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 238, in command
14:06:10:training_objectdetection_YOLOv5_adapter.py: DatabaseService.find_mongod(),
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 286, in find_mongod
14:06:10:training_objectdetection_YOLOv5_adapter.py: raise ServiceExecutableNotFound("Could not find `mongod`")
14:06:10:training_objectdetection_YOLOv5_adapter.py: fiftyone.core.service.ServiceExecutableNotFound: Could not find `mongod`
14:06:10:training_objectdetection_YOLOv5_adapter.py: During handling of the above exception, another exception occurred:
14:06:10:training_objectdetection_YOLOv5_adapter.py: Traceback (most recent call last):
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/fiftyone_dataset_creator.py", line 51, in init_fiftyone
14:06:10:training_objectdetection_YOLOv5_adapter.py: import fiftyone.zoo as foz
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/__init__.py", line 25, in
14:06:10:training_objectdetection_YOLOv5_adapter.py: from fiftyone.__public__ import *
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/__public__.py", line 16, in
14:06:10:training_objectdetection_YOLOv5_adapter.py: _foo.establish_db_conn(config)
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/odm/database.py", line 206, in establish_db_conn
14:06:10:training_objectdetection_YOLOv5_adapter.py: raise FiftyOneConfigError(
14:06:10:training_objectdetection_YOLOv5_adapter.py: fiftyone.core.config.FiftyOneConfigError: MongoDB could not be installed on your system. Please define a `database_uri` in your `fiftyone.core.config.FiftyOneConfig` to connect to yourown MongoDB instance or cluster
14:06:10:training_objectdetection_YOLOv5_adapter.py: During handling of the above exception, another exception occurred:
14:06:10:training_objectdetection_YOLOv5_adapter.py: Traceback (most recent call last):
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/training_objectdetection_YOLOv5_adapter.py", line 92, in initialise
14:06:10:training_objectdetection_YOLOv5_adapter.py: self.dataset_creator = YoloV5DatasetCreator(self,
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/fiftyone_dataset_creator.py", line 30, in __init__
14:06:10:training_objectdetection_YOLOv5_adapter.py: self.init_fiftyone()
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/fiftyone_dataset_creator.py", line 54, in init_fiftyone
14:06:10:training_objectdetection_YOLOv5_adapter.py: shutil.rmtree(fiftyone_path)
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/lib/python3.8/shutil.py", line 709, in rmtree
14:06:10:training_objectdetection_YOLOv5_adapter.py: onerror(os.lstat, path, sys.exc_info())
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/lib/python3.8/shutil.py", line 707, in rmtree
14:06:10:training_objectdetection_YOLOv5_adapter.py: orig_st = os.lstat(path)
14:06:10:training_objectdetection_YOLOv5_adapter.py: FileNotFoundError: [Errno 2] No such file or directory: '/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/fiftyone'
14:06:10:training_objectdetection_YOLOv5_adapter.py: Initializing YoloV5DatasetCreator. Retries left: 1
14:06:10:training_objectdetection_YOLOv5_adapter.py: Traceback (most recent call last):
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/odm/database.py", line 200, in establish_db_conn
14:06:10:training_objectdetection_YOLOv5_adapter.py: _db_service = fos.DatabaseService()
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 80, in __init__
14:06:10:training_objectdetection_YOLOv5_adapter.py: self.start()
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 209, in start
14:06:10:training_objectdetection_YOLOv5_adapter.py: super().start()
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 118, in start
14:06:10:training_objectdetection_YOLOv5_adapter.py: + self.command,
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 238, in command
14:06:10:training_objectdetection_YOLOv5_adapter.py: DatabaseService.find_mongod(),
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/service.py", line 286, in find_mongod
14:06:10:training_objectdetection_YOLOv5_adapter.py: raise ServiceExecutableNotFound("Could not find `mongod`")
14:06:10:training_objectdetection_YOLOv5_adapter.py: fiftyone.core.service.ServiceExecutableNotFound: Could not find `mongod`
14:06:10:training_objectdetection_YOLOv5_adapter.py: During handling of the above exception, another exception occurred:
14:06:10:training_objectdetection_YOLOv5_adapter.py: Traceback (most recent call last):
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/fiftyone_dataset_creator.py", line 51, in init_fiftyone
14:06:10:training_objectdetection_YOLOv5_adapter.py: import fiftyone.zoo as foz
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/__init__.py", line 25, in
14:06:10:training_objectdetection_YOLOv5_adapter.py: from fiftyone.__public__ import *
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/__public__.py", line 16, in
14:06:10:training_objectdetection_YOLOv5_adapter.py: _foo.establish_db_conn(config)
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/bin/linux/python38/venv/lib/python3.8/site-packages/fiftyone/core/odm/database.py", line 206, in establish_db_conn
14:06:10:training_objectdetection_YOLOv5_adapter.py: raise FiftyOneConfigError(
14:06:10:training_objectdetection_YOLOv5_adapter.py: fiftyone.core.config.FiftyOneConfigError: MongoDB could not be installed on your system. Please define a `database_uri` in your `fiftyone.core.config.FiftyOneConfig` to connect to yourown MongoDB instance or cluster
14:06:10:training_objectdetection_YOLOv5_adapter.py: During handling of the above exception, another exception occurred:
14:06:10:training_objectdetection_YOLOv5_adapter.py: Traceback (most recent call last):
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/training_objectdetection_YOLOv5_adapter.py", line 92, in initialise
14:06:10:training_objectdetection_YOLOv5_adapter.py: self.dataset_creator = YoloV5DatasetCreator(self,
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/fiftyone_dataset_creator.py", line 30, in __init__
14:06:10:training_objectdetection_YOLOv5_adapter.py: self.init_fiftyone()
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/fiftyone_dataset_creator.py", line 54, in init_fiftyone
14:06:10:training_objectdetection_YOLOv5_adapter.py: shutil.rmtree(fiftyone_path)
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/lib/python3.8/shutil.py", line 709, in rmtree
14:06:10:training_objectdetection_YOLOv5_adapter.py: onerror(os.lstat, path, sys.exc_info())
14:06:10:training_objectdetection_YOLOv5_adapter.py: File "/usr/lib/python3.8/shutil.py", line 707, in rmtree
14:06:10:training_objectdetection_YOLOv5_adapter.py: orig_st = os.lstat(path)
14:06:10:training_objectdetection_YOLOv5_adapter.py: FileNotFoundError: [Errno 2] No such file or directory: '/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/fiftyone'
14:06:10:training_objectdetection_YOLOv5_adapter.py: An exception occurred initialising the module: [Errno 2] No such file or directory: '/usr/bin/codeproject.ai-server-2.6.4/modules/TrainingObjectDetectionYOLOv5/fiftyone'
codeproject is installed on the same mechine that agent dvr is
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Quote: Operating System: Linux (Ubuntu 24.04)
First: how is 24.04? I've not updated my system yet (waiting till August for RTM) but I'd be interested in your take.
Second, the error is that MongoDB can't be installed. I'm guessing this is related to 24.04 given the 24.04 release notes specifically state there will be app incompatibilities until full release in August.
cheers
Chris Maunder
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Hello,
I have been having this issue where I do not get motion alert at night, even though AI detects a car in my driveway at over 90%, but then it says "nothing found" and I do not get an alert.
My son comes home at night around 2am, so I am able to test this very regularly.
This only happens at night. My driveway is very well lit and very clear. Some nights it works fine and other nights it's a complete miss.
Why would AI not send an alert if it sees a car at 93%, but other other times it will send it?
My CPU hovers around 8-10% with 8 cameras and I use sub streams. I also have 16 Gigs of ram.. so I doubt it's a system limitation.
Any ideas?
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The first question I have is whether you are using BlueIris or AgentDVR. I use AgentDVR.
If you are using AgentDVR, you can check its logging around that time (2am or whenever the car shows up). See what the log says when the car arrives.
If the logs do not say anything about that time, it could be that the motion detection did not go off at all. This means that when the car arrived, the motion it caused on the camera was not enough to trigger the motion detection. In this case, you would need to increase the sensitivity of the motion detector for the camera so that it would trigger.
There could be other reasons too, but I think you can start with checking the log and seeing what it says. My suggestion above is based on my own experience.
And, your system is fine based on your description.
modified 3 days ago.
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I use Blue Iris. The other thing is sometimes it says nothing found, but other times it detects the car, but it will not send a motion alert. It's like nothing happened in front of the camera. It won't say nothing found or cancelled motion... just does nothing.
It seemed like it just completely ignored the motion, but you can clearly see it was tracking it using "Analyze with AI".
This is very odd, but like I said, it only happens at night.
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I see. Unfortunately, I don't know about BlueIris, so my suggestion might not help solve the problem. However, you could check the settings for the motion detector, the schedule of the detector (if there is one), the confidence setting of the AI, etc.
I think the best way to debug this is to test it on the spot. Have someone go in front of the camera to trigger the motion detector at the time when you are having problems with the detection, while another person is on BlueIris testing different settings to figure out what is wrong.
Hopefully it helps.
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This sounds like a Blue Iris issue, rather than a detection issue. Is that correct?
cheers
Chris Maunder
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I'm not sure. This is an example of AI "seeing" an object, but not firing an alert.
But other times, it just says.."nothing found".
I noticed it was seeing deer as sheep..so I added "sheep" to the settings, but I still not get alerts.
   
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I tend to see a large amount of failed inferences using the Coral ver 2.2.2 module as opposed to using the YOLOv5 .NET module. What are failed inferences? What does this mean?
Coral ver 2.2.2:
Module 'Object Detection (Coral)' 2.2.2 (ID: ObjectDetectionCoral)
Valid: True
Module Path: <root>\modules\ObjectDetectionCoral
AutoStart: True
Queue: objectdetection_queue
Runtime: python3.9
Runtime Loc: Local
FilePath: objectdetection_coral_adapter.py
Start pause: 1 sec
Parallelism: 16
LogVerbosity:
Platforms: all
GPU Libraries: installed if available
GPU Enabled: enabled
Accelerator:
Half Precis.: enable
Environment Variables
CPAI_CORAL_MODEL_NAME = MobileNet SSD
CPAI_CORAL_MULTI_TPU = true
MODELS_DIR = <root>\modules\ObjectDetectionCoral\assets
MODEL_SIZE = Small
Status Data: {
"inferenceDevice": "TPU",
"inferenceLibrary": "TF-Lite",
"canUseGPU": "false",
"successfulInferences": 22839,
"failedInferences": 9402,
"numInferences": 32241,
"averageInferenceMs": 7.730373483952888
}
Started: 08 May 2024 3:36:09 PM Central Standard Time
LastSeen: 09 May 2024 5:05:38 PM Central Standard Time
Status: Stopped
Requests: 32268 (includes status calls)
YOLOv5 .NET:
Module 'Object Detection (YOLOv5 .NET)' 1.10.0 (ID: ObjectDetectionYOLOv5Net)
Valid: True
Module Path: <root>\modules\ObjectDetectionYOLOv5Net
AutoStart: True
Queue: objectdetection_queue
Runtime: dotnet
Runtime Loc: System
FilePath: bin\ObjectDetectionYOLOv5Net.exe
Start pause: 1 sec
Parallelism: 0
LogVerbosity:
Platforms: all
GPU Libraries: installed if available
GPU Enabled: enabled
Accelerator:
Half Precis.: enable
Environment Variables
CUSTOM_MODELS_DIR = <root>\modules\ObjectDetectionYOLOv5Net\custom-models
MODELS_DIR = <root>\modules\ObjectDetectionYOLOv5Net\assets
MODEL_SIZE = MEDIUM
Status Data: {
"InferenceDevice": "GPU",
"inferenceLibrary": "DirectML",
"canUseGPU": true,
"successfulInferences": 62370,
"failedInferences": 0,
"numInferences": 62370,
"averageInferenceMs": 278,
"Histogram": {
"car": 112067,
"truck": 2855,
"person": 3756,
"bus": 166,
"bird": 6,
"motorcycle": 43,
"bicycle": 332,
"dog": 18,
"squirrel": 17,
"horse": 4,
"cow": 2,
"cat": 5,
"Car": 5310,
"Person": 81,
"Motorcycle": 3,
"Dog": 9
},
"NumItemsFound": 124674
}
Started: 10 May 2024 6:30:55 AM Central Standard Time
LastSeen: 11 May 2024 2:21:28 PM Central Standard Time
Status: Started
Requests: 62372 (includes status calls)
For reference, System Info:
Server version: 2.6.2
System: Windows
Operating System: Windows (Microsoft Windows 10.0.19045)
CPUs: Intel(R) Core(TM) i5-7500 CPU @ 3.40GHz (Intel)
1 CPU x 4 cores. 4 logical processors (x64)
GPU (Primary): Intel(R) HD Graphics 630 (1,024 MiB) (Intel Corporation)
Driver: 31.0.101.2111
System RAM: 16 GiB
Platform: Windows
BuildConfig: Release
Execution Env: Native
Runtime Env: Production
Runtimes installed:
.NET runtime: 7.0.5
.NET SDK: Not found
Default Python: Not found
Go: Not found
NodeJS: Not found
Rust: Not found
Video adapter info:
Intel(R) HD Graphics 630:
Driver Version 31.0.101.2111
Video Processor Intel(R) HD Graphics Family
System GPU info:
GPU 3D Usage 4%
GPU RAM Usage 0
Global Environment variables:
CPAI_APPROOTPATH = <root>
CPAI_PORT = 32168
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The difference between Failed Inferences in the Coral module and the YOLOv5 .NET module is dramatic.
Coral = ~18% of total Failed.
Yolo = 0 failed out of 150,000 total inferences.
This leads me to not use the Coral module, as it indicates there is something wrong.
Again, what does failed inferences mean? How does the program identify an inference as failed? Does anyone know?
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Just to confirm, are you using a Small model size for Coral in all these tests, and Medium for YOLOv5.NET?
Also, are you using other software with this, like Blue Iris?
Thanks,
Sean Ewington
CodeProject
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Blue Iris latest stable version.
Coral small
YOLOv5 .NET medium
In ver. 2.6.2, with the Coral I tried changing models, and changing to medium from small.
This just resulted in apparent crashes of the module, and a lot of difficulty getting results to Blue Iris. Also, during that period, trying to make those changes, I kept losing the TPU Multi TPU setting. I have uninstalled and reinstalled CPAI 2.6.2 with no good results, and also uninstalled and reinstalled the Coral version 2.2.2 module with Do not use download cache checked.
I may be imagining things, but I thought I saw a period of good results with the Coral ver 2.2.0 module, but I can't seem to get back to it.
For me, the Yolo modules with ipcam-combined and ipcam-dark work pretty well.
I suppose I should download the current code and see if I can figure out what increments the failed inferences counter. It must be in there somewhere.
In some discussions with @mailseth, he asked if I see anything in the logs. I looked, but replied that nothing jumps out at me. Of course, I don't know what I'm looking for.
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Coral on Windows is just not great. There's no getting around it I'm sorry. Failed inferences could be anything from the device overheating and shutting down, to a memory issue, to the USB driver failing. I constantly have issues on Windows with Coral, far less so on Linux (or even macOS 11).
cheers
Chris Maunder
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The Coral accelerator in the Windows Blue Iris Code Project machine is an M.2, not USB.
My impression is that although it is very fast, accuracy seems poor, and lack of any custom models, it finds everything, even if it's wrong.
My Coral USB device on the Ubuntu machine has been flawless with Frigate NVR for about 5 months. It is the only detector specified, so I know it is working.
My big gripe with Coral is that the test program that you are supposed to use according to the installation web page will not run without an older version of py and pycoral. (I think. I haven't tried it for several months.) I like to be able to use the manufacturer's diagnostics to verify functionality.
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I am seeing the YOLOv5 .NET module displaying CPU (DirectML) despite the module info screen showing that GPU is enabled. Did I read somewhere that there is something wrong in the code that displays CPU instead of GPU on the status screen? I don't remember where I heard this.
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Thanks very much for your report. Could you please share your System Info tab from your CodeProject.AI Server dashboard?
Thanks,
Sean Ewington
CodeProject
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