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Codeproject.AI

Codeproject.AI

CodeProject.AI Server is a self-hosted, free and Open Source Artificial Intelligence Server for any platform, any language. Just like you would install a database server to provide data storage, you install CodeProject.AI Server to provide AI services.

It can be installed locally, requires no off-device or out of network data transfer, and is easy to use.

Head over to the CodeProject.AI Documentation for installation instructions.

Configuration

Configuration example
codeprojectai:
host: codeprojectai
port: 32168
object_detector:
cameras:
viseron_CHANGEME_camera:
fps: 1
log_all_objects: true
labels:
- label: person
confidence: 0.8
trigger_recorder: true
- label: cat
confidence: 0.8
zones:
- name: zone1
coordinates:
- x: 0
y: 500
- x: 1920
y: 500
- x: 1920
y: 1080
- x: 0
y: 1080
labels:
- label: person
confidence: 0.8
trigger_recorder: true
mask:
- coordinates:
- x: 400
y: 200
- x: 1000
y: 200
- x: 1000
y: 750
- x: 400
y: 750
camera_two:
fps: 1
labels:
- label: person
confidence: 0.7
trigger_recorder: true
- label: cat
confidence: 0.8
trigger_recorder: false
face_recognition:
save_unknown_faces: true
cameras:
camera_two:
labels:
- person
license_plate_recognition:
cameras:
camera_one:
labels:
- vehicle
- car
- truck
known_plates:
- ABC123
codeprojectaimap required
CodeProject.AI configuration.

Face recognition

Face recognition runs as a post processor when a specific object is detected.

Labels

Labels are used to tell Viseron when to run a post processor.

Any label configured under the object_detector for your camera can be added to the post processors labels section.

note

Only objects that are tracked by an object_detector can be sent to a post_processor. The object also has to pass all of its filters (confidence, height, width etc).

Train

On startup images are read from face_recognition_path and a model is trained to recognize these faces.
The folder structure of the faces folder is very strict. Here is an example of the default one:

/config
|── face_recognition
| └── faces
| ├── person1
| | ├── image_of_person1_1.jpg
| | ├── image_of_person1_2.png
| | └── image_of_person1_3.jpg
| └── person2
| | ├── image_of_person2_1.jpeg
| | └── image_of_person2_2.jpg
danger

You need to follow this folder structure, otherwise training will not be possible.

License plate recognition

License plate recognition runs as a post processor when a specific object is detected.

You can track known license plates by adding them to the known_plates list in the configuration. Known plates will be reported as binary sensors.

There is also a sensor entity that reports all the plates that have been detected. This can be used to trigger actions in other platforms, eg Home Assistant, when an unknown plate is detected.

Object detector

An object detector scans an image to identify multiple objects and their position.

tip

Object detectors can be taxing on the system, so it is wise to combine it with a motion detector

Labels

Labels are used to tell Viseron what objects to look for and keep recordings of. The available labels depends on what detection model you are using.

The max/min width/height is used to filter out any unreasonably large/small objects to reduce false positives.
Objects can also be filtered out with the use of an optional mask.

Zones

Zones are used to define areas in the cameras field of view where you want to look for certain objects (labels).
Say you have a camera facing the sidewalk and have labels setup to record the label person.
This would cause Viseron to start recording people who are walking past the camera on the sidewalk. Not ideal.
To remedy this you define a zone which covers only the area that you are actually interested in, excluding the sidewalk.

codeprojectai:
object_detector:
cameras:
camera_one:
...
zones:
- name: sidewalk
coordinates:
- x: 522
y: 11
- x: 729
y: 275
- x: 333
y: 603
- x: 171
y: 97
labels:
- label: person
confidence: 0.8
trigger_recorder: true
tip

See Mask for how to get the coordinates for a zone.

Mask

Masks are used to exclude certain areas in the image from object detection. If a detected object has its lower portion inside of the mask it will be discarded.

The coordinates form a polygon around the masked area.
To easily generate coordinates you can use a tool like image-map.net.
Just upload an image from your camera, choose the Poly shape and start drawing your mask.
Then click Show me the code! and adapt it to the config format.
Coordinates coords="522,11,729,275,333,603,171,97" should be turned into this:

codeprojectai:
object_detector:
cameras:
camera_one:
...
mask:
- coordinates:
- x: 522
y: 11
- x: 729
y: 275
- x: 333
y: 603
- x: 171
y: 97

Paste your coordinates here and press Get config to generate a config example

Troubleshooting

To enable debug logging for codeprojectai, add the following to your config.yaml
/config/config.yaml
logger:
logs:
viseron.components.codeprojectai: debug