VisionAI¶
Documentation for VisionAI toolkit.
Overview¶
VisionAI provides a set of command line utilities for you to manage different Vision AI scenarios that have been pre-developed and pre-tested. VisionAI focuses on workplace health and safety models - and majority of the models you see here have been developed with that in mind.
These are production-ready model trained from open-source and academic datasets. We are continuously working on new scenarios - and our current scenario repo consists of over 60 scenarios that are listed here. They are developed with the intent of being easy-to-use for business. The framework also supports a whole bunch of custom scenarios.
Install VisionAI¶
Install VisionAI application through PyPI. There are other options available for install - including a Docker container option. These are detailed in installation section.
Deploy to Azure¶
Deploy a fully configured and tested solution directly from Azure Marketplace. VisionAI runs computer vision models, most of which run orders of magnitude faster if executed on a GPU machine. Our Azure Marketplace offer VisionAI Community Edition is available through Azure Marketplace here. The community edition deploys a fully configured Virtual Machine with the recommended hardware and software options. Get more details here.
- TODO: Point to ARM template that needs to be deployed (using these instructions and here is an example JSON file).
List available Scenarios¶
VisionAI is organized in terms of scenarios. Consider each scenario as being a business use-case, that is solved by a combination of Machine Learning models and an inference algorithm. For example Warn me when max occupancy of this area exceeds 80 people is a business scenario, where as the People detection is an ML model.
VisionAI supports 60 scenarios currently and more are being added continuously. Our current focus is on Workplace Safety scenarios. Please contact us if a scenario you need is not present in our repo and we will look into it.
$ visionai scenarios list
------------------------------------------------
Privacy Suite
blur-faces
blur-text
Fire safety
early-smoke-and-fire-detection
smoking-and-vaping-detection
Personnel safety
ppe-detection
pfas-system-detection
railings-detection
Suspicious activity
shipping-activity-detection
agressive-behaivior
Compliance Policies
max-occupancy
Equipment
rust-and-corrosion-detection
IR Camera
temperature-monitoring
------------------------------------------------
✨ More scenarios are added regularly ✨
Get details for a Scenario¶
You can get details about a scenario using visionai scenario details command. Specify the scenario you want additional details for. The details of a scenario include the dataset size, model accuracy metrics,
$ visionai scenario --name early-smoke-and-fire-detection details
------------------------------------------------
Category: Fire safety
Scenario: early-smoke-and-fire-detection
This scenario has been trained on open-source datasets consisting of 126,293 images. The datasets images are primarily outdoors (70%), but do contain a good number of indoor images (30%). There is a ~50-50% mix of day vs night images. You can find more details about this scenario at visionify.ai/early-smoke-and-fire-detection.
Model: smoke-and-fire-detection-1.0.1.pt
Model size: 127MB
Model type: Object Detection
Framework: PyTorch
Model performance:
Dataset size: 126,293 images
Accuracy: 94.1%
Recall: 93%
F1-Score: 93.5%
Events:
smoke-detected | Immediate
fire-detected | Immediate
Event examples:
{
"scenario": "smoke-and-fire-detection",
"event_name": "smoke-detected",
"event_details": {
"camera": "camera-01",
"date": "2023-01-04 11:05:02",
"confidence": 0.92
}
}
------------------------------------------------
Run a Scenario¶
Use visionai run command to run a scenario. In its simplest sense, you can run a single scenario on your web-cam. In a more complex use-case, you can specify a pipeline of scenarios, configure notification logic for each scenario, timings for each scenario etc.
Get help on commands¶
You can get more help on any command by adding --help at the end of the command. For example, if you want to get details about pipeline commands, you can run the following commands.
$ visionai pipeline --help
Usage: visionai pipeline [OPTIONS] COMMAND [ARGS]...
Manage pipelines
Pipeline is a sequence of preprocess routines and
scenarios to be run on a given set of cameras. Each
pipeline can be configured to run specific scenarios -
each scenario with their own customizations for event
notifications. This module provides robust methods for
managing pipelines, showing their details, adding/remove
cameras from pipelines and running a pipeline.
╭─ Options ────────────────────────────────────────────────╮
│ --help Show this message and exit. │
╰──────────────────────────────────────────────────────────╯
╭─ Commands ───────────────────────────────────────────────╮
│ add-camera Add a camera to a pipeline │
│ add-preprocess Add a preprocess routine to a pipeline │
│ add-scenario Add a scenario to a pipeline │
│ create Create a named pipeline │
│ remove-camera Remove a camera from a pipeline │
│ reset Reset the pipeline to original state. │
│ run Run a pipeline of scenarios on given │
│ cameras │
│ show Show details of a pipeline │
╰──────────────────────────────────────────────────────────╯
$ visionai pipeline add-scenario --help
Usage: visionai pipeline add-scenario [OPTIONS]
Add a scenario to a pipeline
The order of the scenarios does not matter. All added
scenarios are run in different threads. All scenarios are
run after pre-processing stage is done.
``` Ex: visionai pipeline --name test_pipe add-scenario
--name smoke-and-fire visionai pipeline --name test_pipe
add-scenario --name ppe-detection visionai pipeline --name
test_pipe run ```
@arg pipeline - specify a named pipeline @arg scenario -
specify name of the scenario to run
@return None
╭─ Options ────────────────────────────────────────────────╮
│ * --pipeline TEXT pipeline name [default: None] │
│ [required] │
│ * --scenario TEXT scenario to add │
│ [default: None] │
│ [required] │
│ --help Show this message and exit. │
╰──────────────────────────────────────────────────────────╯
Next steps¶
Congratulations! You have successfully run the first scenario. Now go through tutorials to learn about how to run multiple scnearios, how to configure each scenario for the events you need, how to set up the dependencies etc.
Or you can also go through our scenarios page to explore the different scenarios available and their model details. If you have a need for a scenario to be implemented, do not hesitate to submit a request.