Checking The Output
After we deploy the application, we can check the output HDMI output or use Amazon CloudWatch Logs.
For more information, see Setting up the AWS Panorama Appliance Developer Kit or Viewing AWS Panorama event logs in CloudWatch Logs, respectively.
If we have an HDMI output connected to the device, we should see the output from the device on the HDMI.
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Computer Vision Model
A CV model helps us extract useful information from images and video frames.
We can detect and localize objects in a scene, and identity and classify images and action recognition.
You can choose from a variety of frameworks such as TensorFlow, MXNet, and PyTorch to build your CV models, or you can choose from a variety of pre-trained models availa.
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Conclusion
The applications of computer vision at the edge are only now being imagined and built out.
As a data scientist, I’m very excited to be innovating in lockstep with AWS Panorama customers to help you ideate and build CV models that are uniquely tailored to solve your problems.
And we’re just scratching the surface of what’s possible with CV at the ed.
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Creating The Application
To create your application, complete the following steps: 1.
On the AWS Panorama console, choose My applications.
2) Choose Create application.
1) Choose Begin creation.
1) For Name, enter car_counter.
2) For Description, enter an optional description.
3) Choose Next.
1) Click Choose model.
1) For Model artifact path, enter the model S3 URI.
2) For.
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Deploying Your Application
To deploy your new application, complete the following steps:.
1) Choose Choose appliance.
1) Choose the appliance you created.
2) Choose Choose camera streams.
1) Select your camera stream.
1) Choose Deploy.
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Extending The Solution
We can do so much more with this application and extend it to other parking-related use cases, such as the following:.
1) Parking lot routing– Where are the vacant parking spots?.
2) Parking lot monitoring– Are cars parked in appropriate spots.
Are they too close to each other.
You can integrate these use cases with other AWS services like QuickSight.
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How do I use API-driven computer vision services?
Integrate API-driven computer vision services into any application or use pre-trained models.
For example, integrate a facial recognition API into an existing application to easily add visual analysis features.
Only pay for the number of images or minutes of video you analyze and the metadata you store.
No minimum fees or upfront commitments.
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Parking Lot Car Counter Application
Parking facilities, like the one in the image below, need to know how many cars are parked in a given facility at any point of time, to assess vacancy and intake more customers.
You also want to keep track of the number of cars that enter and exit your facility during any given time.
You can use this information to improve operations, such as addin.
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The Business Logic Code
After we upload the model artifacts to an S3 bucket, let’s turn our attention to the business logic code.
For more information about the sample developer code, see Sample application code.
For a comparative example of code samples, see AWS Panorama People Counter Exampleon GitHub.
Before we look at the full code, let’s look at a skeleton of the bus.
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Uploading The Model Artifacts
We need to upload the model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket.
The bucket name should have aws-panorama- in the beginning of the name.
After downloading the model artifacts, we upload the ssd_512_resnet50_v1_voc.tar.gzfile to the S3 bucket.
To create your bucket, complete the following steps:.
1) Download the model art.
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What are computer vision services on AWS?
Computer vision services on AWS range from API-driven services that require no machine learning experience, to customizable deep learning frameworks for machine learning practitioners.
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What is AWS & how does it work?
AWS provides the broadest and most complete set of artificial intelligence and machine learning (AI/ML) services connected to a comprehensive set of data sources for customers of all levels of expertise.
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What is computer vision & how does it work?
Computer vision allows machines to identify people, places, and things in images with accuracy at or above human levels with much greater speed and efficiency.
Often built with deep learning models, it automates extraction, analysis, classification and understanding of useful information from a single image or a sequence of images.
