Getting Started Guide

Time to Complete: 30 minutes
Programming Language: Python3, Bash

Prerequisites for Target System

Intel® Core™ processor
At least 16 GB RAM
At least 64 GB hard drive
An Internet connection
Docker*
Docker Compose* v2 (Optional)
Git*
Ubuntu* LTS Boot Device

If Ubuntu is not installed on the target system, follow the instructions and install Ubuntu.

Install Automated Self-Checkout Package Software

Do the following to install the software package:

Download the reference implementation package: Automated Self-Checkout Retail Reference Implementation.
Open a new terminal and navigate to the download folder to unzip the automated-self-checkout package:
```
   unzip automated-self-checkout.zip
```
Navigate to the automated-self-checkout/ directory:
```
 cd automated-self-checkout
```
Change permission of the executable edgesoftware file:
```
   chmod 755 edgesoftware
```
Install the package:
```
   ./edgesoftware install
```
You will be prompted for the Product Key during the installation. The Product Key is in the email you received from Intel confirming your download.

When the installation is complete, you will see the message “Installation of package complete” and the installation status for each module.

If the installation fails because of proxy-related issues, follow the troubleshooting steps.

Run and Evaluate Pre-Configured Pipelines

In a retail environment, self-checkout solutions analyze video streams from multiple cameras to streamline the checkout process. The system detects and classifies products as items are scanned. Barcode and text recognition ensure accuracy. This data is processed to verify purchases and update inventory in real time. Factors such as latency and frames per second (FPS) help assess the automated self-checkout solution's real-time responsiveness and efficiency.

This demonstration shows how to run the pre-configured pipeline, view a simulation that detects and tracks objects, and check the pipeline's status.

Step 1: Run Pipeline

Do the following to run the pre-configured pipeline:

Navigate to the automated-self-checkout directory:
```
   cd automated-self-checkout
```
Modify the following host IP addresses to match the IP address of the system running the reference implementation:
- HOST_IP and RSTP_CAMERA_IP in the src/pipeline-server/.env file.
- host_ip in the src/pipeline-server/postman/env.json file.
Run the pipeline server:
```
   make run-pipeline-server
```
The containers will start to run.

Step 2: Launch Grafana Dashboard

Do the following to launch the Grafana* dashboard to view the objects being detected and tracked:

Open a web browser and enter the following URL to access the Grafana dashboard: http://<target_system_IP>:3000.

To get <target_system_IP>, run the hostname -I command.
When prompted, provide the following credentials:
- Username: root
- Password: evam123
On the dashboard, go to Menu > Home, and select Video Analytics Dashboard.

The dashboard visualizes the object detection and tracking pipelines. The bounding boxes around the products indicate their detection and tracking. The dashboard also shows the active streams and their corresponding average FPS.

Step 3: Check Pipeline Status

Do the following to check the metrics:

Check whether the docker containers are running:

   docker ps --format 'table{{.Names}}\t{{.Image}}\t{{.Status}}'

Check the MQTT inference output:

   mosquitto_sub -v -h localhost -p 1883 -t 'AnalyticsData0'
   mosquitto_sub -v -h localhost -p 1883 -t 'AnalyticsData1'
   mosquitto_sub -v -h localhost -p 1883 -t 'AnalyticsData2'

Here is the result for AnalyticsData0:

   AnalyticsData0 {"objects":[{"detection":{"bounding_box":{"x_max":0.3163176067521043,"x_min":0.20249048400491532,"y_max":0.7995593662281202,"y_min":0.12237883070032396},"confidence":0.868196964263916,"label":"bottle","label_id":39},"h":731,"region_id":6199,"roi_type":"bottle","w":219,"x":389,"y":132},{"detection":{"bounding_box":{"x_max":0.7833052431819754,"x_min":0.6710088227893136,"y_max":0.810283140877349,"y_min":0.1329853767638305},"confidence":0.8499506711959839,"label":"bottle","label_id":39},"h":731,"region_id":6200,"roi_type":"bottle","w":216,"x":1288,"y":144}],"resolution":{"height":1080,"width":1920},"tags":{},"timestamp":67297301635}

   AnalyticsData0 {"objects":[{"detection":{"bounding_box":{"x_max":0.3163306922646063,"x_min":0.20249845268772138,"y_max":0.7984013488063937,"y_min":0.12254781445953},"confidence":0.8666459321975708,"label":"bottle","label_id":39},"h":730,"region_id":6201,"roi_type":"bottle","w":219,"x":389,"y":132},{"detection":{"bounding_box":{"x_max":0.7850104587729607,"x_min":0.6687324296210857,"y_max":0.7971464600783804,"y_min":0.13681757042794374},"confidence":0.8462932109832764,"label":"bottle","label_id":39},"h":713,"region_id":6202,"roi_type":"bottle","w":223,"x":1284,"y":148}],"resolution":{"height":1080,"width":1920},"tags":{},"timestamp":67330637174}

Check the pipeline status:

   ./src/pipeline-server/status.sh

The pipeline status should be like:

   --------------------- Pipeline Status ---------------------
   ----------------8080----------------
   [
   {
         "avg_fps": 11.862402507697258,
         "avg_pipeline_latency": 0.5888091060475129,
         "elapsed_time": 268.07383918762207,
         "id": "95204aba458211efa9080242ac180006",
         "message": "",
         "start_time": 1721361269.6349292,
         "state": "RUNNING"
   }
   ]

The pipeline status displays the average FPS and average pipeline latency, among other metrics.

Stop the services:
```
   make down-pipeline-server
```

Summary

In this get started guide, you learned how to:

Install the automated self-checkout package software.
Verify the installation.
Run pre-configured pipelines, visualize object detection and tracking, and extract data from them.

Learn More

To apply custom environment variables, see Advanced Settings.
To evaluate the pipeline system performance across different hardware, see Test Performance.

Troubleshooting

Issues with Docker Installation

If you are behind a proxy and if you experience connectivity issues, the Docker installation might fail. Do the following to install Docker manually:

Install Docker from a package.
Complete the post-installation steps to manage Docker as a non-root user.
Configure the Docker CLI to use proxies.

Error Logs

To access the Docker Logs for EVAM server 0, run the following command:

   docker logs evam_0

Here is an example of the error log when the RSTP stream is unreachable for a pipeline:

   {"levelname": "INFO", "asctime": "2024-07-31 23:26:47,257", "message": "===========================", "module": "pipeline_manager"}
   {"levelname": "INFO", "asctime": "2024-07-31 23:26:47,257", "message": "Completed Loading Pipelines", "module": "pipeline_manager"}
   {"levelname": "INFO", "asctime": "2024-07-31 23:26:47,257", "message": "===========================", "module": "pipeline_manager"}
   {"levelname": "INFO", "asctime": "2024-07-31 23:26:47,330", "message": "Starting Tornado Server on port: 8080", "module": "__main__"}
   {"levelname": "INFO", "asctime": "2024-07-31 23:26:51,177", "message": "Creating Instance of Pipeline detection/yolov5", "module": "pipeline_manager"}
   {"levelname": "INFO", "asctime": "2024-07-31 23:26:51,180", "message": "Gstreamer RTSP Server Started on port: 8555", "module": "gstreamer_rtsp_server"}
   {"levelname": "ERROR", "asctime": "2024-07-31 23:26:51,200", "message": "Error on Pipeline 5d5b3b0a4f9411efb60d0242ac120007: gst-resource-error-quark: Could not open resource for reading. (5): ../gst/rtsp/gstrtspsrc.c(6427): gst_rtspsrc_setup_auth (): /GstPipeline:pipeline3/GstURISourceBin:source/GstRTSPSrc:rtspsrc0:\nNo supported authentication protocol was found", "module": "gstreamer_pipeline"}

Known Issues

For the list of known issues, see known issues.