Using the solution

Batch processing inference against a set of image files

The client application also supports batch processing, as a handy way to test trained ML models against a large set of images. In batch processing mode, the flow is as follows:

The user prepares a set of images to run inference against. These are typically images saved from the camera earlier, with known labels - i.e, known to be good and known to be defective examples
The user stores the images on the VIAI Edge server, in a directory that the camera client application can access
The user runs the camera client in batch mode against the root directory that contains the images
The application finds all image files inside the directory and its subdirectories
The application forwards each image in turn to the VIAI ML model and gets the inference results
Optionally, the inference results can be forwarded to an MQTT message queue, to be received by another host and/or for the results to be redirected to a text file for further analysis

The following steps will run batch mode inference against a set of images.

Run on Edge Server

Log in to the VIAI Edge server as user viai-admin
Check which physical volumes are mounted to the camera application
```
 kubectl -n ${NAMESPACE} get pods

 kubectl -n ${NAMESPACE} describe pod viai-edge-camera-integration-<suffix>

 kukbectl describe pv <pv-id>
```
You should see a mount point similar to: /mnt/localpv-share/pvs/3/ which is mounted as /var/lib/viai/camera-data inside the camera client container.
Copy your testing image dataset to the VIAI Edge server, to the directory found above

 kubectl exec -it viai-camera-integration -- /bin/bash

Verify that the container can see the image files
```
 ls -l /var/lib/viai/camera-data/
```
(Reccomended) On another host, start receiving ML inference results, by subscribing to an MQTT topic

Run on Setup Workstation
```
 mosquitto_sub -h 192.168.1.24 -t viai/results
```
Where 192.168.1.24 is the IP address of the mosquitto service running in Kubernetes on the same server.

You can find the mosquitto service IP and ML model service name with:
```
 kubectl -n ${NAMESPACE} get services
```
Start batch processing all images in the dataset, and forwarding the inference results to MQTT

Run on Edge Server
```
 python3 camera_client.py --protocol file --address /var/lib/viai/camera-data/good/ --device_id good_batch --ml \
     --ml_host <ml-model-service-name> --ml_port 8602 --mode batch --mqtt --mqtt_host ${MQTT_HOST}
```
You should see the camera application iterate through all the input files, and in the second host mosquitto_sub window, you should start receiving the ML model inference result JSON records.

You can redirect the results to a text file on the second host, by modifying the mosquitto_sub command as follows:
```
 mosquitto_sub -h 192.168.1.24 -t viai/results >> good_batch_model-version-xyz.txt
```