Change Video Source In Vision AI Detection Apps#

Typically, a pipeline is started with a cURL request with JSON payload containing source, destination and parameters. For example, the following cURL request start an AI pipeline on a file inferencing on either the pallet defect detection model or the PCB anomaly detection model.

Note

If you are running multiple instances of the application, ensure to provide NGINX_HTTPS_PORT number in the URL for the app instance, i.e., replace <HOST_IP> with <HOST_IP>:<NGINX_HTTPS_PORT>. If you are running a single instance and using an NGINX_HTTPS_PORT other than the default 443, replace <HOST_IP> with <HOST_IP>:<NGINX_HTTPS_PORT>.

Pallet Defect Detection

curl -k https://<HOST_IP>/api/pipelines/user_defined_pipelines/<pipeline_name> -X POST -H 'Content-Type: application/json' -d '{
  "source": {
     "uri": "file:///home/pipeline-server/resources/videos/warehouse.avi",
     "type": "uri"
  },
  "parameters": {
     "detection-properties": {
        "model": "/home/pipeline-server/resources/models/pallet-defect-detection/deployment/Detection/model/model.xml",
        "device": "CPU"
     }
  }
}'

PCB Anomaly Detection

curl -k https://<HOST_IP>/api/pipelines/user_defined_pipelines/<pipeline_name> -X POST -H 'Content-Type: application/json' -d '{
  "source": {
     "uri": "file:///home/pipeline-server/resources/videos/anomalib_pcb_test.avi",
     "type": "uri"
  },
  "parameters": {
     "classification-properties": {
        "model": "/home/pipeline-server/resources/models/pcb-anomaly-detection/deployment/Anomaly classification/model/model.xml",
        "device": "CPU"
     }
  }
}'

To change the input video source for the pipeline, refer to the following table:

Pallet Defect Detection

Video Source	Source Section of the cURL Request	Remark
File	`”source”: { “uri”: “file://path”, “type”: “uri” }`
RTSP	`”source”: { “uri”: “rtsp://url”, “type”: “uri” }`	In the .env file at the root of the repository for Docker Compose based deployment, update RTSP_CAMERA_IP to the IP of the machine where the RTSP stream is coming from: RTSP_CAMERA_IP=<IP_where_RTSP_stream_is_originating_from>
Web Camera	`”source”: { “device”: “/dev/video0”, “type”: “webcam” }`	The pipeline in pipeline-server-config.json needs to be changed as follows: `”pipeline”: “v4l2src device=/dev/video0 name=source ! video/x-raw,format=YUY2 ! videoconvert ! video/x-raw,format=RGB ! gvadetect name=detection model-instance-id=inst0 ! queue ! gvawatermark ! gvafpscounter ! appsink name=destination”,` `

PCB Anomaly Detection

Video Source	Source Section of the cURL Request	Remark
File	`”source”: { “uri”: “file://path”, “type”: “uri” }`
RTSP	`”source”: { “uri”: “rtsp://url”, “type”: “uri” }`	In the .env file at the root of the repository for docker compose based deployment, update RTSP_CAMERA_IP to the IP of the machine where the RTSP stream is coming from: RTSP_CAMERA_IP=<IP_where_RTSP_stream_is_originating_from>
Web Camera	`”source”: { “device”: “/dev/video0”, “type”: “webcam” }`	The pipeline in pipeline-server-config.json needs to be changed as follows: `”pipeline”: “v4l2src device=/dev/video0 name=source ! video/x-raw,format=YUY2 ! videoconvert ! video/x-raw,format=RGB ! gvaclassify name=classification model-instance-id=inst0 ! queue ! gvawatermark ! gvafpscounter ! appsink name=destination”,` `