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[DOCS] Fixing formatting in Pallet Defect Detection docs (open-edge-platform#1534)
Signed-off-by: Sebastian Golebiewski <sebastianx.golebiewski@intel.com>
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‎manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/environment-variables.md‎

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@@ -7,8 +7,7 @@ This reference application's configuration has the following environment variabl
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- **APP_DIR**
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- (String). Optional. Refers to absolute path to the sample app directory. It gets auto-populated during app installation.
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In addtion to the ones above, the application also uses environment variables of following two Microservices:
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In addition to the ones above, the application also uses environment variables of following two Microservices:
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1. DL Streamer Pipeline Server
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- DL Streamer Pipeline Server reference document on environment variables is present [here](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer-pipeline-server/environment-variables.html)

‎manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/get-started.md‎

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# Get Started
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- **Time to Complete:** 30 minutes
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- **Programming Language:** Python 3
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- **Time to Complete:** 30 minutes
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- **Programming Language:** Python 3
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## Prerequisites
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1. Clone the **edge-ai-suites** repository and change into industrial-edge-insights-vision directory. The directory contains the utility scripts required in the instructions that follows.
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```bash
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git clone https://github.com/open-edge-platform/edge-ai-suites.git
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cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/
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```
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```bash
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git clone https://github.com/open-edge-platform/edge-ai-suites.git
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cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/
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```
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2. Set app-specific environment variable file
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```bash
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cp .env_pallet_defect_detection .env
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```
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2. Set app-specific environment variable file
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3. Edit the below mentioned environment variables in the `.env` file as follows:
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```bash
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cp .env_pallet_defect_detection .env
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```
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```bash
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HOST_IP=<HOST_IP> # IP address of server where DL Streamer Pipeline Server is running.
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3. Edit the below mentioned environment variables in the `.env` file as follows:
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MR_PSQL_PASSWORD= #PostgreSQL service & client adapter e.g. intel1234
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```bash
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HOST_IP=<HOST_IP> # IP address of server where DL Streamer Pipeline Server is running.
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MR_MINIO_ACCESS_KEY= # MinIO service & client access key e.g. intel1234
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MR_MINIO_SECRET_KEY= # MinIO service & client secret key e.g. intel1234
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MR_PSQL_PASSWORD= #PostgreSQL service & client adapter e.g. intel1234
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MTX_WEBRTCICESERVERS2_0_USERNAME=<username> # WebRTC credentials e.g. intel1234
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MTX_WEBRTCICESERVERS2_0_PASSWORD=<password>
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MR_MINIO_ACCESS_KEY= # MinIO service & client access key e.g. intel1234
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MR_MINIO_SECRET_KEY= # MinIO service & client secret key e.g. intel1234
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# application directory
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SAMPLE_APP=pallet-defect-detection
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```
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MTX_WEBRTCICESERVERS2_0_USERNAME=<username> # WebRTC credentials e.g. intel1234
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MTX_WEBRTCICESERVERS2_0_PASSWORD=<password>
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4. Install the pre-requisites. Run with sudo if needed.
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# application directory
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SAMPLE_APP=pallet-defect-detection
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```
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```bash
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./setup.sh
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```
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4. Install the pre-requisites. Run with sudo if needed.
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This script sets up application pre-requisites, download artifacts, sets executable permissions for scripts etc. Downloaded resource directories are made available to the application via volume mounting in docker compose file automatically.
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```bash
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./setup.sh
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```
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This script sets up application pre-requisites, download artifacts, sets executable permissions for scripts etc. Downloaded resource directories are made available to the application via volume mounting in docker compose file automatically.
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## Deploy the Application
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5. Start the Docker application:
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1. Start the Docker application:
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The Docker daemon service should start automatically at boot. If not, you can start it manually:
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```bash
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sudo systemctl start docker
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```
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docker compose up -d
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```
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6. Fetch the list of pipeline loaded available to launch:
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```bash
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./sample_list.sh
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```
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This lists the pipeline loaded in DL Streamer Pipeline Server.
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Example Output:
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```bash
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# Example output for Pallet Defect Detection
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Environment variables loaded from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/.env
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Running sample app: pallet-defect-detection
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Checking status of dlstreamer-pipeline-server...
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Server reachable. HTTP Status Code: 200
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Loaded pipelines:
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[
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...
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{
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"description": "DL Streamer Pipeline Server pipeline",
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"name": "user_defined_pipelines",
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"parameters": {
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"properties": {
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"detection-properties": {
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"element": {
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"format": "element-properties",
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"name": "detection"
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}
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}
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},
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"type": "object"
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},
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"type": "GStreamer",
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"version": "pallet_defect_detection"
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}
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...
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]
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```
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7. Start the sample application with a pipeline.
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```bash
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./sample_start.sh -p pallet_defect_detection
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```
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This command will look for the payload for the pipeline specified in the `-p` argument above, inside the `payload.json` file and launch a pipeline instance in DL Streamer Pipeline Server. Refer to the table, to learn about different available options.
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> **IMPORTANT**: Before you run `sample_start.sh` script, make sure that
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> `jq` is installed on your system. See the
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> [troubleshooting guide](./troubleshooting-guide.md#unable-to-parse-json-payload-due-to-missing-jq-package)
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> for more details.
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Output:
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```bash
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# Example output for Pallet Defect Detection
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Environment variables loaded from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/.env
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Running sample app: pallet-defect-detection
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Checking status of dlstreamer-pipeline-server...
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Server reachable. HTTP Status Code: 200
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Loading payload from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/apps/pallet-defect-detection/payload.json
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Payload loaded successfully.
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Starting pipeline: pallet_defect_detection
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Launching pipeline: pallet_defect_detection
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Extracting payload for pipeline: pallet_defect_detection
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Found 1 payload(s) for pipeline: pallet_defect_detection
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Payload for pipeline 'pallet_defect_detection' {"source":{"uri":"file:///home/pipeline-server/resources/videos/warehouse.avi","type":"uri"},"destination":{"frame":{"type":"webrtc","peer-id":"pdd"}},"parameters":{"detection-properties":{"model":"/home/pipeline-server/resources/models/pallet-defect-detection/model.xml","device":"CPU"}}}
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Posting payload to REST server at https://<HOST_IP>/api/pipelines/user_defined_pipelines/pallet_defect_detection
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Payload for pipeline 'pallet_defect_detection' posted successfully. Response: "4b36b3ce52ad11f0ad60863f511204e2"
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```
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> **NOTE:** This will start the pipeline. To view the inference stream on WebRTC, open a browser and navigate to https://<HOST_IP>/mediamtx/pdd/ for Pallet Defect Detection
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8. Get the status of running pipeline instance(s):
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```bash
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./sample_status.sh
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```
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This command lists the statuses of pipeline instances launched during the lifetime of sample application.
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Output:
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```bash
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# Example output for Pallet Defect Detection
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Environment variables loaded from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/.env
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Running sample app: pallet-defect-detection
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[
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{
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"avg_fps": 30.00446179356829,
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"elapsed_time": 36.927825689315796,
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"id": "4b36b3ce52ad11f0ad60863f511204e2",
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"message": "",
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"start_time": 1750956469.620569,
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"state": "RUNNING"
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}
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]
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```
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9. Stop pipeline instances.
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```bash
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./sample_stop.sh
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```
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This command will stop all instances that are currently in the `RUNNING` state and return their last status.
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Output:
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```bash
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# Example output for Pallet Defect Detection
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No pipelines specified. Stopping all pipeline instances
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Environment variables loaded from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/.env
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Running sample app: pallet-defect-detection
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Checking status of dlstreamer-pipeline-server...
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Server reachable. HTTP Status Code: 200
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Instance list fetched successfully. HTTP Status Code: 200
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Found 1 running pipeline instances.
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Stopping pipeline instance with ID: 4b36b3ce52ad11f0ad60863f511204e2
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Pipeline instance with ID '4b36b3ce52ad11f0ad60863f511204e2' stopped successfully. Response: {
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"avg_fps": 30.002200575353214,
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"elapsed_time": 63.72864031791687,
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"id": "4b36b3ce52ad11f0ad60863f511204e2",
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"message": "",
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"start_time": 1750956469.620569,
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"state": "RUNNING"
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}
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```
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To stop a specific instance, identify it with the `--id` argument.
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For example, `./sample_stop.sh --id 4b36b3ce52ad11f0ad60863f511204e2`
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10. Stop the Docker application.
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```bash
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docker compose down -v
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```
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This will bring down the services in the application and remove any volumes.
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2. Fetch the list of pipeline loaded available to launch:
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```bash
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./sample_list.sh
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```
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This lists the pipeline loaded in DL Streamer Pipeline Server.
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Example Output:
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```bash
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# Example output for Pallet Defect Detection
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Environment variables loaded from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/.env
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Running sample app: pallet-defect-detection
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Checking status of dlstreamer-pipeline-server...
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Server reachable. HTTP Status Code: 200
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Loaded pipelines:
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[
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...
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{
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"description": "DL Streamer Pipeline Server pipeline",
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"name": "user_defined_pipelines",
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"parameters": {
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"properties": {
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"detection-properties": {
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"element": {
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"format": "element-properties",
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"name": "detection"
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}
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}
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},
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"type": "object"
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},
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"type": "GStreamer",
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"version": "pallet_defect_detection"
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}
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...
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]
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```
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3. Start the sample application with a pipeline.
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```bash
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./sample_start.sh -p pallet_defect_detection
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```
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This command will look for the payload for the pipeline specified in the `-p` argument above, inside the `payload.json` file and launch a pipeline instance in DL Streamer Pipeline Server. Refer to the table, to learn about different available options.
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> **IMPORTANT**: Before you run `sample_start.sh` script, make sure that
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> `jq` is installed on your system. See the
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> [troubleshooting guide](./troubleshooting-guide.md#unable-to-parse-json-payload-due-to-missing-jq-package)
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> for more details.
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Output:
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```bash
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# Example output for Pallet Defect Detection
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Environment variables loaded from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/.env
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Running sample app: pallet-defect-detection
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Checking status of dlstreamer-pipeline-server...
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Server reachable. HTTP Status Code: 200
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Loading payload from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/apps/pallet-defect-detection/payload.json
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Payload loaded successfully.
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Starting pipeline: pallet_defect_detection
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Launching pipeline: pallet_defect_detection
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Extracting payload for pipeline: pallet_defect_detection
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Found 1 payload(s) for pipeline: pallet_defect_detection
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Payload for pipeline 'pallet_defect_detection' {"source":{"uri":"file:///home/pipeline-server/resources/videos/warehouse.avi","type":"uri"},"destination":{"frame":{"type":"webrtc","peer-id":"pdd"}},"parameters":{"detection-properties":{"model":"/home/pipeline-server/resources/models/pallet-defect-detection/model.xml","device":"CPU"}}}
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Posting payload to REST server at https://<HOST_IP>/api/pipelines/user_defined_pipelines/pallet_defect_detection
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Payload for pipeline 'pallet_defect_detection' posted successfully. Response: "4b36b3ce52ad11f0ad60863f511204e2"
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```
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> **NOTE:** This will start the pipeline. To view the inference stream on WebRTC, open a browser and navigate to https://<HOST_IP>/mediamtx/pdd/ for Pallet Defect Detection
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4. Get the status of running pipeline instance(s):
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```bash
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./sample_status.sh
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```
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This command lists the statuses of pipeline instances launched during the lifetime of sample application.
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Output:
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```bash
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# Example output for Pallet Defect Detection
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Environment variables loaded from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/.env
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Running sample app: pallet-defect-detection
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[
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{
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"avg_fps": 30.00446179356829,
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"elapsed_time": 36.927825689315796,
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"id": "4b36b3ce52ad11f0ad60863f511204e2",
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"message": "",
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"start_time": 1750956469.620569,
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"state": "RUNNING"
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}
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]
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```
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5. Stop pipeline instances.
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```bash
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./sample_stop.sh
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```
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This command will stop all instances that are currently in the `RUNNING` state and return their last status.
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Output:
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```bash
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# Example output for Pallet Defect Detection
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No pipelines specified. Stopping all pipeline instances
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Environment variables loaded from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/.env
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Running sample app: pallet-defect-detection
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Checking status of dlstreamer-pipeline-server...
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Server reachable. HTTP Status Code: 200
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Instance list fetched successfully. HTTP Status Code: 200
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Found 1 running pipeline instances.
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Stopping pipeline instance with ID: 4b36b3ce52ad11f0ad60863f511204e2
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Pipeline instance with ID '4b36b3ce52ad11f0ad60863f511204e2' stopped successfully. Response: {
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"avg_fps": 30.002200575353214,
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"elapsed_time": 63.72864031791687,
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"id": "4b36b3ce52ad11f0ad60863f511204e2",
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"message": "",
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"start_time": 1750956469.620569,
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"state": "RUNNING"
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}
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```
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To stop a specific instance, identify it with the `--id` argument.
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For example, `./sample_stop.sh --id 4b36b3ce52ad11f0ad60863f511204e2`
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6. Stop the Docker application.
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```bash
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docker compose down -v
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```
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This will bring down the services in the application and remove any volumes.
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## Further Reading
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