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Example:\n\n```sh\nmkdir -p /home/pi/facerec/people/\n```\n---\n## Creating Person Profiles\n- Within the base folder, create individual folders for each person\n- Name folders as you want the person to be identified (e.g., `/home/pi/facerec/people/Dave`)\n- Add photos of each person to their respective folders\n- **Important:** Each image should contain only one face\n---\n## Image Requirements\n- Images are internaly resized to 640x640 pixels, when detecting faces. So high-resolution images should be cropped to focus on the face (similar to passport photos).\n This ensures that the 640 pixels capture more relevant facial details.\n- Quality is more important than quantity - a few clear photos are likley to work better than many poor ones.\n- It's a good idea to include images from different angles, including from the actual camera in place if posible.\n---\n\n## Operation\n- This flow functions as a dashboard for testing and optimizing face recognition. \n- When you click **Enrole**, it iterates through the person folders and creates `flow.people` which contains names, filenames and corresponding vectors.\nSeveral nodes will display status info, so you can see what is happening during this process.\n- If you add new images to the folders, you can run **Enrole** again, existing records will be skipped.\n- After completion any problems will be logged in `flow.errors` and summarised in the `Valid face ?` status.\n- If not using persistant storeage, you can configure the cosine similarity node to read from a file. Click **Write to File** (_edit filename as required_) \n---\n\n## Troubleshooting Enrollment\nIf you encounter \"No Face\" errors:\n- Check image quality. (e.g. very low resolution, poor contrast etc)\n- Increase the **Confidence Threshold** in the FACE Detection enrollment node.\n- Try using the `YoloV8s-face` model\n\nFor > 1 face :\n- Crop images to show only one face.\n- Decrease the **Confidence Threshold** in the FACE Detection enrollment node.\n\n_Sometimes it's easier to remove problematic images rather than troubleshoot them._\n\nTo attempt to force detection, click **Fix Errors**\n- This will use the error file list and either increase or decrease threshold by 1.\n- After completion if there are still errors, you can click **Fix Errors** again.\n\n- To start fresh, click **Delete ALL** to remove `flow.people`.\n---\n\n## Testing\n- Use the inject file path for testing face identification.\n- Any image can be sent to the recognition flow.\n---\n\n## Reolink Doorbell Setup:\n\nA Specific example using Reolink -\n\n- Add your doorbell's IP address and password to the flow environent variables\n- If using FTP:\n - Configure your server for FTP access.\n - Setup the camera to FTP images on person detection.\n - Set the watch node path (e.g., `/home/pi/FTP/files/face`)\n - New images arriving will trigger the flow.\n- If using Webhook:\n - [See this thread](https://discourse.nodered.org/t/reolink-doorbell-finally-supports-webhooks/93834/73)\n\n\n\n\n\n\n","x":670,"y":120,"wires":[]},{"id":"a765530bfca6fbb1","type":"face-vectorization-plus","z":"7b0af20c5f713b06","g":"6c0bc9c1bf2dce87","name":"","data":"data.face","dataType":"msg","inputType":"1","returnType":"0","method":"0","path":"","x":1190,"y":280,"wires":[["425c5148b378a7df"]]},{"id":"425c5148b378a7df","type":"function","z":"7b0af20c5f713b06","g":"6c0bc9c1bf2dce87","name":"Add vectors","func":"const name = msg.name\nnode.status({ fill: \"green\", shape: \"ring\", text: name });\n\n\n // Clear previous timeout if it exists\n let activeTimeoutId = context.statusTimeoutId;\n if (activeTimeoutId) {\n clearTimeout(activeTimeoutId); 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