Due to a lack of time and other commitments, I've decided to archive this project. Personally I'm now using Frigate with a cheap £60 Google Coral device - it's far better than this solution.
A Synology Surveillance Station notification system utilising DeepStack AI, inspired by Christopher Adams' sssAI implementation.
The aim of the solution is to reduce the noise generated by Synology Surveillance Station's motion detection by routing all motion events via a Deepstack docker image to look for particular objects, e.g. people.
While sssAI is a great solution, it is hamstrung by the Synology notification system to send motion alerts. Due to the delay between fetching the snapshot, processing the image using the AI and requesting the alert, it means that the image attached to the Synology notification is sometimes 5-10 seconds after the motion alert was originally triggered.
SynoAI aims to solve this problem by side-stepping the Synology notifications entirely by allowing other notification systems to be used.
I made this application mostly for myself in order to improve upon Christopher Adams' original idea and don't expect anything in return. However, if you find it useful and would like to buy me a coffee, feel free to do it at Buy me a coffee! ☕. This is entirely optional, but would be appreciated! Or even better, help supported this project by contributing changes such as expanding the supported notification systems (or even AIs).
The documentation you see here corresponds to the branch/tag selected above. The documentation will be accurate for the version tag you have selected, however if viewing the main branch, this is assumed to be the documentation that corresponds to the latest commit and latest image.
For example, if you are using the docker image/version v1.1.0, then ensure you have selected the tag for v1.1.0, otherwise you may see features or options which are not available on your version of SynoAI.
- Features
- Config
- Support AIs
- Notifications
- Caveats
- Configuration
- Updating
- Docker
- Example appsettings.json
- Problems/Debugging
- FAQ
- Triggered via an Action Rule from Synology Surveillance Station
- Works using the camera name and requires no technical knowledge of the Surveillance Station API in order to retrieve the unique camera ID
- Uses an AI for object/person detection
- Produces an output image with highlighted objects using the original image at the point of motion detection
- Sends notification(s) at the point of notification with the processed image attached
- The AI does not need to run on the Synology box and can be run on an another server.
An example appsettings.json configuration file can be found here and all configuration for notifications and AI can be found under their respective sections. The following are the top level configs for communication with Synology Surveillance Station:
- Url [required]: The URL and port of your NAS, e.g. http://{IP}:{Port}
- User [required]: The user that will be used to request API snapshots
- Password [required]: The password of the user above
- AllowInsecureUrl [optional] (Default
false): Whether to allow an insecure HTTPS connection to the Synology API - SynoAIUrl [optional]: The URL that SynoAI is reachable at. E.g. Used to provide URLs to captures in some notifications.
- Cameras [required]: An array of camera objects - see Camera Config
- Notifiers [required]: See notifications
- Quality [optional] (Default:
Balanced): The quality, aka "profile type" to use when taking a snapshot. This will be based upon the settings of the streams you have configured in Surveillance Station. i.e. if your low, balanced and high streams have the same settings in Surveillance Station, then this setting will make no difference. But if you have a high quality 4k stream, a balance 1080p stream and a low 720p stream, then setting to high will return and process a 4k image. Note that the higher quality the snapshot, the longer the notification will take. Additionally, the larger the image, the smaller your detected objects may be, so ensure you set the MinSizeX/MinSizeY values respectively.High: Takes the snapshot using the profile type "High quality"Balanced: Takes the snapshot using the profile type "Balanced"Low: Takes the snapshot using the profile type "Low bandwidth"
- MinSizeX [optional] (Default:
50): The minimum size in pixels that the object must be to trigger a change (will be ignored if specified on the Camera) - MinSizeY [optional] (Default:
50): The minimum size in pixels that the object must be to trigger a change (will be ignored if specified on the Camera). - Delay [optional] (Default:
5000): The period of time in milliseconds (ms) that must occur between the end of the last motion detection of camera and the next time it'll be processed.- i.e. if your delay is set to 5000 and your camera reports motion 4 seconds after SynoAI finished processing the previous request, then the check will be ignored.
- However, if the report from Surveillance Station is more than 5000ms, then the cameras image will be processed.
- DelayAfterSuccess [optional] (Default:
NULL): The period of time in milliseconds (ms) that must occur between the end of the last motion detection of camera, which resulted in a successful detection and notification being sent, and the next time it'll be processed.- i.e. if your delay is set to 5000 and your camera reports motion 4 seconds after SynoAI finished processing the previous request, then the check will be ignored.
- However, if the report from Surveillance Station is more than 5000ms, then the cameras image will be processed. If this value isn't specified, then
Delaywill be used.
- MaxSnapshots [optional] (Default:
1): Upon movement, the maximum number of snapshots sequentially retrieved from SSS until finding an object of interest. e.g. if 4 is specified, then SynoAI will make a maximum of 4 requests until it finds a type of interest. If a matching type is found on the 1st snapshot, then no further snapshots will be taken. If nothing is found within the 4 requests, then no further snapshots will be taken until the next time Surveillance Station detects motion - DrawMode [optional] (Default:
Matches): Whether to draw all predictions from the AI on the capture image:Matches: Will draw boundary boxes over any object/person that matches the types defined on the camerasAll: Will draw boundary boxes over any object/person that the AI detectedOff: Will not draw boundary boxes (note - this will speed up time between detection and notification as SynoAI will not have to manipulate the image)
- DrawExclusions [optional] (Default:
false): Whether to draw the exclusion zone boundary boxes on the image. Useful for setting up the initial exclusion zones - BoxColor [optional] (Default:
#FF0000): The colour of the border of the boundary box - TextBoxColor [opional] (Default:
#00FFFFFFakatransparent): The colour of the box to draw behind the text to aid in making text more visible - ExclusionBoxColour [optional] (Default:
#00FF00): The colour of the border of the exclusion boundary box - StrokeWidth [optional] (Default:
2): The width, in pixels, of the border around the boundary box - Font [optional] (Default:
Tahoma): The font to use when labelling the boundary boxes on the output image - FontSize [optional] (Default:
12): The size of the font to use (in pixels) when labelling the boundary boxes on the output image - FontColor [optional] (Default:
#00FF00akagreen): The colour of the text for the labels when labelling the boundary boxes on the output image - TextOffsetX [optional] (Default:
2) : The number of pixels to offset the label from the left of the inside of the boundary image on the output image - TextOffsetY [optional] (Default:
2) : The number of pixels to offset the label from the top of the inside of the boundary image on the output image - SaveOriginalSnapshot [optional] (Default:
Off): A mode determining whether to save the source snapshot that was captured from the API before it was sent to and processed by the AI:Off: Will never save the original snapshotAlways: Will save every single snapshot every time motion is detectedWithPredictions: Will save the snapshot if the AI makes one or more predictions (note that this will include predictions which aren't valid)WithValidPredictions: Will save the snapshot only if the AI makes one or more predictions which are deemed as valid, e.g. within size limits, boundaries and expected types
- DaysToKeepCaptures [optional] (Default:
0): The number of days to keep images for. Every time motion is detected, the captures directory will be processed and any images older than the specified number of days will be deleted. A value of0means that captures will be kept forever.
- Name: [required]: The name of the camera on Surveillance Station
- Types: [required]: An array of types that will trigger a notification when detected by the AI, e.g. ["Person", "Car"]
- Threshold [required]: An integer denoting the required confidence of the AI to trigger the notification, e.g. 40 means that the AI must be 40% sure that the object detected was a person before SynoAI sends a notification
- MinSizeX [optional] (Default:
NULL): Will override the default set on the top levelMinSizeX - MinSizeY [optional] (Default:
NULL): Will override the default set on the top levelMinSizeY - Wait [optional]: An integer for the number of milliseconds to wait before requesting a snapshot once triggered, e.g. 2500 will wait for 2500ms (2.5 seconds) before requesting a snapshot from Surveillance Station
- Delay [optional] (Default:
NULL): Will override the value set on the top levelDelay - DelayAfterSuccess [optional] (Default:
0): Will override the value set on the top levelDelayAfterSuccess - MaxSnapshots [optional] (Default:
NULL): Upon movement, the maximum number of snapshots sequentially retrieved from SSS until finding an object of interest (i.e. 4 snapshots). If not specified, will use the value from the main config. - Rotate [optional] (Default:
0): The degrees to rotate the image after it's captured from SurveillanceStation. The rotation will be applied before it's passed to the AI - Exclusions [optional]: An array of exclusion zones to ignore found objects within. If the entirity of an object is within the exclusion zone, then it won't be reported by the notifiers.
- Start [required]
- X: The start X co-ordinate of the exclusion zone
- Y: The start Y co-ordinate of the exclusion zone
- End [required]
- X: The end X co-ordinate of the exclusion zone
- Y: The end Y co-ordinate of the exclusion zone
- Mode [optional] (Default:
Contains)Contains: The entire detected object must be contained within the exclusion zone for it to be ignored, i.e. if 1 or more pixel is outside of the boundary, then it will not be ignoredIntersect: Any part of the detected object must overlap with the exclusion zone for it to be ignored, i.e. if 1 or more pixel is within the boundary, then it will be ignored
- Start [required]
The following settings can be applied to the camera while SynoAI is running. All the following settings can be applied by performing a JSON POST to the respective Camera endpoint. Only the values that you wish to set need to be provided in the JSON body.
- Enabled [optional]: Whether the camera should be enabled or disabled.
As an example, to disable the camera with the name Driveway on the SynoAI URL 10.0.0.10 port 8080, you POST to http://10.0.0.10:8080/Camera/Driveway with the following JSON body:
{
"Enabled": false
}To re-enable the camera, just POST again with Enabled set to true.
{
"Enabled": true
}Configs which should be changed for debugging (change at own risk):
- ApiVersionInfo [optional] (Default:
6): The API version to use for the SYNO.API.Info API. According to the function spec for DSM, 6 is the correct version for DSM 6+ - ApiVersionCamera [optional] (Default:
9): The API version to use for SYNO.SurveillanceStation.Camera. According to the functional spec for DSM, 9 is the correct version for SSS 8+.
In order to specify the AI to use, set the Type property against the AI section in the config:
"AI": {
"Type": "DeepStack"
}The Deepstack API is a free to use AI that can identify objects, faces and more. Currently SynoAI makes use of the object detection model, which allows detection of people, cars, bicycles, trucks and even giraffes! For a full list of supported types see the Deepstack documentation.
"AI": {
"Type": "DeepStack",
"Url": "http://10.0.0.10:83"
}- Url [required]: The URL of the AI to POST the image to
For a full list of supported types see the CodeProject.AI documentation.
"AI": {
"Type": "CodeProjectAIServer",
"Url": "http://10.0.0.10:83"
}- Url [required]: The URL of the AI to POST the image to
Multiple notifications can be triggered when an object is detected. Each notification will have a defined "Type" and the sections below explain how each notification should be defined. An optional feature allows notifications to be triggered only by specified cameras.
"Notifiers": [
{
"Type": "{Type}",
"Cameras": [ "Driveway", "Garden"]
}
]- Type [required]: One of the supported notification types (see each type for additional required and optional configs below)
- Cameras [optional]: A list of camera names that the notification will be triggered for. Allows certain notifications to only trigger for specific cameras; not specifying the value, or setting cameras to an empty array, will result in the notification being sent for all cameras.
- Types [optional]: A list of types that the notification will be triggered for. Allows certain notifications to only trigger for specific types; not specifying the value, or setting types to an empty array, will result in the notification being sent for all detected types.
The Pushbullet notification will send an image and a message containing a list of detected object types. An API key will need to be obtained from your Pushbullet account. Currently the notification will be sent to all devices that the API key belongs to.
{
"Type": "Pushbullet",
"ApiKey": "0.123456789"
}- ApiKey [required]: The API key for the Pushbullet service
The webhook notification can be used to make web requests (e.g. API calls) either with or without the image that triggered the alert.
{
"Type": "Webhook",
"Url": "http://servername/resource",
"Method": "POST",
"Authentication": "Bearer",
"Token": "XYZ.123456",
"SendImage": false,
"AllowInsecureUrl": false
}- Url [required]: The URL to send the image to
- Method [optional] (Default:
POST): The HTTP method to use:- GET
- POST
- PATCH
- PUT
- DELETE
- Authorization [optional] (Default:
None): The type of Authorization to use if any- Basic
- Username [optional]: The username to use when using Basic Authorization
- Password [optional]: The password to use when using Basic Authorization
- Bearer
- Token [optional]: The token to use when using Basic Authorization
- Basic
- ImageField [optional] (Default:
image): The field name of the image in the POST data - SendImage [optional] (Default:
true): The image will be sent to the webhook when the method is POST, PATCH or PUT - AllowInsecureUrl [optional] (Default:
false): Whether to allow an insecure HTTPS connection to the Webhook.
The following is example data for when SendImage is false and SynoAIUrl is "http://192.168.1.2".
{
"camera": "Driveway",
"foundTypes": [
"Car"
],
"predictions": [
{
"Label": "car",
"Confidence": 67.89117,
"MinX": 1738,
"MinY": 420,
"MaxX": 2304,
"MaxY": 844,
"SizeX": 566,
"SizeY": 424
}
],
"message": "Motion detected on Driveway\n\nDetected 1 objects:\nCar",
"imageUrl": "http://192.168.1.2/CameraName/capture.jpeg"
}The telegram bot will send notifications and images when motion has been detected. To use this notification, you will need to set up your own Telegram bot using one of the many guides available.
{
"Type": "Telegram",
"ChatID": "000000000",
"Token": "",
"PhotoBaseURL": ""
}- Chat ID [required]: The ID of the chat with your bot. There are a number of ways of retrieving your ID, including this one.
- Token [required]: The API token provided to you by Telegram's BotFather when creating your bot
- PhotoBaseURL [optional]: Should only be filled in if you're using the Synology Web Station and can self host your images. If left blank, the file will be uploaded to Telegram for you. However, if you exposed your Captures directory on Web Station, this would be the URL to your captures folder.
The email notification will send and email with the attached image to the specified recipient.
{
"Type": "Email",
"Sender": "youremail@example.com",
"Destination": "youremail@example.com",
"Host": "smtp.server.com",
"Port": 465,
"Username": "username",
"Password": "password",
"Encryption": "Auto"
}- Sender [optional]: The email address that the notifications should be sent from; if missed/empty, then Destination will be used as the sender
- Destination [required]: The email recipient for SynoAi notifications
- Host [required]: The hostname of the SMTP server e.g. "smtp.gmail.com"
- Port [optional] (Default: 25): The port used by the SMTP server e.g 25, 465, 587
- Username [optional] (Default: ``````): The email used to authenticate with the smtp server
- Password [optional] (Default: ``````) : The password used to authenticate with the smtp server
- Encryption [optional] (Default:
None): The encryption method used by the host:- None: No SSL or TLS encryption should be used
- Auto: Allow SynoAI to decide which SSL or TLS options to use. If the server does not support SSL or TLS, then the connection will continue without any encryption
- SSL: The connection should use SSL or TLS encryption immediately
- STARTTLS: Elevates the connection to use TLS encryption immediately after reading the greeting and capabilities of the server. If the server does not support the STARTTLS extension, then the connection will fail and a NotSupportedException will be thrown
- STARTTLSWHENAVAILABLE: Elevates the connection to use TLS encryption immediately after reading the greeting and capabilities of the server, but only if the server supports the STARTTLS extension.
Note that to send email using GMail you may need to log into your Google Account (or Admin Console) and allow "Less secure app access".
{
"Type": "Email",
"Sender": "youremail@gmail.com",
"Destination": "youremail@gmail.com",
"Host": "smtp.gmail.com",
"Port": 587,
"Encryption": "StartTLS",
"Username": "youremail@gmail.com",
"Password": "yourpassword"
},Integration with HomeAssistant can be achieved using the Push integration and by calling the HomeAssistant webhook with the SynoAI Webhook notification.
Example HomeAssistant Configuration.yaml:
camera:
- platform: push
name: Motion Driveway
webhook_id: motion_driveway
timeout: 1
buffer: 1HomeAssistant requires the POSTed image field to be called "image" (which is the default for SynoAI), so from the SynoAI side the integration is simply a case of creating a Webhook and pointing it at your HomeAssistant's IP and Webhook ID:
{
"Type": "Webhook",
"Url": "http://nas-ip:8123/api/webhook/motion_driveway"
} Automations can be created using this webhook by checking for changes for the camera entity state. When the Push camera is not receiving any data, it will be in the state of idle. When the state switches to recording, it means that the webhook has started receiving data. For the fastest automation responses, perform your actions immediately on that state change.
Multiple webhooks can be set up, each pointed at a different HomeAssistant Push camera. Additionally, you can create an automation that is triggered on a Webhook call. Then just use the SynoAI webhook notification to call that webhook. Note that it's wasteful to send an image when triggering the non-Push webhooks on HomeAssistant, so ensure that SendImage is set to false.
The Pushover notification will send an image and a message containing a list of detected object types. An API key and user key will need to be obtained from your Pushover account. An array of devices can be specified to limit the devices that receive the notifications, or the device field can be left blank
{
"Type": "Pushover",
"ApiKey": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
"UserKey": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
"Device": [
"iphone"
]
}- ApiKey [required]: The API key for the Pushover service
- UserKey [required]: The User key for the Pushover service
- Device [optional]: An array of device names to send the notifications to. If left blank, the notifications will be sent to all devices
- Sound [optional]: The sound to override the user's default sound choice
- Priority [optional]: The priority with which to send the message
Send notifications to a Discord server via Discord Webhooks. You can get a Discord Webhook URL for your Discord server by following the instructions here.
{
"Type": "Discord",
"Url": " https://discord.com/api/webhooks/F4K3W3BH00K"
}- Url [required]: The URL of the Discord Webhook you want to send messages to.
Send notifications as MQTT messages. Messages are JSON-encoded and sent to the {BaseTopic}\{CameraName}\notification topic. You can optionally include the capture as a base64 encoded JPG, but this is disabled by default.
{
"Type": "MQTT",
"Host": "mqtt.domain.com",
"Port": 1883,
"Username": "user",
"Password": "password",
"BaseTopic": "synoai",
"SendImage": "false"
}- Host [required]: The hostname or IP for the MQTT broker
- Port [optional] (Default:
1883): The port the MQTT broker is listening to - Username [optional]: The username to use
- Password [optional]: The password to use
- BaseTopic [optional] (Default:
"synoai") 9E12 : The base topic for messages - SendImage [optional] (Default:
false): Whether to send a base64-encoded JPG in theimagefield.
The following is example data for when SendImage is false and SynoAIUrl is "https://synoai.example.com".
{
"camera": "Driveway",
"foundTypes": [
"Car"
],
"predictions": [
{
"Label": "car",
"Confidence": 67.89117,
"MinX": 1738,
"MinY": 420,
"MaxX": 2304,
"MaxY": 844,
"SizeX": 566,
"SizeY": 424
}
],
"message": "Motion detected on Driveway\n\nDetected 1 objects:\nCar",
"imageUrl": "https://synoai.example.com/CameraName/capture.jpeg"
}- SynoAI still relies on Surveillance Station triggering the motion alerts
- Looking for an object, such as a car on a driveway, will continually trigger alerts if that object is in view of the camera when Surveillance Station detects movement, e.g. a tree blowing in the wind.
- To avoid this, exclusion zones can be defined to ignore matching objects.
The configuration instructions below are primarily aimed at running SynoAI in a docker container on DSM (Synology's operating system). Docker will be required anyway as Deepstack is assumed to be setup inside a Docker container. It is entirely possible to run SynoAI on a webserver instead, or to install it on a Docker instance that's not running on your Synology NAS, however that is outside the scope of these instructions. Additionally, the configuration of the third party notification systems (e.g. generating a Pushbullet API Key) is outside the scope of these instructions and can be found on the respective applications help guides.
The top level steps are:
- Setup the Deepstack Docker image on DSM
- Setup the SynoAI image on DSM
- Add Action Rules to Synology Surveillance Station's motion alerts in order to trigger the SynoAI API.
The following instructions explain how to set up the Deepstack image using the Docker app built into DSM.
- Download the deepquestai/deepstack:latest image
- Run the image
- Enter a name for the image, e.g. deepstack
- Edit the advanced settings
- Enable auto restarts
- On the port settings tab;
- Enter a port mapping to port 5000 from an available port on your NAS, e.g. 83
- On the Environment tab;
- Set MODE: Low
- Set VISION-DETECTION: True
- Accept the advanced settings and then run the image
- Open a web browser and go to the Deepstack page by navigating to http://{YourIP}:{YourDeepstackPort}
- If you've set everything up successfully and you're using the latest version of DeepStack, then you'll see a message saying "DeepStack Activated"
The following instructions explain how to set up the SynoAI image using the Docker app built into DSM. For docker-compose, see the example file in the src, or in the documentation below.
- Create a folder called synoai (this will contain your Captures directory and appsettings.json)
- Put your appsettings.json file in the folder
- Create a folder called Captures
- Open Docker in DSM
- Download the djdd87/synoai:latest image by either;
- Searching the registry for djdd87/synoai
- Going to the image tab and;
- Add > Add from URL
- Enter https://hub.docker.com/r/djdd87/synoai
- Run the image
- Enter a name for the image, e.g. synoai
- Edit the advanced settings
- Enable auto restarts
- On the volumes tab;
- Add a file mapping from your appsettings.json to /app/appsettings.json
- Add a folder mapping from your captures directory to /app/Captures (optional)
- On the port settings tab;
- Enter a port mapping to port 80 from an available port on your NAS, e.g. 8080
- On the environment tab;
- To change the timezone to your local time, add a variable called TZ and set the value to your locale.
The next step is to configure actions inside Surveillance Station that will call the SynoAI API.
- Open up Surveillance Station
- Open Action Rules
- Create a new rule and enter;
- Name: A name for the action e.g. Trigger SynoAI - Driveway
- Rule type: Triggered (Default)
- Action type: Interruptible (Default)
- Click next to open the Event tab and enter;
- Event source: Camera
- Device: Your camera, e.g. Driveway
- Event: Motion Detected
- Click next to open the Action tab and enter;
- Action device: Webhook
- URL: http://{YourIP}:{YourPort}/Camera/{CameraName}, e.g. http://10.0.0.10:8080/Camera/Driveway, where
- YourIP: Is the IP of your NAS, or the Docker server where SynoAI is deployed
- YourPort: The port that the SynoAI image is listening on as you configured above.
- CameraName: The name of the camera, e.g. Driveway
- Username: Blank
- Password: Blank
- Method: GET
- Times: 1
- Click test send and if everything is set up correctly, then you'll get a green tick
- Click next and the action will now be created.
Congratulations, you should now have a trigger calling the SynoAI API for your camera every time Surveillance Station detects motion. In order to set up multiple cameras, just create a new Action Rule for each camera.
Note that SynoAI is still reliant on Surveillance Station detecting the motion, so this will need some tuning on your part. However, it's now possible to up the sensitivity and avoid false-positives as SynoAI will only notify you (via your preferred notification system/app) when an object is detected, e.g. a Person.
If you have followed the above instructions to setup the container using the DSM UI, then the following instructions are how to correctly update:
- Pull the :latest image down again from the Image tab
- Wait until DSM shows an alert to say that djdd87/synoai has finished downloading
- Stop your SynoAI container
- Select the container and go to Actions > Clear
- Start the container.
SynoAI can be installed as a docker image, which is available from DockerHub.
The following is a list of the available images and their meaning:
- Latest: The latest version. This is the live development version - YMMV
- v*..: A known stable release.
The image can be pulled using the Docker cli by calling:
docker pull djdd87/synoai:latest
To run the image a volume must be specified to map your appsettings.json file. Additionally a port needs mapping to port 80 in order to trigger the API. Optionally, the Captures directory can also be mapped to easily expose all the images output from SynoAI.
docker run
-v /path/appsettings.json:/app/appsettings.json
-v /path/captures:/app/Captures
-p 8080:80 djdd87/synoai:latest
version: '3.4'
services:
synoai:
image: djdd87/synoai:latest
build:
context: .
dockerfile: ./Dockerfile
ports:
- "8080:80"
volumes:
- /docker/synoai/captures:/app/Captures
- /docker/synoai/appsettings.json:/app/appsettings.json{
"Logging": {
"LogLevel": {
"Default": "Warning",
"Microsoft": "Warning",
"Microsoft.Hosting.Lifetime": "Warning"
}
},
"Url": "http://10.0.0.10:5000",
"User": "SynologyUser",
"Password": "SynologyPassword",
"MinSizeX": 100,
"MinSizeY": 100,
"DaysToKeepCaptures": 14,
"AI": {
"Type": "DeepStack",
"Url": "http://10.0.0.10:83"
},
"Notifiers": [
{
"Type": "Pushbullet",
"ApiKey": "0.123456789"
},
{
"Type": "Webhook",
"Url": "http://server/images",
"Method": "POST",
"Field": "image"
}
],
"Cameras": [
{
"Name": "Driveway",
"Types": [ "Person", "Car" ],
"Threshold": 45,
"MinSizeX": 250,
"MinSizeY": 500
},
{
"Name": "SideGate",
"Types": [ "Person" ],
"Wait": 2500
},
{
"Name": "BackDoor",
"Types": [ "Person" ],
"Threshold": 30,
"Exclusions": [
{
"Start": { "X": 1800, "Y": 400 },
"End": { "X": 2350, "Y": 900 }
},
{
"Start": { "X": 0, "Y": 0 },
"End": { "X": 200, "Y": 500 }
}
]
}
]
}If issues are encountered, to get more verbose information in the logs, change the logging to the following:
"Logging": {
"LogLevel": {
"Default": "Information",
"Microsoft": "Warning",
"Microsoft.Hosting.Lifetime": "Information"
}
}This will output the full information log and help identify where things are going wrong, as well as displaying the confidence percentages from Deepstack.
SynoAI is fast, but reliant on SSS to both notify it and return the current image. If you believe that a notification should have been triggered, then it's likely a configuration or performance issue with your configuration.
- Ensure that there is no delay set in your SSS camera configuration to ignore short-lived motion. For example, if you tell SSS to ignore 2 seconds of motion, that's a 2 second delay between the camera seeing the motion and then running the action rule to tell SynoAI to process the image
- Investigate your logs for performance issues.
Ensure your TZ environmental variable is set in your docker configuration.
Security cameras video stream includes I, P and B frames (See: Wikipedia). An I-Frame is like a jpg image, holding a complete snapshot of the scene. Between each I-Frame, the camera sends a bunch of P and B frames, each one holding only those parts of the scene that had any change / movement along time. I.e: In still scenes this saves lots of bandwidth.
At play back time, the video algorithm reconstructs the scene over time by first placing the I-frame as background and then composing each successive P or B frames on top, as time advances.
When SynoAI requests a snapshot from your NAS, Synology API just fetches the latest I-Frame. While this action is fast and simple, depending on your camera brand and configuration, the most recent I-Frame may be several seconds old and may not even include the actual moving object!
Some cameras are quite savvy on their bandwidth by really stretching the time between each I-Frame sent. I.e. DAHUA cameras brand got a configuration setting, labeled "SMART CODEC" which does just that when "ON". If this is your case, you should turn this "OFF", otherwise SynoAI may be fed old snapshots!
Generally snapshots should be returned within a second or, at most, two. If taking a snapshot is taking too long, please try the following before reporting an issue.
- Try using the IP to your DSM instance instead of a hostname as DNS lookups can caused a performance bottleneck depending on your system
- Try updating the base image with
apt-get updatefrom a new bash terminal - Try all three quality profiles to determine if it's a 9C3F n issue with the performance of your hardware.
Example slow log excerpt:
info: SynoAI.Controllers.CameraController[0] Front Courtyard: Snapshot received in 4167ms.
Spaces in URLs should be encoded using "%20". Most programs and applications, including SynoAI, handle this for you, but unfortunately the action rule on Surveillance Station does not. Therefor if your camera names contains a space, then you will need to ensure the URL in your action rule has all spaces replaced with %20.
Wrong: http://10.0.0.10:8080/Camera/Back Door
Right: http://10.0.0.10:8080/Camera/Back%20Door
Logs show timeout exceptions and SynoAI fails to initialise or the logs show crashes when passing snapshot requests to the AI
Example error:
System.Threading.Tasks.TaskCanceledException: The request was canceled due to the configured HttpClient.Timeout of 100 seconds elapsing.
The issue is a networking/firewall issue and is not a fault with SynoAI. This has been reported when using the dockerbridge network when all the containers are running on Synology DSM. In order to connect to all the containers, use the dockerbridge gateway IP address instead of the local IP from the Synology NAS.
- 400 Invalid password.
- If your password is definitely correct and you are still getting a 400 error code, then there's potentially an issue with the Synology DSM user configuration. However, if you cannot see any issues with the permissions, try creating a new user from SurveillanceStation directly.
- 401 Guest or disabled account.
- 402 Permission denied.
- 403 One time password not specified.
- You have Two-Factor Authentication enabled and this is not currently support. Please create a dedicated user account without 2FA limited to just SurveillanceStation.
- 404 One time password authenticate failed.
- 405 App portal incorrect.
- 406 OTP code enforced.
- 407 Max Tries (if auto blocking is set to true).
- 408 Password Expired Can not Change.
- 409 Password Expired.
- 410 Password must change (when first time use or after reset password by admin).
- 411 Account Locked (when account max try exceed).
- 100: Unknown error
- 101: Invalid parameters
- 102: API does not exist
- 103: Method does not exist
- 104: This API version is not supported
- 105: Insufficient user privilege
- If this occurs, check your username and password, or;
- Try creating a specific user for Synology Surveillance Station, or;
- Ensure your user has permission to the Surveillance Station application
- 106: Connection time out
- 107: Multiple login detected
- DeepStackAI: Failed to call API with HTTP status code 'Forbidden'
- Ensure that you have enabled VISION-DETECTION and correctly spelled it
You need to specify a list of cameras against each notification. If a notification has cameras specified, then it will only send it to those cameras. If the Cameras list is empty or not specified, then the notification will be used for all cameras:
"Notifiers": [
{
"Type": "Webhook",
"URL": "https://server/url_1"
"Cameras": [ "Camera1" ]
},
{
"Type": "Webhook",
"URL": "https://server/url_2"
"Cameras": [ "Camera2" ]
},
{
"Type": "Webhook",
"URL": "https://server/url_3and4"
"Cameras": [ "Camera3", "Camera4" ]
}
]