Pipeline Protocol

Basic Format

The task pipeline is described using JSON format and consists of several nodes. Each node contains the following core attributes:

{
    "NodeA": {
        "recognition": "OCR",    // Recognition algorithm
        "action": "Click",       // Action to be executed
        "next": [                // List of subsequent nodes
            "NodeB",
            "NodeC"
        ],
        // Other extended properties...
    },
    // Other node definitions...
}

Execution Logic

Process Control Mechanism

1. Task Triggering

   • Start the task by specifying the entry node through the tasker.post_task interface

2. Sequential Detection:

   • Perform sequential detection on the current node's next list
   • Attempt to identify the recognition features configured for each sub-node in sequence

3. Interruption Mechanism:

   • When a sub-node is successfully matched, immediately terminate subsequent node detections
   • Execute the operation defined by the matched node's action

4. Successor Processing:

   • After the operation is completed, switch the active node to the current node
   • Repeat the above detection process

Termination Conditions

The task process terminates when any of the following conditions are met:

• The current node's next list is empty
• All subsequent node detections continuously fail until timeout

Application Examples

Scenario Description

There are menus Display/Storage/Accessibility in the Android settings interface, among which Storage contains the submenus Games/Apps after opening.

Configuration Example

{
    "Start Example": {
        "next": [
            "Enter Display",
            "Enter Storage",
            "Enter Accessibility"
        ]
    },
    "Enter Display": {
        "recognition": "XXX",
        "action": "Click",
        // ...
    },
    "Enter Storage": {
        "recognition": "XXX",
        "action": "Click",
        "next": [
            "Enter Games Submenu",
            "Enter Applications Submenu"
        ]
    },
    "Enter Accessibility": {
        // ...
    },
    "Enter Games Submenu": {
        "action": "Swipe",
        "next": []
    },
    "Enter Applications Submenu": {
        // ...
    },
    // ...
}

Execution Process Simulation

Case 1

Case 2

Property Fields

TIP

For mandatory fields, they may remain empty in the Pipeline JSON file, but must be set via the interface before actual execution.

Pipeline v1

• recognition: string
  Recognition algorithm type. Optional, default is DirectHit.
  Possible values: DirectHit | TemplateMatch | FeatureMatch | ColorMatch | OCR | NeuralNetworkClassify | NeuralNetworkDetect | Custom
  See Algorithm Types for details.

• action: string
  Action to execute. Optional, default is DoNothing.
  Possible values: DoNothing | Click | LongPress | Swipe | MultiSwipe | ClickKey | LongPressKey | InputText | StartApp | StopApp | StopTask | Command | Custom
  See Action Types for details.

• next: string | list<string, >
  List of nodes to execute next. Optional, default is empty.
  It recognizes each node in sequence and executes the first one it recognizes.

• interrupt : string | list<string, >
  The list of candidate nodes when all nodes in next are not recognized, and similar interrupt operations will be performed. Optional, empty by default.
  If all nodes in next are not recognized, each node in the interrupt list will be recognized in order, and the first recognized one will be executed. After all subsequent nodes are executed, jump back to this node to try to recognize it again.
  For example: A: { next: [B, C], interrupt: [D, E] }
  When B and C are not recognized and D is recognized, D and D.next will be fully executed. But when the pipeline of D is fully executed. It will return to node A again and continue to try to recognize B, C, D, E.
  This field is mostly used for exception handling. For example, D is to recognize the "network disconnection prompt box". After clicking confirm and waiting for the network connection to succeed, continue the previous node flow.

• is_sub: bool
  (Deprecated in version 2.x, but retains compatibility. interrupt is recommended instead.)
  Whether it is a sub-node. Optional, default is false.
  If it's a sub-node, after completing this node (and subsequent nodes such as "next"), it will return to re-recognize the "next" list of this node.
  For example: A.next = [B, Sub_C, D], where Sub_C.is_sub = true. If Sub_C is matched, after fully executing Sub_C and subsequent nodes, it will return to re-recognize [B, Sub_C, D] and execute the matching items and subsequent nodes.

• rate_limit: uint
  Identification rate limit, in milliseconds. Optional, default 1000.
  Each round of identification "next" + "interrupt" consumes at least rate_limit milliseconds, and sleep will wait if the time is less than that.

• timeout: uint
  Timeout for recognizing "next" + "interrupt" nodes, in milliseconds. Optional, Default is 20,000 milliseconds (20 seconds).
  The detailed logic is while(!timeout) { foreach(next + interrupt); sleep_until(rate_limit); }

• on_error : string | list<string, >
  When recognition timeout or the action fails to execute, the nodes in this list will be executed next. Optional, empty by default.

• inverse: bool
  Reverse the recognition result: recognized as not recognized, and not recognized as recognized. Optional, default is false.
  Please note that nodes recognized through this setting will have their own clicking actions disabled (because nothing was actually recognized). If there is a need, you can set the target separately.

• enabled: bool
  Whether to enable this node. Optional, default is true.
  If set to false, this node will be skipped when it appears in the "next" lists of other nodes, meaning it won't be recognized or executed.

• pre_delay: uint
  Delay in milliseconds between recognizing a node and executing the action. Optional, default is 200 milliseconds.
  It is recommended to add intermediate nodes whenever possible and use less delay to maintain both speed and stability.

• post_delay: uint
  Delay in milliseconds between executing the action and recognizing the "next" nodes. Optional, default is 200 milliseconds.
  It is recommended to add intermediate nodes whenever possible and use less delay to maintain both speed and stability.

• pre_wait_freezes: uint | object
  Time in milliseconds to wait for the screen to stop changing between recognizing a node and executing the action. Optional, default is 0 (no waiting).
  It will exit the action only when the screen has not had significant changes for "pre_wait_freezes" milliseconds in a row.
  If it's an object, more parameters can be set, see Waiting for the Screen to Stabilize for details. The specific order is pre_wait_freezes - pre_delay - action - post_wait_freezes - post_delay.

• post_wait_freezes: uint | object
  Time in milliseconds to wait for the screen to stop changing between executing the action and recognizing the "next" nodes. Optional, default is 0 (no waiting).
  Other logic is the same as pre_wait_freezes.

• focus: any
  Focus on the node, resulting in additional callback messages. Optional, default is null (no messages).
  See Node Notifications for details.

Pipeline v2

NOTE

MaaFW supports Pipeline v2 protocol since version v4.4.0, and is compatible with v1.

Compared with v1, the main difference is that the fields related to recognition and action are put into the secondary dictionary (the type is put into the type field, and the other parameters are put into the param field), and the rest are the same. Example:

{
    "NodeA": {
        "recognition": {
            "type": "TemplateMatch",
            "param": {
                // Recognition related fields are placed in recognition.param, and the keys and values ​​remain unchanged from v1
                "template": "A.png",
                "roi": [100, 100, 10, 10]
            }
        },
        "action": {
            "type": "Click",
            "param": {
                // Action related fields are placed in action.param, and the keys and values ​​remain unchanged from v1
                "target": "XXX"
            }
        },
        // Non-recognition and action fields remain unchanged from v1
        "next": ["NodeB"],
        "pre_delay": 1000,
        // ...
    }
}

Default Properties

Please refer to default_pipeline.json
Default can set the default values ​​of all fields. And the object of algorithm/action name can set the default parameter value of the corresponding algorithm/action.

Algorithm Types

DirectHit

Direct hit, meaning no recognition is performed, and the action is executed directly.

TemplateMatch

Template matching, also known as "find image."

This algorithm property requires additional fields:

• roi: array<int, 4> | string
  Recognition area coordinates. Optional, default [0, 0, 0, 0], i.e. full screen.

  • array<int, 4>: Recognition area coordinates, [x, y, w, h], if you want full screen, you can set it to [0, 0, 0, 0].
  • string: Fill in the node name, and identify within the target range identified by a previously executed node.

• roi_offset: array<int, 4>
  Move additionally based on roi as the range, and add the four values ​​separately. Optional, default [0, 0, 0, 0].

• template: string | list<string, >
  Path to the template image, relative to the "image" folder. Required. The images used need to be cropped from the lossless original image and scaled to 720p. Reference to here.
  Supports filling in folder paths, and all image files in them will be recursively loaded.

• threshold: double | list<double, >
  Template matching threshold. Optional, default is 0.7.
  If it's an array, its length should match the length of the template array.

• order_by: string
  How the results are sorted. Optional, default is Horizontal
  Possible values: Horizontal | Vertical | Score | Random
  You can use it with the index field.

• index: int
  Index to hit. Optional, default is 0.
  If there are N results in total, the value range of index is [-N, N - 1], where negative numbers are converted to N - index using Python-like rules. If it exceeds the range, it is considered that there is no result in the current identification.

• method: int
  Template matching algorithm, equivalent to cv::TemplateMatchModes. Optional, default is 5.
  Only supports 1, 3, and 5, with higher values providing greater accuracy but also taking more time.
  For more details, refer to the OpenCV official documentation.

• green_mask: bool
  Whether to apply a green mask. Optional, default is false.
  If set to true, you can paint the unwanted parts in the image green with RGB: (0, 255, 0), and those green parts won't be matched.

FeatureMatch

Feature matching, a more powerful "find image" with better generalization, resistant to perspective and size changes.

This algorithm property requires additional fields:

• roi: array<int, 4> | string
  Same as TemplateMatch.roi.

• roi_offset: array<int, 4>
  Same as TemplateMatch.roi_offset.

• template: string | list<string, >
  Path to the template image, relative to the "image" folder. Required.
  Supports filling in folder paths, and all image files in them will be recursively loaded.

• count: uint
  The minimum number of matching feature points required (threshold). Optional, default is 4.

• order_by: string
  How the results are sorted. Optional, default is Horizontal
  Possible values: Horizontal | Vertical | Score | Area | Random You can use it with the index field.

• index: int
  Index to hit. Optional, default is 0.
  If there are N results in total, the value range of index is [-N, N - 1], where negative numbers are converted to N - index using Python-like rules. If it exceeds the range, it is considered that there is no result in the current identification.

• green_mask: bool
  Whether to apply a green mask. Optional, default is false.
  If set to true, you can paint the unwanted parts in the image green with RGB: (0, 255, 0), and those green parts won't be matched.

• detector: string
  Feature detector. Optional, default is SIFT.
  Currently, it supports the following algorithms:

  • SIFT
    High computational complexity, scale invariance, and rotation invariance. Best performance.
  • KAZE
    Suitable for 2D and 3D images, scale invariance, and rotation invariance.
  • AKAZE
    Faster computation speed, scale invariance, and rotation invariance.
  • BRISK
    Very fast computation speed, scale invariance, and rotation invariance.
  • ORB
    Very fast computation speed, rotation invariance, but lacks scale invariance.

  You can look up detailed characteristics of each algorithm on your own.

• ratio: double
  The distance ratio for KNN matching, [0 - 1.0], where larger values make the matching more lenient (easier to connect). Optional, default is 0.6.

ColorMatch

Color matching, also known as "find color."

This algorithm property requires additional fields:

• roi: array<int, 4> | string
  Same as TemplateMatch.roi.

• roi_offset: array<int, 4>
  Same as TemplateMatch.roi_offset.

• method: int
  Color matching method, equivalent to cv::ColorConversionCodes. Optional, default is 4 (RGB).
  Common values are 4 (RGB, 3 channels), 40 (HSV, 3 channels), and 6 (GRAY, 1 channel).
  For more details, refer to the OpenCV official documentation.

• lower: list<int, > | list<list<int, >>
  Lower bound for colors. Required. The innermost list length should match the number of channels in the method.

• upper: list<int, > | list<list<int, >>
  Upper bound for colors. Required. The innermost list length should match the number of channels in the method.

• count: uint
  The minimum number of matching pixels required (threshold). Optional, default is 1.

• order_by: string
  How the results are sorted. Optional, default is Horizontal
  Possible values: Horizontal | Vertical | Score | Area | Random
  You can use it with the index field.

• index: int
  Index to hit. Optional, default is 0.
  If there are N results in total, the value range of index is [-N, N - 1], where negative numbers are converted to N - index using Python-like rules. If it exceeds the range, it is considered that there is no result in the current identification.

• connected: bool
  Whether to count only connected points. Optional, default is false.
  If set to true, after applying color filtering, it will only count the maximum connected block of pixels. If set to false, it won't consider whether these pixels are connected.

OCR

Text recognition.

This algorithm property requires additional fields:

• roi: array<int, 4> | string
  Same as TemplateMatch.roi.

• roi_offset: array<int, 4>
  Same as TemplateMatch.roi_offset.

• expected: string | list<string, >
  The expected results, supports regular expressions. Required.

• threshold: double
  Model confidence threshold. Optional, default is 0.3.

• replace: array<string, 2> | list<array<string, 2>>
  Some text recognition results may not be accurate, so replacements are performed. Optional.

• order_by: string
  How the results are sorted. Optional, default is Horizontal
  Possible values: Horizontal | Vertical | Area | Length | Random
  You can use it with the index field.

• index: int
  Index to hit. Optional, default is 0.
  If there are N results in total, the value range of index is [-N, N - 1], where negative numbers are converted to N - index using Python-like rules. If it exceeds the range, it is considered that there is no result in the current identification.

• only_rec: bool
  Whether to recognize only (without detection, requires precise roi). Optional, default is false.

• model: string
  Model folder path. Use a relative path to the "model/ocr" folder. Optional, default is empty.
  If empty, it will use the models in the root of the "model/ocr" folder. The folder should include three files: rec.onnx, det.onnx, and keys.txt.

NeuralNetworkClassify

Deep learning classification, to determine if the image in a fixed position matches the expected "category."

This algorithm property requires additional fields:

• roi: array<int, 4> | string
  Same as TemplateMatch.roi.

• roi_offset: array<int, 4>
  Same as TemplateMatch.roi_offset.

• labels: list<string, >
  Labels, meaning the names of each category. Optional.
  It only affects debugging images and logs. If not filled, it will be filled with "Unknown."

• model: string
  Model file path. Use a relative path to the "model/classify" folder. Required.
  Currently, only ONNX models are supported. Refer to NNClassify Recipe.

• expected: int | list<int, >
  The expected category index. Required.

• order_by: string
  How the results are sorted. Optional, default is Horizontal
  Possible values: Horizontal | Vertical | Score | Random You can use it with the index field.

• index: int
  Index to hit. Optional, default is 0.
  If there are N results in total, the value range of index is [-N, N - 1], where negative numbers are converted to N - index using Python-like rules. If it exceeds the range, it is considered that there is no result in the current identification.

For example, if you want to recognize whether a cat or a mouse appears in a fixed position in the image, and you've trained a model that supports this three-category classification, and you want to click when it recognizes a cat or a mouse but not when it recognizes a dog, the relevant fields would be:

{
    "labels": ["Cat", "Dog", "Mouse"],
    "expected": [0, 2]
}

Please note that these values should match the actual model output.

NeuralNetworkDetect

Deep learning object detection, an advanced version of "find image."

The main difference from classification is the flexibility to find objects at arbitrary positions. However, this often requires more complex models, more training data, longer training times, and significantly higher resource usage during inference.

This algorithm property requires additional fields:

• roi: array<int, 4> | string
  Same as TemplateMatch.roi.

• roi_offset: array<int, 4>
  Same as TemplateMatch.roi_offset.

• labels: list<string, >
  Labels, meaning the names of each category. Optional.
  It only affects debugging images and logs. If not filled, it will be filled with "Unknown."

• model: string
  Model file path. Use a relative path to the "model/detect" folder. Required.
  Currently supports YoloV8 ONNX models. Other Yolo models with the same input/output should theoretically be supported but are untested.
  Training reference: NNDetect Recipe.

• expected: int | list<int, >
  The expected category index.

• threshold: double | list<double, >
  Model confidence threshold. Optional, default is 0.3.
  If it's an array, its length should match the length of the expected array.

• order_by: string
  How the results are sorted. Optional, default is Horizontal
  Possible values: Horizontal | Vertical | Score | Area | Random
  You can use it with the index field.

• index: int
  Index to hit. Optional, default is 0.
  If there are N results in total, the value range of index is [-N, N - 1], where negative numbers are converted to N - index using Python-like rules. If it exceeds the range, it is considered that there is no result in the current identification.

For example, if you want to detect cats, dogs, and mice in an image and only click when a cat or a mouse is detected but not when a dog is detected, the relevant fields would be:

{
    "labels": ["Cat", "Dog", "Mouse"],
    "expected": [0, 2]
}

Please note that these values should match the actual model output.

Custom

Execute the recognition handle passed in through the MaaResourceRegisterCustomRecognition interface

This algorithm property requires additional fields:

• custom_recognition: string
  Recognition name, same as the one passed in through the registration interface. It will also be passed through MaaCustomRecognitionCallback.custom_recognition_name. Required.

• custom_recognition_param: any
  Recognition parameter, any type, will be passed through MaaCustomRecognitionCallback.custom_recognition_param. Optional, default null.

• roi: array<int, 4> | string
  Same as TemplateMatch.roi, will be passed through MaaCustomRecognitionCallback.roi. Optional, default [0, 0, 0, 0].

• roi_offset: array<int, 4>
  Same as TemplateMatch.roi_offset.

Action Types

DoNothing

Does nothing.

Click

Clicks.

Additional properties for this action:

• target: true | string | array<int, 4>
  The position of the click target. Optional, default is true.

  • true: The target is the position just recognized in this node (i.e., itself).
  • string: Enter the node name, as the target, to use the position recognized by a previously executed node.
  • array<int, 4>: The target is a random point within a fixed coordinate area [x, y, w, h]. To target the entire screen, set it to [0, 0, 0, 0].

• target_offset: array<int, 4>
  Additional movement from the target before clicking, where the four values are added together. Optional, default is [0, 0, 0, 0].

LongPress

Long press。

Additional properties for this action:

• target: true | string | array<int, 4>
  The position of the long press target. Optional, default is true. The values are the same as Click.target.

• target_offset: array<int, 4>
  Additional movement from the target before long pressing, where the four values are added together. Optional, default is [0, 0, 0, 0].

• duration: uint
  Duration of the long press in milliseconds. Optional, default is 1000.

Swipe

Linear swipe.

Additional properties for this action:

• begin: true | string | array<int, 4>
  The starting point of the swipe. Optional, default is true. The values are the same as Click.target.

• begin_offset: array<int, 4>
  Additional movement from the begin before swiping, where the four values are added together. Optional, default is [0, 0, 0, 0].

• end: true | string | array<int, 4> | list<true | string | array<int, 4>>
  The end point of the swipe. Optional, default is true. The values are the same as Click.target.
  💡 v4.5.x version adds support for list, which can be used to add swipe waypoints! Unlike multiple swipes, multiple ends will not lift between them, i.e., a single polyline swipe.

• end_offset: array<int, 4> | list<array<int, 4>>
  Additional movement from the end before swiping, where the four values are added together. Optional, default is [0, 0, 0, 0].

• duration: uint | list<uint,>
  Duration of the swipe in milliseconds. Optional, default is 200.

• end_hold: uint | list<uint,>
  After swiping to the end point, wait for an additional time before lifting, in ms. Optional, default 0.

• only_hover: bool
  Only move the mouse cursor, no press/release action. Optional, default false.

MultiSwipe

Multi-finger linear swipe.

Additional properties for this action:

• swipes: list<object,>
  Multi swipe array. Required.
  The order of the array elements has no effect, only based on starting.

  • starting: uint
    Swipe start time, in milliseconds. Optional, default 0.
    MultiSwipe additional field, the swipe will start pressing at the starting millisecond in this action.

  • begin: true | string | array<int, 4>
    The starting point of the swipe. Optional, default is true. The values are the same as Click.target.

  • begin_offset: array<int, 4>
    Additional movement from the begin before swiping, where the four values are added together. Optional, default is [0, 0, 0, 0].

  • end: true | string | array<int, 4> | list<true | string | array<int, 4>>
    The end point of the swipe. Optional, default is true. The values are the same as Click.target.
    💡 v4.5.x version adds support for list, which can be used to add swipe waypoints! Unlike multiple swipes, multiple ends will not lift between them, i.e., a single polyline swipe.

  • end_offset: array<int, 4> | list<array<int, 4>>
    Additional movement from the end before swiping, where the four values are added together. Optional, default is [0, 0, 0, 0].

  • duration: uint | list<uint,>
    Duration of the swipe in milliseconds. Optional, default is 200.

  • end_hold: uint | list<uint,>
    After swiping to the end point, wait for an additional time before lifting, in ms. Optional, default 0.

  • only_hover: bool
    Only move the mouse cursor, no press/release action. Optional, default false.

For example:

{
    "A": {
        "action": "MultiSwipe",
        "swipes": [
            {
                "begin": [],
                "end": []
            },
            {
                "starting": 500,
                "begin": [],
                "end": []
            }
        ]
    }
}

ClickKey

Click a key.

Additional properties for this action:

• key: int | list<int, >
  The key(s) to click, supporting only virtual key code of corresponding controller. Required.

  • Adb Controller
  • Win32 Controller

LongPressKey

Long Press a key.

Additional properties for this action:

• key: int
  The key to press, supporting only virtual key code of corresponding controller. Required.

• duration: uint
  Duration of the long press in milliseconds. Optional, default is 1000.

InputText

Inputs text.

Additional properties for this action:

• input_text: string
  The text to input, some controller only supports ascii. Required.

StartApp

Starts an app.

Additional properties for this action:

• package: string
  Launch entry. Required.
  You need to enter the package name or activity, for example, com.hypergryph.arknights or com.hypergryph.arknights/com.u8.sdk.U8UnityContext.

StopApp

Closes an app.

Additional properties for this action:

• package: string
  The app to close. Required.
  You need to enter the package name, for example, com.hypergryph.arknights.

StopTask

Stops the current task chain (the individual task chain passed to MaaTaskerPostTask).

Command

Execute a command.

This action attribute requires additional fields:

• exec: string
  The path of the program to be executed. Required.

• args: list<string,>
  The arguments to be executed. Optional.
  supports runtime parameters replacement:

  • {ENTRY}: Entry name.
  • {NODE}: Node name.
  • {IMAGE}: The path to the file where the screenshot is saved. The file is deleted before the process exits. Please copy it by yourself if you want to save it permanently.
  • {BOX}: Identify the hit target, the format is [x, y, w, h].
  • {RESOURCE_DIR}: The path of the resource folder loaded last time.
  • {LIBRARY_DIR}: The path of the folder where the MaaFW library is located.

• detach: bool
  Detach the child process, that is, do not wait for the child process to complete, and continue directly to the next task. Optional, default false.

Example:

{
    "NodeA": {
        "action": "Command",
        "exec": "Python",
        "args": [
            "{RESOURCE_DIR}/my_script/test.py"
            "Haha",
            "{IMAGE}",
            "{NODE}",
            "{BOX}"
        ]
    },
    "NodeB": {
        "action": "Command",
        "exec": "{RESOURCE_DIR}/my_exec/my_exec.exe"
    }
}

The actual command is:

# NodeA
Python C:/MaaXXX/resource/my_script/test.py Haha C:/temp/123.png NodeA [0,0,0,0]

# NodeB
C:/MaaXXX/resource/my_exec/my_exec.exe

Custom

Execute the action handle passed in through the MaaResourceRegisterCustomAction interface

This action attribute requires additional fields:

• custom_action: string
  Action name, same as the one passed in through the registration interface. It will also be passed through MaaCustomActionCallback.custom_action_name. Required.

• custom_action_param: any
  Action parameter, any type, will be passed through MaaCustomActionCallback.custom_action_param. Optional, default null.

• target: true | string | array<int, 4>
  The position of the target, which will be passed through MaaCustomActionCallback.box. Optional, default true. Same as Click.target.

• target_offset: array<int, 4>
  Same as Click.target_offset.

Waiting for the Screen to Stabilize

Waits for the screen to stabilize. It exits the action only when there is no significant change in the screen for a certain continuous time.

The field value can be a uint or an object. For example:

{
    "NodeA": {
        "pre_wait_freezes": 500
    },
    "NodeB": {
        "pre_wait_freezes": {
            // more properties ...
        }
    }
}

If the value is an object, you can set additional fields:

• time: uint
  It exits the action only when there has been no significant change in the screen for "time" milliseconds in a row. Optional, default is 1.

• target: true | string | array<int, 4>
  The position of the target to wait for. Optional, default is true. The values are the same as Click.target.

• target_offset: array<int, 4>
  Additional movement from the target to be used as the waiting target, where the four values are added together. Optional, default is [0, 0, 0, 0].

• threshold: double
  The template matching threshold to determine "no significant change." Optional, default is 0.95.

• method: int
  The template matching algorithm to determine "no significant change," i.e., cv::TemplateMatchModes. Optional, default is 5. The same as TemplateMatch.method.

• rate_limit: uint
  Identification rate limit, in milliseconds. Optional, default 1000.
  Each identification consumes at least rate_limit milliseconds, and sleep will be executed if the time is less than that.

• timeout: uint
  Timeout for recognizing, in milliseconds. Optional, default is 20,000 milliseconds (20 seconds).

Node Notifications

The specific implementation of node notifications is determined by the upper-level UI. A message template mechanism based on JSON objects is recommended, using placeholder replacements to achieve dynamic content display.

Message Template Mechanism

When MaaFW sends a callback message, it provides the following information:

• message: Message type identifier

• details: An object containing specific data

Node Notification Configuration Example

// Configure message templates in the Pipeline
{
  "NodeA": {
    "focus": {
      "Node.Recognition.Succeeded": "{name} recognition hit, ready to start execution",
      "Node.Action.Starting": "{name} starts execution, task ID: {task_id}",
    }
  }
}

// When this node starts execution, the UI will receive a callback
{
  "message": "Node.Action.Starting",
  "details_json": {
    "task_id": 12345,
    "name": "NodeA",
    "list": ["action1", "action2"],
    "focus": {
      "Node.Recognition.Succeeded": "{name} recognition hit, ready to start execution",
      "Node.Action.Starting": "{name} starts execution, task ID: {task_id}"
    }
  }
}

Processing Flow

1. UI receives the node notification message sent by the framework
2. Find the corresponding template string in focus based on the message
3. Use data in details_json to replace placeholders in the template (such as {name}, {task_id})
4. Display the processed text to the user in the interface. In the example, it displays NodeA starts execution, task ID: 12345

More Message Types

For a complete list of callback messages and detailed descriptions, please refer to Callback Protocol.