Settings Commands




The `AnimateCursor` command allows you to control whether the movement of the mouse cursor is visible during script execution. By setting this command to “Yes”, you make the cursor’s movement visible, providing a clear visual cue of where and how the cursor is moving across the screen. Conversely, setting it to “No” hides the cursor’s movement, making the transitions from one point to another invisible to the user.


The `ClearCache` command is used within EyeAutomate to reset the image recognition algorithm’s “memory”. This memory feature allows the algorithm to remember the locations of images it interacted with in previous script executions, stored temporarily to speed up subsequent runs by looking for images in their last known locations. By using `ClearCache`, you effectively erase this memory, which means the script might run slower as it will have to locate images without prior knowledge of their positions. This command can be particularly useful when testing web applications where internet latency might affect the timing of actions.


The `UseMatchCache` command controls whether the image recognition system utilizes its “memory” of previously located images. By toggling this feature on, the system remembers and reuses the locations of images it has found in past script executions, potentially speeding up future searches by looking in known locations first. When turned off, the system does not use this cached information for its current operations, although it does not delete these cached locations, unlike the `ClearCache` command. This means you can re-enable the use of cached locations at any time without needing to rerun the script to rebuild the cache. This command offers flexibility in managing how image recognition tasks are optimized for speed and efficiency based on the specific requirements of your automation process.


The `ConfirmationLevel` command is used to define the accuracy needed for EyeAutomate’s image recognition to consider an image a match. You can set this accuracy level by specifying a percentage, such as `ConfirmationLevel “50”` for a lower accuracy requirement or `ConfirmationLevel “80”` for a higher one. The default setting is “80%”, which means that 80% of the pixels must match for an image to be recognized as the same. This command is especially useful when your screen has many similar images, and you need to ensure that the script accurately identifies the correct one by adjusting how strict the match criteria are.


The `ManualRecovery` command activates a feature where, if the image recognition process fails to locate a specified image, it prompts a user intervention window. This prompt asks the user to manually perform the action that the script was unable to execute automatically due to the missing image. By setting this command to “Yes”, you enable this interactive mode, whereas “No” keeps the process entirely automated, without seeking user input during such failures. This functionality can be particularly useful in scenarios where the graphical user interface (GUI) of the application being automated is frequently updated or under maintenance, leading to potential mismatches in image recognition. Instead of updating the script’s images every time there’s a change in the GUI, `ManualRecovery` allows for a flexible workaround by involving the user directly when automation fails.


The `MinimumContrast` command adjusts the contrast sensitivity of the image recognition algorithm, affecting how it distinguishes between colors in the images it searches for. This setting is part of the pre-processing phase, where the image recognition system analyzes the visual elements before attempting to find a match on the screen. By setting the `MinimumContrast`, you can fine-tune the algorithm’s ability to detect differences in color and contrast, which can be particularly useful in environments where images may not be clearly defined or where color variation is minimal. Adjusting this parameter helps in improving the accuracy of image recognition by ensuring that only images with sufficient contrast differences are considered for matching.


The `Recognition` command allows you to switch between different image recognition algorithms used by the system, specifically between pixel-based and vector-based approaches. You have several options for setting this command:

  • both: The system uses both pixel and vector algorithms to identify images, enhancing accuracy and reliability by leveraging the strengths of both methods.

  • either: The system will consider an image recognized if either the pixel or vector algorithm identifies it, offering a balance between speed and accuracy.

  • pixels: This option restricts the system to use only the pixel-based algorithm, which is effective for detailed image matching based on pixel patterns.

  • vectors: Selects only the vector-based algorithm, which is useful for recognizing shapes and general forms, making it advantageous in scenarios where images may scale or where pixel-perfect accuracy is less critical.

By adjusting the `Recognition` command, you can optimize the image recognition process based on the specific needs of your application or task, balancing between speed, accuracy, and tolerance for variations in the images being searched.


The `Region` command narrows down the area of the screen where the image recognition algorithm looks for a specified image. By defining a specific region, the search for the image is confined to this area only. If the image you’re searching for is located outside the defined region, the image recognition attempt will not succeed. This command is particularly useful for optimizing search times and improving accuracy by focusing on areas where the image is most likely to be found, reducing the likelihood of false matches from other parts of the screen.


The `Sensitivity` command adjusts how sensitive the image recognition algorithm is when searching for images on the screen. By increasing sensitivity, the algorithm becomes more likely to recognize images even if they are partially obscured or appear slightly different from their stored reference. Conversely, decreasing sensitivity makes the algorithm stricter, requiring a closer match to the reference image for a successful recognition. This command allows you to fine-tune the balance between recognizing variations of an image and avoiding false positives, optimizing the performance of your automated tasks based on the specific requirements and conditions of the environment in which they operate.


The `StepDelay` command introduces a specified delay, measured in milliseconds, between the execution of each command in your script. Implementing this command is akin to manually inserting a sleep command after every action, but it streamlines the process by automatically applying the delay to all actions. This feature can be particularly useful for slowing down the execution of a script to match the response times of the application being automated or to make the automated actions more closely mimic human interaction patterns


The `Timeout` command adjusts the maximum duration the image recognition algorithm will spend searching for a specified image before giving up. By default, this timeout is set to 30 seconds. When you use this command, you specify the timeout in seconds, not milliseconds. This is particularly useful for managing the script’s efficiency and responsiveness, especially in scenarios where waiting too long for an image could cause unnecessary delays.