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Std. of Image
The
Standard Deviation of Image node in AugeLab Studio calculates the standard deviation value of an input image. The standard deviation is a measure of the amount of variation or dispersion in a set of values. In the case of an image, it indicates the amount of variation in pixel intensities.The
Standard Deviation of Image node calculates the standard deviation of an input image. It provides a numeric output socket that contains the calculated standard deviation value. This node is useful for analyzing the variation in pixel intensities within an image.- Node Title: Std. of Image
- Node ID: OP_NODE_STD_IMAGE
The
Standard Deviation of Image node has the following input socket:- Image: The input image for which the standard deviation will be calculated. Connect an image to this socket.
The
Standard Deviation of Image node has the following output socket:- Standard Deviation: The calculated standard deviation value of the input image.
The
Standard Deviation of Image node does not require any additional configuration.- 1.Drag and drop the
Standard Deviation of Imagenode from the node library onto the canvas in AugeLab Studio. - 2.Connect the input image to the Image input socket of the
Standard Deviation of Imagenode. - 3.Run the pipeline.
- 4.The
Standard Deviation of Imagenode will calculate the standard deviation of the input image. - 5.The calculated standard deviation value will be available at the Standard Deviation output socket of the node.
- 6.Use the standard deviation value for further analysis or processing within your data pipeline.
- The
Standard Deviation of Imagenode assumes that the input image is represented as a numpy array with pixel intensities ranging from 0 to 255. - The standard deviation value represents the amount of variation or spread in pixel intensities within the image. A higher standard deviation indicates greater variation, while a lower standard deviation indicates less variation.
- The
Standard Deviation of Imagenode can be useful for tasks such as image quality assessment, noise detection, or identifying regions of high or low contrast in an image. - The standard deviation value is calculated using the numpy library's
std()function applied to the pixel intensities of the image.
That concludes the documentation for the
Standard Deviation of Image node in AugeLab Studio. This node allows you to calculate the standard deviation of an input image, providing a measure of the variation in pixel intensities within the image.