The Scripting Index provides examples for processing images. In JMP, select Help > Scripting Index to view this interactive resource.
Additional resources are available from the JMP File Exchange at https://community.jmp.com/community/file-exchange.
Note: All of the JMP image filters are supported at the operating system level. Images that are processed on Windows might differ from images processed on macOS.
Argument
name
Specifies the quoted name of a JMP image filter. The following filters are available:
– "Despeckle" removes defects (that is, speckles) from a scanned or captured image (for example, scratches, dust, etc.).
– "Edge" identifies pixels in an image where the brightness changes sharply and darkens pixels with no sharp change. Edge detection is used to detect changes in surface, depth, material, and lighting.
– "Enhance" reduces the contrast between pixels in a noisy image.
– "Median" reduces noise (that is, the random variation) and smooths an image by comparing each pixel’s brightness with its neighbors’ and, if the value is very different, replaces it with the average of the neighbors’ values.
– "Negate" creates the negative of the color or gray-scale image by changing each pixel color to its complementary color.
– "Normalize" changes a color image’s pixels to use the full range of the file format’s number system. Normalization will make the image’s colors more intense.
– "Sharpen" reduces blur by sharpening edges of an image.
– "Contrast", n brightens or darkens an image. A higher number (>0.0) brightens an image; a lower number (<0.0) darkens an image.
– "Gamma", n corrects the image visual display (brightness and intensity) to account for differences in monitor hardware. A higher number (> 1.0) lightens the image; a lower number (< 1.0) darkens the image.
– "Reduce Noise", n reduces the random variation (or noise) that occurs with higher ISO sensitivity or longer exposure times.
– "Gaussian Blur", radius, sigma reduces image noise and detail creating a smoother image. Radius is equal to the blur radius around each pixel and sigma is the standard deviation of the Gaussian distribution. Gaussian blur is commonly used when resizing or performing edge detection.
Description
Example
The following example places a four-frame TIF file in a new window and shows the image that is in the first frame.
img = New Image( "$DOWNLOADS/Multiframe.tif" );
nframes = img << Get N Frames(); // return 4
img << Set Current Frame( 1 ); // show image 1
win = New Window( "Multi-Frame TIFF", img );
Examples
img = New Image( "$SAMPLE_IMAGES/tile.jpg" );
xs = 2;
img << Scale( xs );
New Window( "Tilex 2", img );
img = New Image( "$SAMPLE_IMAGES/tile.jpg" );
img << Scale( 2, 0.5 ); // scale image width by 2 and height by 1/2
New Window( "Tile squished", img );
Notes
Using Scale is an alternative to getting the size of the image, multiplying by the scale factor, and then setting the size.
Description
See Also