CLIJ2
GPU accelerated image processing for everyone
generateLabelFeatureImage
Generates a feature image for Trainable Weka Segmentation.
Use this terminology to specify which features should be generated:
- BOUNDING_BOX_DEPTH
- BOUNDING_BOX_WIDTH
- BOUNDING_BOX_HEIGHT
- CENTROID_X
- CENTROID_Y
- CENTROID_Z
- MASS_CENTER_X
- MASS_CENTER_Y
- MASS_CENTER_Z
- MAX_DISTANCE_TO_CENTROID
- MAX_DISTANCE_TO_MASS_CENTER
- MEAN_DISTANCE_TO_CENTROID
- MEAN_DISTANCE_TO_MASS_CENTER
- MAX_MEAN_DISTANCE_TO_CENTROID_RATIO
- MAX_MEAN_DISTANCE_TO_MASS_CENTER_RATIO
- MAXIMUM_INTENSITY
- MEAN_INTENSITY
- MINIMUM_INTENSITY
- SUM_INTENSITY
- STANDARD_DEVIATION_INTENSITY
- PIXEL_COUNT
- count_touching_neighbors
- average_touch_pixel_count
- average_distance_of_touching_neighbors
- MEAN_OF_LAPLACIAN
Example: “MEAN_INTENSITY count_touching_neighbors”
Availability: Available in Fiji by activating the update sites clij and clij2. This function is part of clijx-weka_-0.32.0.1.jar.
Usage in ImageJ macro
Ext.CLIJx_generateLabelFeatureImage(Image input, Image label_map, Image label_feature_image_destination, String feature_definitions);
Usage in object oriented programming languages
Java
// init CLIJ and GPU import net.haesleinhuepf.clijx.CLIJx; import net.haesleinhuepf.clij.clearcl.ClearCLBuffer; CLIJx clijx = CLIJx.getInstance(); // get input parameters ClearCLBuffer input = clijx.push(inputImagePlus); ClearCLBuffer label_map = clijx.push(label_mapImagePlus); label_feature_image_destination = clijx.create(input);
// Execute operation on GPU clijx.generateLabelFeatureImage(input, label_map, label_feature_image_destination, feature_definitions);
// show result label_feature_image_destinationImagePlus = clijx.pull(label_feature_image_destination); label_feature_image_destinationImagePlus.show(); // cleanup memory on GPU clijx.release(input); clijx.release(label_map); clijx.release(label_feature_image_destination);
Matlab
% init CLIJ and GPU clijx = init_clatlabx(); % get input parameters input = clijx.pushMat(input_matrix); label_map = clijx.pushMat(label_map_matrix); label_feature_image_destination = clijx.create(input);
% Execute operation on GPU clijx.generateLabelFeatureImage(input, label_map, label_feature_image_destination, feature_definitions);
% show result label_feature_image_destination = clijx.pullMat(label_feature_image_destination) % cleanup memory on GPU clijx.release(input); clijx.release(label_map); clijx.release(label_feature_image_destination);