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2D K Nearest Neighbors Python script when we have a point selection in ImageJ #Fiji #Python #ImageJ
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| # @ImagePlus imp | |
| # @Integer(label="Number of Neighbors") k | |
| ''' | |
| Simple 2D KNN script | |
| Olivier Burri, BioImaging & Optics Platform | |
| Ecole Polytechnique Fédérale de Lausanne | |
| July 12th 2016 | |
| Code provided as-is in reply to an ImageJ mailing list question | |
| http://imagej.1557.x6.nabble.com/distance-between-adjacent-particles-td3699485.html#a5016864 | |
| ''' | |
| from ij.gui import Overlay, Line, OvalRoi | |
| from ij import IJ | |
| from ij.measure import ResultsTable | |
| from ij.measure import Calibration | |
| from java.awt import Color | |
| import math | |
| ### Some functions ### | |
| # Brute-Force KNN | |
| def knn(data, k): | |
| # Just take the data, find the K nearest neighbors to each point | |
| the_knn=[] | |
| for i in range(len(data)): | |
| d = [] | |
| for j in range(len(data)): | |
| d.append([j, dist(data[i], data[j])]) | |
| # Sort | |
| d_sort = sorted(d, key=lambda thed: thed[1]) | |
| # Keep only the k nearest | |
| the_knn.append(d_sort[1:(k+1)]) | |
| return the_knn | |
| # Euclidean Distance 2D | |
| def dist(p0, p1): | |
| return math.sqrt((p0[0] - p1[0])**2 + (p0[1] - p1[1])**2) | |
| # Convenience function to go from a ROI to points | |
| def roiToPointList(image): | |
| p = [image.getRoi().getFloatPolygon().xpoints, image.getRoi().getFloatPolygon().ypoints] | |
| points = map(list, zip(*p)) | |
| return points | |
| def showKNNResults(the_knn): | |
| # Overlay | |
| ov = Overlay() | |
| the_avg = [] | |
| # Draw each neighbor as a line and draw the average as a circle | |
| for i in range(len(the_knn)): | |
| avg = sum([d[1] for d in the_knn[i]]) / k | |
| the_avg.append(avg) | |
| o = OvalRoi(points[i][0] - avg, points[i][1] - avg, 2*avg, 2*avg) | |
| o.setStrokeColor(Color.decode("#00FFFF")) | |
| ov.add(o) | |
| for j in range(k): | |
| # XY Coordinates of current point | |
| a = points[i]; | |
| b = points[the_knn[i][j][0]] | |
| # XY Coordinates of neighbor k | |
| l = Line(a[0], a[1], b[0], b[1]) | |
| l.setStrokeColor(Color.decode("#FF00FF")) | |
| ov.add(l) | |
| # Build Results Table | |
| cal = imp.getCalibration() | |
| frt = ResultsTable() | |
| for i in range(len(the_knn)): | |
| frt.incrementCounter() | |
| frt.addValue("Point", i) | |
| frt.addValue("Average distance [px]", the_avg[i]) | |
| if( cal.scaled() ): | |
| frt.addValue("Average distance ["+cal.getXUnit()+"]", cal.getX(the_avg[i])) | |
| frt.show(str(k)+" Nearest Neightbors Average Distances") | |
| imp.setOverlay(ov) | |
| ### Starting the script ### | |
| # Get Coordinates | |
| points = roiToPointList(imp) | |
| # Compute K Nearest Neighbors | |
| the_knn = knn(points,k) | |
| # Display the result table and an overlay | |
| showKNNResults(the_knn) |
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