-
Notifications
You must be signed in to change notification settings - Fork 75
/
DetectionUtils.java
331 lines (301 loc) · 10.9 KB
/
DetectionUtils.java
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
package fiji.plugin.trackmate.detection;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import fiji.plugin.trackmate.Spot;
import fiji.plugin.trackmate.detection.util.MedianFilter2D;
import net.imglib2.Cursor;
import net.imglib2.FinalInterval;
import net.imglib2.Interval;
import net.imglib2.Point;
import net.imglib2.RandomAccess;
import net.imglib2.RandomAccessible;
import net.imglib2.RandomAccessibleInterval;
import net.imglib2.algorithm.localextrema.LocalExtrema;
import net.imglib2.algorithm.localextrema.LocalExtrema.LocalNeighborhoodCheck;
import net.imglib2.algorithm.localextrema.RefinedPeak;
import net.imglib2.algorithm.localextrema.SubpixelLocalization;
import net.imglib2.algorithm.neighborhood.RectangleShape;
import net.imglib2.converter.RealFloatConverter;
import net.imglib2.img.Img;
import net.imglib2.img.ImgFactory;
import net.imglib2.img.array.ArrayCursor;
import net.imglib2.img.array.ArrayImg;
import net.imglib2.img.array.ArrayImgs;
import net.imglib2.img.basictypeaccess.array.FloatArray;
import net.imglib2.type.NativeType;
import net.imglib2.type.numeric.RealType;
import net.imglib2.type.numeric.real.FloatType;
import net.imglib2.util.Intervals;
import net.imglib2.view.IntervalView;
import net.imglib2.view.Views;
public class DetectionUtils
{
/**
* Creates a laplacian of gaussian (LoG) kernel tuned for blobs with a
* radius specified <b>using calibrated units</b>. The specified calibration
* is used to determine the dimensionality of the kernel and to map it on a
* pixel grid.
*
* @param radius
* the blob radius (in image unit).
* @param nDims
* the dimensionality of the desired kernel. Must be 1, 2 or 3.
* @param calibration
* the pixel sizes, specified as <code>double[]</code> array.
* @return a new image containing the LoG kernel.
*/
public static final Img< FloatType > createLoGKernel( final double radius, final int nDims, final double[] calibration )
{
// Optimal sigma for LoG approach and dimensionality.
final double sigma = radius / Math.sqrt( nDims );
final double[] sigmaPixels = new double[ nDims ];
for ( int i = 0; i < sigmaPixels.length; i++ )
{
sigmaPixels[ i ] = sigma / calibration[ i ];
}
final int n = sigmaPixels.length;
final long[] sizes = new long[ n ];
final long[] middle = new long[ n ];
for ( int d = 0; d < n; ++d )
{
// From Tobias Gauss3
final int hksizes = Math.max( 2, ( int ) ( 3 * sigmaPixels[ d ] + 0.5 ) + 1 );
sizes[ d ] = 3 + 2 * hksizes;
middle[ d ] = 1 + hksizes;
}
final ArrayImg< FloatType, FloatArray > kernel = ArrayImgs.floats( sizes );
final ArrayCursor< FloatType > c = kernel.cursor();
final long[] coords = new long[ nDims ];
// Work in image coordinates
while ( c.hasNext() )
{
c.fwd();
c.localize( coords );
double sumx2 = 0.;
double mantissa = 0.;
for ( int d = 0; d < coords.length; d++ )
{
final double x = calibration[ d ] * ( coords[ d ] - middle[ d ] );
sumx2 += ( x * x );
mantissa += 1. / sigmaPixels[ d ] / sigmaPixels[ d ] * ( x * x / sigma / sigma - 1 );
}
final double exponent = -sumx2 / 2. / sigma / sigma;
/*
* LoG normalization factor, so that the filtered peak have the
* maximal value for spots that have the size this kernel is tuned
* to. With this value, the peak value will be of the same order of
* magnitude than the raw spot (if it has the right size). This
* value also ensures that if the image has its calibration changed,
* one will retrieve the same peak value than before scaling.
* However, I (JYT) could not derive the exact formula if the image
* is scaled differently across X, Y and Z.
*/
final double C = 1. / Math.PI / sigmaPixels[ 0 ] / sigmaPixels[ 0 ];
c.get().setReal( -C * mantissa * Math.exp( exponent ) );
}
return kernel;
}
/**
* Copy an interval of the specified source image on a float image.
*
* @param img
* the source image.
* @param interval
* the interval in the source image to copy.
* @param factory
* a factory used to build the float image.
* @return a new float Img. Careful: even if the specified interval does not
* start at (0, 0), the new image will have its first pixel at
* coordinates (0, 0).
*/
public static final < T extends RealType< T >> Img< FloatType > copyToFloatImg( final RandomAccessible< T > img, final Interval interval, final ImgFactory< FloatType > factory )
{
final Img< FloatType > output = factory.create( interval );
final long[] min = new long[ interval.numDimensions() ];
interval.min( min );
final RandomAccess< T > in = Views.offset( img, min ).randomAccess();
final Cursor< FloatType > out = output.cursor();
final RealFloatConverter< T > c = new RealFloatConverter< >();
while ( out.hasNext() )
{
out.fwd();
in.setPosition( out );
c.convert( in.get(), out.get() );
}
return output;
}
/**
* Returns a new {@link Interval}, built by squeezing out singleton
* dimensions from the specified interval.
*
* @param interval
* the interval to squeeze.
* @return a new interval.
*/
public static final Interval squeeze( final Interval interval )
{
int nNonSingletonDimensions = 0;
for ( int d = nNonSingletonDimensions; d < interval.numDimensions(); d++ )
{
if ( interval.dimension( d ) > 1 )
{
nNonSingletonDimensions++;
}
}
final long[] min = new long[ nNonSingletonDimensions ];
final long[] max = new long[ nNonSingletonDimensions ];
int index = 0;
for ( int d = 0; d < interval.numDimensions(); d++ )
{
if ( interval.dimension( d ) > 1 )
{
min[ index ] = interval.min( d );
max[ index ] = interval.max( d );
index++;
}
}
return new FinalInterval( min, max );
}
/**
* Apply a simple 3x3 median filter to the target image.
*/
public static final < R extends RealType< R > & NativeType< R >> Img< R > applyMedianFilter( final RandomAccessibleInterval< R > image )
{
final MedianFilter2D< R > medFilt = new MedianFilter2D< >( image, 1 );
if ( !medFilt.checkInput() || !medFilt.process() ) { return null; }
return medFilt.getResult();
}
public static final List< Spot > findLocalMaxima( final RandomAccessibleInterval< FloatType > source, final double threshold, final double[] calibration, final double radius, final boolean doSubPixelLocalization, final int numThreads )
{
/*
* Find maxima.
*/
final FloatType val = new FloatType();
val.setReal( threshold );
final LocalNeighborhoodCheck< Point, FloatType > localNeighborhoodCheck = new LocalExtrema.MaximumCheck< >( val );
final IntervalView< FloatType > dogWithBorder = Views.interval( Views.extendMirrorSingle( source ), Intervals.expand( source, 1 ) );
final ExecutorService service = Executors.newFixedThreadPool( numThreads );
List< Point > peaks;
try
{
peaks = LocalExtrema.findLocalExtrema( dogWithBorder, localNeighborhoodCheck, new RectangleShape( 1, true ), service, numThreads );
}
catch ( InterruptedException | ExecutionException e )
{
e.printStackTrace();
peaks = Collections.emptyList();
}
service.shutdown();
if ( peaks.isEmpty() ) { return Collections.emptyList(); }
final List< Spot > spots;
if ( doSubPixelLocalization )
{
/*
* Sub-pixel localize them.
*/
final SubpixelLocalization< Point, FloatType > spl = new SubpixelLocalization< >( source.numDimensions() );
spl.setNumThreads( numThreads );
spl.setReturnInvalidPeaks( true );
spl.setCanMoveOutside( true );
spl.setAllowMaximaTolerance( true );
spl.setMaxNumMoves( 10 );
final ArrayList< RefinedPeak< Point >> refined = spl.process( peaks, dogWithBorder, source );
spots = new ArrayList< >( refined.size() );
final RandomAccess< FloatType > ra = source.randomAccess();
/*
* Deal with different dimensionality manually. Profound comment:
* this is the proof that this part of the code is sloppy. ImgLib2
* is supposed to be dimension-generic. I just did not use properly
* here.
*/
if ( source.numDimensions() > 2 )
{ // 3D
for ( final RefinedPeak< Point > refinedPeak : refined )
{
ra.setPosition( refinedPeak.getOriginalPeak() );
final double quality = ra.get().getRealDouble();
final double x = refinedPeak.getDoublePosition( 0 ) * calibration[ 0 ];
final double y = refinedPeak.getDoublePosition( 1 ) * calibration[ 1 ];
final double z = refinedPeak.getDoublePosition( 2 ) * calibration[ 2 ];
final Spot spot = new Spot( x, y, z, radius, quality );
spots.add( spot );
}
}
else if ( source.numDimensions() > 1 )
{ // 2D
final double z = 0;
for ( final RefinedPeak< Point > refinedPeak : refined )
{
ra.setPosition( refinedPeak.getOriginalPeak() );
final double quality = ra.get().getRealDouble();
final double x = refinedPeak.getDoublePosition( 0 ) * calibration[ 0 ];
final double y = refinedPeak.getDoublePosition( 1 ) * calibration[ 1 ];
final Spot spot = new Spot( x, y, z, radius, quality );
spots.add( spot );
}
}
else
{ // 1D
final double z = 0;
final double y = 0;
for ( final RefinedPeak< Point > refinedPeak : refined )
{
ra.setPosition( refinedPeak.getOriginalPeak() );
final double quality = ra.get().getRealDouble();
final double x = refinedPeak.getDoublePosition( 0 ) * calibration[ 0 ];
final Spot spot = new Spot( x, y, z, radius, quality );
spots.add( spot );
}
}
}
else
{
spots = new ArrayList< >( peaks.size() );
final RandomAccess< FloatType > ra = source.randomAccess();
if ( source.numDimensions() > 2 )
{ // 3D
for ( final Point peak : peaks )
{
ra.setPosition( peak );
final double quality = ra.get().getRealDouble();
final double x = peak.getDoublePosition( 0 ) * calibration[ 0 ];
final double y = peak.getDoublePosition( 1 ) * calibration[ 1 ];
final double z = peak.getDoublePosition( 2 ) * calibration[ 2 ];
final Spot spot = new Spot( x, y, z, radius, quality );
spots.add( spot );
}
}
else if ( source.numDimensions() > 1 )
{ // 2D
final double z = 0;
for ( final Point peak : peaks )
{
ra.setPosition( peak );
final double quality = ra.get().getRealDouble();
final double x = peak.getDoublePosition( 0 ) * calibration[ 0 ];
final double y = peak.getDoublePosition( 1 ) * calibration[ 1 ];
final Spot spot = new Spot( x, y, z, radius, quality );
spots.add( spot );
}
}
else
{ // 1D
final double z = 0;
final double y = 0;
for ( final Point peak : peaks )
{
ra.setPosition( peak );
final double quality = ra.get().getRealDouble();
final double x = peak.getDoublePosition( 0 ) * calibration[ 0 ];
final Spot spot = new Spot( x, y, z, radius, quality );
spots.add( spot );
}
}
}
return spots;
}
}