com.actelion.research.orbit.imageAnalysis.deeplearning.DLSegment Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of orbit-image-analysis Show documentation
Show all versions of orbit-image-analysis Show documentation
Orbit, a versatile image analysis software for biological image-based quantification
/*
* Orbit, a versatile image analysis software for biological image-based quantification.
* Copyright (C) 2009 - 2018 Idorsia Pharmaceuticals Ltd., Hegenheimermattweg 91, CH-4123 Allschwil, Switzerland.
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see input = constructAndExecuteGraphToNormalizeImage(image);
long[] mask = executeInceptionGraph(graphDef, input);
BufferedImage bufferedImage = decodeLabels(mask, Color.black, Color.white);
ImageIO.write(bufferedImage, "jpg", new File(OUTPUT_IMAGE));
long elapsedTimeInSec = (new Date().getTime() - startDate.getTime()) / 1000;
System.out.println(String.format("Ended in %ds .", elapsedTimeInSec));
}
private static BufferedImage decodeLabels(long[] mask, Color bg, Color fg) {
// int[][] label_colours = {
// new int[] { 0, 0, 0 },
// new int[] { 255, 255, 255 } //Glomeruli
// };
int[][] label_mask = convert1DVectorTo2D(mask, DESIRED_SIZE, DESIRED_SIZE);
BufferedImage image = new BufferedImage(DESIRED_SIZE, DESIRED_SIZE, BufferedImage.TYPE_INT_RGB);
for (int y = 0; y < image.getWidth(); y++) {
for (int x = 0; x < image.getHeight(); x++) {
// Color color = new Color(label_colours[label_mask[y][x]][0],
// label_colours[label_mask[y][x]][1],
// label_colours[label_mask[y][x]][2]);
Color color = label_mask[y][x]==0? bg : fg;
image.setRGB(x, y, color.getRGB());
}
}
return image;
}
public static int[][] convert1DVectorTo2D(long[] values, int rows, int cols) {
int[][] array = new int[rows][cols];
for (int j = 0; j < rows; j++) {
for (int i = 0; i < cols; i++) {
array[j][i] = (int) values[j * cols + i];
}
}
return array;
}
private static Tensor constructAndExecuteGraphToNormalizeImage(byte[] imageBytes) {
try (Graph g = new Graph()) {
GraphBuilder b = new GraphBuilder(g);
final int H = DESIRED_SIZE;
final int W = DESIRED_SIZE;
final float[] mean = new float[]{104.00698793f,116.66876762f,122.67891434f};
final float scale = 1f;
final Output input = b.constant("input", imageBytes);
final Output output = b.div(b.sub(b.resizeBilinear(
b.expandDims(b.cast(b.decodeJpeg(input, 3), Float.class),
b.constant("make_batch", 0)),
b.constant("size", new int[] { H, W })), b.constant("mean", mean)),
b.constant("scale", scale));
try (Session s = new Session(g)) {
return s.runner().fetch(output.op().name()).run().get(0).expect(Float.class);
}
}
}
private static long[] executeInceptionGraph(byte[] graphDef, Tensor input) {
try (Graph g = new Graph()) {
g.importGraphDef(graphDef);
try (Session s = new Session(g);
Tensor result = s
.runner()
.feed("image_batch", input)
.fetch("predictions")
.run().get(0).expect(Long.class)) {
return result.copyTo(new long[result.numElements()]);
}
}
}
public Session buildSession() {
byte[] graphDef = readAllBytesOrExit(Paths.get(MODEL_NAME));
Graph g = new Graph();
g.importGraphDef(graphDef);
Session s = new Session(g);
return s;
}
public BufferedImage segmentInput(byte[] inputBytes, Session s, Color bg, Color fg) {
Tensor inputTensor = constructAndExecuteGraphToNormalizeImage(inputBytes);
Tensor outputTensor = s
.runner()
.feed("image_batch", inputTensor)
.fetch("predictions")
.run().get(0).expect(Long.class);
long[] mask = outputTensor.copyTo(new long[outputTensor.numElements()]);
BufferedImage bufferedImage = decodeLabels(mask,bg,fg);
return bufferedImage;
}
private static byte[] readAllBytesOrExit(Path path) {
try {
return Files.readAllBytes(path);
} catch (IOException e) {
System.err.println("Failed to read [" + path + "]: " + e.getMessage());
System.exit(1);
}
return null;
}
}