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public Trajectory generateTrajectory() {
		Trajectory t = new Trajectory(dimension);
		t.add(new Point3d(0, 0, 0));
		double[] incrx = generateIncrements();
		double[] incry = generateIncrements();
		for(int i = 1; i <= numberOfSteps; i++) {
			Point3d pos = new Point3d();
			pos.setX(t.get(i-1).x + incrx[i-1]);
			pos.setY(t.get(i-1).y + incry[i-1]);
			t.add(pos);

thorstenwagner

package fiji.plugin.trackmate.detection.findmaxima;

import ij.IJ;

import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Stack;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

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<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi=
"http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0
http://maven.apache.org/xsd/maven-4.0.0.xsd">
	<modelVersion>4.0.0</modelVersion>
 
	<groupId>de.biomedical-imaging.imagej</groupId>
	<artifactId>ij_trajectory_classifier</artifactId>
	<version>0.0.1-SNAPSHOT</version>
	<packaging>jar</packaging>

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java.lang.IllegalArgumentException: Cannot handle replace call to list in ij.Menus's public static synchronized java.lang.String[] getPlugins()
	at net.imagej.patcher.CodeHacker.replaceCallInMethod(CodeHacker.java:725)
	at net.imagej.patcher.CodeHacker.replaceCallInMethod(CodeHacker.java:650)
	at net.imagej.patcher.LegacyExtensions.addExtraPlugins(LegacyExtensions.java:477)
	at net.imagej.patcher.LegacyExtensions.injectHooks(LegacyExtensions.java:175)
	at net.imagej.patcher.LegacyInjector.inject(LegacyInjector.java:308)
	at net.imagej.patcher.LegacyInjector.injectHooks(LegacyInjector.java:109)
	at net.imagej.patcher.LegacyEnvironment.initialize(LegacyEnvironment.java:101)
	at net.imagej.patcher.LegacyEnvironment.applyPatches(LegacyEnvironment.java:495)
	at net.imagej.patcher.LegacyInjector.preinit(LegacyInjector.java:397)

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{
    "model" : {
        "architecture":         "Full Yolo",
        "input_size":           4096,
        "anchors":              [260, 260],
        "max_box_per_image":    150,        
        "labels":               ["box"]
    },

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<?xml version="1.0" encoding="UTF-8" standalone="no"?><annotation verified="no"><folder>boxes</folder><filename>TcdA1-0010_frames_sum.jpg</filename><path>/Users/twagner/Documents/Datasets/deep_training_set/training/img/TcdA1-0010_frames_sum.jpg</path><source><database>unknown</database></source><size><width>4096</width><height>4096</height><depth>1</depth></size><segmented>0</segmented><object><name>box</name><pose>Unspecified</pose><truncated>0</truncated><difficult>0</difficult><bndbox><xmin>3170</xmin><ymin>3324</ymin><xmax>3430</xmax><ymax>3584</ymax></bndbox></object><object><name>box</name><pose>Unspecified</pose><truncated>0</truncated><difficult>0</difficult><bndbox><xmin>917</xmin><ymin>952</ymin><xmax>1177</xmax><ymax>1212</ymax></bndbox></object><object><name>box</name><pose>Unspecified</pose><truncated>0</truncated><difficult>0</difficult><bndbox><xmin>541</xmin><ymin>3348</ymin><xmax>801</xmax><ymax>3608</ymax></bndbox></object><object><name>box</name><pose>Unspecified</pose><truncated>0</truncat

thorstenwagner

public FloatProcessor generateMask(int mask_size, int filamentwidth, int maskwidth, int type){
		FloatProcessor fp = new FloatProcessor(mask_size, mask_size);
		double x0 = fp.getWidth()/2 + 0.5;
		double y0 = fp.getHeight()/2 + 0.5;
		double sigmax = maskwidth/2.355; //Full width at half maximum
		double varx = sigmax*sigmax;
		double sigmay = filamentwidth/2.355;
		double vary = sigmay*sigmay;
		
		for(int i = 0; i < mask_size; i++){

thorstenwagner

from . import imagereader
from .augmentation import Augmentation
import cv2
import copy
from .utils import BoundBox, bbox_iou
import numpy as np
from keras.utils import Sequence
import threading
import multiprocessing
from multiprocessing import sharedctypes

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from __future__ import print_function
import argparse
import json
import os
from keras.utils import multi_gpu_model
import numpy as np
import time
from . import imagereader
from .frontend import YOLO
from .preprocessing import parse_annotation2

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from scipy.spatial.transform import Rotation as R
import numpy as np
import star
import transphire_transform.dump_load.star as trs
import os


input_path = "data/run_it024_data.star"
results_path = "results/test_results/"
result_filename = "run_it024_data"