Manual segmentation in ImageJ/FIJI

1 Load the image sequence in FIJI/ImageJ

• Select File \rightarrowimport$\rightarrow$Image Sequence...

• Find the directory where the image sequence is using Browse

• Click OK to load the image sequence

• 3D viewer for image sequence

​ Select Plugins \rightarrow3D viewer \rightarrowOK. Wait for a while you will see the 3D view of the image sequence as below:

2 Image transformation between warp and weft direction

• load the image sequence oriented in the weft direction into ImageJ;

  

• Reslice the images as below to show the warp tows and binders;

You should get:

• Recover to the original weft view:

3 Manual selection of ROI

ROI: Region of Interest

• A label is assigned to each fiber tow. In the case of warp tows, for instance, the labels are assigned as below:

Let's use Tow 8 to show the procedure of manual segmentation.

4 Import existing selections:

1. Load the image sequence which the ROI information belonging to.
2. Drag and put the ROI file (.zip file containing multi .roi files)

If the ROI information was stored in csv files, the following code to can be used in imageJ (save the code as xxx.ijm file) to import.

csv to ROI

run("Select None");//Remove all the selections
roiManager("reset");//Reset the ROI manager
dir_ROI = getDirectory("Choose a Directory to import")
list = getFileList(dir_ROI);
image_roi = getTitle();


for(i = 0; i < list.length; i++){

Table.open(dir_ROI+'Long_0_F_'+ 4*i+1 +'.csv');
xpoints = Table.getColumn("x");
ypoints = Table.getColumn("y");

zpoints = Table.get("z", 2);
print(zpoints);

selectWindow(image_roi);
setSlice(zpoints);
makeSelection("polygon", xpoints, ypoints);
roi_name = "0"+zpoints;
Roi.setName(roi_name);
roiManager("Add");
roiManager("select", i)
RoiManager.setPosition(zpoints);
close(Table.title);
}

5 Export the selections (ROI) to csv and .npz file

Then run the following code that stored in a xxxx.ijm file:

5.1 ROI to csv

ROI_output_csv.ijm code for imageJ

//导单个截面结果为csv
//只需要修改纤维数据保存文件的名称
dir_saving = getDirectory("Choose a Directory to save");
run("Clear Results");
n = roiManager('count');
for (i = 0; i < n; i++) { 
    run("Clear Results");
    roiManager('select', i);
    getSelectionCoordinates(x, y);
    z=getSliceNumber();
    for (j=0; j<x.length; j++) {
      setResult("x", j, x[j]);
      setResult("y", j, y[j]);
      setResult("z", j, z);
      }
    setOption("ShowRowNumbers", true);
    updateResults;
    saveAs("Results", dir_saving + 'warp_1_'+z+'.csv');
}

A single csv file contains the key points/pixel positions to describe a cross-section of a fiber tow in the following format:

5.2 csv to pcd

csv2pcd_DataPreparation.py

.pcd is a new defined format for point cloud dataset.

可以用pk_load加载

from polykriging import utility
import polykriging as pk
import numpy as np
import zipfile, os
import pandas as pd

# 压缩某个目录下所有文件
def compress_file(zipfilename, dirname):
    # zipfilename是压缩包名字，dirname是要打包的目录
    if os.path.isfile(dirname):
        with zipfile.ZipFile(zipfilename, 'w') as z:
            z.write(dirname)
    else:
        with zipfile.ZipFile(zipfilename, 'w') as z:
            for root, dirs, files in os.walk(dirname):
                for single_file in files:
                    if single_file != zipfilename:
                        filepath = os.path.join(root, single_file)
                        z.write(filepath)

path = utility.choose_directory(titl =
                                "Choose the directory that contains ROIs stored in csv files")
cwd = utility.cwd_chdir(path)
files = utility.filenames(path)

yarn = files[0][:files[0].rfind("_")]

compress_file(yarn + ".zip", "./")
for i in np.arange(len(files)):
    # File loading order control
    section = files[0][ : files[0].rfind("_") + 1] + str(i+1) + ".csv"
    csPoints = np.loadtxt(section, comments= section,
                            delimiter=",", skiprows=1
                            )
    try:
        surfPoints = np.vstack((surfPoints, csPoints))
    except NameError:
        surfPoints = csPoints
    os.remove(section)  # 处理后删除文件

# save the csv files into a npy file