This notebook is part of the PyImageJ Tutorial Series, and assumes familiarity with the ImageJ API. Dedicated tutorials for ImageJ can be found here.
8 Discover ImageJ commands with the Recorder
The original ImageJ contains many useful commands that can be hard to use in PyImageJ without some prior knowledge on the their parameters. This notebook demonstrates how to use ImageJ’s Recorder feature to record ImageJ commands in a supported language (IJ Macro, BeanScript, Java and JavaScript).
8.1 Example 1: Apply “Find Maxima…” command to an image
In this example we will use “Find Maxima” on a test image. Once the image is loaded, open the Recorder (see image below) and begin running commands to capture the code lines.
Now that the Recorder window is open and listening for commands, set the recorder language to either Java or JavaScript before running the “Find Maxima…” command. Next run the “Find Maxima…” command (Process > Find Maxima…) to capture the code lines.
Running “Find Maxima…” (set to prominence=1000 and Point Selection) on test_still.tif results in 21 detections which are then overlayed over the input image.:
Now that we have the Java code for the “Find Maxima…” command we can replicate this workflow in PyImageJ. The “Find Maxima…” command will overlay detections ontop of a displayed input image, therefore we will need to initialize ImageJ in interactive mode. Please note that MacOS users will have to change the mode to gui due to architecture limitations. For more information please visit the initialization documentation.
import imagej
# initialize ImageJ2
ij = imagej.init(mode='headless')
print(f"ImageJ2 version: {ij.getVersion()}")
ImageJ2 version: 2.14.0/1.54f
Because the “Find Maxima…” and other original ImageJ commands work with the ImagePlus object type (instead of the newer ImageJ2/ImgLib2 Dataset and ImgPlus object types) we need to first convert the Dataset returned from ij.io().open() to an ImagePlus.
# open test image and convert from Dataset to ImagePlus
dataset = ij.io().open('sample-data/test_still.tif')
imp = ij.py.to_imageplus(dataset)
# show the image
ij.py.show(imp)
Operating in headless mode - the original ImageJ will have limited functionality.
Next, show the image with ImageJ and run Find Maxima... using the same Java syntax generated from the recorder.
# show image and then find maxima
imp.getProcessor().resetMinAndMax()
ij.ui().show(imp)
ij.IJ.run(imp, "Find Maxima...", "prominence=1000 output=[Point Selection]")
Operating in headless mode - the IJ class will not be fully functional.
[INFO] test_still.tif = net.imagej.display.DefaultDatasetView@1e4c6583
[INFO] null = img["test_still.tif" (-2), 16-bit, 300x300x1x1x1]
8.2 Example 2: Extract a slice and run “Analyze Particles…”
Let’s try a more complicated example next. This Java code was generated with the ImageJ Recorder while analyzing some data.
imp = IJ.openImage("sample-data/test_timeseries.tif");
imp2 = new Duplicator().run(imp, 3, 3, 1, 1, 14, 14);
IJ.run(imp, "Enhance Contrast", "saturated=0.35");
IJ.setAutoThreshold(imp, "Moments dark");
IJ.run(imp, "Analyze Particles...", " show=Overlay display clear");
The Java code takes the test_timeseries.tif sample data (4D: [X, Y, Channel, Time]) and performs the following operations:
Open the test data.
Duplicate channel 3, frame 14 (extracts a single still from the timeseries).
Enhance the contrast of the image.
Threshold with “Moments dark”.
Analyze particles and display results via overlay.
Results:
Note that in this example we will use the orginal ImageJ’s image opener (IJ.openImage()) instead of ImageJ2’s (ij.io().open()). The original ImageJ’s opener is more limited than ImageJ2’s however it will return an ImagePlus image object instead of a Dataset, thus no conversion step is needed like in the previous example.
Just like in the Example 1 the Java code generated from the Recorder can be typically used with little to no modification for language syntax (take note of the different syntax needed to use the Duplicator):
from scyjava import jimport
# get ImageJ's duplicator
Duplicator = jimport('ij.plugin.Duplicator')
# run ImageJ commands
imp_timeseries = ij.IJ.openImage("sample-data/test_timeseries.tif")
imp_extract = Duplicator().run(imp_timeseries, 3, 3, 1, 1, 14, 14) # visit the Javadoc for more info https://imagej.nih.gov/ij/developer/api/ij/ij/plugin/Duplicator.html
ij.IJ.run(imp_extract, "Enhance Contrast", "saturated=0.35")
ij.ui().show(imp_extract)
ij.IJ.setAutoThreshold(imp_extract, "Moments dark")
ij.IJ.run(imp_extract, "Analyze Particles...", " show=Overlay display clear")
[INFO] null = img["DUP_test_timeseries.tif" (-5), 16-bit, 250x250x1x1x1]
Area Mean Min Max X Y Circ. Feret FeretX FeretY FeretAngle MinFeret AR Round Solidity
1 0.211 1293 1141 1445 48.588 13.975 1.000 0.727 149 42 116.565 0.325 2.000 0.500 1.000
2 0.739 1216.143 1085 1473 28.716 14.927 0.800 1.340 88 44 104.036 0.975 1.387 0.721 0.737
3 0.106 1333.000 1333 1333 25.838 16.738 1.000 0.460 79 51 135.000 0.325 1.000 1.000 1.000
4 0.528 1193.400 1099 1295 31.753 17.518 1.000 1.172 96 53 146.310 0.650 1.553 0.644 0.909
5 0.317 1241.667 1087 1384 43.604 17.604 1.000 0.919 133 55 45.000 0.650 1.464 0.683 0.857
6 180.091 1513.540 1083 4462 52.667 27.076 0.077 32.384 112 68 148.543 13.747 2.528 0.396 0.542
7 0.106 1104.000 1104 1104 58.988 17.388 1.000 0.460 181 53 135.000 0.325 1.000 1.000 1.000
8 3.803 1478.194 1085 2510 29.728 19.121 0.600 3.250 89 54 126.870 2.103 1.913 0.523 0.742
9 0.211 1268.000 1161 1375 50.213 17.875 1.000 0.727 154 54 116.565 0.325 2.000 0.500 1.000
10 0.211 1171.000 1108 1234 25.838 18.200 1.000 0.727 79 55 116.565 0.325 2.000 0.500 1.000
11 0.106 1257.000 1257 1257 32.663 18.038 1.000 0.460 100 55 135.000 0.325 1.000 1.000 1.000
12 0.423 1371.250 1186 1686 32.825 19.175 1.000 0.919 100 58 135.000 0.650 1.000 1.000 1.000
13 0.317 1140.667 1109 1157 43.929 19.446 1.000 0.919 134 59 135.000 0.650 1.464 0.683 0.857
14 0.423 1187.500 1097 1241 54.925 19.825 1.000 0.919 168 60 135.000 0.650 1.000 1.000 1.000
15 0.528 1182.800 1132 1312 55.998 20.053 0.873 1.379 171 63 45.000 0.872 1.882 0.531 0.714
16 0.423 1302.500 1117 1634 21.938 20.556 1.000 1.028 66 63 161.565 0.650 1.468 0.681 0.800
17 0.739 1215.286 1102 1352 57.223 20.684 0.898 1.340 174 63 165.964 0.975 1.387 0.721 0.737
18 0.423 1234.500 1192 1276 49.075 21.125 1.000 0.919 150 64 135.000 0.650 1.000 1.000 1.000
19 1.479 1192.214 1086 1398 50.468 21.473 0.587 2.081 153 68 38.660 1.453 1.436 0.697 0.737
20 0.845 1422.125 1150 1897 22.750 21.938 1.000 1.340 68 67 165.964 0.975 1.195 0.837 0.800
21 0.211 1116.500 1113 1120 55.088 21.775 1.000 0.727 169 66 116.565 0.325 2.000 0.500 1.000
22 0.211 1141.500 1128 1155 27.463 22.425 1.000 0.727 84 68 116.565 0.325 2.000 0.500 1.000
23 0.106 1168.000 1168 1168 19.013 22.588 1.000 0.460 58 69 135.000 0.325 1.000 1.000 1.000
24 0.106 1163.000 1163 1163 39.813 22.588 1.000 0.460 122 69 135.000 0.325 1.000 1.000 1.000
25 0.211 1126.000 1102 1150 59.150 23.075 0.785 0.919 181 72 45.000 0.460 2.646 0.378 0.667
26 0.423 1222.750 1086 1383 34.775 23.400 1.000 0.919 106 71 135.000 0.650 1.000 1.000 1.000
27 22.498 1762.784 1084 5446 26.759 25.966 0.317 8.387 74 90 35.538 5.378 1.747 0.573 0.652
28 1.162 1330.364 1092 1829 32.278 24.951 0.976 1.625 97 78 36.870 1.300 1.147 0.871 0.815
29 0.634 1167.667 1110 1227 37.375 26.163 1.000 1.172 114 79 123.690 0.650 1.500 0.667 1.000
30 0.739 1328.571 1137 1482 18.641 27.138 0.564 1.750 55 82 158.199 0.788 2.898 0.345 0.636
31 1.162 1216.818 1089 1455 23.031 27.403 0.691 1.895 69 82 120.964 1.149 2.046 0.489 0.647
32 0.106 1124.000 1124 1124 48.263 27.788 1.000 0.460 148 85 135.000 0.325 1.000 1.000 1.000
33 0.317 1166.333 1101 1208 64.296 28.004 1.000 0.919 197 85 135.000 0.650 1.464 0.683 0.857
34 0.106 1092.000 1092 1092 48.588 28.438 1.000 0.460 149 87 135.000 0.325 1.000 1.000 1.000
35 0.106 1136.000 1136 1136 43.063 28.763 1.000 0.460 132 88 135.000 0.325 1.000 1.000 1.000
36 0.211 1106.000 1092 1120 40.788 29.250 1.000 0.727 125 89 116.565 0.325 2.000 0.500 1.000
37 3.063 1362.310 1083 2069 20.772 30.219 0.705 2.907 63 98 63.435 1.950 1.463 0.684 0.817
38 0.106 1088.000 1088 1088 29.413 29.413 1.000 0.460 90 90 135.000 0.325 1.000 1.000 1.000
39 0.211 1150.500 1104 1197 44.850 29.413 1.000 0.727 137 90 153.435 0.325 2.000 0.500 1.000
40 0.211 1123.500 1110 1137 49.563 29.900 1.000 0.727 152 91 116.565 0.325 2.000 0.500 1.000
41 1.901 1434.944 1103 2129 41.600 31.038 1.000 2.081 126 98 51.340 1.300 1.362 0.734 0.947
42 0.211 1128.500 1100 1157 24.700 30.713 1.000 0.727 75 94 153.435 0.325 2.000 0.500 1.000
43 8.344 1414.139 1091 2321 44.638 33.275 0.353 6.106 134 111 64.799 3.417 2.518 0.397 0.568
44 0.951 1351.111 1134 1652 48.479 31.074 1.000 1.453 147 95 153.435 0.975 1.340 0.746 0.857
45 0.951 1435.444 1088 1798 64.838 31.038 1.000 1.379 198 94 135.000 0.975 1.000 1.000 1.000
46 0.528 1161.400 1106 1248 28.178 31.233 1.000 1.172 85 97 33.690 0.650 1.553 0.644 0.909
47 0.528 1120.200 1102 1144 49.758 31.428 1.000 1.172 152 98 56.310 0.650 1.553 0.644 0.909
48 0.528 1277.400 1110 1547 21.743 31.623 1.000 1.172 66 99 56.310 0.650 1.553 0.644 0.909
49 0.951 1182.667 1088 1279 27.643 32.735 0.832 1.750 84 103 68.199 0.650 2.386 0.419 0.947
50 0.211 1184.000 1110 1258 48.425 32.338 1.000 0.727 148 99 153.435 0.325 2.000 0.500 1.000
51 3.274 1360.774 1098 1866 19.453 33.805 0.795 3.083 55 106 18.435 1.625 1.947 0.514 0.873
52 0.423 1267.250 1116 1458 41.031 33.313 0.857 1.028 126 101 108.435 0.650 1.468 0.681 0.800
53 3.908 1232.703 1083 1597 54.894 34.577 0.272 3.500 164 103 158.199 2.758 1.327 0.754 0.565
54 11.302 1550.327 1096 2918 48.742 37.273 0.583 6.629 149 125 78.690 2.600 2.647 0.378 0.849
55 0.211 1088.500 1087 1090 57.525 34.288 1.000 0.727 176 105 153.435 0.325 2.000 0.500 1.000
56 0.106 1138.000 1138 1138 59.638 34.288 1.000 0.460 183 105 135.000 0.325 1.000 1.000 1.000
57 1.901 1470.667 1102 2386 21.847 35.624 1.000 1.895 66 107 120.964 1.609 1.130 0.885 0.857
58 0.106 1084.000 1084 1084 57.038 35.263 1.000 0.460 175 108 135.000 0.325 1.000 1.000 1.000
59 1.373 1266.846 1145 1573 64.263 35.538 0.956 1.895 195 108 149.036 0.975 1.796 0.557 0.929
60 2.113 1330.600 1131 1581 54.584 37.278 0.898 2.344 166 118 56.310 1.532 1.701 0.588 0.851
61 2.958 1209.321 1083 1670 58.187 37.607 0.667 2.796 176 112 125.538 1.912 1.573 0.636 0.737
62 1.056 1364.300 1106 1748 65.033 37.148 0.887 1.625 199 112 126.870 1.149 1.587 0.630 0.800
63 0.423 1334.500 1289 1374 19.500 37.375 1.000 0.919 59 114 135.000 0.650 1.000 1.000 1.000
64 0.106 1141.000 1141 1141 17.388 37.863 1.000 0.460 53 116 135.000 0.325 1.000 1.000 1.000
65 1.162 1330.818 1083 1694 52.103 38.601 0.976 1.625 158 120 36.870 1.300 1.147 0.871 0.815
66 0.423 1250.500 1131 1375 54.113 39.081 1.000 1.028 165 120 161.565 0.650 1.468 0.681 0.800
67 0.423 1182.250 1102 1337 51.594 39.813 0.857 1.028 158 121 108.435 0.650 1.468 0.681 0.800
68 0.211 1120.500 1120 1121 21.775 39.813 1.000 0.727 66 122 153.435 0.325 2.000 0.500 1.000
69 0.106 1097.000 1097 1097 36.888 40.138 1.000 0.460 113 123 135.000 0.325 1.000 1.000 1.000
70 25.561 1346.455 1084 2808 62.117 45.300 0.197 10.205 178 149 52.765 5.891 1.883 0.531 0.593
71 1.162 1133.455 1084 1221 36.976 41.142 0.333 2.538 111 129 39.806 1.117 3.160 0.316 0.537
72 6.338 1389.550 1113 2426 59.508 41.540 0.600 4.420 177 127 162.897 2.275 1.781 0.561 0.822
73 0.634 1148.333 1089 1231 45.717 41.329 0.686 1.625 139 129 53.130 0.727 2.778 0.360 0.706
74 1.796 1360.118 1084 1974 55.202 41.380 0.763 2.081 167 129 38.660 1.625 1.209 0.827 0.791
75 0.951 1351.000 1095 1746 67.113 41.763 1.000 1.379 205 127 135.000 0.975 1.000 1.000 1.000
76 0.106 1113.000 1113 1113 24.538 42.088 1.000 0.460 75 129 135.000 0.325 1.000 1.000 1.000
77 0.634 1129.167 1089 1212 35.913 43.496 0.769 1.379 109 132 135.000 0.975 1.295 0.772 0.750
78 0.106 1108.000 1108 1108 25.838 44.038 1.000 0.460 79 135 135.000 0.325 1.000 1.000 1.000
79 0.106 1090.000 1090 1090 59.638 44.688 1.000 0.460 183 137 135.000 0.325 1.000 1.000 1.000
80 0.106 1105.000 1105 1105 26.813 45.013 1.000 0.460 82 138 135.000 0.325 1.000 1.000 1.000
81 0.106 1138.000 1138 1138 31.688 45.013 1.000 0.460 97 138 135.000 0.325 1.000 1.000 1.000
82 1.584 1573.533 1103 2296 67.156 45.489 0.804 2.081 204 138 141.340 1.300 1.279 0.782 0.833
83 0.106 1108.000 1108 1108 34.938 45.338 1.000 0.460 107 139 135.000 0.325 1.000 1.000 1.000
84 0.211 1103.500 1085 1122 37.050 45.338 1.000 0.727 113 139 153.435 0.325 2.000 0.500 1.000
85 0.106 1092.000 1092 1092 42.088 45.338 1.000 0.460 129 139 135.000 0.325 1.000 1.000 1.000
86 0.211 1103.000 1084 1122 56.550 45.338 1.000 0.727 173 139 153.435 0.325 2.000 0.500 1.000
87 2.852 1268.963 1091 1598 25.705 47.035 0.836 2.796 77 141 125.538 1.838 1.662 0.602 0.806
88 0.423 1100.750 1083 1122 36.806 46.556 0.857 1.172 112 142 146.310 0.650 2.000 0.500 0.800
89 0.106 1110.000 1110 1110 43.063 46.313 1.000 0.460 132 142 135.000 0.325 1.000 1.000 1.000
90 0.317 1136.333 1102 1188 27.354 46.854 1.000 0.919 83 145 45.000 0.650 1.464 0.683 0.857
91 0.106 1126.000 1126 1126 44.038 47.288 1.000 0.460 135 145 135.000 0.325 1.000 1.000 1.000
92 0.106 1116.000 1116 1116 25.188 47.938 1.000 0.460 77 147 135.000 0.325 1.000 1.000 1.000
93 0.317 1141.667 1084 1233 56.279 48.696 1.000 0.919 172 149 135.000 0.650 1.464 0.683 0.857
94 0.106 1083.000 1083 1083 57.038 48.588 1.000 0.460 175 149 135.000 0.325 1.000 1.000 1.000
95 1.479 1183.571 1085 1314 23.354 49.284 0.911 2.180 69 150 153.435 0.975 2.363 0.423 0.875
96 0.211 1138.000 1135 1141 57.200 49.238 1.000 0.727 175 151 153.435 0.325 2.000 0.500 1.000
97 2.429 1494.130 1084 2113 65.643 50.142 0.938 2.344 199 152 146.310 1.609 1.465 0.683 0.836
98 0.211 1125.000 1102 1148 18.850 50.050 0.785 0.919 57 153 135.000 0.460 2.646 0.378 0.667
99 0.211 1098.500 1085 1112 34.125 49.888 1.000 0.727 104 153 153.435 0.325 2.000 0.500 1.000
100 0.423 1157.250 1100 1208 57.200 50.050 1.000 0.919 175 153 135.000 0.650 1.000 1.000 1.000
101 0.739 1273.714 1125 1414 27.370 50.491 1.000 1.379 83 157 45.000 0.975 1.380 0.725 0.875
102 0.634 1142.833 1096 1169 62.075 50.375 0.916 1.453 189 156 26.565 0.650 2.082 0.480 0.857
103 0.211 1127.500 1099 1156 29.900 50.863 1.000 0.727 91 156 153.435 0.325 2.000 0.500 1.000
104 0.739 1239.714 1095 1509 28.345 51.513 1.000 1.172 86 157 123.690 0.975 1.069 0.936 0.875
105 0.106 1091.000 1091 1091 34.938 51.513 1.000 0.460 107 158 135.000 0.325 1.000 1.000 1.000
106 0.845 1167.000 1084 1255 31.159 52.325 0.785 1.625 94 163 36.870 0.919 2.014 0.496 0.727
107 0.211 1085.000 1085 1085 60.125 51.838 1.000 0.727 184 159 153.435 0.325 2.000 0.500 1.000
108 1.479 1309.357 1097 1704 62.307 52.998 0.993 1.750 189 162 158.199 1.300 1.164 0.859 0.824
109 0.528 1316.600 1211 1587 26.098 53.593 1.000 1.172 79 166 33.690 0.650 1.553 0.644 0.909
110 0.951 1314.778 1133 1693 29.124 54.221 1.000 1.453 89 169 63.435 0.975 1.340 0.746 0.857
111 0.423 1148.250 1097 1244 56.144 54.519 0.857 1.172 172 166 123.690 0.650 2.000 0.500 0.800
112 0.423 1146.500 1103 1165 59.719 54.438 0.857 1.028 183 166 108.435 0.650 1.468 0.681 0.800
113 1.373 1357.538 1092 1626 61.513 54.513 1.000 1.750 187 169 21.801 1.300 1.420 0.704 0.812
114 0.106 1156.000 1156 1156 33.313 54.763 1.000 0.460 102 168 135.000 0.325 1.000 1.000 1.000
115 10.563 1473.170 1084 2716 56.940 57.346 0.364 5.592 170 183 54.462 3.627 1.591 0.629 0.725
116 0.211 1161.000 1156 1166 59.963 55.575 1.000 0.727 184 170 116.565 0.325 2.000 0.500 1.000
117 0.106 1095.000 1095 1095 26.488 55.738 1.000 0.460 81 171 135.000 0.325 1.000 1.000 1.000
118 0.106 1101.000 1101 1101 63.863 56.063 1.000 0.460 196 172 135.000 0.325 1.000 1.000 1.000
119 5.176 1373.796 1089 2209 32.397 57.502 0.433 4.275 93 178 8.746 2.432 2.008 0.498 0.645
120 0.317 1266.333 1203 1303 61.046 57.146 1.000 0.919 187 177 45.000 0.650 1.464 0.683 0.857
121 0.528 1288.200 1110 1490 64.253 57.493 1.000 1.172 196 176 146.310 0.650 1.553 0.644 0.909
122 2.113 1429.950 1098 2307 36.465 58.760 0.838 2.538 109 178 140.194 1.498 1.710 0.585 0.833
123 1.584 1249.067 1090 1491 39.033 58.858 0.804 2.081 118 179 141.340 1.300 1.739 0.575 0.769
124 2.007 1558.421 1099 2453 59.638 58.919 1.000 1.895 181 180 149.036 1.625 1.121 0.892 0.864
125 0.106 1112.000 1112 1112 33.963 58.988 1.000 0.460 104 181 135.000 0.325 1.000 1.000 1.000
126 1.056 1243.600 1085 1465 48.750 60.353 0.887 1.838 148 184 135.000 1.149 1.864 0.537 0.800
127 2.218 1518.762 1094 2666 53.261 60.613 1.000 2.180 161 185 153.435 1.609 1.355 0.738 0.857
128 0.211 1172.000 1159 1185 39.650 60.613 1.000 0.727 121 186 153.435 0.325 2.000 0.500 1.000
129 0.634 1275.167 1099 1602 50.808 60.883 1.000 1.172 155 186 123.690 0.919 1.291 0.775 0.800
130 0.106 1131.000 1131 1131 41.113 60.938 1.000 0.460 126 187 135.000 0.325 1.000 1.000 1.000
131 0.106 1277.000 1277 1277 62.563 60.938 1.000 0.460 192 187 135.000 0.325 1.000 1.000 1.000
132 0.106 1119.000 1119 1119 43.063 61.263 1.000 0.460 132 188 135.000 0.325 1.000 1.000 1.000
133 0.317 1191.000 1130 1236 56.171 61.479 1.000 0.919 172 188 135.000 0.650 1.464 0.683 0.857
134 0.106 1163.000 1163 1163 41.438 61.588 1.000 0.460 127 189 135.000 0.325 1.000 1.000 1.000
135 0.211 1141.500 1113 1170 44.688 62.075 1.000 0.727 137 190 116.565 0.325 2.000 0.500 1.000
136 0.106 1119.000 1119 1119 53.463 64.188 1.000 0.460 164 197 135.000 0.325 1.000 1.000 1.000
ImageJ macro language
It is possible to record workflows in the original ImageJ macro language and run those macros in PyImageJ with ij.py.run_macro() (see the07-Running-Macros-Scripts-and-Plugins notebook for more information). While this is functional, we do not recommend using the macro language as it is more fragile and less powerful due to the orignal ImageJ internal operations. For example, macros are single threaded only and do not take advantage of multi-threaded processing. For more information on ImageJ macro limitations please visit the macro wiki page.