Last modified by puchades on 2022/09/30 16:01
From version 41.1
edited by tomazvieira
on 2022/09/11 12:43
Change comment: There is no comment for this version
To version 45.2
edited by tomazvieira
on 2022/09/11 16:02
Change comment: There is no comment for this version

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... ... @@ -49,20 +49,24 @@
49 49  
50 50  === Opening a Dataset from the data-proxy ===
51 51  
52 -You can also load Neuroglancer Precomputed Chunks data from the data-proxy; The URLs for this kind of data follow the following scheme:
53 -\\##precomputed:~/~/https:~/~/data-proxy.ebrains.eu/api/buckets/(% style="background-color:#3498db; color:#ffffff" %)my-bucket-name(% style="background-color:#9b59b6; color:#ffffff" %)/path/inside/your/bucket(%%)##
52 +You can also load Neuroglancer Precomputed Chunks data from the data-proxy (e.g. the [[ana-workshop-event bucket>>https://wiki.ebrains.eu/bin/view/Collabs/ana-workshop-event/Bucket]]); The URLs for this kind of data follow the following scheme:
53 +\\##precomputed:~/~/https:~/~/data-proxy.ebrains.eu/api/v1/buckets/(% style="background-color:#3498db; color:#ffffff" %)my-bucket-name(% style="color: rgb(0, 0, 0); background-color: rgb(255, 255, 255)" %)/(% style="background-color:#9b59b6; color:#ffffff" %)path/inside/your/bucket(%%)##
54 54  
55 -So, for example, to load the sample data inside the (% style="background-color:#3498db; color:#ffffff" %)quint-demo(%%) bucket, under the path (% style="background-color:#9b59b6; color:#ffffff" %)tg-ArcSwe_mice_precomputed/hbp-00138_122_381_423_s001.precomputed(% style="color:#000000" %) (%%) like in the example below:
55 +where (% style="background-color:#9b59b6; color:#ffffff" %)path/inside/your/bucket(%%)  should be the path to the folder containing the dataset "info" file.
56 56  
57 57  
58 -[[image:image-20220128142757-1.png]]
58 +So, for example, to load the sample data inside the (% style="background-color:#3498db; color:#ffffff" %)ana-workshop-event(%%) bucket, under the path (% style="background-color:#9b59b6; color:#ffffff" %)tg-ArcSwe_mice_precomputed/hbp-00138_122_381_423_s001.precomputed(% style="color:#000000" %) (%%) like in the example below:
59 59  
60 +(% style="display:none" %) (%%)
61 +
62 +[[image:webilastik_bucket_paths.png]]
63 +
60 60  === ===
61 61  
62 62  you would type a URL like this:
63 63  
64 64  
65 -##precomputed:~/~/https:~/~/data-proxy.ebrains.eu/api/buckets/(% style="background-color:#3498db; color:#ffffff" %)quint-demo(%%)/(% style="background-color:#9b59b6; color:#ffffff" %)tg-ArcSwe_mice_precomputed/hbp-00138_122_381_423_s001.precomputed(%%)##
69 +{{{precomputed://https://data-proxy.ebrains.eu/api/v1/buckets/ana-workshop-event/tg-ArcSwe_mice_precomputed/hbp-00138_122_381_423_s001.precomputed}}}
66 66  
67 67  this scheme is the same whether you're loading data into the Neuroglancer viewer or specifying an input URL in the export applet.
68 68  
... ... @@ -92,23 +92,21 @@
92 92  (% class="wikigeneratedid" %)
93 93  [[image:webilastik_click_recenter_button.png]]
94 94  
95 -(% class="wikigeneratedid" %)
96 96  == Allocating a Compute Session ==
97 97  
98 98  Normal ilastik operation can be computationally intensive, requiring dedicated compute resources to be allocated to every user working with it.
99 99  
100 -
101 101  The "Session Management" widget allows you to request a compute session where webilastik will run; Select a session duration and click 'Create' to create a new compute session. Eventually the compute session will be allocated, opening up the other workflow widgets.
102 102  
105 +Don't forget to close your compute session by clicking the "Close Session" button once you're done to prevent wasting your quota in the HPC. If you have a long running job, though, you can just leave the session and rejoin it later by pasting its session ID in the "Session Id" field of the "Session Management" widget and clicking "rejoin Session".
103 103  
104 -
105 105  == Training the Pixel Classifier ==
106 106  
107 107  === Selecting Image Features ===
108 108  
109 -Pixel Classification uses different characteristics ("features") of your image to determine which class each pixel should belong to. These take into account, for example, color and texture of each pixel as well as that of the neighboring pixels. Each one of this characteristics requires some computational power, which is why you can select only the ones that are sensible for your particular dataset.
111 +Pixel Classification uses different characteristics ("features") of each pixel from your image to determine which class that pixel should belong to. These take into account, for example, color and texture of each pixel as well as that of the neighboring pixels. Each one of this characteristics requires some computational power, which is why you can select only the ones that are sensible for your particular dataset.
110 110  
111 -Use the checkboxes in the applet "Select Image Features" applet to select some image features and their corresponding sigma (which determines the radius around the pixel that will be considered when computing that feature).
113 +Use the checkboxes in the applet "Select Image Features" applet to select some image features and their corresponding sigma. The higher the sigma, the bigger the vicinity considered when computing values for each pixel, and the bigger its influence over the final value of that feature. Higher sigmas also require more computations to be done and can increase the time required to do predictions.
112 112  
113 113  You can read more about image features in [[ilastik's documentation.>>https://www.ilastik.org/documentation/pixelclassification/pixelclassification\]]
114 114  
... ... @@ -118,12 +118,15 @@
118 118  
119 119  === Labeling the image ===
120 120  
121 -In order to classify the pixels of an image into different classes (e.g.: 'foreground' and 'background') ilastik needs you to provide it with samples of each class.
123 +In order to classify the pixels of an image into different classes (e.g.: 'foreground' and 'background') ilastik needs you to provide it with examples of each class.
122 122  
123 -To do so, first select a particular resolution of your dataset (your viewer might interpolate between multiple scales of the dataset, but ilastik operates on a single resolution):
124 124  
125 -[[image:image-20220125165642-1.png]]
126 +==== Picking an Image Resolution (for multi-resolution images only) ====
126 126  
128 +If your data has multiple resolutions (**not the case in any of the sample datasets**), you'll have to pick one of them in the "Training" widget. Neuroglancer interpolates between multiple scales of the dataset, but ilastik operates on a single resolution:
129 +
130 +[[image:image-20220911155827-1.png]]
131 +
127 127  Once you've selected a resolution to train on, you should see a new "training" tab at the top of the viewer:
128 128  
129 129  [[image:image-20220125165832-2.png]]
... ... @@ -132,9 +132,12 @@
132 132  
133 133  [[image:image-20220222151117-1.png]]
134 134  
140 +==== ====
135 135  
136 -The status display in this applet will show "training on [datasource url]" when you're in training mode.
142 +==== Painting Labels ====
137 137  
144 +The status display in the "Training" applet will show "training on [datasource url]" when it's ready to start painting.
145 +
138 138  Now you can start adding brush strokes. Select a color from the color picker, check the "Enable Brushing" checkbox to enable brushing (and disable navigation), and click and drag over the image to add brush strokes. Ilastik will map each used color to a "class", and will try to figure out a class for every pixel in the image based on the examples provided by the brush strokes. By painting, you provide ilastik with samples of what a pixel in that particular class should look like. The following image shows an example with 2 classes: teal, representing the "foreground" or the "cell class", and magenta, representing the "background" class.
139 139  
140 140  [[image:image-20220222153157-4.png]]
image-20220911155827-1.png
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