Last modified by puchades on 2022/09/30 16:01
From version 52.1
edited by tomazvieira
on 2022/09/11 17:09
Change comment: There is no comment for this version
To version 48.1
edited by tomazvieira
on 2022/09/11 16:31
Change comment: Uploaded new attachment "image-20220911163127-4.png", version {1}

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143 143  
144 144  The status display in the "Training" applet will show "training on [datasource url]" when it's ready to start painting.
145 145  
146 -Now you can start adding brush strokes. By default, webilastik will create two kinds of labels: "Background" and "Foreground". You can rename them to your liking or change their colors to something more suitable for you or your dataset. You can also add more labels if you'd like ilastik to classify the pixels of your image into more than two categories.
146 +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.
147 147  
148 -Select one of the labels from the "Current Label" dropdown or by using the "Select Label" button, check the "Enable Brushing" checkbox to enable brushing mode (**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: magenta, representing the "foreground" and green, representing the "background" class.
148 +[[image:image-20220222153157-4.png]]
149 149  
150 -[[image:image-20220911162555-3.png]]
151 -
152 152  Once you have some image features selected and some brush annotation of at least 2 colors, you can check "Live Update" and ilastik will automatically use your examples to predict what classes the rest of your dataset should be, displaying the results in a "predictions" tab.
153 153  
154 -[[image:image-20220911163127-4.png]]
152 +[[image:image-20220222153610-5.png]]
155 155  
156 156  
157 157  You can keep adding or removing brush strokes to improve your predictions.
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162 162  1. Adjust the layer opacity to better view the predictions or underlying raw data;
163 163  1. Advanced users: edit the shader to render the predictions in any arbitrary way;
164 164  
165 -The image below shows the "predictions" tab with an opacity set to 0.88 using the steps described above:
163 +The image below shows the "predictions" tab with an opacity set to 0.68 using the steps described above:
166 166  
167 -[[image:image-20220911163504-5.png]]
165 +[[image:image-20220125172238-8.png]]
168 168  
169 -You can keep adding or removing features to your model, as well as adding and removing annotations, which will automatically refresh the predictions tab.
167 +You can keep adding or removing features to your model, as well as adding and removing annotations, which will automatically update the predictions tab.
170 170  
171 171  === Exporting Results and Running Jobs ===
172 172  
173 -Once you trained your pixel classifier with the previous applets, you can apply it to other datasets or even the same dataset that was used to do the training on. You can export your results in two ways:
171 +Once you trained your pixel classifier with the previous applets, you can apply it to other datasets or even the same dataset that was used to do the training on.
174 174  
175 -~1. As a "Predictions Map", which is a float32 image with as many channels as the number of Label colors you've used, or;
173 +To do so, select a data source by typing in the URL of the data source in the Data Source Url field and select a scale from the data source as they appear beneath the URL field.
176 176  
177 -2. As a "Simple Segmentation", which is one 3-channel uint8 image for each of the Label colors you've used. The imag will be red where that pixel is more likely to belong to the respective Label and black everywhere else.
175 +Then, configure a Data Sink, i.e., a destination that will receive the results of the pixel classification. For now, webilastik will only export to ebrains' data-proxy buckets; Fill in the name of the bucket and then the prefix (i.e.: path within the bucket) where the results in Neuroglancer's precomputed chunks format should be written to.
178 178  
179 -To do so, select a data source by typing in the URL of the data source in the "Url" field of the "Input" fieldset and select a scale from the data source as they appear beneath the URL field. You can also click the "Suggestions..." button to select one of the annotated datasources.
177 +[[image:image-20220125190311-2.png]]
180 180  
181 -Then, configure the Output, i.e., the destination that will receive the results of the pixel classification. For now, webilastik will only export to ebrains' data-proxy buckets:
182 -
183 -1. Fill in the name of the data-proxy bucket where the results in Neuroglancer's precomputed chunks format should be written to;
184 -1. Fill in the directory path inside the bucket where the results should be saved to. This path will also contain the "info" file of the precomputed chunks format.
185 -
186 -[[image:image-20220911170735-7.png]]
187 -
188 -
189 189  Finally, click export button and eventually a new job shall be created if all the parameters were filled in correctly.
190 190  
191 191  You'll be able to find your results in the data-proxy GUI, in a url that looks something like this:
192 192  
193 -https:~/~/data-proxy.ebrains.eu/your-bucket-name?prefix=your/info/directory/path
183 +https:~/~/data-proxy.ebrains.eu/your-bucket-name?prefix=your/selected/prefix
194 194  
195 195  [[image:image-20220125191847-3.png]]
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