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Title

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1		-4. How to use Webilastik
	1	+4. How to segment your objcets with Webilastik

Author

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1		-XWiki.tomazvieira
	1	+XWiki.puchades

Content

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50	50	=== Opening a Dataset from the data-proxy ===
51	51
52	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(%%)##
	53	+\\##precomputed:~/~/https:~/~/data-proxy.ebrains.eu/api/v1/buckets/(% style="background-color:#3498db; color:#ffffff" %)my-bucket-name(% style="background-color:#ffffff; color:#000000" %)/(% style="background-color:#9b59b6; color:#ffffff" %)path/inside/your/bucket(%%)##
54	54
55	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
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80	80
81	81	[[image:image-20220125164557-4.png]]
82	82
83		-You can also click the
84	84
85	85	==== A Note on Neuroglancer and 2D data ====
86	86
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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. 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.
	145	+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.
147	147
148		-[[image:image-20220222153157-4.png]]
	147	+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.
149	149
	149	+[[image:image-20220911162555-3.png]]
	150	+
150	150	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.
151	151
152		-[[image:image-20220222153610-5.png]]
	153	+[[image:image-20220911163127-4.png]]
153	153
154	154
155	155	You can keep adding or removing brush strokes to improve your predictions.
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160	160	1. Adjust the layer opacity to better view the predictions or underlying raw data;
161	161	1. Advanced users: edit the shader to render the predictions in any arbitrary way;
162	162
163		-The image below shows the "predictions" tab with an opacity set to 0.68 using the steps described above:
	164	+The image below shows the "predictions" tab with an opacity set to 0.88 using the steps described above:
164	164
165		-[[image:image-20220125172238-8.png]]
	166	+[[image:image-20220911163504-5.png]]
166	166
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.
	168	+You can keep adding or removing features to your model, as well as adding and removing annotations, which will automatically refresh the predictions tab.
168	168
169	169	=== Exporting Results and Running Jobs ===
170	170
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.
	172	+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:
172	172
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.
	174	+~1. As a "Predictions Map", which is a float32 image with as many channels as the number of Label colors you've used, or;
174	174
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.
	176	+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.
176	176
177		-[[image:image-20220125190311-2.png]]
	178	+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.
178	178
	180	+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:
	181	+
	182	+1. Fill in the name of the data-proxy bucket where the results in Neuroglancer's precomputed chunks format should be written to;
	183	+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.
	184	+
	185	+[[image:image-20220911170735-7.png]]
	186	+
	187	+
179	179	Finally, click export button and eventually a new job shall be created if all the parameters were filled in correctly.
180	180
181	181	You'll be able to find your results in the data-proxy GUI, in a url that looks something like this:
182	182
183		-https:~/~/data-proxy.ebrains.eu/your-bucket-name?prefix=your/selected/prefix
	192	+https:~/~/data-proxy.ebrains.eu/your-bucket-name?prefix=your/info/directory/path
184	184
185	185	[[image:image-20220125191847-3.png]]