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12	12	=== Pixel classification workflow ===
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	14	+For a quick introduction, watch: [[https:~~/~~/www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be>>url:https://www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be]]
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15		-==== ====
	16	+**Basic steps:**
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	18	+-Train the classifier with two classes (labeling and background)
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18		-Photo by David Clode
	20	+-Apply the classifier to the rest of the images (Batch processing)
19	19
20		-==== Or code ====
	22	+-Export the probability maps in HDH5 format, and simple_segmentation images in .png format, with the default settings.
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22		-Code blocks can be added by using the code macro:
	24	+=== Object classification workflow ===
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24		-{{code language="python"}}
25		-x = 1
26		-if x == 1:
27		- # indented four spaces
28		- print("x is 1.")
29		-{{/code}}
	26	+There are three options on the ilastik start up page for running Object Classification.  Choose the //Object Classification with Raw Data and Pixel Prediction Maps as input//**.**
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31		-(% class="wikigeneratedid" id="HH4Won27tAppearinToC" %)
	28	+-Save the object classification file in the same folder as the raw images for analysis.  If the images are moved after the ilastik file is created, the link between the ilastik file and the images may be lost, resulting in a corrupted file.
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	30	+-In the **Input Data** applet, upload the original images and their respective probability maps in HDH5 format (output from the pixel classification).
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	32	+-Train the classifier with two classes (labeling and artefacts)
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	34	+(% class="wikigeneratedid" id="H" %)
	35	+-In the **Object Information Export** applet, export “Object Predictions” in PNG format.  Do not change the default export location
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