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11	11
12	12	=== Pixel classification workflow ===
13	13
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]]
15	15
16		-**Basic steps:**
	15	+==== ====
17	17
18		--Train the classifier with two classes (labeling and background)
19	19
20		--Apply the classifier to the rest of the images (Batch processing)
	18	+Photo by David Clode
21	21
22		--Export the probability maps in HDH5 format, and simple_segmentation images in .png format, with the default settings.
	20	+==== Or code ====
23	23
24		-=== Object classification workflow ===
	22	+Code blocks can be added by using the code macro:
25	25
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//**.**
	24	+{{code language="python"}}
	25	+x = 1
	26	+if x == 1:
	27	+ # indented four spaces
	28	+ print("x is 1.")
	29	+{{/code}}
27	27
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.
29		-
30		--
31		-
32		-==== ====
33		-
34		-
	31	+(% class="wikigeneratedid" id="HH4Won27tAppearinToC" %)
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