Mercurial > repos > bgruening > cp_track_objects

comparison test-data/identify_primary_objects_adv_global_rb.cppipe @ 0:644e5e32a83c draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit 35da2dcd86747c9bff138e100dbe08c6106f3780"
author bgruening
date Sat, 06 Feb 2021 10:03:00 +0000
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children 1fd453cd757b
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-1:000000000000 0:644e5e32a83c
1 CellProfiler Pipeline: http://www.cellprofiler.org
2 Version:3
3 DateRevision:319
4 GitHash:
5 ModuleCount:5
6 HasImagePlaneDetails:False
7
8 Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
9 :
10 Filter images?:Images only
11 Select the rule criteria:and (extension does isimage) (directory doesnot startwith ".")
12
13 Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
14 Extract metadata?:Yes
15 Metadata data type:Text
16 Metadata types:{}
17 Extraction method count:1
18 Metadata extraction method:Extract from file/folder names
19 Metadata source:File name
20 Regular expression to extract from file name:(?P<field1>.*)_(?P<field2>[a-zA-Z0-9]+)_(?P<field3>[a-zA-Z0-9]+)_(?P<field4>[a-zA-Z0-9]+)
21 Regular expression to extract from folder name:(?P<folderField1>.*)
22 Extract metadata from:All images
23 Select the filtering criteria:and (file does contain "")
24 Metadata file location:
25 Match file and image metadata:[]
26 Use case insensitive matching?:No
27
28 NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
29 Assign a name to:Images matching rules
30 Select the image type:Grayscale image
31 Name to assign these images:DNA
32 Match metadata:[]
33 Image set matching method:Order
34 Set intensity range from:Image metadata
35 Assignments count:1
36 Single images count:0
37 Maximum intensity:255.0
38 Process as 3D?:No
39 Relative pixel spacing in X:1.0
40 Relative pixel spacing in Y:1.0
41 Relative pixel spacing in Z:1.0
42 Select the rule criteria:and (file does startwith "im")
43 Name to assign these images:DNA
44 Name to assign these objects:Cell
45 Select the image type:Grayscale image
46 Set intensity range from:Image metadata
47 Select the image type:Grayscale image
48 Maximum intensity:255.0
49
50 Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
51 Do you want to group your images?:Yes
52 grouping metadata count:1
53 Metadata category:field1
54
55 IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\'Identify the nuclei from the DNA channel.\', \'PARAMS\x3A\', \'- Typical diameter of objects (Min,Max)\', \'- Method to distinguish clumped objects\x3A Shape/None. With Shape, the distance between the 2 centers can be changed.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
56 Select the input image:DNA
57 Name the primary objects to be identified:Nuclei
58 Typical diameter of objects, in pixel units (Min,Max):10,40
59 Discard objects outside the diameter range?:Yes
60 Discard objects touching the border of the image?:Yes
61 Method to distinguish clumped objects:Shape
62 Method to draw dividing lines between clumped objects:Shape
63 Size of smoothing filter:1
64 Suppress local maxima that are closer than this minimum allowed distance:7
65 Speed up by using lower-resolution image to find local maxima?:Yes
66 Fill holes in identified objects?:After both thresholding and declumping
67 Automatically calculate size of smoothing filter for declumping?:No
68 Automatically calculate minimum allowed distance between local maxima?:No
69 Handling of objects if excessive number of objects identified:Continue
70 Maximum number of objects:500
71 Use advanced settings?:Yes
72 Threshold settings version:10
73 Threshold strategy:Global
74 Thresholding method:RobustBackground
75 Threshold smoothing scale:1.4000
76 Threshold correction factor:1.0
77 Lower and upper bounds on threshold:0.0,1.0
78 Manual threshold:0
79 Select the measurement to threshold with:None
80 Two-class or three-class thresholding?:Two classes
81 Assign pixels in the middle intensity class to the foreground or the background?:Foreground
82 Size of adaptive window:50
83 Lower outlier fraction:0.06
84 Upper outlier fraction:0.07
85 Averaging method:Median
86 Variance method:Median absolute deviation
87 # of deviations:3.00
88 Thresholding method:RobustBackground