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comparison mesmer.xml @ 2:187918c47051 draft

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planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/mesmer commit 40737a3341bb2352f4f8560889bb53362fd624be
author goeckslab
date Wed, 28 Dec 2022 19:26:02 +0000
parents 02abff468d60
children c60b810d570d
comparison
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1:02abff468d60 2:187918c47051
5 <import>macros.xml</import> 5 <import>macros.xml</import>
6 </macros> 6 </macros>
7 7
8 <expand macro="requirements"/> 8 <expand macro="requirements"/>
9 <expand macro="stdio"/> 9 <expand macro="stdio"/>
10 <expand macro="version_cmd"/>
11 10
12 <command detect_errors="exit_code"><![CDATA[ 11 <command detect_errors="exit_code"><![CDATA[
13 ln -s '$nuclear_image' 'input.tif' && 12 python $script
14 @CMD_BEGIN@
15 --output-directory ./
16 --output-name 'mask.tif'
17 --nuclear-image 'input.tif'
18 --nuclear-channel $nuclear_channel
19 --compartment $compartment
20 --image-mpp $image_mpp
21 $squeeze
22
23 #if $membrane_select.membrane_segment == "True":
24 --membrane-image '$membrane_select.membrane_image'
25 --membrane-channel '$membrane_select.membrane_channel'
26 #end if
27 ]]></command> 13 ]]></command>
28 14 <configfiles>
15 <configfile name="script">
16 import argparse
17 import os
18 import sys
19
20 import numpy as np
21 import tifffile
22 import zarr
23
24 from deepcell.applications import Mesmer
25
26 level = 0
27 is_ome=False
28 #if $image.file_ext == "ome.tiff":
29 is_ome=True
30 #end if
31
32 ## Grab params
33 nuc_kwargs = {}
34 wc_kwargs = {}
35
36
37 with tifffile.TiffFile("$image", is_ome=is_ome) as tiff:
38
39 # Read single pyramid level
40 level_array = zarr.open(tiff.aszarr(series = 0, level = level))
41
42 ## grab the nuclear and membrane channels based on their indices add the markers along the channel axis
43 ## Tifffile should always read as (channel,X,Y)
44 #if $compartment_select.compartment != 'whole-cell':
45 nuc_kwargs = {
46 #if $compartment_select.nuclear_options.pixel_expansion != '':
47 'pixel_expansion': $compartment_select.nuclear_options.pixel_expansion,
48 #end if
49 #for $key, $value in $dict.items($compartment_select.nuclear_options.adv_options)
50 '$key': $value,
51 #end for
52 'maxima_threshold': $compartment_select.nuclear_options.maxima_threshold
53 }
54 #end if
55
56 nuclear_indices = [int(x) for x in $nuclear_channels.split(',')]
57 nuclear_channels = level_array.oindex[nuclear_indices, :, :]
58 nuclear_channels = np.sum(nuclear_channels, axis = 0)
59
60 #if $compartment_select.compartment != 'nuclear':
61 wc_kwargs = {
62 #for $key, $value in $dict.items($compartment_select.wc_options.adv_options)
63 '$key': $value,
64 #end for
65 'maxima_threshold': $compartment_select.wc_options.maxima_threshold
66 }
67
68 membrane_indices = [int(x) for x in $compartment_select.wc_options.wc_channels.split(',')]
69 membrane_channels = level_array.oindex[membrane_indices, :, :]
70 membrane_channels = np.sum(membrane_channels, axis = 0)
71 #end if
72
73 ## stack the nuclear and membrane composite channels with nuclear in channel 0 and mem in channel 1
74 ## mesmer expects dimensions to be (X,Y,Channel) so axis = -1
75 #if $compartment_select.compartment == 'nuclear':
76 membrane_channels = np.zeros(nuclear_channels.shape)
77 #end if
78 formatted_image = np.stack((nuclear_channels,membrane_channels), axis=-1)
79
80 ## add batch dimension. Will have to be squeezed out later
81 formatted_image = np.expand_dims(formatted_image, 0)
82
83 ## Create the application
84 app = Mesmer()
85
86 ## Run segmentation
87 mask = app.predict(
88 formatted_image,
89 image_mpp = $image_mpp,
90 compartment = "$compartment_select.compartment",
91 pad_mode = 'constant',
92 postprocess_kwargs_whole_cell = wc_kwargs,
93 postprocess_kwargs_nuclear = nuc_kwargs)
94
95 #if $squeeze:
96 mask = np.squeeze(mask)
97 #end if
98
99 #if $compartment_select.compartment == 'both':
100 ## split the two-channel mask into separate outputs
101 #if $squeeze:
102 tifffile.imsave( "WC_output_mask.tif", mask[:,:,0])
103 tifffile.imsave("NU_output_mask.tif", mask[:,:,1])
104 #else:
105 tifffile.imsave( "WC_output_mask.tif", mask[:,:,:,0])
106 tifffile.imsave("NU_output_mask.tif", mask[:,:,:,1])
107 #end if
108 #else:
109 ## save single-channel mask outputs as a tiff
110 tifffile.imsave("mask.tif", mask)
111 #end if
112 </configfile>
113 </configfiles>
29 <inputs> 114 <inputs>
30 <param name="nuclear_image" type="data" format="tiff, ome.tiff" label="Image containing the nuclear marker(s)"/> 115 <param name="image" type="data" format="tiff, ome.tiff" label="Image containing the all marker(s)"/>
31 <param name="nuclear_channel" type="integer" value="0" label="The numerical index of the channel(s) from nuclear-image"/> 116 <param name="nuclear_channels" type="text" value="0" label="The numerical indices of the channel(s) for the nuclear markers" help="No quotes, separated by comma. e.g. 0, 1."/>
32 <param name="compartment" type="select" label="Compartment for segmentation prediction: ">
33 <option selected="true" value="whole-cell">Whole cell</option>
34 <option value="nuclear">Nuclear</option>
35 </param>
36 <param name="image_mpp" type="float" value="0.5" label="Resolution of the image in microns-per-pixel"/> 117 <param name="image_mpp" type="float" value="0.5" label="Resolution of the image in microns-per-pixel"/>
37 <param name="squeeze" type="boolean" truevalue="--squeeze" falsevalue="" checked="false" label="Whether to np.squeeze the outputs before saving"/> 118 <param name="squeeze" type="boolean" checked="true" label="Whether to np.squeeze the outputs before saving"/>
38 <conditional name="membrane_select"> 119 <conditional name="compartment_select">
39 <param name="membrane_segment" type="select" label="Segment with Cell Membrane"> 120 <param name="compartment" type="select" label="Compartment for segmentation prediction: ">
40 <option selected="True" value="False">No</option> 121 <option selected="true" value="whole-cell">Whole cell</option>
41 <option value="True">Yes</option> 122 <option value="nuclear">Nuclear</option>
123 <option value="both">Both</option>
42 </param> 124 </param>
43 <when value="True"> 125 <when value="nuclear">
44 <param name="membrane_image" type="data" format="tiff, ome.tiff" label="The path to an image containing the membrane marker(s)"/> 126 <expand macro="nuclear_options_macro"/>
45 <param name="membrane_channel" type="integer" value="0" label="The numerical index of the channel(s) from membrane-image"/>
46 </when> 127 </when>
47 <when value="False" /> 128 <when value="whole-cell">
129 <expand macro="wc_options_macro"/>
130 </when>
131 <when value="both">
132 <expand macro="nuclear_options_macro"/>
133 <expand macro="wc_options_macro"/>
134 </when>
48 </conditional> 135 </conditional>
49 </inputs> 136 </inputs>
137
50 <outputs> 138 <outputs>
51 <data format="tiff" name="mask" from_work_dir="mask.tif" label="${tool.name} on ${on_string}: Mask"/> 139 <data format="tiff" name="nu_mask" from_work_dir="NU_output_mask.tif" label="${tool.name} on ${on_string}: Nuclear Mask">
140 <filter>compartment_select['compartment'] == 'both'</filter>
141 </data>
142 <data format="tiff" name="wc_mask" from_work_dir="WC_output_mask.tif" label="${tool.name} on ${on_string}: Whole Cell Mask">
143 <filter>compartment_select['compartment'] == 'both'</filter>
144 </data>
145 <data format="tiff" name="mask" from_work_dir="mask.tif" label="${tool.name} on ${on_string}: Mask">
146 <filter>compartment_select['compartment'] != 'both'</filter>
147 </data>
52 </outputs> 148 </outputs>
53 <tests> 149 <tests>
54 <test> 150 <test>
55 <param name="nuclear_image" value="test.tiff" /> 151 <param name="image" value="deepcell_test.tiff" ftype="tiff"/>
56 <param name="compartment" value="nuclear" /> 152 <param name="compartment" value="nuclear" />
57 <param name="membrane_segment" value="False" />
58 <param name="image_mpp" value="0.65" /> 153 <param name="image_mpp" value="0.65" />
59 <param name="squeeze" value="--squeeze" /> 154 <param name="squeeze" value="True" />
60 <output name="mask" ftype="tiff"> 155 <output name="mask" ftype="tiff">
61 <assert_contents> 156 <assert_contents>
62 <has_size value="360000" delta="1000" /> 157 <has_size value="1049000" delta="1000" />
63 </assert_contents> 158 </assert_contents>
64 </output> 159 </output>
65 </test> 160 </test>
66 </tests> 161 <test>
162 <param name="image" value="deepcell_test.tiff" ftype="tiff"/>
163 <param name="compartment" value="whole-cell" />
164 <param name="wc_channels" value="0, 1" />
165 <param name="maxima_threshold" value="0.075" />
166 <param name="image_mpp" value="0.65" />
167 <param name="squeeze" value="True" />
168 <output name="mask" ftype="tiff">
169 <assert_contents>
170 <has_size value="1049000" delta="1000" />
171 </assert_contents>
172 </output>
173 </test>
174 <test>
175 <param name="image" value="deepcell_test.tiff" ftype="tiff"/>
176 <param name="compartment" value="both" />
177 <param name="image_mpp" value="0.65" />
178 <param name="wc_channels" value="1" />
179 <param name="nuclear_channels" value="0" />
180 <param name="squeeze" value="True" />
181 <output name="wc_mask" ftype="tiff">
182 <assert_contents>
183 <has_size value="1049000" delta="1000" />
184 </assert_contents>
185 </output>
186 <output name="nu_mask" ftype="tiff">
187 <assert_contents>
188 <has_size value="1049000" delta="1000" />
189 </assert_contents>
190 </output>
191 </test>
192 </tests>
67 <help><![CDATA[ 193 <help><![CDATA[
68 ------ 194 ------
69 Mesmer 195 Mesmer
70 ------ 196 ------
71 197