annotate filter.xml @ 4:d51310ab328a draft default tip

planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c86a1b93cb7732f7331a981d13465653cc1a2790

<tool id="ip_filter_standard" name="Filter 2-D image" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="20.05">
    <description>with scipy</description>
    <macros>
        <import>creators.xml</import>
        <import>tests.xml</import>
        <token name="@TOOL_VERSION@">1.12.0</token>
        <token name="@VERSION_SUFFIX@">1</token>
    </macros>
    <creator>
        <expand macro="creators/bmcv" />
    </creator>
    <edam_operations>
        <edam_operation>operation_3443</edam_operation>
    </edam_operations>
    <xrefs>
        <xref type="biii">scipy</xref>
    </xrefs>
    <requirements>
        <requirement type="package" version="@TOOL_VERSION@">scipy</requirement>
        <requirement type="package" version="1.26.4">numpy</requirement>
        <requirement type="package" version="0.22.0">scikit-image</requirement>
        <requirement type="package" version="2024.2.12">tifffile</requirement>
        <requirement type="package" version="0.1">giatools</requirement>
    </requirements>
    <command detect_errors="aggressive"><![CDATA[

        python '$__tool_directory__/filter_image.py'

        '$input'
        '$output'

        $filter_type
        $size

    ]]></command>
    <inputs>
        <param name="input" type="data" format="tiff,png" label="Input image" />
        <conditional name="filter">
            <param name="filter_type" type="select" label="Filter type">
                <option value="gaussian" selected="True">Gaussian</option>
                <option value="median">Median</option>
                <option value="prewitt_h">Prewitt horizontal</option>
                <option value="prewitt_v">Prewitt vertical</option>
                <option value="sobel_h">Sobel horizontal</option>
                <option value="sobel_v">Sobel vertical</option>
            </param>
            <when value="gaussian">
                <param name="size" type="float" value="3" min="0.1" label="Sigma" help="The half width of the Gaussian bell (in pixels)." />
            </when>
            <when value="median">
                <param name="size" type="integer" value="3" label="Radius" help="Radius of the neighborhood (in pixels)." />
            </when>
            <when value="prewitt_h">
                <param name="size" type="hidden" value="0" />
            </when>
            <when value="prewitt_v">
                <param name="size" type="hidden" value="0" />
            </when>
            <when value="sobel_h">
                <param name="size" type="hidden" value="0" />
            </when>
            <when value="sobel_v">
                <param name="size" type="hidden" value="0" />
            </when>
        </conditional>
    </inputs>
    <outputs>
       <data format="tiff" name="output" />
    </outputs> 
    <tests>
        <!-- Tests with uint8 TIFF input image -->
        <test>
            <param name="input" value="input1_uint8.tif" />
            <param name="filter_type" value="gaussian" />
            <expand macro="tests/intensity_image_diff" name="output" value="input1_gaussian.tif" ftype="tiff">
                <!--

                The input file `input1_uint8.tif` has values ranging between 23 and 254, with a mean value of 63.67.

                Below, we use an assertion in addition to the `image_diff` comparison, to ensure that the range of
                values is preserved. The motiviation behind this is that the expectation images are usually checked
                visually, which means that the `image_diff` comparison is likely to ensure that the brightness of
                the image is correct, thus it's good to double-check the range of values (hence the comparably large
                value for `eps`). This also concerns the median filter.

                -->
                <has_image_mean_intensity mean_intensity="63.67" eps="10"/>
            </expand>
        </test>
        <test>
            <param name="input" value="input1_uint8.tif" />
            <param name="filter_type" value="median" />
            <expand macro="tests/intensity_image_diff" name="output" value="input1_median.tif" ftype="tiff">
                <!-- See note for Gaussian filter above. -->
                <has_image_mean_intensity mean_intensity="63.67" eps="10"/>
            </expand>
        </test>
        <test>
            <param name="input" value="input1_uint8.tif" />
            <param name="filter_type" value="prewitt_h" />
            <expand macro="tests/intensity_image_diff" name="output" value="input1_prewitt_h.tif" ftype="tiff"/>
        </test>
        <test>
            <param name="input" value="input1_uint8.tif" />
            <param name="filter_type" value="prewitt_v" />
            <expand macro="tests/intensity_image_diff" name="output" value="input1_prewitt_v.tif" ftype="tiff"/>
        </test>
        <test>
            <param name="input" value="input1_uint8.tif" />
            <param name="filter_type" value="sobel_h" />
            <expand macro="tests/intensity_image_diff" name="output" value="input1_sobel_h.tif" ftype="tiff"/>
        </test>
        <test>
            <param name="input" value="input1_uint8.tif" />
            <param name="filter_type" value="sobel_v" />
            <expand macro="tests/intensity_image_diff" name="output" value="input1_sobel_v.tif" ftype="tiff"/>
        </test>
        <!-- Tests with float TIFF input image -->
        <test>
            <param name="input" value="input2_float.tif" />
            <param name="filter_type" value="gaussian" />
            <expand macro="tests/intensity_image_diff" name="output" value="input2_gaussian.tif" ftype="tiff">
                <!-- See note for Gaussian filter above. -->
                <has_image_mean_intensity mean_intensity="0.25" eps="0.01"/>
            </expand>
        </test>
    </tests>
    <help>

        **Applies a standard filter to a single-channel 2-D image.**

        Mean filters like the Gaussian filter or the median filter preserve both the brightness of the image, and the range of values.

    </help>
    <citations>
       <citation type="doi">10.1016/j.jbiotec.2017.07.019</citation>
    </citations>
</tool>