add by-slice to gaussian

active_plugins/gaussian_byslice.py

@@ -0,0 +1,108 @@
+"""
+Gaussian_BySlice
+==============
+
+**Gaussian_BySlice** will blur an image and remove noise. Filtering an
+image with a Gaussian filter can be helpful if the foreground signal is
+noisy or near the noise floor.
+
+|
+
+============ ============ ===============
+Supports 2D? Supports 3D? Respects masks?
+============ ============ ===============
+YES          YES          NO
+============ ============ ===============
+"""
+
+import numpy
+import skimage.filters
+from cellprofiler_core.image import Image
+from cellprofiler_core.module import ImageProcessing
+from cellprofiler_core.setting import Binary
+from cellprofiler_core.setting.text import Integer
+
+
+class Gaussian_BySlice(ImageProcessing):
+    category = "Advanced"
+
+    module_name = "Gaussian_BySlice"
+
+    variable_revision_number = 2
+
+    def create_settings(self):
+        super(Gaussian_BySlice, self).create_settings()
+
+        self.sigma = Integer(
+            text="Sigma",
+            value=1,
+            doc="Standard deviation of the kernel to be used for blurring. Larger sigmas induce more blurring.",
+        )
+
+        self.by_slice = Binary(
+            text="Process by slice",
+            value=False,
+            doc="If enabled, for 3D images, the Gaussian filter is applied to each Z-plane independently. Setting ignored if images are not 3D.",
+        )
+
+    def run(self, workspace):
+        x_name = self.x_name.value
+
+        y_name = self.y_name.value
+
+        images = workspace.image_set
+
+        x = images.get_image(x_name)
+
+        dimensions = x.dimensions
+
+        x_data = x.pixel_data
+
+        sigma = numpy.divide(self.sigma.value, x.spacing)
+
+        if self.by_slice.value and x.volumetric:
+            y_data = numpy.zeros_like(x_data, dtype=float)
+            for index, plane in enumerate(x_data):
+                y_data[index] = skimage.filters.gaussian(plane, sigma=self.sigma.value)
+        else:
+            y_data = skimage.filters.gaussian(x_data, sigma=sigma)
+
+        y = Image(dimensions=dimensions, image=y_data, parent_image=x)
+
+        images.add(y_name, y)
+
+        if self.show_window:
+            workspace.display_data.x_data = x_data
+
+            workspace.display_data.y_data = y_data
+
+            workspace.display_data.dimensions = dimensions
+
+    def settings(self):
+        __settings__ = super(Gaussian_BySlice, self).settings()
+
+        return __settings__ + [self.sigma, self.by_slice]
+
+    def visible_settings(self):
+        __settings__ = super(Gaussian_BySlice, self).visible_settings()
+
+        __settings__ += [self.sigma, self.by_slice]
+
+        return __settings__
+
+    def upgrade_settings(self, setting_values, variable_revision_number, module_name):
+        """Adjust the setting_values for older save file versions
+
+        setting_values - a list of strings representing the settings for
+                         this module.
+        variable_revision_number - the variable revision number of the module
+                                   that saved the settings
+        module_name - the name of the module that saved the settings
+
+        returns the modified settings, revision number
+        """
+        if variable_revision_number == 1:
+            setting_values = setting_values + ["No"]
+            variable_revision_number = 2
+
+        return setting_values, variable_revision_number