Viewer slider

.github/workflows/conda_build_and_publish.yml

@@ -14,14 +14,14 @@ jobs:
   build:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
       with:
         fetch-depth: 0 # All history for use later with the meta.yaml file for conda-recipes
     - name: publish-to-conda
-      uses: paskino/conda-package-publish-action@v1.4.4
+      uses: TomographicImaging/conda-package-publish-action@v2
       with:
         subDir: 'Wrappers/Python/conda-recipe'
-        channels: '-c conda-forge -c paskino -c ccpi'
+        channels: '-c conda-forge -c ccpi'
         AnacondaToken: ${{ secrets.ANACONDA_TOKEN }}
         publish: ${{ github.event_name == 'push' && startsWith(github.event.ref, 'refs/tags') }}
         test_all: ${{(github.event_name == 'push' && startsWith(github.event.ref, 'refs/tags')) || (github.ref == 'refs/heads/master')}}

CHANGELOG.md

@@ -1,7 +1,11 @@
 # Changelog
-
 ## vx.x.x
+- Add slider widget #365
+- Removed VTK 8 variants from conda recipe.
+- Change Python variants: removed 3.6 and 3.7, added 3.11 and 3.12.
+- Bugfix on resample reader #359. Standalone viewer app defaults to downsample in all dimensions.
 - Fix bug with setInputAsNumpy() using deprecated `numpy2vtkImporter` in `CILViewer2D` - now uses `numpy2vtkImage`
+- Removed the `paskino` channel from the install command as eqt is on `conda-forge`
 
 ## v23.1.0
 - Raise error if try to add multiple widgets with the same name to CILViewer or CILViewer2D.

README.md

@@ -15,7 +15,7 @@ An example of use of the viewers in another app is the [iDVC app](https://github
 To install via `conda`, create a new environment using:
 
 ```bash
-conda create --name cilviewer ccpi-viewer=23.1.0 -c ccpi -c paskino -c conda-forge
+conda create --name cilviewer ccpi-viewer=23.1.0 -c ccpi -c conda-forge
 ```
 
 ## Running the CILViewer app

Wrappers/Python/ccpi/viewer/CILViewer2D.py

@@ -22,7 +22,7 @@
 from ccpi.viewer.CILViewerBase import CILViewerBase
 from ccpi.viewer.utils import Converter
 
-from ccpi.viewer.widgets import cilviewerBoxWidget
+from ccpi.viewer.widgets import cilviewerBoxWidget, SliceSliderRepresentation, SliderCallback
 
 
 class CILInteractorStyle(vtk.vtkInteractorStyle):
@@ -991,7 +991,7 @@ class CILViewer2D(CILViewerBase):
     IMAGE_WITH_OVERLAY = 0
     RECTILINEAR_WIPE = 1
 
-    def __init__(self, dimx=600, dimy=600, ren=None, renWin=None, iren=None, debug=True):
+    def __init__(self, dimx=600, dimy=600, ren=None, renWin=None, iren=None, debug=False, enableSliderWidget=True):
         CILViewerBase.__init__(self, dimx=dimx, dimy=dimy, ren=ren, renWin=renWin, iren=iren, debug=debug)
 
         self.setInteractorStyle(CILInteractorStyle(self))
@@ -1140,6 +1140,10 @@ def __init__(self, dimx=600, dimy=600, ren=None, renWin=None, iren=None, debug=T
         self.imageTracer.AutoCloseOn()
         self.imageTracer.AddObserver(vtk.vtkWidgetEvent.Select, self.style.OnTracerModifiedEvent, 1.0)
 
+        # Slider widget
+        self.sliderWidget = None
+        self._sliderWidgetEnabled = enableSliderWidget
+
         self.__vis_mode = CILViewer2D.IMAGE_WITH_OVERLAY
         self.setVisualisationToImageWithOverlay()
 
@@ -1153,6 +1157,7 @@ def setInput3DData(self, imageData):
 
     def setInputData(self, imageData):
         self.log("setInputData")
+        self.reset()
         self.img3D = imageData
         self.installPipeline()
         self.axes_initialised = True
@@ -1310,6 +1315,9 @@ def installPipeline(self):
         elif self.vis_mode == CILViewer2D.RECTILINEAR_WIPE:
             self.installRectilinearWipePipeline()
 
+        if self.getSliderWidgetEnabled():
+            self.installSliceSliderWidgetPipeline()
+
         self.ren.ResetCamera()
         self.ren.Render()
 
@@ -1460,6 +1468,59 @@ def installRectilinearWipePipeline(self):
 
         self.AddActor(wipeSlice, WIPE_ACTOR)
 
+    def installSliceSliderWidgetPipeline(self):
+        '''Create the pipeline for the slice slider widget
+
+        The slider widget and representation are created if not already present.
+        Currently the slider widget enabled flag is not used.
+        '''
+        if self.sliderWidget is not None:
+            # reset the values to the appropriate ones of the new loaded image
+            self.sliderCallback.update_orientation(self.style, 'reset')
+            return
+
+        sr = SliceSliderRepresentation()
+        sr.SetValue(self.getActiveSlice())    
+        sr.SetMaximumValue(self.img3D.GetDimensions()[2] - 1)
+        sr.SetMinimumValue(0)
+
+        sw = vtk.vtkSliderWidget()
+        sw.SetInteractor(self.getInteractor())
+        sw.SetRepresentation(sr)
+        sw.SetAnimationModeToAnimate()
+        sw.EnabledOn()
+
+        cb = SliderCallback(self, sw)
+
+        # Add interaction observers
+        # propagate events from the slider to the viewer
+        sw.AddObserver(vtk.vtkCommand.InteractionEvent, cb)
+
+        # propagate events from the viewer to the slider
+        self.style.AddObserver("MouseWheelForwardEvent", cb.update_from_viewer, 0.9 )
+        self.style.AddObserver("MouseWheelBackwardEvent", cb.update_from_viewer, 0.9 )
+        self.style.AddObserver("CharEvent", cb.update_orientation, 0.9 )
+
+        # reset the slider
+        cb.update_from_viewer(self.style, 'reset')
+
+        # save references
+        self.sliderWidget = sw
+        self.sliderCallback = cb
+
+    def uninstallSliderWidget(self):
+        '''remove the slider widget from the viewer'''
+        if self.sliderWidget is not None:
+            sr = self.sliderWidget.GetRepresentation()
+            if sr is not None:
+                sr.RemoveAllObservers()
+                coll = vtk.vtkPropCollection()
+                sr.GetActors(coll)
+                print ("coll", coll)
+                for actor in coll:
+                    print ("actor", actor)
+                    self.ren.RemoveActor(actor)
+
     def AdjustCamera(self, resetcamera=False):
         self.ren.ResetCameraClippingRange()
 
@@ -1801,3 +1862,18 @@ def uninstallPipeline2(self):
         elif self.vis_mode == CILViewer2D.RECTILINEAR_WIPE:
             # rectilinear wipe visualises 2 images in the same pipeline
             pass
+
+    def reset(self):
+        self.uninstallPipeline()
+        if self.image2 is not None:
+            self.uninstallPipeline2()
+        if self.getSliderWidgetEnabled():
+            self.uninstallSliderWidget()
+
+    def getSliderWidgetEnabled(self):
+        return self._sliderWidgetEnabled
+    def setSliderWidgetEnabled(self, enable):
+        if enable:
+            self._sliderWidgetEnabled = enable
+
+

Wrappers/Python/ccpi/viewer/ui/main_windows.py

@@ -275,7 +275,7 @@ def setViewersInput(self, image, viewers, input_num=1, image_name=None):
         raw_image_attrs = self.raw_attrs
         hdf5_image_attrs = self.hdf5_attrs
         dataset_name = hdf5_image_attrs.get('dataset_name')
-        resample_z = hdf5_image_attrs.get('resample_z')
+        resample_z = hdf5_image_attrs.get('resample_z', True)
         target_size = self.getTargetImageSize()
         if isinstance(image, str) or isinstance(image, list):
             image_reader = ImageReader(file_name=image)

Wrappers/Python/ccpi/viewer/utils/conversion.py

@@ -898,6 +898,65 @@ def ReadDataSetInfo(self):
 
 
 # ---------------------- RESAMPLE READERS -------------------------------------------------------------
+def calculate_target_downsample_magnification(max_size, total_size, acq=False):
+    '''calculate the magnification of each axis and the number of slices per chunk
+
+    Parameters
+    ----------
+    max_size: int
+        target size of the image in number of pixels
+    total_size: int
+        actual size of the image in number of pixels
+    acq: bool, default: False
+        whether the data is acquisiton data or not
+
+    Returns
+    -------
+    slice_per_chunk: int
+        number of slices per chunk
+    xy_axes_magnification: float
+        magnification of the xy axes
+    '''
+    if not acq:
+        # scaling is going to be similar in every axis
+        # (xy the same, z possibly different)
+        xy_axes_magnification = np.power(max_size / total_size, 1 / 3)
+        slice_per_chunk = int(np.round(1 / xy_axes_magnification))
+    else:
+        # If we have acquisition data we don't want to resample in the z
+        # direction because then we would be averaging projections together,
+        # so we have one slice per chunk
+        slice_per_chunk = 1
+        xy_axes_magnification = np.power(max_size / total_size, 1 / 2)
+
+    return (slice_per_chunk, xy_axes_magnification)
+
+
+def calculate_target_downsample_shape(max_size, total_size, shape, acq=False):
+    '''calculate the shape of the resampled image
+
+    Parameters
+    ----------
+    max_size: int
+        target size of the image in number of pixels
+    total_size: int
+        actual size of the image in number of pixels
+    acq: bool, default: False
+        whether the data is acquisition data or not
+
+    Returns
+    -------
+    target_image_shape: tuple
+        shape of the resampled image
+    '''
+    slice_per_chunk, xy_axes_magnification = \
+        calculate_target_downsample_magnification(max_size, total_size, acq)
+    num_chunks = 1 + len([i for i in range(slice_per_chunk, shape[2], slice_per_chunk)])
+
+    target_image_shape = (int(xy_axes_magnification * shape[0]), int(xy_axes_magnification * shape[1]), num_chunks)
+    return target_image_shape
+
+
 class cilBaseResampleReader(cilReaderInterface):
     '''vtkAlgorithm to load and resample a file to an approximate memory footprint.
     This BaseClass provides the methods needed to resample a file, if the filename
@@ -1007,20 +1066,10 @@ def RequestData(self, request, inInfo, outInfo):
                 outData.ShallowCopy(reader.GetOutput())
 
             else:
-
-                # scaling is going to be similar in every axis
-                # (xy the same, z possibly different)
-                if not self.GetIsAcquisitionData():
-                    xy_axes_magnification = np.power(max_size / total_size, 1 / 3)
-                    num_slices_per_chunk = int(
-                        1 / xy_axes_magnification)  # number of slices in the z direction we are resampling together.
-                else:
-                    # If we have acquisition data we don't want to resample in the z
-                    # direction because then we would be averaging projections together,
-                    # so we have one slice per chunk
-                    num_slices_per_chunk = 1  # number of slices in the z direction we are resampling together.
-                    xy_axes_magnification = np.power(max_size / total_size, 1 / 2)
-
+                num_slices_per_chunk, xy_axes_magnification = \
+                    calculate_target_downsample_magnification(max_size,
+                                                              total_size,
+                                                              self.GetIsAcquisitionData())
                 # Each chunk will be the z slices that we will resample together to form one new slice.
                 # Each chunk will contain num_slices_per_chunk number of slices.
                 self._SetNumSlicesPerChunk(num_slices_per_chunk)
@@ -1850,19 +1899,10 @@ def RequestData(self, request, inInfo, outInfo):
             outData.ShallowCopy(inData)
 
         else:
-
-            # scaling is going to be similar in every axis
-            # (xy the same, z possibly different)
-            if not self.GetIsAcquisitionData():
-                xy_axes_magnification = np.power(max_size / total_size, 1 / 3)
-                num_slices_per_chunk = int(
-                    1 / xy_axes_magnification)  # number of slices in the z direction we are resampling together.
-            else:
-                # If we have acquisition data we don't want to resample in the z
-                # direction because then we would be averaging projections together,
-                # so we have one slice per chunk
-                num_slices_per_chunk = 1  # number of slices in the z direction we are resampling together.
-                xy_axes_magnification = np.power(max_size / total_size, 1 / 2)
+            num_slices_per_chunk, xy_axes_magnification = \
+                    calculate_target_downsample_magnification(max_size,
+                                                              total_size,
+                                                              self.GetIsAcquisitionData())
 
             # Each chunk will be the z slices that we will resample together to form one new slice.
             # Each chunk will contain num_slices_per_chunk number of slices.

Wrappers/Python/ccpi/viewer/widgets/__init__.py

@@ -1 +1,2 @@
 from .box_widgets import cilviewerBoxWidget, cilviewerLineWidget
+from .slider import SliderCallback, SliceSliderRepresentation