Make feature maps work for all layers #80
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## master #80 +/- ##
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+ Coverage 86.08% 87.63% +1.55%
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Files 13 14 +1
Lines 1272 1351 +79
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+ Hits 1095 1184 +89
+ Misses 177 167 -10
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Hi Johannes @jo-mueller ,
thanks for working on this! I have some doubts about backwards-compatibility. I think this PR is changing former behaviour. Could you comment on this? See comments below.
Thanks!
Best,
Robert
| if "label" in self._table.keys(): | ||
| selected_column = list(self._table.keys())[self._view.currentColumn()] | ||
| print("Selected column", selected_column) | ||
| if selected_column is not None: | ||
| if isinstance(self._layer, napari.layers.Labels): | ||
| from ._parametric_images import visualize_measurement_on_labels, map_measurements_on_labels | ||
| new_layer = self._viewer.add_image( map_measurements_on_labels(self._layer, selected_column, self._viewer) , | ||
| name=selected_column + " in " + self._layer.name , | ||
| affine=self._layer.affine , | ||
| scale=self._layer.scale, | ||
| rotate=self._layer.rotate | ||
| ) | ||
| new_layer.contrast_limits = [np.min(self._table[selected_column]), np.max(self._table[selected_column])] | ||
| new_layer.colormap = "jet" | ||
| elif isinstance(self._layer, napari.layers.Points): | ||
| features = self._layer.features | ||
| new_layer = self._viewer.add_points(self._layer.data, | ||
| features=features, | ||
| face_color=selected_column, | ||
| face_colormap="jet", | ||
| size=self._layer.size, | ||
| name=selected_column + " in " + self._layer.name, | ||
| affine=self._layer.affine, | ||
| scale=self._layer.scale, | ||
| rotate=self._layer.rotate | ||
| ) | ||
| new_layer.contrast_limits = [np.min(self._table[selected_column]), np.max(self._table[selected_column])] | ||
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| if "vertex_index" in self._table.keys(): | ||
| selected_column = list(self._table.keys())[self._view.currentColumn()] | ||
| print("Selected column (T)", selected_column) | ||
| if selected_column is not None and isinstance(self._layer, napari.layers.Surface): | ||
| values = np.asarray(self._table[selected_column]) | ||
| data = self._layer.data | ||
| data = [np.asarray(data[0]).copy(), np.asarray(data[1]).copy(), values] | ||
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| new_layer = self._viewer.add_surface(data, | ||
| name=selected_column + " in " + self._layer.name) | ||
| new_layer.contrast_limits = [np.min(self._table[selected_column]), np.max(self._table[selected_column])] | ||
| if "annotation" in selected_column or "CLUSTER_ID" in selected_column: | ||
| new_layer.colormap = "hsv" | ||
| else: | ||
| new_layer.colormap = "jet" |
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Are you sure, you can remove this entire code? It seems that this breaks backwards compatibility. E.g. where is the new_layer.colormap going?
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I moved the setting of the colormap into the new create_feature_map function. The colormaps for the respective different layers are set at the following locations:
- Here for labels layers (which will be returned as
ImageData) - Here for
PointsData - Here for
VectorsData - Here for
SurfaceData
The reason for doing it this way is that the color displays for different layers are controlled by different attributes (e.g., colormap, face_colormap, edge_colormap)
Co-authored-by: Robert Haase <[email protected]>
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@haesleinhuepf Thanks for having a look at this. I think what I'm still missing here is taking the associated transformations ( Otherwise I would say what I did is 70% refactoring, mainly. |
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I added the |
…b.com/jo-mueller/napari-skimage-regionprops into make-feature-maps-work-for-all-layers
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Hi @haesleinhuepf , is there something I could do to see this through? I would be really happy to see this as part of the plugin :) |
| content = layer.features.to_dict('list') | ||
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| # to accomodate passing features to surfaces through the metadata | ||
| elif 'features' in layer.metadata.keys(): |
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Can you give an example (plugin) where there is data in layer.metadata['features'] but not in layer.features? Might it be possible to talk to the developers of these plugins so that they use layer.features? It would make more plugjns compatible to each other if we all use the same API 😉
Hi @haesleinhuepf,
I made a little addon to the results table. I created a function
create_featuremapsthat generalizes the functionality that was previously in the_on_double_click()function of theTableWidget, which is triggered when the table header is doubleclicked and which generated a featuremap.The
_on_double_click()function now just calls the newcreate_featuremapsfunction and passes the layer and the feature column to it. The new function then visualizes the feature accordingly for every layer:Labelslayerface_coloraccording to featureedge_coloraccording to featuresurface[2]overwritten with the values of the selected feature.Since
viewer.add_surface(data, features=features)is not allowed whereasviewer.add_surface(data, metadata = {'features':features})is, I added a little snipped in the__init__of theTableWidget, which moves the features from the metadata tolayer.features.QA
Let me know what you think!