Closed CameronGBell closed 3 years ago
Merging #71 (b0d17ca) into master (5225e33) will increase coverage by
2.10%
. The diff coverage is94.18%
.
@@ Coverage Diff @@
## master #71 +/- ##
==========================================
+ Coverage 86.39% 88.49% +2.10%
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Files 15 15
Lines 992 1156 +164
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+ Hits 857 1023 +166
+ Misses 135 133 -2
Impacted Files | Coverage Δ | |
---|---|---|
ParticleSpy/segimgs.py | 91.57% <91.07%> (+11.81%) |
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ParticleSpy/ParticleAnalysis.py | 94.22% <100.00%> (+1.33%) |
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ParticleSpy/custom_kernels.py | 100.00% <100.00%> (ø) |
|
ParticleSpy/segptcls.py | 94.56% <100.00%> (+0.05%) |
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ParticleSpy/tests/test_particle_clustering.py | 100.00% <100.00%> (ø) |
I think you'd modified the canvas_size to be larger (1024 vs 512) but if you're using a small screen resolution it's then not possible to see the bottom of the window. I've modified to vary the canvas_size with screen resolution. Maybe canvas_size should be the smaller of 0.8*screen height or 1024?
What do you think?
In the long term it would be great to make segUI resizeable.
I think you'd modified the canvas_size to be larger (1024 vs 512) but if you're using a small screen resolution it's then not possible to see the bottom of the window. I've modified to vary the canvas_size with screen resolution. Maybe canvas_size should be the smaller of 0.8*screen height or 1024?
What do you think?
In the long term it would be great to make segUI resizeable.
I think that makes sense, a resizable segUI would make sense to implement alongside a zoomable canvas
Okay, I added an if statement to set to 1024 if smaller than the calculated height. I'll leave a resizeable window to you for another PR.
Everything else seems to work for me, so all looks okay. I will go through and change everything to lower case in a PR next week.
One last thing before merging. Could you add an example of training the classifier using an imported mask and not using the segUI? Just for those who don't want to run segUI (for those using cloud-based services etc).
Improves and fleshes out trainable segmentation interface: