Labels QOL enhancements #81
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WalkthroughThe recent updates bring significant enhancements to data management in the Changes
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| elif type(key) == slice: | ||
| return [self.labeled_frames[i] for i in range(*key.indices(len(self)))] | ||
| elif type(key) == list: | ||
| return [self.labeled_frames[i] for i in key] | ||
| elif isinstance(key, np.ndarray): | ||
| return [self.labeled_frames[i] for i in key.tolist()] | ||
| elif type(key) == tuple and len(key) == 2: | ||
| video, frame_idx = key | ||
| res = self.find(video, frame_idx) | ||
| if len(res) == 1: | ||
| return res[0] | ||
| elif len(res) == 0: | ||
| raise IndexError( | ||
| f"No labeled frames found for video {video} and " | ||
| f"frame index {frame_idx}." | ||
| ) | ||
| elif type(key) == Video: | ||
| res = self.find(key) | ||
| if len(res) == 0: | ||
| raise IndexError(f"No labeled frames found for video {key}.") | ||
| return res |
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Consider using isinstance() for type checking instead of direct type comparison. This is more Pythonic and supports inheritance.
- elif type(key) == slice:
+ elif isinstance(key, slice):
- elif type(key) == list:
+ elif isinstance(key, list):
- elif type(key) == tuple and len(key) == 2:
+ elif isinstance(key, tuple) and len(key) == 2:
- elif type(key) == Video:
+ elif isinstance(key, Video):Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| elif type(key) == slice: | |
| return [self.labeled_frames[i] for i in range(*key.indices(len(self)))] | |
| elif type(key) == list: | |
| return [self.labeled_frames[i] for i in key] | |
| elif isinstance(key, np.ndarray): | |
| return [self.labeled_frames[i] for i in key.tolist()] | |
| elif type(key) == tuple and len(key) == 2: | |
| video, frame_idx = key | |
| res = self.find(video, frame_idx) | |
| if len(res) == 1: | |
| return res[0] | |
| elif len(res) == 0: | |
| raise IndexError( | |
| f"No labeled frames found for video {video} and " | |
| f"frame index {frame_idx}." | |
| ) | |
| elif type(key) == Video: | |
| res = self.find(key) | |
| if len(res) == 0: | |
| raise IndexError(f"No labeled frames found for video {key}.") | |
| return res | |
| elif isinstance(key, slice): | |
| return [self.labeled_frames[i] for i in range(*key.indices(len(self)))] | |
| elif isinstance(key, list): | |
| return [self.labeled_frames[i] for i in key] | |
| elif isinstance(key, np.ndarray): | |
| return [self.labeled_frames[i] for i in key.tolist()] | |
| elif isinstance(key, tuple) and len(key) == 2: | |
| video, frame_idx = key | |
| res = self.find(video, frame_idx) | |
| if len(res) == 1: | |
| return res[0] | |
| elif len(res) == 0: | |
| raise IndexError( | |
| f"No labeled frames found for video {video} and " | |
| f"frame index {frame_idx}." | |
| ) | |
| elif isinstance(key, Video): | |
| res = self.find(key) | |
| if len(res) == 0: | |
| raise IndexError(f"No labeled frames found for video {key}.") | |
| return res |
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #81 +/- ##
==========================================
+ Coverage 95.16% 95.33% +0.17%
==========================================
Files 13 13
Lines 1551 1609 +58
==========================================
+ Hits 1476 1534 +58
Misses 75 75 ☔ View full report in Codecov by Sentry. |
| save_jabs, | ||
| load_video, | ||
| load_file, | ||
| save_file, | ||
| ) |
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📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [7-28]
Consider addressing the unused imports.
Many imports in this file are flagged as unused. If these are meant to be available for external use, add them to the __all__ list to explicitly export them. Otherwise, consider removing them to clean up the codebase. Here's an example of how you might modify the code:
- from sleap_io.model.skeleton import Node, Edge, Skeleton, Symmetry
- from sleap_io.model.video import Video
- from sleap_io.model.instance import (
- Point,
- PredictedPoint,
- Track,
- Instance,
- PredictedInstance,
- )
- from sleap_io.model.labeled_frame import LabeledFrame
- from sleap_io.model.labels import Labels
- from sleap_io.io.main import (
- load_slp,
- save_slp,
- load_nwb,
- save_nwb,
- load_labelstudio,
- save_labelstudio,
- load_jabs,
- save_jabs,
- load_video,
- load_file,
- save_file,
- )
+ __all__ = ['Node', 'Edge', 'Skeleton', 'Symmetry', 'Video', 'Point', 'PredictedPoint', 'Track', 'Instance', 'PredictedInstance', 'LabeledFrame', 'Labels', 'load_slp', 'save_slp', 'load_nwb', 'save_nwb', 'load_labelstudio', 'save_labelstudio', 'load_jabs', 'save_jabs', 'load_video', 'load_file', 'save_file']
LabeledFrame.remove_predictions: Remove predicted instances from a labeled frame.LabeledFrame.remove_empty_instances: Remove instances with no visible points from a labeled frame.Labels.save: Instance-level convenience wrapper forsio.save_file.Labels.clean: Remove unused or empty frames, instances, videos, skeletons and tracks.Labels.remove_predictions: Remove predicted instances from all labeled frames (Remove predictions from labels #69).Labels.__getitem__: Now supports lists, slices, numpy arrays, tuples of(Video, frame_idx)andVideo.Summary by CodeRabbit
LabeledFrameclass with methods to remove specific predictions and empty instances.LabeledFrame.