Closed zhiltsov-max closed 3 days ago
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The recent changes in the cvat/apps/dataset_manager
streamline shape conversions and annotation handling by renaming and refactoring methods in the formats.transformations
module. Notably, the CVATRleToCOCORle
class has been replaced with MaskConverter
, providing a clearer and more modular approach to mask conversions.
File(s) | Change Summary |
---|---|
cvat/apps/dataset_manager/bindings.py | Updated import and function calls to use MaskConverter instead of CVATRleToCOCORle . Adjusted mask conversion method names accordingly. |
cvat/apps/dataset_manager/formats/transformations.py | Renamed CVATRleToCOCORle class to MaskConverter . Split convert_mask into cvat_rle_to_dm_rle and dm_mask_to_cvat_rle . Added a new rle method to MaskConverter . |
sequenceDiagram
participant DatasetManager
participant MaskConverter
DatasetManager->>MaskConverter: cvat_rle_to_dm_rle(mask)
MaskConverter-->>DatasetManager: Converted RLE data
DatasetManager->>MaskConverter: dm_mask_to_cvat_rle(mask)
MaskConverter-->>DatasetManager: Converted mask points
In lines of code where data molds,
Conversions now more clear, less old,
MaskConverter takes the stage,
Transformations on a cleaner page.
Shapes evolve, annotations play,
Coding ease, a brighter day. 🌟
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Attention: Patch coverage is 51.28205%
with 19 lines
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Please upload report for BASE (
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Issues
0 New issues
0 Accepted issues
Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code
Motivation and context
CVAT uses correct, but suboptimal algorithms for RLE enconding and decoding. This results in bad import and export performance for masks. Quality reports are also affected, as they use the same code to represent datasets.
In tests, the average speedup is:
How has this been tested?
Checklist
develop
branchLicense
Summary by CodeRabbit
rle
method to theMaskConverter
class, enhancing functionality for mask-related operations.