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perf(pose): read dense video frames sequentially
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pioritize BiLSTM model
NLag
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Refactor(pipeline): improve code readability and update function calls for clarity
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Merge branch 'main' into NLag/issue4
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feat(report): stream analysis progress
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Enhance UnknownExercise class with debug data handling and add test for manual unknown exercise in pipeline
NLag
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feat(report): add annotated issue clips
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Merge remote issue 4 updates
NLag
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Add issue 10 training release assets
NLag
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Merge branch 'main' into NLag/issue4
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fix(renderer): preserve video colors in annotations
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refactor(exercises): route pipeline through exercise objects
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Implement frame-level issue markers
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Merge branch 'main' into NLag/issue4
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refactor(exercises): introduce exercise strategies
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Merge branch 'main' into NLag/issue4
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fix(rep-analysis): split exercise analyzers
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Merge branch 'main' into NLag/issue4
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Merge main into issue 4 branch
NLag
# Conflicts:
# app.py
# uv.lock -
Add temporal router model selection
NLag
Resolves #17 -
Implement exercise classifier router
NLag
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Merge branch 'main' into issue-6-rep-analysis-variation
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Test rep metric variation detection
Daphne
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feat(rep_counter): implement exercise-specific rep counters for push-ups, shoulder presses, and squats; add dependency on fastapi
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feat(pose): add pluggable pose backends
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feat(pose): use dense frames for real pose extraction
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fix(classifier): default auto routing to squat
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feat(renderer): generate annotated pose videos
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feat(pipeline): persist rep debug artifacts
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feat(rep): implement deterministic exercise rep segmentation
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refactor(exercises): centralize exercise definitions
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test(pose): allow src path imports in pose tests
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feat(pose): add pluggable pose backends
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feat(video): add intake quality gate
Closes #2 -
feat(pipeline): harden mock artifact contracts
Closes #1
