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* Fix E2E tests on macOS ARM64 by using multi-arch base image - Add _is_macos_arm64() helper to detect Apple Silicon - Conditionally apply --platform linux/amd64 flag only on non-ARM64 systems - Handle ConnectionResetError in service_initializer for startup timing - Add unit tests for ARM64 detection and conditional platform behavior This allows native ARM64 builds on Apple Silicon while maintaining AMD64 compatibility elsewhere. * refactor: simplify architecture - use docker-compose directly - Remove service_manager module (Python wrapper around Docker) - Remove config.py and start.py entry points - Users now use docker-compose directly - Migrate Dockerfile to uv 0.9.26 for faster builds - Upgrade Python 3.12 → 3.13 - Upgrade TensorFlow 2.18.0 → 2.20.0 - Update documentation with new usage instructions - Fix Docker build context for uv.lock access * refactor: rename extractor_service to perfectframe and simplify project structure Module changes: - Rename extractor_service/ to perfectframe/ - Move main.py to app.py as the FastAPI entry point - Update all internal imports to use perfectframe module Project structure: - Move Dockerfile from extractor_service/ to project root - Remove extractor_service/.dockerignore (no longer needed) - Remove extractor_service/requirements.txt (using pyproject.toml) - Remove .github/README.pl.md (Polish README) Test reorganization: - Flatten test structure from tests/extractor_service/* and tests/service_manager/* to tests/unit/, tests/integration/, tests/e2e/ - Merge common test fixtures into tests/common.py - Update all test imports to use perfectframe module - Rename docker e2e tests for clarity (docker_best_frames_extractor_test.py, etc.) Configuration updates: - Update docker-compose.yaml to use root Dockerfile - Update pyproject.toml with new module path - Update .github/workflows/run_tests.yml for new test paths - Add perfectframe/ to .gitignore patterns - Exclude perfectframe/ from ty pre-commit check (isolated env lacks dependencies) * feat: migrate from TensorFlow to ONNX Runtime - Replace TensorFlow (~500MB) with ONNX Runtime (~50MB) - Update Python to 3.14.2 and all dependencies to latest versions - Add conversion scripts for model weights (TF -> ONNX) - Update Dockerfile with fixed versions for production - Reduce Docker image size by ~450MB * chore: configure Ruff linter and pre-commit hooks - Add .pre-commit-config.yaml with ruff, docformatter, detect-secrets, pytest - Configure ruff in pyproject.toml with ALL rules and sensible ignores - Add docstrings to all modules and public functions (Google style) - Fix all linting issues: RET504, PLR2004, D401, D205, D404 - Migrate tests from unittest.mock to pytest-mock - Add pytest-mock to test dependencies - Remove obsolete scripts/ directory (ONNX conversion) - Add .secrets.baseline for detect-secrets - Update .gitignore with *.onnx, *.h5 * fix: resolve ty type checker errors in pre-commit Remove return type annotations from yield-based pytest fixtures that ty incorrectly infers as generators. Exclude tests/ from ty-check since test files use dev dependencies not available in pre-commit environment. * chore: update Python target version to 3.13 * test: enforce 100% coverage in CI and pre-commit - Add unit tests for app.py endpoints - Add --cov=perfectframe --cov-fail-under=100 to pytest - Remove unused if __name__ == "__main__" block * feat: switch to onnxruntime-gpu with automatic GPU/CPU fallback - Change onnxruntime to onnxruntime-gpu for CUDA support (Linux/Windows) - Keep onnxruntime for macOS (no CUDA support on Apple Silicon) - Rename docker-compose services: extractor_service -> perfectframe - Remove TensorFlow leftover environment variable - Add CPU Mode and GPU Mode sections to README - Update Built with section: Tensorflow -> ONNX Runtime * refactor: clean up docstrings and README - Remove redundant Args sections from docstrings (types are self-documenting) - Remove "Built with" section from README - Add usage comments to dependencies in pyproject.toml * refactor: improve type annotations and add runtime type checks Replace type: ignore comments with proper runtime checks and add type aliases (Image, Images, ImagesBatch, ScoresArray) for better code clarity. Fix tuple type annotations and Generator return type. * fix: race condition between extractor check and flag set Set _active_extractor immediately after check in start_extractor instead of inside background task to prevent concurrent requests from bypassing the single-extractor constraint. * refactor: use ExtractorName enum for active_extractor type Replace str | None with ExtractorName | None in ExtractorStatus and ExtractorManager for type consistency across the codebase. * refactor: improve type annotations in video_processors - Rename batch_size to frames_batch_size in get_next_frames - Use Images type for frames batch return type - Use Image type for _read_next_frame return - Skip None frames when building batch - Remove unused numpy import * refactor: rename target_image_size to input_size with ImageResolution type * fix: update tests and code to use enum .value for string representation - Fix extractor_manager to use .value when building error messages - Update unit tests to expect enum .value in assertions - Update integration tests to use ImageExtension/VideoExtension enums instead of removed config attributes - Use ExtractorName enum instead of string in integration test - Remove unused config fixture arguments from tests - Remove curl from Dockerfile and healthcheck from docker-compose * docs: improve README Usage section, remove Tests/Roadmap/Polski * refactor: merge InceptionResNetNIMA and _ONNXModel into NIMAEvaluator Consolidate legacy class structure after TensorFlow to ONNX migration. The InceptionResNetNIMA name was misleading as it no longer reflects architecture-specific knowledge - it's now a generic ONNX loader. * ci: add release-please and PR title validation * feat: add Docker macOS ARM64 support and CI improvements * ci: add SBOM attestation and versioned filename on release * ci: add OpenSSF Scorecard workflow * docs: add OpenSSF Scorecard badge * ci: replace lint job with pre-commit in CI workflow Replace separate ruff/ty lint job with pre-commit/action to run all pre-commit hooks in CI, ensuring checks can't be bypassed with --no-verify. Test jobs now depend on pre-commit passing first. * ci: add dev branch to CI and CodeQL workflows * ci: add SonarCloud integration * security: move workflow permissions to job level * ci: pin trivy-action to v0.28.0 * ci: add --group test to uv run commands * ci: add pytest-timeout to test dependencies * ci: update all GitHub Actions to latest versions * ci: fix docker image name for Trivy scanner * ci: remove SonarCloud CI workflow in favor of Automatic Analysis * security: run container as non-root user * fix: ensure proper permissions for non-root container user * fix: address silent failures, typos, and license inconsistency - Add exception logging in ExtractorManager.__run_extractor() - Check cv2.imwrite() return value and log errors on failure - Fix test bug: add missing parentheses to assert_not_called() - Fix typo: rename compering_group_size to comparing_group_size - Fix typo: change "ulr" to "url" in log message - Update license in pyproject.toml from GPL-3.0 to Apache-2.0 - Add requests.RequestException handling for model download * refactor: merge consecutive RUN instructions in Dockerfile * ci: add uv package ecosystem to Dependabot Enable automatic dependency updates for Python packages managed by uv. * chore: change license from GPL v3 to Apache 2.0 - Replace LICENSE.md with Apache License 2.0 text - Remove GPL license blocks from Python module docstrings - Update README license reference and add v3.0 to comparison table - Remove license badge (GitHub displays it automatically) * fix: improve README badge consistency and order - Move GitHub stars badge to the end - Add flat style to OpenSSF Scorecard badge - Use shields.io proxy for SonarCloud badge for consistent styling * fix: remove wide badges for single-line layout Remove OpenSSF Scorecard and SonarCloud Quality Gate badges to ensure all badges fit on one line.
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