VideoAesthetics/pyproject.toml at master · ashish-code/VideoAesthetics · GitHub

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[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "video-aesthetics"
version = "0.1.0"
description = "Automated aesthetic quality assessment of movie trailers via ILGnet + SVM."
readme = "README.md"
license = { file = "LICENSE" }
requires-python = ">=3.10"
authors = [{ name = "Ashish Gupta", email = "ashish@example.com" }]
keywords = [
    "computer-vision", "video-aesthetics", "image-quality", "svm",
    "caffe", "nima", "deep-learning",
]
classifiers = [
    "Development Status :: 4 - Beta",
    "Intended Audience :: Science/Research",
    "License :: OSI Approved :: MIT License",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
    "Topic :: Scientific/Engineering :: Image Processing",
]

dependencies = [
    "numpy>=1.24",
    "scipy>=1.11",
    "scikit-learn>=1.4",
    "matplotlib>=3.8",
    "Pillow>=10.0",
]

[project.optional-dependencies]
acquisition = [
    "requests>=2.31",
    "beautifulsoup4>=4.12",
    "google-api-python-client>=2.100",
    "yt-dlp>=2024.1",
    "cinemagoer>=2023.5",     # modern fork of IMDbPy
]
scene = [
    "scenedetect>=0.6",
    "pims>=0.6",
    "opencv-python>=4.8",
]
dev = [
    "pytest>=8.0",
    "pytest-cov>=5.0",
]

[project.scripts]
video-aesthetics = "video_aesthetics.cli:main"

[tool.hatch.build.targets.wheel]
packages = ["src/video_aesthetics"]

[tool.uv]
dev-dependencies = [
    "pytest>=8.0",
    "pytest-cov>=5.0",
]

[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "-v --tb=short"

[tool.coverage.run]
source = ["src/video_aesthetics"]
omit = ["tests/*"]

[tool.coverage.report]
show_missing = true