[project]
name = "fall-detection-pose"
version = "0.1.0"
description = "Rule-based fall detection from YOLO26 pose keypoints with ByteTrack multi-object tracking, event-level evaluation on URFD"
readme = "README.md"
requires-python = ">=3.10"
license = "MIT"
authors = [{ name = "tun", email = "doinb03131047@gmail.com" }]

# 核心依賴刻意保持輕量:rules/events/eval 不 import torch/ultralytics/cv2,
# 使得規則引擎與評估可以在無 GPU 的環境(本機、CI)秒級測試。
dependencies = [
    "numpy>=1.26",
    "pandas>=2.0",
    "pyarrow>=15",
    "pydantic>=2.5",
    "pyyaml>=6",
]

[project.optional-dependencies]
# 推論相關(Colab GPU 環境安裝): pip install -e ".[infer]"
infer = [
    "ultralytics>=8.4",     # YOLO26-pose 權重自 v8.4.0 起釋出
    "opencv-python>=4.9",
    "imageio-ffmpeg>=0.5",  # 保證有 ffmpeg 二進位可做 H.264 重編碼
    "requests>=2.31",
]
demo = ["gradio>=6,<7"]     # Gradio 6.x 語法與 5.x 不相容,鎖主版本
plot = ["matplotlib>=3.8"]  # 失敗分析特徵時序圖
all = ["fall-detection-pose[infer,demo,plot]"]

[project.scripts]
fdp = "fall_detection.cli:main"

[dependency-groups]
dev = ["pytest>=8"]

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

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

[tool.pytest.ini_options]
testpaths = ["tests"]