Metadata-Version: 2.4
Name: cy-yologic
Version: 1.0.2
Summary: Extreme Speed YOLO Pose Pipeline with Cythonized Logic
Author: choiungyo
Author-email: choiungyo@example.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: ultralytics>=8.0.0
Requires-Dist: numpy>=1.20.0
Requires-Dist: opencv-python>=4.5.0
Requires-Dist: Cython>=0.29.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: requires-dist
Dynamic: requires-python
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Markdown
# 🚀 yologic (v1.0.0)

**Extreme Speed Real-Time YOLO Pose Pipeline with Cythonized C-Logic**

`yologic` is a high-performance wrapper for `ultralytics` YOLOv8-Pose, specifically engineered to eliminate Python's runtime overhead in real-time vision applications. Developed by **choiungyo**.

---

## 🔥 Key Features

1. **⚡ Cythonized Core (`logic_yolo`)**: All heavy mathematical computations (Spine angle calculation, movement delta analysis) are pre-compiled into native C code, reducing latency to near zero.
2. **📸 FastVideoStream**: A specialized non-blocking camera buffer that prevents frame-drop and synchronization lag common in standard OpenCV `read()` loops.
3. **🛠️ Hardware Agnostic**: Optimized to work seamlessly with both NVIDIA CUDA and OpenVINO (Intel iGPU) environments.

---

## 📦 Installation

```bash
pip install cy_yologic
```
💻 Quick Start

```Python
from ultralytics import YOLO
import cy_yologic

# 1. Load your YOLO model
model = YOLO('yolov8n-pose.pt')

# 2. Run the extreme-speed pipeline
cy_yologic.run_yolo_pipeline(model_instance=model, cam_src=0)
```
📄 License
MIT License. Developed with passion for extreme performance by choiungyo.
