clean readme

Author: AriNguyen

README.md

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 Real‑time multithreaded webcam/video capture in modern C++20 & OpenCV that keeps your main thread free for computer vision or ML inference.
 
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 ## Why?
 
 OpenCV's `VideoCapture` is synchronous: every `read()` blocks on USB/RTSP I/O and decoding. This library adds a **producer/consumer** queue so frame acquisition runs on a dedicated thread, lifting throughput up to **32%** on a 4‑core laptop while keeping latency bounded.
 
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 ## Features
 
 | Category      | What you get                                                                 |
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 | Metrics      | Built‑in FPS / latency stats returned as a C++ struct or JSON.                |
 | Extensible   | Optional CUDA path (-DENABLE_CUDA=ON), gRPC frame streaming, ONNXRuntime inference hooks. |
 
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 ## Quick Start
 
 ### Docker (zero install)
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 ./build/threaded_capture --device 0 --num_frames 500
 ```
 
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 ## Build from Source
 
 ```sh
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 ./utils/clean.bash < .gitignore
 ```
 
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 ## Webcam Stream
 
 The `detach` method (`t1.detach()`) is used so we don't need to wait for thread 1 to finish. Instead, it will get the dataframe. The process happens simultaneously.
 
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 ## Measuring FPS and Elapsed Time
 
 I first used the **chrono** library to measure the time but found that it's hard to convert to seconds for calculating FPS. So, I use **ctime**:
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 double fps = numFrames / elapsed_secs;
 ```
 
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 ## Face Detection using dlib
 
 See: [dlib webcam_face_pose_ex.cpp example](http://dlib.net/webcam_face_pose_ex.cpp.html)
 
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 ## Benchmark
 
 ### Just streaming webcam
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 The elapsed time doesn't change much; however, the FPS of streaming 100 and 1000 frames increases by 23.5% and 31.5%, respectively.
 
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 ## License
 
 This project is licensed under the MIT License — see [LICENSE](LICENSE) for details.
 
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 ## Acknowledgements
 
 - Inspired by [PyImageSearch: "How to increase FPS with multithreading in OpenCV"](https://www.pyimagesearch.com/2015/12/21/increasing-webcam-fps-with-python-and-opencv/)