yolonas-cpp

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An CPP library for object detection with full processing using OpenCV DNN (for ONNX) on YOLO-NAS model.

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# yolonas-cpp
An CPP library for object detection with full processing using OpenCV DNN (for ONNX) on YOLO-NAS model. 
[Showcase, demo and quckstart YouTube video](https://youtu.be/t4x2_ebe988)

## Introduction
As normal world of AI today becomed reality, we use it as much as we can. Everyone knows that today to run an AI model, or anything similar to that, you need intermediate processing power that many people cannot afford it. As we, humans, we always choose the easiest path to do something. That is similar here. If you search online for some machine vision thing, you will probably 99% of time get something written in **Python**. Currently, **Python** is the easiest path, but in inference time not the fastest. If you understand what Python **_Interpreter_** is, you will understand how is it slow to do some complex tasks. There comes **CPP**. It almost runs everything today under the hood, and it's incredibly **FAST**. But **ONLY KNOWLEDGABLE PEOPLE** knows how to use it. So, here comes this libary. You can in **TWO LINES** of code run machine detection. It runs using **OpenCV**, and it can use **YOLO-NAS**, that model which is really good, with fastest detection (inference) time, and it CAN detect extremly small objects. Here is it's efficiency comparing to other models: 
![efficency](https://github.com/ukicomputers/yolonas-cpp/assets/84191191/3c991abb-e1ed-49da-9cc0-0c37fcab7fe8)

## Detection
![detected](https://github.com/ukicomputers/yolonas-cpp/assets/84191191/888a729e-bdc2-4c59-ae74-6cf855fc5cf3)
 Library uses **OpenCV** and it's **DNN** to run the model. Model is under **ONNX** format. It's also able to run with **CUDA** (not tested yet).

## Requirements
- GCC
- CMake
- OpenCV (installed as CMake library) with `dnn` submodule enabled
- CUDA compatible GPU + `nvcc` (_optionally, if you want to use GPU inference_)
### Attention!
I got issues when I used precompiled version of OpenCV, `libopencv-dev`, installed with `apt` package manager. Then, I was not able to start a inference at all. To fix this issue, I compiled and installed OpenCV using this [gist](https://gist.github.com/ukicomputers/e559854ac0b4fba7ea722722f96d5fbf). Please use it to avoid any issues.

## Setup
### via Docker
To setup this library and interact with it via Docker, you can simply pull it and run by using commands:
```bash
docker pull ukicomputers/yolonas-cpp
docker run ukicomputers/yolonas-cpp
```
By default, container will use default model and picture for detection.
Currently, Docker container **DOES NOT** support GPU accelerated, and video inference.
These features are comming soon.
You can interact with it by setting enviroment variables when running it:
```bash
docker run -v /path/to/write/out/imgs:/output -e model="path/to/model" -e metadata="path/to/metadata" -e source="path/to/source" ukicomputers/yolonas-cpp
```
It is important that if you want to get images of detections, you need to make a bridge from folder on your device, exactly to folder on container located on `/output`. You may also want to create bridges for models, etc located on your local device.
You can also find image on [Docker Hub](https://hub.docker.com/r/ukicomputers/yolonas-cpp).
### Local setup
To install this library on your computer, firstly clone this repository:
```bash
git clone https://github.com/ukicomputers/yolonas-cpp.git
```
Then change directory to it, and execute **install.bash** with superuser permission, installing as CMake libary:
```bash
cd yolonas-cpp
sudo bash install.bash
```
After that library will be installed. **Aditionally**, if you want to download YOLO-NAS S COCO model to use directly from example, you can just execute this bash script:
```bash
bash download_models.bash
```
Download size is about ~47MB (_maybe more, as the models are constantly updated_).

## Quick usage
**CPP** code (_not full code, just minimal example_):
```cpp
// include
#include <ukicomputers/YoloNAS.hpp>
// modelpath metadata Labels CUDA
YoloNAS net("./yolo_nas_s.onnx", "./metadata", {"lbl1", "lbl2"}, false);
// img draw score threshold
net.predict(img, true, 0.5);
```
Include library in **CMake**:
```cmake
find_package(YoloNAS REQUIRED)
target_link_libraries(${PROJECT_NAME} ukicomputers::YoloNAS)
```
Also required to include **OpenCV**:
```cmake
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})
target_link_libraries(${PROJECT_NAME} ${OpenCV_LIBS})
```

## Full library usage
### `YoloNAS` class
Acceptable arguments:
```cpp
YoloNAS::YoloNAS(string netPath, string config, vector<string> lbls, bool cuda = false);
```
**Initializes YoloNAS class.**
- modelpath (`std::string`),
- Metadata file (`std::string`),
- labels in a vector (`std::vector<std::string>`)
- CUDA support (`bool`, default `false`),

```cpp
YoloNAS net(modelPath, metadata, CUDA, labels);
```
### Function `predict`
Acceptable arguments:
```cpp
vector<YoloNAS::detectionInfo> YoloNAS::predict(cv::Mat &img, bool applyOverlayOnImage = true, float scoreThresh = -1.00)
```
**Predicts (detects) objects from image.**
- image (`cv::Mat`)
- visualy display detection (`bool`, writing on given image, default `true`)
- score thereshold (`float`, if thereshold is not given, library will use thereshold from metadata, otherwise, it will use from given argument, **values are entered from 0.1-1.0 not 1-100**, may vary for if different model is used)
```cpp
net.predict(image, overlayOnImage, scoreThreshold);
```
**Returns `detectionInfo` struct(s) in vector for each detection.** 
Accesible elements from `detectionInfo` struct:
```cpp
struct detectionInfo
{
 int x, y, w, h;
 float score;
 string label;
};
```
**Score is returned as float value from 0.1-1.0**. May vary if different model is used.

## Demo
Demo is located in folder `demo` from downloaded repository. To use it out-of-box, you can download example models by executing `download_models.bash`. To compile and run it, execute `build.bash` from `demo` folder.

## Custom model & metadata
To use your own model, and run it also inside library, use `metadata.py` script, [link here](https://github.com/ukicomputers/yolonas-cpp/blob/main/metadata.py). To use it, in `metadata.py`, first few variables needs to be changed according to your model (model path, model type, number of classes). **IMPORTANT: `metadata.py` DOES NOT ACCEPT `.onnx` FILE FORMAT!** It only accepts the standard YOLO `.pt` format. Script will convert your model to ONNX, and return required `metadata` file, that can be later used in inference.

## TODO
- fix required Docker things
- make detection visualisation look cooler

## License & contributions
Please add my name to top of a document to use library. It helps for me to get reputation.
```cpp
// YOLO-NAS CPP library written by Uglješa Lukešević (github.com/ukicomputers)
```
Also, feel free to open new issues, and contribute it by opening pull request.

## References
- [https://github.com/Deci-AI/super-gradients](https://github.com/Deci-AI/super-gradients)
- [https://github.com/Hyuto/yolo-nas-onnx](https://github.com/Hyuto/yolo-nas-onnx/tree/master/yolo-nas-cpp)

**ukicomputers.**

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