# EasYoloD
Easy Yolo Detect
用户快速部署yolo的识别程序，支持onnxruntime, opencv(dnn), openvino
仅需简短几行代码即可实现yolo目标检测
## Provider 介绍
1. onnxruntime:
+ cpu: 适配性最高的版本，不需要GPU即可执行
+ gpu: onnxruntime-gpu 需要英伟达GPU，并且安装对应版本cuda，cudnn之后才能使用，速度快
+ onnxdml: onnxruntime-directml 不需要使用特定GPU，核显也可以允许，而且不需要安装任何额外程序，速度一般，而且仅适用与windos系统
1. openvino:
+ cpu: 同onnx的cpu一样
+ gpu: 仅适用于intel的GPU，其他GPU不可用
1. opencv:
+ cpu: 同上
+ gpu: 需要单独编译带有cuda的opencv包，并正确配置路径，并且安装好cuda和cudnn，速度快
## 安装和使用
```bash
pip install EasYoloD
```
Requirements
+ Python 3.8-3.12
+ opencv-python <= 4.10.0.84
+ numpy <= 1.26
使用:
```python
import EasYoloD
EasYoloD.init(provider='onnxruntime',gpu=False) # onnxruntime-directml 则使用onnxdml，openvino使用 openvino
model = EasYoloD.Model()
# conf 置信度
# ious
# namse 可以是文件，也可以是一个list
model.load('modelpath', conf, ious, names)
# or 你使用的是opencv dnn yolov4的weight模型
# model.load('config path', 'weight path', inputsize, names, conf, nms)
result = model.detect(img=image)
# or 你希望自己处理输出
# result = model.detect_only(img=image)
```
输出示例:
detect:
```
{
1: [
{'confidence': 0.89, 'box': [(614, 202), (732, 242)], 'center': (673, 222)},
{'confidence': 0.87, 'box': [(975, 227), (1105, 268)], 'center': (1040, 247)},
{'confidence': 0.87, 'box': [(845, 241), (962, 284)], 'center': (903, 262)},
{'confidence': 0.86, 'box': [(418, 203), (495, 243)], 'center': (456, 223)},
{'confidence': 0.85, 'box': [(713, 233), (822, 273)], 'center': (767, 253)},
{'confidence': 0.83, 'box': [(776, 222), (888, 261)], 'center': (832, 241)}
],
2: [],
3: [
{'confidence': 0.8, 'box': [(664, 265), (687, 289)], 'center': (675, 277)}
],
4: [
{'confidence': 0.86, 'box': [(846, 195), (955, 236)], 'center': (900, 215)},
{'confidence': 0.84, 'box': [(1108, 227), (1208, 273)], 'center': (1158, 250)}
],
5: [],
6: [],
7: []
}
```
detect_only:
```
(array([[ 614.5011 , 202.27354, 732.4082 , 242.74388],
[ 975.4805 , 227.59409, 1105.0723 , 268.69995],
[ 845.77277, 241.3953 , 962.0877 , 284.1887 ],
[ 418.44012, 203.71834, 495.6739 , 243.37538],
[ 846.04956, 195.53143, 955.15515, 236.9972 ],
[ 713.3884 , 233.3027 , 822.95776, 273.27628],
[1108.0188 , 227.39557, 1208.6423 , 273.43536],
[ 776.30786, 222.16605, 888.85815, 261.70145],
[ 664.80615, 265.0358 , 687.7573 , 289.32138]], dtype=float32), array([0.88843024, 0.86892086, 0.8652373 , 0.8610253 , 0.858262 ,
0.84596515, 0.8361889 , 0.83084583, 0.8002863 ], dtype=float32), array([0, 0, 0, 0, 3, 0, 3, 0, 2], dtype=int64))
```
Raw data
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