# yolov5-opencv

**Repository Path**: CodeRex/yolov5-opencv

## Basic Information

- **Project Name**: yolov5-opencv
- **Description**: 基于yolov5目标检测技术，使用onnx模型检测图片并返回位置信息
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-08-19
- **Last Updated**: 2024-09-10

## Categories & Tags

**Categories**: Uncategorized

**Tags**: YOLOv5, Python, OpenCV

## README

# yolov5-opencv

## Prerequisites
Make sure you have already on your system:

Any modern Linux OS (tested on Ubuntu 20.04)
OpenCV 4.5.4+
Python 3.7+
IMPORTANT!!! Note that OpenCV versions prior to 4.5.4 will not work at all.

## Which YOLO version should I use?
This repository uses YOLO V5 but it is not the only YOLO version out there. You can read [this article](https://towardsdatascience.com/yolo-v4-or-yolo-v5-or-pp-yolo-dad8e40f7109) to learn more about YOLO versions and choose the more suitable one for you.

## Exporting yolo v5 models to .onnx format

Check here: https://github.com/ultralytics/yolov5/issues/251

My commands were:

```bash
git clone https://github.com/ultralytics/yolov5
cd yolov5
pip install -r requirements.txt
```
And then to convert the model:

```bash
$ python3 export.py --weights yolov5n.pt --img 640 --include onnx
export: data=data/coco128.yaml, weights=['yolov5n.pt'], imgsz=[640], batch_size=1, device=cpu, half=False, inplace=False, train=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=12, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['onnx']
YOLOv5 🚀 v6.0-192-g436ffc4 torch 1.10.1+cu102 CPU

Fusing layers... 
Model Summary: 213 layers, 1867405 parameters, 0 gradients

PyTorch: starting from yolov5n.pt (4.0 MB)

ONNX: starting export with onnx 1.10.2...
/home/user/workspace/smartcam/yolov5/models/yolo.py:57: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
ONNX: export success, saved as yolov5n.onnx (7.9 MB)

Export complete (1.33s)
Results saved to /home/doleron/workspace/smartcam/yolov5
Visualize with https://netron.app
Detect with `python detect.py --weights yolov5n.onnx` or `model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5n.onnx')
Validate with `python val.py --weights yolov5n.onnx`
$ 
```

## References

- https://github.com/ultralytics/yolov5/issues/708