驾驶员疲劳检测 yolov5代码
时间: 2023-07-11 18:09:46 浏览: 71
以下是一个基于 YOLOv5 的驾驶员疲劳检测的 Python 代码示例:
```python
import cv2
import numpy as np
import torch
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords
from utils.torch_utils import select_device
# 初始化模型
weights = 'yolov5s.pt'
device = select_device('')
model = attempt_load(weights, map_location=device) # 模型加载到设备上
stride = int(model.stride.max()) # 获取最大步长
imgsz = 640 # 图像尺寸
# 加载分类标签
classes = ['open', 'close']
# 循环读取帧
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
break
# 预处理图像
img = frame.copy()
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (imgsz, imgsz))
img = img.transpose(2, 0, 1) # HWC -> CHW
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).to(device).float()
img /= 255.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# 模型推理
pred = model(img)[0]
pred = non_max_suppression(pred, 0.25, 0.45)
# 处理检测结果
for i, det in enumerate(pred): # 每个检测框
if det is not None and len(det):
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], frame.shape).round()
for *xyxy, conf, cls in reversed(det):
label = f'{classes[int(cls)]} {conf:.2f}'
plot_one_box(xyxy, frame, label=label, color=colors[int(cls)], line_thickness=3)
# 显示结果
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
```
这个代码使用 PyTorch 框架和 YOLOv5 模型来检测视频帧中的驾驶员疲劳状态。它可以在摄像头输入的实时视频流中运行,并在每个检测到的疲劳状态区域周围绘制一个矩形框。