写一段python基于yolov5框架人脸识别的代码

实现基于yolov5框架的人脸识别代码如下：

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

# 加载模型
model = attempt_load("yolov5s.pt", map_location='cpu')

# 设置设备
device = select_device("cpu")

# 设置阈值
confidence = 0.4
iou_thresh = 0.5

# 加载类别名称
classes = ["face"]

# 开启摄像头
cap = cv2.VideoCapture(0)

# 循环读取帧
while True:
    
    # 读取帧
    ret, frame = cap.read()
    
    if not ret:
        break
    
    # 转化为RGB
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    
    # 处理大小
    img = cv2.resize(frame, (640, 640))

    # 转化为张量
    img = torch.from_numpy(img).to(device)
    
    # 添加维度
    img = img.permute(2, 0, 1).unsqueeze(0)

    # 模型推理
    with torch.no_grad():
        output = model(img.float(), augment=False)[0]

    # 处理坐标
    boxes = output[..., :4]
    boxes = scale_coords(img.shape[2:], boxes, frame.shape).round()

    # 处理置信度和类别
    confidence_scores = output[..., 4]
    class_indexes = output[..., 5].long()

    # 多类别处理
    detections = []
    for class_index in range(output.shape[2]-5):

        class_mask = class_indexes == class_index
        if not class_mask.any():
            continue

        class_boxes = boxes[class_mask]
        class_confidence_scores = confidence_scores[class_mask]

        # 多类别非极大抑制
        class_detections = non_max_suppression(
            torch.cat((class_boxes, class_confidence_scores.unsqueeze(1)), dim=1), 
            conf_thres=confidence, 
            iou_thres=iou_thresh, 
            multi_label=False, 
            classes=None, 
            agnostic=True
        )

        for detection in class_detections:
            detection = detection.cpu().numpy()
            detection = detection[0:4].astype(np.int)

            detections.append(detection)

    # 画人脸框
    for box in detections:
        x_min, y_min, x_max, y_max = box
        cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)

    # 显示结果
    cv2.imshow("face detection", frame)

    # 退出
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放摄像头
cap.release()

# 关闭窗口
cv2.destroyAllWindows()

该代码首先加载yolov5模型，然后设置设备、阈值和类别名称。接下来开启摄像头，循环读取每一帧，并进行模型推理。模型输出包含置信度、类别和坐标信息，需要对其进行处理，得到每一个人脸框的坐标。最后，将每一个人脸框用矩形框住，并显示结果。