yolo v5 txt检测框显示代码

以下是使用YOLOv5检测文本，并在检测结果上绘制检测框的代码示例：

import cv2
import torch
import numpy as np
from models.experimental import attempt_load
from utils.general import non_max_suppression

# 加载YOLOv5模型
weights = 'path/to/weights.pt'
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = attempt_load(weights, map_location=device)

# 定义类别名称和颜色
class_names = ['text']
colors = [[255, 0, 0]]

# 加载测试图片
img_path = 'path/to/image.jpg'
img = cv2.imread(img_path)

# 图片预处理
img_size = 640
img = cv2.resize(img, (img_size, img_size))
img = img[:, :, ::-1].transpose(2, 0, 1)
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).to(device)

# 模型推理
model.eval()
with torch.no_grad():
    pred = model(img.unsqueeze(0))[0]
    pred = non_max_suppression(pred, conf_thres=0.5, iou_thres=0.5)

# 绘制检测框
if pred[0] is not None:
    pred = pred[0]
    for det in pred:
        x1, y1, x2, y2, conf, cls = det
        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
        label = f'{class_names[int(cls)]} {conf:.2f}'
        color = colors[int(cls)]
        cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
        cv2.putText(img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

# 显示结果
cv2.imshow('result', img[:, :, ::-1])
cv2.waitKey(0)

其中，`attempt_load`函数用于加载YOLOv5模型，`non_max_suppression`函数用于进行非极大值抑制，过滤重叠的检测结果。使用`cv2.rectangle`和`cv2.putText`函数在检测结果上绘制检测框和标签，最后使用`cv2.imshow`函数显示结果。