用opencv推理yolo v5模型，输出带矩形检测框的图片

以下是用OpenCV推理Yolo V5模型并输出带矩形检测框的图片的Python代码实现：

import cv2
import numpy as np

# 加载模型和类别标签
net = cv2.dnn.readNetFromDarknet("yolov5.cfg", "yolov5.weights")
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

# 加载图片
img = cv2.imread("test.jpg")

# 获取图片尺寸
height, width, channels = img.shape

# 创建输入blob并执行前向推理
blob = cv2.dnn.blobFromImage(img, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
outputs = net.forward(net.getUnconnectedOutLayersNames())

# 获取检测框及其置信度和类别
class_ids = []
confidences = []
boxes = []
for output in outputs:
    for detection in output:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.5:
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)
            x = int(center_x - w/2)
            y = int(center_y - h/2)
            boxes.append([x, y, w, h])
            confidences.append(float(confidence))
            class_ids.append(class_id)

# 应用非最大抑制算法
indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

# 输出带矩形检测框的图片
for i in indices.flatten():
    x, y, w, h = boxes[i]
    label = str(classes[class_ids[i]])
    confidence = str(round(confidences[i], 2))
    color = (0, 255, 0)
    cv2.rectangle(img, (x, y), (x+w, y+h), color, 2)
    cv2.putText(img, label + " " + confidence, (x, y-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

cv2.imwrite("result.jpg", img)

注：在运行以上代码前，请确保已经成功下载了Yolo V5模型文件（yolov5.cfg和yolov5.weights）、类别标签文件（coco.names）以及待检测的图片文件（test.jpg）。另外，需要安装OpenCV库和Numpy库。