帮我写一段用yolo进行回归预测的代码

抱歉，YOLO算法用于目标检测而不是回归预测。如果您需要使用YOLO进行目标检测，可以使用以下代码：

import cv2
import numpy as np
import argparse

# 构建参数解析器
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="path to input image")
ap.add_argument("-y", "--yolo", required=True, help="base path to YOLO directory")
ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections")
ap.add_argument("-t", "--threshold", type=float, default=0.3, help="threshold when applying non-maxima suppression")
args = vars(ap.parse_args())

# 加载 YOLO 模型
weights_path = os.path.sep.join([args["yolo"], "yolov3.weights"])
config_path = os.path.sep.join([args["yolo"], "yolov3.cfg"])
net = cv2.dnn.readNetFromDarknet(config_path, weights_path)

# 加载图像，获取图像尺寸，并构建一个 blob 用于输入网络
image = cv2.imread(args["image"])
(H, W) = image.shape[:2]
blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB=True, crop=False)

# 设置网络输入和输出层名称
net.setInput(blob)
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# 前向传递，获取边界框和置信度
outputs = net.forward(output_layers)
boxes = []
confidences = []
class_ids = []
for output in outputs:
    for detection in output:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > args["confidence"]:
            box = detection[0:4] * np.array([W, H, W, H])
            (centerX, centerY, width, height) = box.astype("int")
            x = int(centerX - (width / 2))
            y = int(centerY - (height / 2))
            boxes.append([x, y, int(width), int(height)])
            confidences.append(float(confidence))
            class_ids.append(class_id)

# 应用非最大值抑制
idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"], args["threshold"])

# 绘制边界框和标签
if len(idxs) > 0:
    for i in idxs.flatten():
        (x, y) = (boxes[i][0], boxes[i][1])
        (w, h) = (boxes[i][2], boxes[i][3])
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
        text = "{}: {:.4f}".format(classes[class_ids[i]], confidences[i])
        cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

# 显示结果
cv2.imshow("Image", image)
cv2.waitKey(0)

这个代码可以使用YOLOv3算法来检测输入图像中的物体，并在图像上绘制边界框和标签。注意，这段代码需要使用预先训练好的权重文件和配置文件，您需要指定这些文件的路径。