那上面那个程序怎么改成输出x、y、宽和高、置信度、标签

可以按照以下方式修改代码：

import cv2
import numpy as np

# 加载模型和标签
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

# 加载图片并进行预处理
img = cv2.imread("example.jpg")
height, width, _ = img.shape
blob = cv2.dnn.blobFromImage(img, 1/255, (416, 416), swapRB=True, crop=False)

# 将预处理后的图片输入到模型中，并获取输出结果
net.setInput(blob)
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0]-1] for i in net.getUnconnectedOutLayers()]
outs = net.forward(output_layers)

# 处理输出结果
class_ids = []
confidences = []
boxes = []
for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.5: # 设置置信度阈值
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)

            # 计算边框的左上角坐标
            x = int(center_x - w/2)
            y = int(center_y - h/2)

            class_ids.append(class_id)
            confidences.append(float(confidence))
            boxes.append([x, y, w, h])

# 非极大值抑制（NMS）
indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

# 输出结果
for i in indices:
    i = i[0]
    x, y, w, h = boxes[i]
    label = classes[class_ids[i]]
    confidence = confidences[i]
    print("x: {}, y: {}, width: {}, height: {}, confidence: {}, label: {}".format(x, y, w, h, confidence, label))

这样就可以输出每个物体检测框的左上角坐标 x、y，宽和高，置信度以及标签。如果需要输出多张图片的结果，可以将代码封装成一个函数，并在调用时传入图片路径。