把这段代码补充完整：import numpy as np import cv2 # 定义目标检测函数 def detect_objects(image, threshold): # 使用OpenCV加载预训练的目标检测模型 model = cv2.dnn.readNetFromCaffe("deploy.prototxt", "model.caffemodel") # 图像预处理 blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size=(300, 300), mean=(104.0, 177.0, 123.0)) # 输入图像到模型中进行目标检测 model.setInput(blob) detections = model.forward() # 解析检测结果 num_detections = detections.shape[2] filtered_detections = [] for i in range(num_detections): confidence = detections[0, 0, i, 2] if confidence > threshold: x1 = int(detections[0, 0, i, 3] * image.shape[1]) y1 = int(detections[0, 0, i, 4] * image.shape[0]) x2 = int(detections[0, 0, i, 5] * image.shape[1])

import numpy as np
import cv2

# 定义目标检测函数
def detect_objects(image, threshold):
# 使用OpenCV加载预训练的目标检测模型
model = cv2.dnn.readNetFromCaffe("deploy.prototxt", "model.caffemodel")

# 图像预处理
blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size=(300, 300), mean=(104.0, 177.0, 123.0))

# 输入图像到模型中进行目标检测
model.setInput(blob)
detections = model.forward()

# 解析检测结果
num_detections = detections.shape[2]
filtered_detections = []
for i in range(num_detections):
confidence = detections[0, 0, i, 2]
if confidence > threshold:
x1 = int(detections[0, 0, i, 3] * image.shape[1])
y1 = int(detections[0, 0, i, 4] * image.shape[0])
x2 = int(detections[0, 0, i, 5] * image.shape[1])
y2 = int(detections[0, 0, i, 6] * image.shape[0])
class_id = int(detections[0, 0, i, 1])

# 将检测到的目标信息保存到结果列表中
filtered_detections.append((x1, y1, x2, y2, class_id))

return filtered_detections

# 示例用法
image = cv2.imread("image.jpg") # 加载图像
threshold = 0.5 # 置信度阈值
result = detect_objects(image, threshold)
print(result)