请你给出实现这个功能的代码

由于你没有提供具体的上下文，这里我将以一个简单的例子说明如何使用Python和OpenCV库来实现基础的图像处理和车辆检测功能。我们将使用`cv2`库，假设我们已经有了预训练的车辆检测模型（如YoloV3）：

import cv2
import numpy as np

def process_frame(frame):
    # Step 1: 预处理
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    resized_frame = cv2.resize(gray, (608, 608))  # 调整尺寸以适应模型输入

    # Step 2: 车辆检测
    net = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')
    layer_names = net.getLayerNames()
    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    
    blob = cv2.dnn.blobFromImage(resized_frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
    net.setInput(blob)
    outs = net.forward(output_layers)

    # Step 3: 解析检测结果并计数
    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:  # 设置阈值筛选高置信度的对象
                center_x = int(detection[0] * frame.shape[1])
                center_y = int(detection[1] * frame.shape[0])
                w = int(detection[2] * frame.shape[1])
                h = int(detection[3] * frame.shape[0])

                x = center_x - w / 2
                y = center_y - h / 2
                boxes.append([x, y, w, h])
                confidences.append(float(confidence))
                class_ids.append(class_id)

    indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)  # 非极大抑制(NMS)
    vehicles_count = len(indices)

    # Step 4: 可选地，只返回车辆数量，忽略具体位置
    return vehicles_count

# 使用示例
frame = cv2.imread("test_image.jpg")
count = process_frame(frame)
print(f"检测到的车辆数: {count}")

这只是一个基本版本，实际应用中可能需要根据具体需求调整参数和模型。记得替换上述代码中的`yolov3.cfg`和`yolov3.weights`为你的模型路径。