双十二汽车相关识别购买

import cv2
import numpy as np

# 加载预训练模型
model = cv2.dnn.readNetFromCaffe('deploy.prototxt', 'model.caffemodel')

def recognize_car(image_path):
    # 读取图像
    image = cv2.imread(image_path)
    (h, w) = image.shape[:2]
    
    # 创建输入Blob并进行预处理
    blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
    
    # 设置模型输入
    model.setInput(blob)
    
    # 进行推理
    detections = model.forward()
    
    # 解析检测结果
    for i in range(0, detections.shape[2]):
        confidence = detections[0, 0, i, 2]
        if confidence > 0.5:  # 设置置信度阈值
            class_id = int(detections[0, 0, i, 1])
            class_name = CAR_CLASSES[class_id]  # 假设有一个CAR_CLASSES列表存储车型名称
            print(f"Detected car model: {class_name} with confidence {confidence:.2f}")

# 调用函数进行识别
recognize_car('path_to_image.jpg')