10 基于深度学习的目标检测

 import numpy as np

    
 import cv2
    
  
    
 # 解析标签文件
    
 row = open("model1/synset_words.txt").read().strip().split("\n")
    
 class_label = [r[r.find(" "):].split(",")[0] for r in row]
    
  
    
 # 载入caffe所需的配置文件
    
 net = cv2.dnn.readNetFromCaffe("model1/bvlc_googlenet.prototxt",
    
                            "model1/bvlc_googlenet.caffemodel")
    
  
    
 # 读取待分类图像
    
 img = cv2.imread("photos/cat.jpg")  # 确保这里的路径是正确的
    
  
    
 # 转换格式
    
 blob = cv2.dnn.blobFromImage(img, 1, (224, 224), (104, 117, 123))
    
  
    
 # 加载图像
    
 net.setInput(blob)
    
  
    
 # 预测
    
 preds = net.forward()
    
  
    
 # 排序，取概率最大的结果
    
 idx = np.argsort(preds[0])[-1]
    
  
    
 # 获取图片的原始尺寸
    
 (h, w) = img.shape[:2]
    
  
    
 # 等比例缩减图片大小
    
 resized_img = cv2.resize(img, (w // 4, h // 4))
    
  
    
 # 可视化处理，显示图像类别、置信度等信息
    
 text = "label: {}-{:.2f}%".format(class_label[idx], preds[0][idx] * 100)
    
 cv2.putText(resized_img, text, (5, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0))
    
  
    
 # 显示缩减后的图片
    
 cv2.imshow("resized_result", resized_img)
    
  
    
 # 保存缩减后的图片
    
 cv2.imwrite("photos/resized_result.jpg", resized_img)
    
  
    
 cv2.waitKey(0)
    
 cv2.destroyAllWindows()
    
    
    
    
    AI生成项目python
    
    
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 import numpy as np

    
 import cv2
    
  
    
 def prepareDataSet():
    
     # 准备数据集
    
     args = {}
    
     args["prototxt"] = "model2/MobileNetSSD_deploy.prototxt"
    
     args["model"] = "model2/MobileNetSSD_deploy.caffemodel"
    
     return args
    
  
    
 def createNet():
    
     # 构建网络模型对象
    
     args = prepareDataSet()
    
     # load our serialized model from disk
    
     print("[INFO] loading model...")
    
     net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
    
     return net
    
  
    
 if __name__ == "__main__":
    
     # 定义类别名称序列
    
     CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
    
            "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog",
    
            "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
    
  
    
     # 定义边框颜色序列
    
     COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
    
  
    
     # 打开摄像头或视频文件
    
     camera = cv2.VideoCapture("videos/12686501_3840_2160_60fps.mp4")
    
  
    
     # 构建网络模型
    
     net = createNet()
    
  
    
     while True:
    
     ret, frame = camera.read()
    
     if ret:
    
         # 将帧的尺寸调整为1080p
    
         frame = cv2.resize(frame, (1920, 1080))
    
         (h, w) = frame.shape[:2]
    
         blob = cv2.dnn.blobFromImage(cv2.resize(
    
             frame, (300, 300)), 0.007843, (300, 300), 127.5)
    
         net.setInput(blob)
    
         detections = net.forward()
    
  
    
         # 遍历结果
    
         for i in np.arange(0, detections.shape[2]):
    
             # 获得置信度
    
             confidence = detections[0, 0, i, 2]
    
             # 根据置信度阈值过滤执行度
    
             if confidence > 0.2:
    
                 # 根据最大置信度获取类别下标
    
                 idx = int(detections[0, 0, i, 1])
    
                 # 获取位置信息
    
                 box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
    
                 (startX, startY, endX, endY) = box.astype("int")
    
  
    
                 # 显示类别信息和位置边框
    
                 label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
    
                 print("[INFO] {}".format(label))
    
                 cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[idx], 2)
    
                 y = startY - 15 if startY - 15 > 15 else startY + 15
    
                 cv2.putText(frame, label, (startX, y),
    
                             cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
    
  
    
                 if CLASSES[idx] == "person":
    
                     print("raise the alarm")
    
  
    
         # 显示结果
    
         cv2.imshow("result", frame)
    
  
    
         # 按下空格退出 or esc
    
         if cv2.waitKey(1) == ord(' ') or cv2.waitKey(1) == 27:
    
             break
    
     else:
    
         break
    
  
    
     camera.release()
    
     cv2.destroyAllWindows()
    
    
    
    
    AI生成项目python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-07-12/n9wEIyaA7FZOs3rDpoigWtXqfmQM.png)