生成一个python代码实现这个功能

为了实现摆杆的图像识别，我们可以使用Python结合OpenCV（用于图像处理）和TensorFlow（用于深度学习）来创建一个基础的示例。这里是一个简单的例子，展示如何使用预训练的YOLOv3模型进行实时目标检测：

import cv2
import numpy as np
from tensorflow import keras

# 加载预训练的YOLOv3模型
yolo_model = keras.models.load_model('yolov3.weights.h5')  # 替换为你实际的模型路径
yolo_classes = open('coco.names', 'r').read().splitlines()  # 假设你有coco类别文件
net = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights.h5')

# 预处理图像
def preprocess_image(image):
    input_size = (416, 416)
    image = cv2.resize(image, input_size)
    image = image / 255.0
    return image[np.newaxis, :, :, :]

# 实时检测
cap = cv2.VideoCapture(0)  # 使用摄像头作为输入源
while True:
    ret, frame = cap.read()
    if not ret:
        break

    # 进行前向推理
    blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
    net.setInput(blob)
    layer_names = net.getLayerNames()
    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    outs = net.forward(output_layers)

    # 解析并标注结果
    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:  # 设置阈值，只保留置信度高的预测
                center_x, center_y, width, height = detection[:4]
                x, y = center_x - width / 2, center_y - height / 2
                class_ids.append(class_id)
                confidences.append(float(confidence))
                boxes.append([x, y, width, height])

    indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
    for i in indices:
        i = i[0]
        box = boxes[i]
        x, y, w, h = box
        label = f"{yolo_classes[class_ids[i]]} {confidences[i]:.2f}"
        cv2.rectangle(frame, (int(x), int(y)), (int(x+w), int(y+h)), (0, 255, 0), 2)
        cv2.putText(frame, label, (int(x), int(y-10)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)

    # 显示处理后的图像
    cv2.imshow("Barrick Detection", frame)
    key = cv2.waitKey(1)
    if key == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

请注意，这只是一个基本的示例，实际部署时可能需要针对特定摆杆进行更精细的调整，并可能需要调整模型参数和配置文件。