如何将KAN和GLCM相结合生成高质量的目标检测数据集，具体怎么做，写一段代码实现

结合Kan（角分析）和灰度共生矩阵(GLCM)可以增强图像特征并用于目标检测任务。首先，Kan可以帮助提取形状信息，而GLCM则捕捉纹理特性。以下是一个简化的Python代码示例，说明如何结合这两种方法：

import cv2
import numpy as np
from skimage.feature import graycomatrix, greycoprops

# 加载图像
image = cv2.imread('your_image.jpg', 0)

# KAN计算
def kan(image):
    edges = cv2.Canny(image, 50, 150)
    lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=70, minLineLength=50, maxLineGap=20)
    angles = [line[2] for line in lines]
    return angles

# GLCM计算
def glcm(image):
    glcm_matrix = graycomatrix(image, distances=[1], angles=np.radians([45, 90, 135]), symmetric=True, normed=True)
    features = [greycoprops(glcm_matrix, prop).mean() for prop in ['energy', 'contrast', 'correlation']]
    return features

# 结合KAN和GLCM
image_features = {'kan_angles': kan(image), 'glcm_features': glcm(image)}

# 创建数据集样本
dataset_samples = []
for i, (angle_list, feature_list) in enumerate(zip(image_features['kan_angles'], image_features['glcm_features'])):
    sample = {
        'image_id': f'image_{i}',
        'target_coords': None,  # 这里需要替换为实际的目标坐标
        'kan_angles': angle_list,
        'glcm_features': feature_list
    }
    dataset_samples.append(sample)

# 将数据保存为CSV或JSON文件
import pandas as pd
data_df = pd.DataFrame(dataset_samples)
data_df.to_csv('combined_dataset.csv', index=False)