lbp-svm的图像分类python

LBP-SVM（Local Binary Pattern-Support Vector Machine）是一种基于局部二值模式和支持向量机的图像分类方法。在Python中，可以使用OpenCV和scikit-learn库来实现LBP-SVM图像分类。

以下是一个简单的LBP-SVM图像分类示例代码：

import cv2
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 读取图像数据和标签
def read_data():
    data = []
    labels = []
    for i in range(1, 11):
        for j in range(1, 6):
            img = cv2.imread(f"data/{i}_{j}.jpg", 0)
            lbp = local_binary_pattern(img, 8, 1)
            data.append(lbp.ravel())
            labels.append(i)
    return np.array(data), np.array(labels)

# 计算局部二值模式
def local_binary_pattern(img, num_points, radius):
    lbp = np.zeros_like(img)
    for i in range(radius, img.shape[0]-radius):
        for j in range(radius, img.shape[1]-radius):
            center = img[i, j]
            bin_str = ''
            for k in range(num_points):
                x = int(np.round(i + radius * np.cos(2 * np.pi * k / num_points)))
                y = int(np.round(j - radius * np.sin(2 * np.pi * k / num_points)))
                if img[x, y] > center:
                    bin_str += '1'
                else:
                    bin_str += '0'
            lbp[i, j] = int(bin_str, 2)
    return lbp

# 划分训练集和测试集
data, labels = read_data()
X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)

# 训练SVM模型
clf = SVC(kernel='linear', C=1, gamma='auto')
clf.fit(X_train, y_train)

# 预测测试集并计算准确率
y_pred = clf.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print(f"Accuracy: {acc}")

该代码首先读取图像数据和标签，然后使用`local_binary_pattern`函数计算每张图像的局部二值模式。接着，使用`train_test_split`函数将数据划分为训练集和测试集。最后，使用`SVC`函数训练SVM模型并预测测试集，计算准确率。