Matlab实现剪刀石头布的头像分类

实现剪刀石头布的头像分类可以分为以下步骤：

1. 数据集准备：收集足够的剪刀、石头、布的头像数据，并将它们分别存放在三个不同的文件夹中。

2. 数据预处理：对于每张图片，需要将其调整为相同的大小，并转换为灰度图像。

3. 特征提取：使用特征提取算法，如HOG（方向梯度直方图）或LBP（局部二值模式）提取每张图片的特征向量。

4. 分类器训练：将提取的特征向量作为输入，使用分类器算法，如SVM（支持向量机）或神经网络进行训练。

5. 测试与评估：使用测试集对分类器进行评估，并计算准确率、召回率和F1值等指标。

下面是一个基于SVM分类器的Matlab实现示例：

% 数据集准备
rock_dir = 'rock_images/';
paper_dir = 'paper_images/';
scissors_dir = 'scissors_images/';

rock_files = dir(fullfile(rock_dir, '*.jpg'));
paper_files = dir(fullfile(paper_dir, '*.jpg'));
scissors_files = dir(fullfile(scissors_dir, '*.jpg'));

rock_num = length(rock_files);
paper_num = length(paper_files);
scissors_num = length(scissors_files);

data = [];
labels = [];

% 数据预处理和特征提取
for i = 1:rock_num
    file_name = fullfile(rock_dir, rock_files(i).name);
    img = imread(file_name);
    img = imresize(img, [64, 64]); % 调整为64x64大小
    gray_img = rgb2gray(img); % 转换为灰度图像
    hog_feature = extractHOGFeatures(gray_img); % 提取HOG特征
    data = [data; hog_feature];
    labels = [labels; 1]; % 1表示石头
end

for i = 1:paper_num
    file_name = fullfile(paper_dir, paper_files(i).name);
    img = imread(file_name);
    img = imresize(img, [64, 64]);
    gray_img = rgb2gray(img);
    hog_feature = extractHOGFeatures(gray_img);
    data = [data; hog_feature];
    labels = [labels; 2]; % 2表示布
end

for i = 1:scissors_num
    file_name = fullfile(scissors_dir, scissors_files(i).name);
    img = imread(file_name);
    img = imresize(img, [64, 64]);
    gray_img = rgb2gray(img);
    hog_feature = extractHOGFeatures(gray_img);
    data = [data; hog_feature];
    labels = [labels; 3]; % 3表示剪刀
end

% 分类器训练
SVMModel = fitcsvm(data, labels);

% 测试与评估
rock_test_files = dir(fullfile(rock_dir, '*.jpg'));
paper_test_files = dir(fullfile(paper_dir, '*.jpg'));
scissors_test_files = dir(fullfile(scissors_dir, '*.jpg'));

rock_test_num = length(rock_test_files);
paper_test_num = length(paper_test_files);
scissors_test_num = length(scissors_test_files);

correct = 0;
total = rock_test_num + paper_test_num + scissors_test_num;

for i = 1:rock_test_num
    file_name = fullfile(rock_dir, rock_test_files(i).name);
    img = imread(file_name);
    img = imresize(img, [64, 64]);
    gray_img = rgb2gray(img);
    hog_feature = extractHOGFeatures(gray_img);
    label = predict(SVMModel, hog_feature);
    if label == 1
        correct = correct + 1;
    end
end

for i = 1:paper_test_num
    file_name = fullfile(paper_dir, paper_test_files(i).name);
    img = imread(file_name);
    img = imresize(img, [64, 64]);
    gray_img = rgb2gray(img);
    hog_feature = extractHOGFeatures(gray_img);
    label = predict(SVMModel, hog_feature);
    if label == 2
        correct = correct + 1;
    end
end

for i = 1:scissors_test_num
    file_name = fullfile(scissors_dir, scissors_test_files(i).name);
    img = imread(file_name);
    img = imresize(img, [64, 64]);
    gray_img = rgb2gray(img);
    hog_feature = extractHOGFeatures(gray_img);
    label = predict(SVMModel, hog_feature);
    if label == 3
        correct = correct + 1;
    end
end

accuracy = correct / total;
display(['Accuracy: ' num2str(accuracy)]);

在上述代码中，我们使用了HOG算法提取每张图片的特征向量，并使用SVM分类器进行训练和测试。最终输出分类器的准确率。你可以根据自己的需求修改代码，例如使用LBP算法替代HOG算法，或使用其他分类器算法。