matlab水果识别源代码

以下是使用MATLAB实现的简单水果识别源代码示例：

首先，我们需要收集水果的图像数据并将其分为训练集和测试集。在这个例子中，我们收集了苹果、香蕉和橙子的图像，并将它们分为80％的训练数据和20％的测试数据。

% Load image data
apple_folder = 'apple_images/';
banana_folder = 'banana_images/';
orange_folder = 'orange_images/';

apple_files = dir(fullfile(apple_folder, '*.jpg'));
banana_files = dir(fullfile(banana_folder, '*.jpg'));
orange_files = dir(fullfile(orange_folder, '*.jpg'));

% Split data into train and test sets (80% train, 20% test)
apple_train = apple_files(1:round(0.8*length(apple_files)));
apple_test = apple_files(round(0.8*length(apple_files))+1:end);

banana_train = banana_files(1:round(0.8*length(banana_files)));
banana_test = banana_files(round(0.8*length(banana_files))+1:end);

orange_train = orange_files(1:round(0.8*length(orange_files)));
orange_test = orange_files(round(0.8*length(orange_files))+1:end);

接下来，我们将加载训练数据并使用SIFT特征提取器提取每个图像的特征。我们将使用这些特征来训练一个支持向量机分类器，并使用测试数据测试分类器的准确性。

% Load training data
train_files = [apple_train; banana_train; orange_train];
num_train = length(train_files);
train_labels = [ones(length(apple_train),1); 2*ones(length(banana_train),1); 3*ones(length(orange_train),1)];

% Extract SIFT features for each training image
train_features = [];
for i = 1:num_train
    img = imread(fullfile(train_files(i).folder, train_files(i).name));
    img = single(rgb2gray(img));
    [~, descriptors] = vl_sift(img);
    train_features = [train_features, descriptors];
end

% Train SVM classifier
svm_model = fitcecoc(train_features', train_labels);

% Load test data
test_files = [apple_test; banana_test; orange_test];
num_test = length(test_files);
test_labels = [ones(length(apple_test),1); 2*ones(length(banana_test),1); 3*ones(length(orange_test),1)];

% Extract SIFT features for each test image
test_features = [];
for i = 1:num_test
    img = imread(fullfile(test_files(i).folder, test_files(i).name));
    img = single(rgb2gray(img));
    [~, descriptors] = vl_sift(img);
    test_features = [test_features, descriptors];
end

% Predict labels for test data using SVM classifier
predicted_labels = predict(svm_model, test_features');

% Calculate accuracy of classifier
accuracy = sum(predicted_labels == test_labels) / num_test;
disp(['Accuracy: ', num2str(accuracy)]);

最后，我们可以加载一张新的水果图像，并使用我们训练好的支持向量机分类器对其进行分类。

% Load new image and extract SIFT features
new_img = imread('new_fruit_image.jpg');
new_img = single(rgb2gray(new_img));
[~, descriptors] = vl_sift(new_img);

% Classify new image using SVM classifier
new_label = predict(svm_model, descriptors');

% Print predicted label
if new_label == 1
    disp('Apple');
elseif new_label == 2
    disp('Banana');
elseif new_label == 3
    disp('Orange');
end