深度强化学习matlab代码相机标定

以下是使用深度强化学习进行相机标定的Matlab代码示例：

% 清除工作区变量和命令窗口
clc;
clear all;
close all;

% 设置相机参数
K = [1000 0 320; 0 1000 240; 0 0 1]; % 内部参数矩阵
distCoeffs = [0 0 0 0]; % 畸变系数

% 生成标定板
boardSize = [7 10]; % 棋盘格纵横格数
squareSize = 25; % 格子大小
board = generateCheckerboardPoints(boardSize, squareSize);

% 生成相机姿态和图像
nImages = 50; % 生成的图像数量
images = cell(nImages, 1);
poses = cell(nImages, 1);

for i = 1:nImages
    % 随机生成相机姿态
    R = randn(3);
    [U, ~, V] = svd(R);
    R = U * V';
    t = randn(3, 1) * squareSize;
    
    % 计算相机投影矩阵
    P = K * [R t];
    
    % 投影标定板到图像中
    [imagePoints, ~] = projectPoints(board, [0 0 0], [0 0 0], P, distCoeffs);
    
    % 绘制图像
    image = zeros(480, 640, 'uint8');
    for j = 1:size(imagePoints, 1)
        u = round(imagePoints(j, 1));
        v = round(imagePoints(j, 2));
        if u >= 1 && u <= 640 && v >= 1 && v <= 480
            image(v, u) = 255;
        end
    end
    images{i} = image;
    poses{i} = [reshape(R', 1, []), t'];
end

% 定义深度强化学习模型
model = rlDQNAgent(getObservationInfo(),getActionInfo());

% 训练深度强化学习模型
maxepisodes = 1000;
maxsteps = 100;
trainOpts = rlTrainingOptions('MaxEpisodes',maxepisodes,'MaxStepsPerEpisode',maxsteps,...
    'Verbose',false,'Plots','training-progress');
doTraining = true;
if doTraining
    % 执行训练
    trainingStats = train(model,getTrainingData(),trainOpts);
else
    % 加载训练好的模型
    load('camera_calibration_agent.mat','model');
end

% 测试深度强化学习模型
nTests = 10; % 测试次数
errors = zeros(nTests, 1); % 保存测试误差
for i = 1:nTests
    % 随机选择一张图像
    idx = randi(nImages);
    image = images{idx};
    pose = poses{idx};
    
    % 使用深度强化学习模型进行相机标定
    estimatedPose = calibrateCamera(image, model);
    
    % 计算误差
    error = norm(pose - estimatedPose);
    errors(i) = error;
    
    % 显示结果
    subplot(1, 2, 1);
    imshow(image);
    title('Original Image');
    subplot(1, 2, 2);
    drawCamera(pose, 'Color', 'r');
    drawCamera(estimatedPose, 'Color', 'g');
    title(sprintf('Error: %.2f', error));
    drawnow;
end

% 显示平均误差
meanError = mean(errors);
disp(sprintf('Mean Error: %.2f', meanError));

% 定义获取观测值的函数
function obs = getObservationInfo()
    obs = rlNumericSpec([480 640 1]);
    obs.Name = 'image';
end

% 定义获取动作的函数
function act = getActionInfo()
    act = rlFiniteSetSpec([-1 0 1], [3 3]);
    act.Name = 'action';
end

% 定义获取训练数据的函数
function data = getTrainingData()
    data = struct();
    data.image = images;
    data.pose = poses;
end

% 定义相机标定函数
function pose = calibrateCamera(image, model)
    % 预处理图像
    image = imresize(image, [480 640]);
    image = im2double(image);
    image = image(:, :, 1);
    image = 1 - image;
    
    % 使用深度强化学习模型进行相机标定
    state = image;
    done = false;
    while ~done
        action = getAction(model, state);
        switch action
            case -1
                % 减小相机姿态
                pose = decreasePose(model.UserData.pose);
            case 0
                % 不改变相机姿态
                pose = model.UserData.pose;
            case 1
                % 增大相机姿态
                pose = increasePose(model.UserData.pose);
        end
        state = projectBoard(pose);
        done = evaluateState(state);
    end
end

% 定义获取动作的函数
function action = getAction(model, state)
    action = predict(model, state);
    [~, idx] = max(action);
    action = idx - 2;
end

% 定义生成随机相机姿态的函数
function pose = randomPose()
    R = randn(3);
    [U, ~, V] = svd(R);
    R = U * V';
    t = randn(3, 1) * 100;
    pose = [reshape(R', 1, []), t'];
end

% 定义增大相机姿态的函数
function pose = increasePose(pose)
    R = reshape(pose(1:9), 3, 3)';
    t = pose(10:end)';
    t = t + R(:, 3) * 10;
    pose = [reshape(R', 1, []), t];
end

% 定义减小相机姿态的函数
function pose = decreasePose(pose)
    R = reshape(pose(1:9), 3, 3)';
    t = pose(10:end)';
    t = t - R(:, 3) * 10;
    pose = [reshape(R', 1, []), t];
end

% 定义将相机姿态投影到标定板上的函数
function points = projectBoard(pose)
    R = reshape(pose(1:9), 3, 3)';
    t = pose(10:end)';
    P = K * [R t];
    [points, ~] = projectPoints(board, [0 0 0], [0 0 0], P, distCoeffs);
end

% 定义评估状态的函数
function done = evaluateState(state)
    boardPoints = projectBoard(model.UserData.pose);
    done = all(state(sub2ind(size(state), round(boardPoints(:, 2)), round(boardPoints(:, 1)))) == 1);
end

% 定义绘制相机的函数
function drawCamera(pose, varargin)
    R = reshape(pose(1:9), 3, 3)';
    t = pose(10:end)';
    cameraSize = 50;
    cameraColor = 'k';
    if ~isempty(varargin)
        cameraSize = varargin{2};
    end
    if length(varargin) >= 1
        cameraColor = varargin{1};
    end
    camera = [cameraSize cameraSize -cameraSize;
              cameraSize -cameraSize -cameraSize;
              -cameraSize -cameraSize -cameraSize;
              -cameraSize cameraSize -cameraSize];
    camera = bsxfun(@plus, camera * R', t);
    line(camera([1 2 3 4 1], 1), camera([1 2 3 4 1], 2), camera([1 2 3 4 1], 3), 'Color', cameraColor, 'LineWidth', 2);
    line(camera([1 2], 1), camera([1 2], 2), camera([1 2], 3), 'Color', cameraColor, 'LineWidth', 2);
    line(camera([2 3], 1), camera([2 3], 2), camera([2 3], 3), 'Color', cameraColor, 'LineWidth', 2);
    line(camera([3 4], 1), camera([3 4], 2), camera([3 4], 3), 'Color', cameraColor, 'LineWidth', 2);
    line(camera([4 1], 1), camera([4 1], 2), camera([4 1], 3), 'Color', cameraColor, 'LineWidth', 2);
end