meanshift图像跟踪 matlab

以下是一个简单的meanshift图像跟踪的Matlab代码示例：

% 读取第一帧图像
img = imread('frame1.jpg');

% 选择ROI
rect = round(getrect);
x = rect(1);
y = rect(2);
w = rect(3);
h = rect(4);

% 提取ROI的颜色直方图
roi = img(y:y+h-1, x:x+w-1, :);
roi_hsv = rgb2hsv(roi);
roi_hist = hsv_histogram(roi_hsv, 8, 2);

% 初始化跟踪器
prev_rect = rect;
prev_frame = img;
prev_roi_hist = roi_hist;

% 读取后续帧图像并进行跟踪
for i = 2:10
    % 读取当前帧图像
    curr_frame = imread(sprintf('frame%d.jpg', i));
    
    % 进行meanshift跟踪
    curr_rect = meanshift_track(prev_frame, prev_rect, prev_roi_hist);
    
    % 在当前帧图像中绘制跟踪结果
    curr_frame = insertShape(curr_frame, 'Rectangle', curr_rect, 'LineWidth', 2);
    imshow(curr_frame);
    
    % 更新跟踪器参数
    prev_rect = curr_rect;
    prev_frame = curr_frame;
end

% hsv_histogram函数用于计算颜色直方图
function hist = hsv_histogram(img, bins, range)
    h = img(:, :, 1);
    s = img(:, :, 2);
    v = img(:, :, 3);
    h_edges = linspace(0, range, bins+1);
    s_edges = linspace(0, 1, bins+1);
    v_edges = linspace(0, 1, bins+1);
    h_idx = discretize(h, h_edges);
    s_idx = discretize(s, s_edges);
    v_idx = discretize(v, v_edges);
    hist = accumarray([h_idx(:), s_idx(:), v_idx(:)], 1, [bins, bins, bins]);
    hist = hist / sum(hist(:));
end

% meanshift_track函数用于进行meanshift跟踪
function rect = meanshift_track(img, rect, roi_hist)
    % 转换为HSV颜色空间
    hsv_image = rgb2hsv(img);
    
    % 提取H通道
    h = hsv_image(:, :, 1);
    
    % 计算反向投影
    back_proj = apply_back_projection(h, roi_hist);
    
    % 进行meanshift迭代
    [x, y, w, h] = rect;
    roi = back_proj(y:y+h-1, x:x+w-1);
    [cx, cy] = meanshift_iter(roi);
    cx = cx + x - 1;
    cy = cy + y - 1;
    
    % 返回新的跟踪框
    rect = [cx, cy, w, h];
end

% apply_back_projection函数用于计算反向投影
function back_proj = apply_back_projection(img, roi_hist)
    % 计算颜色直方图
    bins = size(roi_hist, 1);
    hist = hsv_histogram(img, bins, 1);
    
    % 计算反向投影
    back_proj = apply_histogram_transform(hist, roi_hist);
end

% apply_histogram_transform函数用于应用直方图变换
function output = apply_histogram_transform(img_hist, ref_hist)
    % 计算直方图比例因子
    img_hist_scale = img_hist / sum(img_hist(:));
    ref_hist_scale = ref_hist / sum(ref_hist(:));
    factor = ref_hist_scale ./ img_hist_scale;
    
    % 应用直方图变换
    output = factor(img_hist);
end

% meanshift_iter函数用于进行meanshift迭代
function [cx, cy] = meanshift_iter(img)
    % 设定停止条件
    tolerance = 0.1;
    max_iter = 100;
    
    % 初始化中心点
    [h, w] = size(img);
    cx = floor(w / 2);
    cy = floor(h / 2);
    
    % 迭代直至收敛
    for iter = 1:max_iter
        % 计算直方图
        bins = 16;
        hist = hsv_histogram(img, bins, 1);
        
        % 计算中心点
        [x, y] = meshgrid(1:w, 1:h);
        cx_new = sum(sum(x .* hist)) / sum(hist(:));
        cy_new = sum(sum(y .* hist)) / sum(hist(:));
        
        % 计算距离
        dist = sqrt((cx_new - cx)^2 + (cy_new - cy)^2);
        
        % 更新中心点
        cx = cx_new;
        cy = cy_new;
        
        % 检查是否收敛
        if dist < tolerance
            break;
        end
    end
end

该代码首先读取第一帧图像并选择ROI，然后提取ROI的颜色直方图并初始化跟踪器。接着，它读取后续帧图像并调用meanshift_track函数进行meanshift跟踪。该函数计算反向投影并进行meanshift迭代以获取新的跟踪框。最后，它在当前帧图像中绘制跟踪结果，并更新跟踪器参数。