1、加载预训练模型 load detector.mat 2、读取图像 [fname,fpath] = uigetfile(".jpg;.png;.png;.bmp"); path = fullfile(fpath,fname); I = imread(path); imshow(I); title("待检测图像") 3、人体检测 [bboxes,scores] = detectPeopleACF(I); Iout = insertObjectAnnotation(I,'rectangle',bboxes,scores,'LineWidth',3); figure,imshow(Iout) title('人体检测结果') 3.1 提取各个人体区域 [croppedImages, croppedBBoxes] = detector.normalizeBBoxes(I, bboxes); figure, montage(croppedImages); title('裁剪区域图') 3.2 估计裁剪区域人体关键点 heatmaps = detector.predict(croppedImages); Iheatmaps = detector.visualizeHeatmaps(heatmaps, croppedImages); montage(Iheatmaps); title("关键点热力图") keypoints = detector.heatmaps2Keypoints(heatmaps); Iheatmaps = detector.visualizeKeyPoints(Iheatmaps,keypoints); montage(Iheatmaps); title('提取的每个人关键点'); Iout3 = detector.visualizeKeyPointsMultiple(I,keypoints,croppedBBoxes); imshow(Iout3); title('最终结果');在这段代码后加检测各个人体坐姿或者站姿的代码

要检测人体的坐姿或站姿，可以使用关键点的信息来进行判断。以下是一个简单的示例代码：

1. 定义坐姿和站姿的判断函数

function [pose, angle] = detectPose(keypoints)
    % 坐姿判断条件：膝盖和腰部都在同一水平线上
    if keypoints(10,2) > keypoints(9,2) && keypoints(13,2) > keypoints(12,2)
        pose = 'sitting';
        angle = atan2(keypoints(9,2)-keypoints(12,2), keypoints(9,1)-keypoints(12,1)) * 180/pi;
    % 站姿判断条件：脚和腰部都在同一垂直线上
    elseif abs(keypoints(10,1)-keypoints(13,1)) < 20 && abs(keypoints(10,1)-keypoints(13,1)) > 0 && keypoints(10,2) < keypoints(9,2) && keypoints(13,2) < keypoints(12,2)
        pose = 'standing';
        angle = atan2(keypoints(9,2)-keypoints(12,2), keypoints(9,1)-keypoints(12,1)) * 180/pi;
    else
        pose = 'unknown';
        angle = 0;
    end
end

2. 在主程序中调用该函数，并对每个人体区域进行坐姿或站姿的判断和标注

% 估计裁剪区域人体关键点
heatmaps = detector.predict(croppedImages);
keypoints = detector.heatmaps2Keypoints(heatmaps);

% 对每个人体区域进行坐姿或站姿的判断和标注
for i = 1:size(croppedBBoxes,1)
    % 提取当前人体区域的关键点信息
    currKeypoints = keypoints{i};
    % 判断坐姿或站姿
    [pose, angle] = detectPose(currKeypoints);
    % 在原图上标注坐姿或站姿
    I = insertText(I, [croppedBBoxes(i,1), croppedBBoxes(i,2)-20], pose, 'FontSize', 16, 'BoxOpacity', 0.8, 'TextColor', 'white', 'BoxColor', 'red');
    I = insertShape(I, 'Line', [croppedBBoxes(i,1)+20, croppedBBoxes(i,2)+20, croppedBBoxes(i,1)+20+50*cosd(angle), croppedBBoxes(i,2)+20-50*sind(angle)], 'LineWidth', 3, 'Color', 'red');
end

% 显示最终结果
imshow(I);
title('检测结果');