numImages = 9;files = cell(1, numImages);为i = 1:numImages文件{i} = fullfile(matlabroot，“工具箱”，“愿景”，“visiondata”，.．.“校准”，“单反”sprintf (“图像% d.jpg”,我));结束显示其中一个校准图像放大倍率= 25;I = imread(files{1});图;imshow(I, InitialMagnification =放大率);标题(“其中一幅校正图像”）;

检测图像中的棋盘角。[imagePoints, boardSize] = detectCheckerboardPoints(files);中棋盘角角的世界坐标%以模式为中心的坐标系，左上角位于(0,0)。squareSize = 29;%(毫米)worldPoints = generateCheckerboardPoints(boardSize, squareSize);校准相机。imageSize = [size(I, 1)， size(I, 2)];cameraParams = estimatecamerparameters (imagePoints, worldPoints，.．.ImageSize = ImageSize);评估校准精度。图;showReprojectionErrors (cameraParams);标题(“Reprojection错误”）;

imOrig = imread(fullfile(matlabroot，“工具箱”，“愿景”，“visiondata”，.．.“校准”，“单反”，“image9.jpg”));图;imshow(imOrig, InitialMagnification =放大率);标题(“输入图像”）;

由于镜头引入了很少的失真，使用'全'输出视图来说明%图像未被解除存储。如果我们使用默认的“same”选项，这将很困难%来注意与原始图像相比的任何差异。注意小的黑色边框。[im, newOrigin] = undistortion timage (imOrig, cameraParams, OutputView =“全部”）;图;imshow(im, InitialMagnification =放大);标题(“无畸变的图像”）;

将图像转换为HSV颜色空间。imHSV = rgb2hsv(im);获取饱和通道。饱和= imHSV(:，:， 2);图像阈值T =灰度(饱和度);imCoin =(饱和度> t);图;imshow(imCoin, InitialMagnification =放大);标题(“分段硬币”）;

查找连接的组件。blobAnalysis =视觉。BlobAnalysis(AreaOutputPort = true，.．.CentroidOutputPort = false，.．.BoundingBoxOutputPort = true，.．.MinimumBlobArea = 200, ExcludeBorderBlobs = true);[areas, boxes] = step(blobAnalysis, imCoin);按面积降序排列连接的组件[~， idx] = sort(区域，“下”）;求两个最大的分量。box = double(box (idx(1:2)，:));缩小显示图像的大小。比例=放大倍数/ 100;imDetectedCoins = imresize(im, scale);为硬币插入标签。imDetectedCoins = insertObjectAnnotation“矩形”，.．.刻度*盒子，“一分钱”）;图;imshow (imDetectedCoins);标题(“发现硬币”）;

检测棋盘。[imagePoints, boardSize] = detectCheckerboardPoints(im);调整图像点，使它们在坐标系统中表示%用于原始图像，在未被扭曲之前。这种调整%使它与为原始图像计算的cameraParameters对象兼容。imagePoints = imagePoints + newOrigin;%将newOrigin添加到每行imagePoints提取相机的内在。camintrinsic = cameraparams . intrinsic;计算相机的外部参数。camExtrinsics = estimateExtrinsics(imagePoints, worldPoints, camintrinsic);

调整包围框左上角的坐标系统移位%由undistortion timage导致，输出视图为'full'。这不是如果输出“相同”，则需要%。这一调整使各点相互兼容%与原始图像的camer参数。boxes = boxes + [newOrigin, 0,0];为宽度和高度添加% 0填充获取左上角和右上角。Box1 = double(boxes(1，:));imagePoints1 = [box1(1:2);.．.Box1 (1) + Box1 (3)， Box1 (2)];获取角落的世界坐标worldPoints1 = img2world2d(imagePoints1, camExtrinsics, camintrinsic);以毫米为单位计算硬币的直径。d = worldPoints1(2，:) - worldPoints1(1，:);diameter inmm = hypot(d(1)， d(2));流(“一便士的测量直径= %0.2f mm\n”, diameterInMillimeters);

获取左上角和右上角。Box2 = double(boxes(2，:));imagePoints2 = [box2(1:2);.．.Box2 (1) + Box2 (3)， Box2 (2)];应用从图像到世界的逆变换worldPoints2 = img2world2d(imagePoints2, camExtrinsics, camintrinsic);以毫米为单位计算硬币的直径。d = worldpoint2 (2，:) - worldPoints2(1，:);diameter inmm = hypot(d(1)， d(2));流(“另一便士的直径= %0.2f mm\n”, diameterInMillimeters);

计算图像中第一枚硬币的中心。。Center1_image = box1(1:2) + box1(3:4)/2;%转换为世界坐标。center1_world = img2world2d(center1_image, camExtrinsics, camintrinsic ics);记得加上z坐标为0。Center1_world = [Center1_world 0];计算到相机的距离。cameraPose = extr2pose(camExtrinsics);cameraLocation = cameraPose.Translation;distanceToCamera = norm(center1_world - cameraLocation);流(“相机到第一枚硬币的距离= %0.2f mm\n”，.．.distanceToCamera);