浙江大学生物系统工程-生物生产机器人-实验报告2

实验报告

课程名称：______生物生产机器人____________指导老师：____崔迪____成绩：__________________ 实验名称：______水果品质检测与分级的方法____________实验类型：____实验室操作结合上机_______ 同组学生姓名：__吴文华 方小敏 邓敏娜 赵懿滢 李艺健 蔡杭杭________ 一、实验目的 二、实验步骤 三、源程序 四、思考题

一、 实验目的

1. 掌握用MER 方法检测水果横径的方法；

2. 掌握机器视觉系统的标定方法。 二、 实验步骤

1. 存储minboundrect 源代码，调用minboundrect ，通过MER 方法获取水果横径；

2. 通过excel 处理数据，进行回归分析，获得图片与参数。

实验名称：_____水果品质检测与分级的方法_____姓名：_____蒋昊铮___学号：_____3120100277_____

三、源程序

minboundrect源代码：

function [rectx,recty,area,perimeter] = minboundrect(x,y,metric)

if (nargin<3) || isempty(metric)

metric = 'a';

elseif ~ischar(metric)

error 'metric must be a character flag if it is supplied.'

else

% check for 'a' or 'p'

metric = lower(metric(:)');

ind = strmatch(metric,{'area','perimeter'});

if isempty(ind)

error 'metric does not match either ''area'' or ''perimeter'''

end

metric = metric(1);

end

x=x(:);

y=y(:);

n = length(x);

if n~=length(y)

error 'x and y must be the same sizes'

end

if n>3

edges = convhull(x,y); % 'Pp' will silence the warnings

% exclude those points inside the hull as not relevant

% also sorts the points into their convex hull as a

% closed polygon

x = x(edges);

y = y(edges);

nedges = length(x) - 1;

elseif n>1

% n must be 2 or 3

nedges = n;

x(end+1) = x(1);

y(end+1) = y(1);

else

nedges = n;

end

switch nedges

case 0

rectx = [];

recty = [];

area = [];

perimeter = [];

return

case 1

rectx = repmat(x,1,5);

recty = repmat(y,1,5);

area = 0;

perimeter = 0;

return

case 2

rectx = x([1 2 2 1 1]);

recty = y([1 2 2 1 1]);

area = 0;

perimeter = 2*sqrt(diff(x).^2 + diff(y).^2);

return

end

Rmat = @(theta) [cos(theta) sin(theta);-sin(theta) cos(theta)]; ind = 1:(length(x)-1);

edgeangles = atan2(y(ind+1) - y(ind),x(ind+1) - x(ind)); edgeangles = unique(mod(edgeangles,pi/2));

nang = length(edgeangles);

area = inf;

perimeter = inf;

met = inf;

xy = [x,y];

for i = 1:nang

rot = Rmat(-edgeangles(i));

xyr = xy*rot;

xymin = min(xyr,[],1);

xymax = max(xyr,[],1);

A_i = prod(xymax - xymin);

P_i = 2*sum(xymax-xymin);

if metric=='a'

M_i = A_i;

else

M_i = P_i;

end

if M_i

% keep this one

met = M_i;

area = A_i;

perimeter = P_i;

rect = [xymin;[xymax(1),xymin(2)];xymax;[xymin(1),xymax(2)];xymin];

rect = rect*rot';

rectx = rect(:,1);

recty = rect(:,2);

end

end

end

调用minboundrect源程序：

>> I=imread('2.1.bmp');

i=imread('1.1.bmp');

i1=rgb2gray(i);%i1灰度图像

i2=im2bw(i1);

>> DG=edge(i2);

bw=im2bw(i2);

[r c]=find(bw==0);

[rectx,recty,area,perimeter] = minboundrect(c,r,'a');

figure

imshow(bw);

line(rectx(:),recty(:),'color','r');

width=sqrt((rectx(2)-rectx(1))^2+(recty(2)-recty(1))^2)

length=sqrt((rectx(1)-rectx(4))^2+(recty(1) -recty(4))^2)

edge(‘1.1.bmp’) ;

DG = edge(‘1.1.bmp’);

imhsow(DG)

四、思考题

1.为什么要对机器视觉系统进行标定？

机器视觉标定的精度大小，直接影响着系统成像的精度，只有做好了前期的这一标定工作，后续工作才能正常展开，所以，提高标定精度是当前科研工作的重要方面之一。机器视觉系统标定方法的存在，尤其是自标定方法，极大地提高了成像精度。