自动阈值分割-场景中直线个数的检测

问题:

在竞速机器人的比赛中,我们使用计算机视觉导航进行跑道路线的识别

目标:

在不同的情况下可以得到采集到的图片中直线的个数,以及直线的斜率,进而判断机器人的具体位置

不同的环境,包括:晴天,阴天,室内,室外,阴影区,和非阴影区,摄像头的曝光区,和非曝光区

具体图片:

自动阈值分割-场景中直线个数的检测

该图像包含阴影区和反光区

先进行RGB到灰度图的转换

clear all;

close all;

clc



img = imread('1.jpg');

img = imresize(img,[240,320]);



%%

%先进行颜色空间的转换

[row col dim] = size(img);

T = zeros([row ,col]);

A = [0.299 0.587 0.114];

for i=1:row

    for j=1:col

        B = [img(i,j,1) img(i,j,2) img(i,j,3)]';

        T(i,j) = A*double(B);

    end

end

new_img = uint8(T);

figure ;imshow(new_img);title('自己转换的图片');

 

紧接着进行阈值分割点的查找

 

%%

%进行阈值分割

Grade_Level = zeros(1,256);

for x = 1:row

    for y = 1:col

        Grade_Level(new_img(x,y)+1) = Grade_Level(new_img(x,y)+1) + 1;

    end

end

figure;plot(1:256,Grade_Level);title('灰度直方图');



%%

%寻找分割点

num_bins=256;

counts = Grade_Level(:);

p = counts / sum(counts);

omega = cumsum(p);

mu = cumsum(p .* (1:num_bins)');

mu_t = mu(end);

sigma_b_squared = (mu_t * omega - mu).^2 ./ (omega .* (1 - omega));



% Find the location of the maximum value of sigma_b_squared.

% The maximum may extend over several bins, so average together the

% locations.  If maxval is NaN, meaning that sigma_b_squared is all NaN,

% then return 0.

maxval = max(sigma_b_squared);

isfinite_maxval = isfinite(maxval);

if isfinite_maxval

    idx = mean(find(sigma_b_squared == maxval));

    pos_threshold = (idx - 1) / (num_bins - 1);

else

    pos_threshold = 0.0;

end

pos_threshold = pos_threshold*(num_bins-1);



%%

pos_index = 1;

for i=1:row

    for j=1:col

        if new_img(i,j) > pos_threshold

            new_img(i,j) = 255;

        else

            new_img(i,j) = 0;

            Img_posX(pos_index) = i;

            Img_posY(pos_index) = j;

            pos_index = pos_index + 1;

        end

    end

end

figure ; imshow(new_img);

自动阈值分割-场景中直线个数的检测

 


自动阈值分割-场景中直线个数的检测


然后检测图片中的直线的条数

 

%%

%进行直线的分割

%利用扫描线算法确定直线的条数

%主要思路:找到一个点,然后直接在周围寻找点

%该点的四邻域内的点如果都是黑色的就把该点放进去

Point.x = -1;

Point.y = -1;

first_line(1) = Point;

iterator = 0;

max_Target_line = 0;

line_Cell = cell(3,1);

for i=2:row-1

    curRow = i;%表明现在做的任何处理都是针对当前行的处理

    num_line = 0;%一行扫描下来得到的目标线的个数

    isChanged = 0;%表示没有改变

    temp_flag = -1;

    for j=2:col-1

        

        num = new_img(i,j);

        

        if num == 255 && (num == new_img(i,j-1)...

                && num == new_img(i,j+1)...

                && num == new_img(i-1,j)...

                && num == new_img(i+1,j))

            

            if isChanged == 1

                temp_flag = temp_flag * -1;

                isChanged = 0;

                num_line = num_line + 1;

                %发现了一条直线,然后记录下直线的位置,作为该直线的大体位置

                

            end

        end

        

        %当前的点为目标点,且直线的四邻域的值也都为目标点

        if num == 0 && (num == new_img(i,j-1)...

                && num == new_img(i,j+1)...

                && num == new_img(i-1,j)...

                && num == new_img(i+1,j))

            

            if isChanged == 0

                isChanged = 1;

            end

            

            iterator = iterator + 1;

            Point.x = i;

            Point.y = j;

            first_line(iterator) = Point;

        end

        

    end

    

    if num_line > max_Target_line

        max_Target_line = num_line;

    end

    

end

max_Target_line


剩下的就是最下二乘法的拟合程序:

 

 

%%

%对直线点集进行最小二乘法拟合,求出直线的斜率

sum_x = 0;

sum_y = 0;

sum_mul = 0;

sum_squar = 0;

first_line = line_Cell{1};

N = length(first_line);



for i=1:N

    sum_x = sum_x + first_line(i).x;

    sum_y = sum_y + first_line(i).y;

    sum_mul = sum_mul + first_line(i).x*first_line(i).y;

    sum_squar = sum_squar + (first_line(i).x)^2;

end

mean_x = sum_x*1.0/n;

mean_y = sum_y*1.0/n;



sum_Xdelta = 0;

sum_Ydelta = 0;

for i=1:N

    sum_Xdelta = sum_Xdelta + (first_line(i).x-mean_x)^2;

    sum_Ydelta = sum_Ydelta + (first_line(i).y-mean_y)^2;

end



delta_x = (sum_Xdelta*1.0/n)^0.5;

delta_y = (sum_Ydelta*1.0/n)^0.5;

temp_sum = 0;

for i=1:N

    temp_sum = temp_sum + (first_line(i).x-mean_x)*(first_line(i).y-mean_y)/(delta_x*delta_y);

end



disp('直线的相关系数');

relative_line = temp_sum/n

disp('直线的斜率');

betha = (n*sum_mul-sum_x*sum_y)*1.0/(n*sum_squar-sum_x^2)


如果要检测图片中的真正直线的个数,可以采用huogh直线检测的算法来检测

 

 

%%

%Hough变换检测直线,使用(a,p)参数空间,a∈[0,180],p∈[0,2d]

a=180; %角度的值为0到180度

d=round(sqrt(m^2+n^2)); %图像对角线长度为p的最大值

s=zeros(a,2*d); %存储每个(a,p)个数

z=cell(a,2*d);  %用元胞存储每个被检测的点的坐标

for i=1:m

    for j=1:n %遍历图像每个点

        if(q(i,j)==1) %只检测图像边缘的白点,其余点不检测

            for k=1:a

                p = round(i*cos(pi*k/180)+j*sin(pi*k/180)); %对每个点从1到180度遍历一遍,取得经过该点的所有直线的p值(取整)

                if(p > 0)%若p大于0,则将点存储在(d,2d)空间

                    s(k,d+p)=s(k,d+p)+1; %(a,p)相应的累加器单元加一

                    z{k,d+p}=[z{k,d+p},[i,j]'];%存储点坐标

                else%相当于a为0到-180

                    ap=abs(p)+1;%若p小于0,则将点存储在(0,d)空间

                    s(k,ap)=s(k,ap)+1;%(a,p)相应的累加器单元加一

                    z{k,ap}=[z{k,ap},[i,j]'];%存储点坐标

                end

            end

        end

    end

end

angle_num=1;

for i=1:a

    for j=1:d*2 %检查每个累加器单元中存储数量

        if(s(i,j) >55) %将提取直线的阈值设为70

            angle(angle_num)=i;

            angle_num=angle_num+1;

            lp=z{i,j};%提取对应点坐标

            for k=1:s(i,j)%对满足阈值条件的累加器单元中(a,p)对应的所有点进行操作

                o(lp(1,k),lp(2,k),1)=255; %每个点R分量=255,G分量=0,B分量=0

                o(lp(1,k),lp(2,k),2)=0;

                o(lp(1,k),lp(2,k),3)=0;  %结果为在原图上对满足阈值要求的直线上的点赋红色

            end

        end

    end

end

figure,imshow(o);title('hough变换提取直线');


自动阈值分割-场景中直线个数的检测

 



自动阈值分割-场景中直线个数的检测

 

 

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