[chapter 04 answer sheet recognition based on Hough change]

The whole system MATLAB Deep learning of intelligent driving hough Transformation to realize case application

Function comparison basis , It is not difficult to apply , Mainly tend to image recognition and simple processing .
The main function is as follows
main.m

clc; clear all; close all;
warning off all;
I = imread('images\\1.jpg');
I1 = Image_Normalize(I, 1);
hsize = [3 3];
sigma = 0.5;
I2 = Image_Smooth(I1, hsize, sigma, 1);
I3 = Gray_Convert(I2, 1);
bw2 = Image_Binary(I3, 1);
figure; subplot(1, 2, 1); imshow(I, []); title(' Original image ');
subplot(1, 2, 2); imshow(bw2, []); title(' Binary image ');
[~, ~, xy_long] = Hough_Process(bw2, I1, 1);
angle = Compute_Angle(xy_long);
[I4, bw3] = Image_Rotate(I1, bw2, angle*1.8, 1);
[bw4, Loc1] = Morph_Process(bw3, 1);
[Len, XYn, xy_long] = Hough_Process(bw4, I4, 1);
[bw5, bw6] = Region_Segmation(XYn, bw4, I4, 1);
[stats1, stats2, Line] = Location_Label(bw5, bw6, I4, XYn, Loc1, 1);
[Dom, Aom, Answer, Bn] = Analysis(stats1, stats2, Line, I4);

Hough Function transformation
namely Hough_Process.m

function [Len, XYn, xy_long] = Hough_Process(bw, Img, flag)

if nargin < 3
    flag = 1;
end
[H, T, R] = hough(bw);
P = houghpeaks(H, 4, 'threshold', ceil(0.3*max(H(:))));
lines = houghlines(bw, T, R, P, 'FillGap', 50, 'MinLength', 7);
max_len = 0;
for k = 1 : length(lines)
    xy = [lines(k).point1; lines(k).point2]; 
    len = norm(lines(k).point1-lines(k).point2); 
    Len(k) = len;
    if len > max_len
        max_len = len;
        xy_long = xy;
    end
    XY{k} = xy; %  Store information 
end
[Len, ind] = sort(Len(:), 'descend'); %  Sort by length 
%  Line information sorting 
for i = 1 : length(ind)
    XYn{i} = XY{ind(i)};
end
xy_long = XYn{1};
x = xy_long(:, 1);
y = xy_long(:, 2);
if abs(diff(x)) < abs(diff(y))
    x = [mean(x); mean(x)];
else
    y = [0.7*y(1)+0.3*y(2); 0.3*y(1)+0.7*y(2)];
end
xy_long = [x y];
if flag
    figure('units', 'normalized', 'position', [0 0 1 1]);
    subplot(2, 2, 1); imshow(bw); title(' Binary image ', 'FontWeight', 'Bold');
    subplot(2, 2, 2); imshow(H, [], 'XData', T, 'YData', R, 'InitialMagnification', 'fit');
    xlabel('\theta'); ylabel('\rho');
    axis on; axis normal; title(' Hough transform domain ', 'FontWeight', 'Bold')
    subplot(2, 2, 3); imshow(Img); title(' Original image ', 'FontWeight', 'Bold');
    subplot(2, 2, 4); imshow(Img); title(' Area identification image ', 'FontWeight', 'Bold');
    hold on;
    plot(xy_long(:,1), xy_long(:,2), 'LineWidth', 2, 'Color', 'r');
end

Region partition subfunction
Region_Segmation.m

function [bw1, bw2] = Region_Segmation(XY, bw, Img, flag)

if nargin < 4
    flag = 1; 
end

for i = 1 : 2
    xy = XY{i}; 
    XY{i} = [1 xy(1, 2); size(bw, 2) xy(2, 2)];
    ri(i) = round(mean([xy(1,2) xy(2,2)]));
end
minr = min(ri);
maxr = max(ri);
bw1 = bw; bw2 = bw;
bw1(1:minr+5, :) = 0;
bw1(maxr-5:end, :) = 0;
bw2(minr-5:end, :) = 0;
bw2(1:round(minr*0.5), :) = 0;

if flag
    figure('units', 'normalized', 'position', [0 0 1 1]);
    subplot(2, 2, 1); imshow(Img, []); title(' Original image ', 'FontWeight', 'Bold');
    subplot(2, 2, 2); imshow(bw, []); title(' Original binary image ', 'FontWeight', 'Bold');
    hold on;
    for i = 1 : 2
        xy = XY{i}; 
        plot(xy(:, 1), xy(:, 2), 'r-', 'LineWidth', 2);
    end
    hold off;
    subplot(2, 2, 3); imshow(bw1, []); title(' Lower area image ', 'FontWeight', 'Bold');
    subplot(2, 2, 4); imshow(bw2, []); title(' Upper area image ', 'FontWeight', 'Bold');
end

Other specific function list files

Full set of file source code –> Portal