我正在尝试在matlab中为行实现hough变换。几天来我一直在努力解决这个问题,我不知道为什么我的代码不能正常工作。是的,这是作业的一部分,但请帮助我,因为我完全放弃了。
输入参数:即 - 逻辑“边缘”图像(0表示不是边缘,1表示边缘)。
function [out_ro, out_theta]=houghTransform(Ie,nBinsRo,nBinsTheta,tresh)
A = zeros(nBinsRo, nBinsTheta);
theta = 1:nBinsTheta;
theta = scale(theta, nBinsTheta, 1, (pi / 2), - (pi / 2));
D = size(diag(Ie));
D = D(1);
ro = 1:nBinsRo;
ro = scale(ro, nBinsRo, 1, D, -D);
len = size(Ie);
%checks all edges
for i = 1:len(1)
for j = 1:len(2)
%if it is an edge
if ((Ie(i,j) == 1))
%generate all functions x cos(theta) + y sin(theta) = ro
for m=1:nBinsTheta
roVal = i * cos(theta(m)) + j * sin(theta(m));
idx = scale2(roVal, D, -D, nBinsRo, 1);
if (idx > 0 && idx < nBinsRo)
A(idx, m) = A(idx, m) + 1;
end
end
end
end
end
figure(1);
clf;
imagesc(A)
% -------------------------------------------------- %
function idx = scale(val, max_val_in, min_val_in, max_val_out, min_val_out)
skalirni_faktor = (max_val_out - min_val_out) / (max_val_in - min_val_in) ;
idx = min_val_out + (val-min_val_in) .* skalirni_faktor;
% -------------------------------------------------- %
function idx = scale2(val, max_val_in, min_val_in, max_val_out, min_val_out)
skalirni_faktor = (max_val_out - min_val_out) / (max_val_in - min_val_in) ;
idx = min_val_out + round((val-min_val_in) .* skalirni_faktor);
非常感谢您的时间和答案。
答案 0 :(得分:2)
我找不到代码的错误,但我认为缩放是有问题的。
如果有人发现这个,这里是另一个hough变换的实现。
function [rho,theta,houghSpace] = houghTransform(theImage,thetaSampleFrequency)
%#Define the hough space
theImage = flipud(theImage);
[width,height] = size(theImage);
rhoLimit = norm([width height]);
rho = (-rhoLimit:1:rhoLimit);
theta = (0:thetaSampleFrequency:pi);
numThetas = numel(theta);
houghSpace = zeros(numel(rho),numThetas);
%#Find the "edge" pixels
[xIndicies,yIndicies] = find(theImage);
%#Preallocate space for the accumulator array
numEdgePixels = numel(xIndicies);
accumulator = zeros(numEdgePixels,numThetas);
%#Preallocate cosine and sine calculations to increase speed. In
%#addition to precallculating sine and cosine we are also multiplying
%#them by the proper pixel weights such that the rows will be indexed by
%#the pixel number and the columns will be indexed by the thetas.
%#Example: cosine(3,:) is 2*cosine(0 to pi)
%# cosine(:,1) is (0 to width of image)*cosine(0)
cosine = (0:width-1)'*cos(theta); %#Matrix Outerproduct
sine = (0:height-1)'*sin(theta); %#Matrix Outerproduct
accumulator((1:numEdgePixels),:) = cosine(xIndicies,:) + sine(yIndicies,:);
%#Scan over the thetas and bin the rhos
for i = (1:numThetas)
houghSpace(:,i) = hist(accumulator(:,i),rho);
end
pcolor(theta,rho,houghSpace);
shading flat;
title('Hough Transform');
xlabel('Theta (radians)');
ylabel('Rho (pixels)');
colormap('gray');
end
答案 1 :(得分:1)
function [ Hough, theta_range, rho_range ] = naiveHough(I)
%NAIVEHOUGH Peforms the Hough transform in a straightforward way.
%
[rows, cols] = size(I);
theta_maximum = 90;
rho_maximum = floor(sqrt(rows^2 + cols^2)) - 1;
theta_range = -theta_maximum:theta_maximum - 1;
rho_range = -rho_maximum:rho_maximum;
Hough = zeros(length(rho_range), length(theta_range));
wb = waitbar(0, 'Naive Hough Transform');
for row = 1:rows
waitbar(row/rows, wb);
for col = 1:cols
if I(row, col) > 0
x = col - 1;
y = row - 1;
for theta = theta_range
rho = round((x * cosd(theta)) + (y * sind(theta)));
rho_index = rho + rho_maximum + 1;
theta_index = theta + theta_maximum + 1;
Hough(rho_index, theta_index) = Hough(rho_index, theta_index) + 1;
end
end
end
end
close(wb);
end
如果您愿意,我也有Hough Naive Implementation的要点。