基于matlab实现图像的道路裂缝识别算法的研究 对于修补裂缝的检测问题

I = imread('a.jpg'); figure,imshow(I,[]) title('原图') I = rgb2gray(I); figure,imshow(I,[]) title('灰度图') I = double(I); [height,width] = size(I); J = I; conv = zeros(5,5);%高斯卷积核 sigma = 1; sigma_2 = sigma * sigma; sum = 0; for i = 1:5 for j = 1:5 conv(i,j) = exp((-(i - 3) * (i - 3) - (j - 3) * (j - 3)) / (2 * sigma_2)) / (2 * 3.14 * sigma_2);%高斯公式 sum = sum + conv(i,j); end end conv = conv./sum; %对图像实施高斯滤波 for i = 1:height for j = 1:width sum = 0; for k = 1:5 for m = 1:5 if (i - 3 + k) > 0 && (i - 3 + k) <= height && (j - 3 + m) > 0 && (j - 3 + m) < width sum = sum + conv(k,m) * I(i - 3 + k,j - 3 + m); end end end J(i,j) = sum; end end figure,imshow(J,[]) title('高斯滤波后的结果') %求梯度 dx = zeros(height,width); dy = zeros(height,width); d = zeros(height,width); %d = J; for i = 1:height - 1 for j = 1:width - 1 dx(i,j) = J(i,j + 1) - J(i,j); dy(i,j) = J(i + 1,j) - J(i,j); d(i,j) = sqrt(dx(i,j) * dx(i,j) + dy(i,j) * dy(i,j)); end end figure,imshow(d,[]) title('求梯度后的结果') %局部非极大值抑制 K = d; for j = 1:width K(1,j) = 0; end for j = 1:width K(height,j) = 0; end for i = 2:width - 1 K(i,1) = 0; end for i = 2:width - 1 K(i,width) = 0; end for i = 2:height - 1 for j = 2:width - 1 if d(i,j) == 0 K(i,j) = 0; else gradX = dx(i,j);%当前点x方向导数 gradY = dy(i,j);%当前点y方向导数 gradTemp = d(i,j);%当前点梯度 %如果Y方向幅度值较大 if abs(gradY) > abs(gradX) weight = abs(gradX) / abs(gradY);%权重 grad2 = d(i - 1,j); grad4 = d(i + 1,j); %如果x、y方向导数符号相同 %像素点位置关系 %g1 g2 % C % g4 g3 if gradX * gradY > 0 grad1 = d(i - 1,j - 1); grad3 = d(i + 1,j + 1); else %如果x、y方向导数符号反 %像素点位置关系 % g2 g1 % C %g3 g4 grad1 = d(i - 1,j + 1); grad3 = d(i + 1,j - 1); end %如果X方向幅度值较大 else weight = abs(gradY) / abs(gradX);%权重 grad2 = d(i,j - 1); grad4 = d(i,j + 1); %如果x、y方向导数符号相同 %像素点位置关系 %g3 %g4 C g2 % g1 if gradX * gradY > 0 grad1 = d(i + 1,j + 1); grad3 = d(i - 1,j - 1); else %如果x、y方向导数符号反 %像素点位置关系 % g1 %g4 C g2 %g3 grad1 = d(i - 1,j + 1); grad3 = d(i + 1,j - 1); end end %利用grad1-grad4对梯度进行插值 gradTemp1 = weight * grad1 + (1 - weight) * grad2; gradTemp2 = weight * grad3 + (1 - weight) * grad4; if gradTemp >= gradTemp1 && gradTemp >= gradTemp2 K(i,j) = gradTemp; else K(i,j) = 0; end end end end figure,imshow(K,[]) title('非极大值抑制后的结果') MIN = 12; MAX = MIN * 2; Large = zeros(height,width); Betueen = zeros(height,width); for i = 1:height for j = 1:width if K(i,j) >= MAX%小于小阈值，不可能为边缘点 Large(i,j) = K(i,j); else if K(i,j) >= MIN Betueen(i,j) = K(i,j); end end end end figure,imshow(Large,[]); MAXSIZE = 999999; Queue = zeros(MAXSIZE,2);%用数组模拟队列 front = 1; rear = 1; edge = zeros(height,width); for i = 2:height for j = 4:width if Large(i,j) > 0 Queue(rear,1) = i; Queue(rear,2) = j; rear = rear + 1; edge(i,j) = Large(i,j); Large(i,j) = 0; end while front ~= rear temp_i = Queue(front,1); temp_j = Queue(front,2); front = front + 1; %8-连通域寻找可能的边缘点 if Betueen(temp_i - 1,temp_j - 1) > 0 Large(temp_i - 1,temp_j - 1) = K(temp_i - 1,temp_j - 1); Betueen(temp_i - 1,temp_j - 1) = 0; Queue(rear,1) = temp_i - 1; Queue(rear,2) = temp_j - 1; rear = rear + 1; end if Betueen(temp_i - 1,temp_j) > 0 Large(temp_i - 1,temp_j) = K(temp_i - 1,temp_j); Betueen(temp_i - 1,temp_j) = 0; Queue(rear,1) = temp_i - 1; Queue(rear,2) = temp_j; rear = rear + 1; end if Betueen(temp_i - 1,temp_j + 1) > 0 Large(temp_i - 1,temp_j + 1) = K(temp_i - 1,temp_j + 1); Betueen(temp_i - 1,temp_j + 1) = 0; %入队 Queue(rear,1) = temp_i - 1; Queue(rear,2) = temp_j + 1; rear = rear + 1; end if Betueen(temp_i,temp_j - 1) > 0 Large(temp_i,temp_j - 1) = K(temp_i,temp_j - 1); Betueen(temp_i,temp_j - 1) = 0; Queue(rear,1) = temp_i; Queue(rear,2) = temp_j - 1; rear = rear + 1; end if Betueen(temp_i,temp_j + 1) > 0 Large(temp_i,temp_j + 1) = K(temp_i,temp_j + 1); Betueen(temp_i,temp_j + 1) = 0; Queue(rear,1) = temp_i; Queue(rear,2) = temp_j + 1; rear = rear + 1; end if Betueen(temp_i + 1,temp_j - 1) > 0 Large(temp_i + 1,temp_j - 1) = K(temp_i + 1,temp_j - 1); Betueen(temp_i + 1,temp_j - 1) = 0; Queue(rear,1) = temp_i + 1; Queue(rear,2) = temp_j - 1; rear = rear + 1; end if Betueen(temp_i + 1,temp_j) > 0 Large(temp_i + 1,temp_j) = K(temp_i + 1,temp_j); Betueen(temp_i + 1,temp_j) = 0; Queue(rear,1) = temp_i + 1; Queue(rear,2) = temp_j; rear = rear + 1; end if Betueen(temp_i + 1,temp_j + 1) > 0 Large(temp_i + 1,temp_j + 1) = K(temp_i + 1,temp_j + 1); Betueen(temp_i + 1,temp_j + 1) = 0; Queue(rear,1) = temp_i + 1; Queue(rear,2) = temp_j + 1; rear = rear + 1; end end i j end end figure,imshow(edge,[]) title('双阈值后的结果')