煤矸多目标区域灰度特征及纹理特征提取

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针对图像多目标区域进行灰度提取纹理提取图像如图所示：

Snipaste_2024-04-18_16-08-22.png

算法设计未采用函数调用可直接运行：

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clear all,clc;
%读录图像
im = imread( '/Users/yons/Desktop/目标区域分别提取并统计灰度值/gan.png' );   
figure;
subplot(1,2,1);imshow(im);title('原始图像');
 %转灰度图像
im=rgb2gray(im);                                             	 
subplot(1,2,2);imshow(im);title('灰度图像');
%对灰度图像进行邻域3x3二维中值滤波
im1=medfilt2(im,[3 3]);                                          
figure;subplot(1,2,1);imshow(im1);title('3x3-中值滤波');
%寻找灰度图像最佳阈值，"Otsu"法阈值分割图像，并进行二值化处理
level=graythresh(im1);                                           
im2=im2bw(im1,level);                                            
subplot(1,2,2);imshow(im2);title('二值化');
im3 = edge(im2,'Canny');
figure,imshow(im3);
%标记连通区域
im4 = bwlabel(im3);
q = max(max(im4));
[r,c] = size(im4);
%构建元胞数组
A = cell(1,q+1);
A{1,1} = im4;
%分离连通区域
for p = 1:q
    im4 = bwlabel(im3);
    for i = 1:r
        for j = 1:c
           if  im4(i,j) ~= p
                 im4(i,j) = 0;
           end
        end
    end
    A{1,p} = im4;
end
%循环输出连通区域
for i = 1:q
    figure,imshow(A{1,i});
end
%多目标连通区域灰度值信息提取
k = cell(1,q);
for i = 1:q
	k{1,i} = ones(r,c);
end

for p = 1:q

	for i = 1:r
    	for j = 1:c
       
       		if sum(A{1,p}(i,1:j)) == 0
            	k{1,p}(i,1:j) = 0;
       		end  
           
       		if sum(A{1,p}(i,j:c)) == 0
           		 k{1,p}(i,j:c) = 0;
       		end
       
       		if A{1,p}(i,j) == 1
          		 k{1,p}(i,j) = 0;
      		end
    	end
	end
end

%T为提取目标区域灰度矩阵
T = cell(1,q);
for p = 1:q
	T{1,p} = zeros(r,c);
end

for p = 1:q
	for i = 1:r
		T{1,p}(i,:) = double(im(i,:)) * diag(k{1,p}(i,:));
	end
end

%%
gray = cell(1,q);

for p = 1:q
	gray{1,p} = T{1,p};
end

for p = 1:q
    [M,N] = size(T{1,p});
  for i = 1:M
	   for j = 1:N
		    for n = 1:256/16
			     if (n-1)*16<=gray{1,p}(i,j)&&gray{1,p}(i,j)<=(n-1)*16+15
				      gray{1,p}(i,j) = n-1;
			     end
        end
     end
  end
  %-----------------------------------------------------------
  % 计算0，45，90，135方向上灰度共生矩阵，距离为1
  %-----------------------------------------------------------
  Q = zeros(16,16,4);
  for m = 1:16
	 for n = 1:16
		 for i = 1:M
			 for j= 1:N
				  if j < N && gray{1,p}(i,j) == m - 1 && gray{1,p}(i,j+1) == n - 1
					   Q(m,n,1) = Q(m,n,1) + 1;
					   Q(m,n,1) = Q(m,n,1);
				  end
				  if i > 1 && j < N && gray{1,p}(i,j) == m - 1 && gray{1,p}(i-1,j+1) == n - 1
					   Q(m,n,2) = Q(m,n,2) + 1;
					   Q(m,n,2) = Q(m,n,2);
				  end
				  if i < M && gray{1,p}(i,j) == m - 1 && gray{1,p}(i+1,j) == n - 1
					 Q(m,n,3) = Q(m,n,3) + 1;
					 Q(m,n,3) = Q(m,n,3);
				  end
				  if i < M && j < N && gray{1,p}(i,j) == m - 1 && gray{1,p}(i+1,j+1) == n - 1
					 Q(m,n,4) = Q(m,n,4) + 1;
					 Q(m,n,4) = Q(m,n,4);
				  end
			 end
		 end
		    if m == n
		       Q(m,n,:) = Q(m,n,:)*2;
        end
   end
  end
  %disp('0度时的灰度共生矩阵：');
  %disp(Q(:,:,1));
  %disp('45度时的灰度共生矩阵：');
  %disp(Q(:,:,2));
  %disp('90度时的灰度共生矩阵：');
  %disp(Q(:,:,3));
  %disp('135度时的灰度共生矩阵：');
  %disp(Q(:,:,4));
  %-----------------------------------------------------------
  % 灰度共生矩阵归一化处理
  %-----------------------------------------------------------
  for n = 1:4
	   Q(:,:,n) = Q(:,:,n)/sum(sum(Q(:,:,n)));
  end
  [kk,ll,mm] = size(Q);

  %-----------------------------------------------------------
  %对共生矩阵计算能量、熵、惯性矩、相关4个纹理参数
  %-----------------------------------------------------------
  H = zeros(1,mm);
  I = H;
  Ux = H;      
  Uy = H;
  deltaX= H;  
  deltaY = H;
  C = H;
  for n = 1:mm
     E(n) = sum(sum(Q(:,:,n).^2)); %能量
      for i = 1:kk
          for j = 1:ll
              if Q(i,j,n)~=0
                  H(n) = -Q(i,j,n)*log(Q(i,j,n))+H(n); %熵
              end
              I(n) = (i-j)^2*Q(i,j,n)+I(n);  %惯性矩

              Ux(n) = i*Q(i,j,n)+Ux(n); %相关性中μx
              Uy(n) = j*Q(i,j,n)+Uy(n); %相关性中μy
          end
      end
  end
  for n = 1:mm
      for i = 1:kk
          for j = 1:ll
              deltaX(n) = (i-Ux(n))^2*Q(i,j,n)+deltaX(n); %相关性中σx
              deltaY(n) = (j-Uy(n))^2*Q(i,j,n)+deltaY(n); %相关性中σy
              C(n) = i*j*Q(i,j,n)+C(n);
          end
      end
      C(n) = (C(n)-Ux(n)*Uy(n))/deltaX(n)/deltaY(n); %相关性
  end

  %--------------------------------------------------------------------------
  %求能量、熵、惯性矩、相关的均值和标准差作为最终8维纹理特征
  %--------------------------------------------------------------------------
  a1 = mean(E);
  b1 = sqrt(cov(E));

  a2 = mean(H);
  b2 = sqrt(cov(H));

  a3 = mean(I);
  b3 = sqrt(cov(I));

  a4 = mean(C);
  b4 = sqrt(cov(C));

  %sprintf('0,45,90,135方向上的能量依次为： %f, %f, %f, %f',E(1),E(2),E(3),E(4))  % 输出数据;
  %sprintf('0,45,90,135方向上的熵依次为： %f, %f, %f, %f',H(1),H(2),H(3),H(4))  % 输出数据;
  %sprintf('0,45,90,135方向上的惯性矩依次为： %f, %f, %f, %f',I(1),I(2),I(3),I(4))  % 输出数据;
  %sprintf('0,45,90,135方向上的相关性依次为： %f, %f, %f, %f',C(1),C(2),C(3),C(4))  % 输出数据;
  wen_li{1,p} = [0,45,90,135;a1,a2,a3,a4];
end

%求取目标区域灰度值平均值
MEAN = cell (1,q);

for p = 1:q 
    m = nnz(T{1,p});
    MEAN{1,p} = sum(sum(T{1,p}))/m;
end

运行代码输出图像单目标灰度均值以及纹理特征，可从元胞数组中查看参量。