基于k均值和Otsu阈值分割算法的水果蔬菜缺陷检测matlab仿真

clc; clear; close all; warning off; addpath(genpath(pwd)); ai = imread('a5.jpg'); mi=imread('mango_infected.png'); m4=imread('m4.PNG'); img = imread('potato.png'); type = 'a'; [L, Centers] = imsegkmeans(img, 3); B = labeloverlay(img,L); rgbImage = B; % Display the original image. subplot(4, 5, 1); imshow(rgbImage); title('Original Image(Potato)'); % Split the original image into color bands. redBand = rgbImage(:,:, 1); greenBand = rgbImage(:,:, 2); blueBand = rgbImage(:,:, 3); % Threshold each color band. redthreshold = 100; greenThreshold = 140; blueThreshold = 170; redMask = (redBand < redthreshold); greenMask = (greenBand > greenThreshold); blueMask = (blueBand < blueThreshold); % Combine the masks to find where all 3 are "true." damagedAreasMask = uint8(redMask & greenMask & blueMask); subplot(4, 5, 2); imshow(damagedAreasMask, []); title('Damaged Areas Mask'); maskedrgbImage = uint8(zeros(size(damagedAreasMask))); % Initialize maskedrgbImage(:,:,1) = rgbImage(:,:,1) .* damagedAreasMask; maskedrgbImage(:,:,2) = rgbImage(:,:,2) .* damagedAreasMask; maskedrgbImage(:,:,3) = rgbImage(:,:,3) .* damagedAreasMask; reqInfo = maskedrgbImage(:, :, 1) > 0 & maskedrgbImage(:, :, 2) > 0 & maskedrgbImage(:, :, 3) > 0; res = sum(reqInfo(:)); disp(res) t = 10000; if type=='b' t = 5000; end if res>t subplot(4, 5, 3); imshow(maskedrgbImage); title('Result: Infected'); else subplot(4, 5, 3); imshow(maskedrgbImage); title('Result: Good'); end %=================================================================================% img = imread('good_potato.jpg'); [L, Centers] = imsegkmeans(img, 3); B = labeloverlay(img,L); rgbImage = B; % Display the original image. subplot(4, 5, 4); imshow(rgbImage); title('Original Image(Potato)'); % Split the original image into color bands. redBand = rgbImage(:,:, 1); greenBand = rgbImage(:,:, 2); blueBand = rgbImage(:,:, 3); % Threshold each color band. redthreshold = 100; greenThreshold = 140; blueThreshold = 170; redMask = (redBand < redthreshold); greenMask = (greenBand > greenThreshold); blueMask = (blueBand < blueThreshold); % Combine the masks to find where all 3 are "true." damagedAreasMask = uint8(redMask & greenMask & blueMask); maskedrgbImage = uint8(zeros(size(damagedAreasMask))); % Initialize maskedrgbImage(:,:,1) = rgbImage(:,:,1) .* damagedAreasMask; maskedrgbImage(:,:,2) = rgbImage(:,:,2) .* damagedAreasMask; maskedrgbImage(:,:,3) = rgbImage(:,:,3) .* damagedAreasMask; %imtool(maskedrgbImage); reqInfo = maskedrgbImage(:, :, 1) > 0 & maskedrgbImage(:, :, 2) > 0 & maskedrgbImage(:, :, 3) > 0; res = sum(reqInfo(:)); disp(res) t = 10000; if type=='b' t = 5000; end if res>t subplot(4, 5, 5); imshow(maskedrgbImage); title('Result: Infected'); else subplot(4, 5, 5); imshow(maskedrgbImage); title('Result: Good'); end %===============================================================================% img = imread('banana.png'); %img = imread('b1.jpg'); %img = imread('b2.jpg'); type = 'b'; [L, Centers] = imsegkmeans(img, 3); B = labeloverlay(img,L); rgbImage = B; % Display the original image. subplot(4, 5, 6); imshow(rgbImage); title('Original Image(Banana)'); % Split the original image into color bands. redBand = rgbImage(:,:, 1); greenBand = rgbImage(:,:, 2); blueBand = rgbImage(:,:, 3); % Threshold each color band. redthreshold = 100; greenThreshold = 140; blueThreshold = 170; redMask = (redBand < redthreshold); greenMask = (greenBand > greenThreshold); blueMask = (blueBand < blueThreshold); % Combine the masks to find where all 3 are "true." damagedAreasMask = uint8(redMask & greenMask & blueMask); subplot(4, 5, 7); imshow(damagedAreasMask, []); title('Damaged Areas Mask'); maskedrgbImage = uint8(zeros(size(damagedAreasMask))); % Initialize maskedrgbImage(:,:,1) = rgbImage(:,:,1) .* damagedAreasMask; maskedrgbImage(:,:,2) = rgbImage(:,:,2) .* damagedAreasMask; maskedrgbImage(:,:,3) = rgbImage(:,:,3) .* damagedAreasMask; %imtool(maskedrgbImage); reqInfo = maskedrgbImage(:, :, 1) > 0 & maskedrgbImage(:, :, 2) > 0 & maskedrgbImage(:, :, 3) > 0; res = sum(reqInfo(:)); disp(res) t = 10000; if type=='b' t = 5000; end if res>t subplot(4, 5, 8); imshow(maskedrgbImage); title('Result: Infected'); else subplot(4, 5, 8); imshow(maskedrgbImage); title('Result: Good'); end %=================================================================================% %img = imread('b1.jpg'); img = imread('b2.jpg'); [L, Centers] = imsegkmeans(img, 3); B = labeloverlay(img,L); rgbImage = B; % Display the original image. subplot(4, 5, 9); imshow(rgbImage); title('Original Image(Banana)'); % Split the original image into color bands. redBand = rgbImage(:,:, 1); greenBand = rgbImage(:,:, 2); blueBand = rgbImage(:,:, 3); % Threshold each color band. redthreshold = 100; greenThreshold = 140; blueThreshold = 170; redMask = (redBand < redthreshold); greenMask = (greenBand > greenThreshold); blueMask = (blueBand < blueThreshold); % Combine the masks to find where all 3 are "true." damagedAreasMask = uint8(redMask & greenMask & blueMask); maskedrgbImage = uint8(zeros(size(damagedAreasMask))); % Initialize maskedrgbImage(:,:,1) = rgbImage(:,:,1) .* damagedAreasMask; maskedrgbImage(:,:,2) = rgbImage(:,:,2) .* damagedAreasMask; maskedrgbImage(:,:,3) = rgbImage(:,:,3) .* damagedAreasMask; %imtool(maskedrgbImage); reqInfo = maskedrgbImage(:, :, 1) > 0 & maskedrgbImage(:, :, 2) > 0 & maskedrgbImage(:, :, 3) > 0; res = sum(reqInfo(:)); disp(res) t = 10000; if type=='b' t = 5000; end if res>t subplot(4, 5, 10); imshow(maskedrgbImage); title('Result: Infected'); else subplot(4, 5, 10); imshow(maskedrgbImage); title('Result: Good'); end %=================================================================================% % display the Original Image subplot(4,5,12); imshow(ai); title('Internal Image(Apple)') %%================================================================================================= n=imhist(ai); % Compute the histogram N=sum(n); % sum the values of all the histogram values max=0; %initialize maximum to zero %%================================================================================================ for i=1:256 P(i)=n(i)/N; %Computing the probability of each intensity level end %%================================================================================================ for T=2:255 % step through all thresholds from 2 to 255 w0=sum(P(1:T)); % Probability of class 1 (separated by threshold) w1=sum(P(T+1:256)); %probability of class2 (separated by threshold) u0=dot([0:T-1],P(1:T))/w0; % class mean u0 u1=dot([T:255],P(T+1:256))/w1; % class mean u1 sigma=w0*w1*((u1-u0)^2); % compute sigma i.e variance(between class) if sigma>max % compare sigma with maximum max=sigma; % update the value of max i.e max=sigma threshold=T-1; % desired threshold corresponds to maximum variance of between class end end %%==================================================================================================== bw=im2bw(ai,threshold/255); % Convert to Binary Image subplot(4,5,13); imshow(bw); title("Otsu threshold"); imgc = uint8(imcomplement(bw)); %imshow(imgc); % Display the Binary Image finalImage = uint8(zeros(size(ai))); finalImage(:,:,1) = ai(:,:,1) .* imgc; finalImage(:,:,2) = ai(:,:,2) .* imgc; finalImage(:,:,3) = ai(:,:,3) .* imgc; %imshow(finalImage); reqInfo = finalImage(:, :, 1) > 0 & finalImage(:, :, 2) > 0 & finalImage(:, :, 3) > 0; res = sum(reqInfo(:)); disp(res) t = 10000; %if type=='b' % t = 5000; %end subplot(4,5,14); if res>t imshow(finalImage); title('Result: Infected'); else imshow(finalImage); title('Result: Good'); end %==========================================================================% subplot(4,5,16); imshow(mi); title('infected mango(Spongy tissue)'); subplot(4,5,17); imshow(m4); title('x-ray image'); m4=rgb2gray(m