how to detect multiple faces on an image using Stephen Peyton and Chee Yi Ong's code

This code can only work on single face image. I want to apply it to a multiple face image. kindly help me with a way out. Thanks.

function [faces,faceBound] = detectFaces(img)

% preprocessing by Gaussian filtering

img2 = img; % keep a copy of the original color 3D image

img = imread(img);

img = rgb2gray(img);

img = conv2(img,fspecial('gaussian',3,3),'same');

% get image parameters

[m,n] = size(img);

% other variables

scanItr = 8; % can be modified depending on how big face is relative to image

% scanItr of 8 works well for images with size about 300x400

faces = []; % empty by default

% compute integral image

intImg = integralImg(img);

% load finalClassifiers

load '../trainHaar/trainedClassifiers.mat' % 286 classifiers

%%%%% Cascaded Detector Structure: 7 levels, 200 classifiers %%%%%

class1 = selectedClassifiers(1:2,:);

class2 = selectedClassifiers(3:12,:);

class3 = selectedClassifiers(13:20,:);

class4 = selectedClassifiers(21:40,:);

class5 = selectedClassifiers(41:70,:);

class6 = selectedClassifiers(71:150,:);

class7 = selectedClassifiers(151:200,:);

% iterate through each window size/pyramid level

for itr = 1:scanItr

printout = strcat('Iteration #',int2str(itr),'\n');

fprintf(printout);

for i = 1:2:m-19

if i + 19 > m

break; % boundary case check

end

for j = 1:2:n-19

if j + 19 > n

break; % boundary case check

end

window = intImg(i:i+18,j:j+18); % 19x19 window as per training

check1 = cascade(class1,window,1);

if check1 == 1

check2 = cascade(class2,window,.5);

if check2 == 1

check3 = cascade(class3,window,.5);

if check3 == 1

check4 = cascade(class4,window,.5);

if check4 == 1

check5 = cascade(class5,window,.6);

if check5 == 1

check6 = cascade(class6,window,.6);

if check6 == 1

fprintf('Passed level 6 cascade.\n');

check7 = cascade(class7,window,.5);

if check7 == 1

% save rectangular corner coordinates

bounds = [j,i,j+18,i+18,itr];

fprintf('Face detected!\n');

faces = [faces;bounds];

end

end

end

end

end

end

end

end

end

% create next image pyramid level

tempImg = imresize(img,.8);

img = tempImg;

[m,n] = size(img);

intImg = integralImg(img);

end

if size(faces,1) == 0 % no faces detected

error('No face detected! Try again with a larger value of scanItr.');

end

%%%%% Get Best Bounding Box %%%%%

% upscale rectangular bound coordinates back to base level of pyramid

faceBound = zeros(size(faces,1),4);

maxItr = max(faces(:,5)); % higher iterations have larger bounding boxes

for i = 1:size(faces,1)

if faces(i,5) ~= maxItr

continue; % only interested in large bounding boxes

end

faceBound(i,:) = floor(faces(i,1:4)*1.25^(faces(i,5)-1));

end

% filter out overlapping rectangular bounding boxes

startRow = 1;

for i = 1:size(faceBound,1)

if faceBound(i,1) == 0

startRow = startRow+1; % start with next row

end

end

faceBound = faceBound(startRow:end,:); % trim faceBound to get rid of 0-filled rows

% get the union of the areas of overlapping boxes

faceBound = [min(faceBound(:,1)),min(faceBound(:,2)),max(faceBound(:,3)),max(faceBound(:,4))];

% Show the detected face(s) with original image

figure,imshow(img2), hold on;

if(~isempty(faceBound));

for n=1:size(faceBound,1)

toleranceX = floor(0.1*(faceBound(n,3)-faceBound(n,1)));

toleranceY = floor(0.1*(faceBound(n,4)-faceBound(n,2)));

% original bounds

x1=faceBound(n,1); y1=faceBound(n,2);

x2=faceBound(n,3); y2=faceBound(n,4);

% adjusted bounds to get wider face capture

x1t=faceBound(n,1)-toleranceX; y1t=faceBound(n,2)-toleranceY;

x2t=faceBound(n,3)+toleranceX; y2t=faceBound(n,4)+toleranceY;

imSize = size(imread(img2));

% if adjusted bounds will lead to out-of-bounds plotting, use original bounds

if x1t < 1 || y1t < 1 || x2t > imSize(2) || y2t > imSize(1)

fprintf('Out of bounds adjustments. Plotting original values...\n');

plot([x1 x1 x2 x2 x1],[y1 y2 y2 y1 y1],'LineWidth',2);

else

plot([x1t x1t x2t x2t x1t],[y1t y2t y2t y1t y1t],'LineWidth',2);

end

end

end

title('Detected face in image');

hold off;

end

Answer 1

[Saurav Chaudhary]

You can go through the following MATLAB answers to get an idea and try to incorporate it in your application.

https://www.mathworks.com/matlabcentral/answers/461391-multiple-face-detection-and-cropping-from-multiple-images?s_tid=answers_rc1-2_p2_MLT

https://www.mathworks.com/matlabcentral/answers/487886-face-detection-of-multiple-faces-inside-one-image?s_tid=answers_rc1-2_p2_Topic

[asirajdin]

Thanks, I really appreciate it.

[asirajdin]

Hi Saurav Chaudhary, I have seen the answers but my problem is which position am I to use "step" and what will be my "num" and "den" while using step. For example: BB= step ("i don't know what should be here", img2). Then, the part of the code plotting the faces marked by the facebound is not really clear to me. when i used this it flagged error.

Error using tf (line 366)

Invalid syntax. Use name/value pairs to specify options and property values.

Error in step (line 96)

sys = tf(a,b);

Error in detectFaces (line 108)

bbox = step(faceBound, img2);

function [faces,faceBound] = detectFaces(img)

% preprocessing by Gaussian filtering

img2 = 'C:\Users\Asirajdin\Documents\T Chapters\Practical Implementation of face detection in matlab\New folder\matlab-viola-jones-master\P24_05.png';% keep a copy of the original color 3D image

img = imread('C:\Users\Asirajdin\Documents\T Chapters\Practical Implementation of face detection in matlab\New folder\matlab-viola-jones-master\P24_05.png');

img = rgb2gray(img);

img = conv2(img,fspecial('gaussian',3,3),'same');

% get image parameters

[m,n] = size(img);

% other variables

scanItr = 8; % can be modified depending on how big face is relative to image

% scanItr of 8 works well for images with size about 300x400

faces = []; % empty by default

% compute integral image

intImg = integralImg(img);

% load finalClassifiers

load '../trainHaar/trainedClassifiers.mat' % 286 classifiers

%%%%% Cascaded Detector Structure: 7 levels, 200 classifiers %%%%%

class1 = selectedClassifiers(1:2,:);

class2 = selectedClassifiers(3:12,:);

class3 = selectedClassifiers(13:20,:);

class4 = selectedClassifiers(21:40,:);

class5 = selectedClassifiers(41:70,:);

class6 = selectedClassifiers(71:150,:);

class7 = selectedClassifiers(151:200,:);

% iterate through each window size/pyramid level

for itr = 1:scanItr

printout = strcat('Iteration #',int2str(itr),'\n');

fprintf(printout);

for i = 1:2:m-19

if i + 19 > m

break; % boundary case check

end

for j = 1:2:n-19

if j + 19 > n

break; % boundary case check

end

window = intImg(i:i+18,j:j+18); % 19x19 window as per training

check1 = cascade(class1,window,1);

if check1 == 1

check2 = cascade(class2,window,.5);

if check2 == 1

check3 = cascade(class3,window,.5);

if check3 == 1

check4 = cascade(class4,window,.5);

if check4 == 1

check5 = cascade(class5,window,.6);

if check5 == 1

check6 = cascade(class6,window,.6);

if check6 == 1

fprintf('Passed level 6 cascade.\n');

check7 = cascade(class7,window,.5);

if check7 == 1

% save rectangular corner coordinates

bounds = [j,i,j+18,i+18,itr];

fprintf('Face detected!\n');

faces = [faces;bounds];

end

end

end

end

end

end

end

end

end

% create next image pyramid level

tempImg = imresize(img,.8);

img = tempImg;

[m,n] = size(img);

intImg = integralImg(img);

end

if size(faces,1) == 0 % no faces detected

error('No face detected! Try again with a larger value of scanItr.');

end

%%%%% Get Best Bounding Box %%%%%

% upscale rectangular bound coordinates back to base level of pyramid

faceBound = zeros(size(faces,1),4);

maxItr = max(faces(:,5)); % higher iterations have larger bounding boxes

for i = 1:size(faces,1)

if faces(i,5) ~= maxItr

continue; % only interested in large bounding boxes

end

faceBound(i,:) = floor(faces(i,1:4)*1.25^(faces(i,5)-1));

end

% filter out overlapping rectangular bounding boxes

startRow = 1;

if faceBound(i,4) == 0

startRow = startRow+1; % start with next row

end

faceBound = faceBound(startRow:end,:); % trim faceBound to get rid of 0-filled rows

% get the union of the areas of overlapping boxes

faceBound = [min(faceBound(:,1)),min(faceBound(:,2)),max(faceBound(:,3)),max(faceBound(:,4))];

bbox = step(faceBound, img2);

figure

imshow(img2)

hold on;

for i = 1:size(bbox,1)

rectangle('Position',bbox(i,:),'LineWidth',2,'LineStyle','-','EdgeColor','g')

end

hold off;

% Show the detected face(s) with original image

% for n=1:size(faceBound,1)

% toleranceX = floor(0.1*(faceBound(n,3)-faceBound(n,1)));

% toleranceY = floor(0.1*(faceBound(n,4)-faceBound(n,2)));

% % original bounds

% x1=faceBound(n,1); y1=faceBound(n,2);

% x2=faceBound(n,3); y2=faceBound(n,4);

% % adjusted bounds to get wider face capture

% % x1t=faceBound(n,1)-toleranceX; y1t=faceBound(n,2)-toleranceY;

% % x2t=faceBound(n,3)+toleranceX; y2t=faceBound(n,4)+toleranceY;

% imSize = size(imread(img2));

% % if adjusted bounds will lead to out-of-bounds plotting, use original bounds

% plot([x1 x1 x2 x2 x1],[y1 y2 y2 y1 y1],'LineWidth',2);

% else

% plot([x1t x1t x2t x2t x1t],[y1t y2t y2t y1t y1t],'LineWidth',2);

% end

% end

end

% title('Detected face in image');

% hold off;

Answer 2

[asirajdin]

When I used the bbox= step(detectFaces, img2), It continued to process endlessly. see it the code

function [faces,faceBound] = detectFaces(img)

% preprocessing by Gaussian filtering

img2 = 'C:\Users\Asirajdin\Documents\T Chapters\Practical Implementation of face detection in matlab\New folder\matlab-viola-jones-master\P24_05.png';% keep a copy of the original color 3D image

img = imread('C:\Users\Asirajdin\Documents\T Chapters\Practical Implementation of face detection in matlab\New folder\matlab-viola-jones-master\P24_05.png');

img = rgb2gray(img);

img = conv2(img,fspecial('gaussian',3,3),'same');

% get image parameters

[m,n] = size(img);

% other variables

scanItr = 8; % can be modified depending on how big face is relative to image

% scanItr of 8 works well for images with size about 300x400

faces = []; % empty by default

% compute integral image

intImg = integralImg(img);

% load finalClassifiers

load '../trainHaar/trainedClassifiers.mat' % 286 classifiers

%%%%% Cascaded Detector Structure: 7 levels, 200 classifiers %%%%%

class1 = selectedClassifiers(1:2,:);

class2 = selectedClassifiers(3:12,:);

class3 = selectedClassifiers(13:20,:);

class4 = selectedClassifiers(21:40,:);

class5 = selectedClassifiers(41:70,:);

class6 = selectedClassifiers(71:150,:);

class7 = selectedClassifiers(151:200,:);

% iterate through each window size/pyramid level

for itr = 1:scanItr

printout = strcat('Iteration #',int2str(itr),'\n');

fprintf(printout);

for i = 1:2:m-19

if i + 19 > m

break; % boundary case check

end

for j = 1:2:n-19

if j + 19 > n

break; % boundary case check

end

window = intImg(i:i+18,j:j+18); % 19x19 window as per training

check1 = cascade(class1,window,1);

if check1 == 1

check2 = cascade(class2,window,.5);

if check2 == 1

check3 = cascade(class3,window,.5);

if check3 == 1

check4 = cascade(class4,window,.5);

if check4 == 1

check5 = cascade(class5,window,.6);

if check5 == 1

check6 = cascade(class6,window,.6);

if check6 == 1

fprintf('Passed level 6 cascade.\n');

check7 = cascade(class7,window,.5);

if check7 == 1

% save rectangular corner coordinates

bounds = [j,i,j+18,i+18,itr];

fprintf('Face detected!\n');

faces = [faces;bounds];

end

end

end

end

end

end

end

end

end

% create next image pyramid level

tempImg = imresize(img,.8);

img = tempImg;

[m,n] = size(img);

intImg = integralImg(img);

end

if size(faces,1) == 0 % no faces detected

error('No face detected! Try again with a larger value of scanItr.');

end

%%%%% Get Best Bounding Box %%%%%

% upscale rectangular bound coordinates back to base level of pyramid

faceBound = zeros(size(faces,1),4);

maxItr = max(faces(:,5)); % higher iterations have larger bounding boxes

for i = 1:size(faces,1)

if faces(i,5) ~= maxItr

continue; % only interested in large bounding boxes

end

faceBound(i,:) = floor(faces(i,1:4)*1.25^(faces(i,5)-1));

end

% filter out overlapping rectangular bounding boxes

startRow = 1;

if faceBound(i,4) == 0

startRow = startRow+1; % start with next row

end

faceBound = faceBound(startRow:end,:); % trim faceBound to get rid of 0-filled rows

% get the union of the areas of overlapping boxes

faceBound = [min(faceBound(:,1)),min(faceBound(:,2)),max(faceBound(:,3)),max(faceBound(:,4))];

bbox = step(detectFaces, img2);

figure

imshow(img2)

hold on;

for i = 1:size(bbox,1)

rectangle('Position',bbox(i,:),'LineWidth',2,'LineStyle','-','EdgeColor','g')

end

hold off;