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

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asirajdin
asirajdin on 31 Oct 2020
Answered: asirajdin on 5 Nov 2020
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

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Answers (2)

Saurav Chaudhary
Saurav Chaudhary on 4 Nov 2020

  2 Comments

asirajdin
asirajdin on 4 Nov 2020
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;

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asirajdin
asirajdin on 5 Nov 2020
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;

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