Problem with defining an argument (face detection)

Question

Pawe? 2011-10-26

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/19531-problem-with-defining-an-argument-face-detection

移动：DGM 2023-12-29

Hello. I downloaded a source code to learn something more about the face detection in matlab, but when I try to run the program I get this kind of massage:

??? Input argument "inputimage" is undefined.
Error in ==> face_detection at 7
[likely_skin]=get_likelyhood(inputimage,rmean,bmean,rbcov);

This is the function that is not working:

function face_detection(inputfile)
[rmean,bmean,rbcov]=make_model();
[likely_skin]=get_likelyhood(inputfile,rmean,bmean,rbcov);
[skinBW,opt_th] = segment_adaptive(likely_skin);
[erodedBW]=label_regions(skinBW);
[eulerBW]=euler_test(erodedBW);
[aspectBW]=aspect_test(eulerBW);
[templateBW]=template_test(aspectBW,inputfile,'template.jpg');
[K,P]=bwlabel(templateBW,8);
s  = regionprops(bwlabel(templateBW), 'centroid');
centroids = cat(1, s.Centroid);
subplot(4,3,12);
imshow(imread(inputfile))
if(P>0)
hold on
plot(centroids(:,1), centroids(:,2), 'b*')
hold off
end
title('Final Detection')
clear all

It would be helpful if someone could help me. If u need i can post all of the sorce code. Or post link to the code.

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Walter Roberson 2011-10-26

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/19531-problem-with-defining-an-argument-face-detection#answer_25818

The source code you show does not match your error message. The error message has inputimage as the first argument to get_likelyhood, but the code you show has inputfile in that location.

Perhaps you did not save the code after you modified it?

4 个评论
显示 2更早的评论隐藏 2更早的评论

Pawe? 2011-10-26

移动：DGM 2023-12-29

在 MATLAB Online 中打开

I will add the whole source code of this program. I tried to change few things, but i get always the same results.

[??? Input argument "inputfile" is undefined.
Error in ==> face_detection at 7
[likely_skin]=get_likelyhood(inputfile,rmean,bmean,rbcov);]

Code:

%This function detect faces in an image
function face_detection(inputfile)
[rmean,bmean,rbcov]=make_model();
[likely_skin]=get_likelyhood(inputfile,rmean,bmean,rbcov);
[skinBW,opt_th] = segment_adaptive(likely_skin);
[erodedBW]=label_regions(skinBW);
[eulerBW]=euler_test(erodedBW);
[aspectBW]=aspect_test(eulerBW);
[templateBW]=template_test(aspectBW,inputfile,'template.jpg');
[K,P]=bwlabel(templateBW,8);
s  = regionprops(bwlabel(templateBW), 'centroid');
centroids = cat(1, s.Centroid);
subplot(4,3,12);
imshow(imread(inputfile))
if(P>0)
hold on
plot(centroids(:,1), centroids(:,2), 'b*')
hold off
end
title('Final Detection')
clear all
%***********************************FUNCTIONS******************************
%**************************************************************************
% Following function returns cromatic values of an input image file
function [cr, cb] = get_crcb(filename)
im= imread(filename);
imycc = rgb2ycbcr(im);
lpf = 1/9 * ones(3);
cr = imycc(:,:,3);
cb = imycc(:,:,2);
cr = filter2(lpf, cr);
cb = filter2(lpf, cb);
cr = reshape(cr, 1, prod(size(cr)));
cb = reshape(cb, 1, prod(size(cb)));
%**************************************************************************
%Following function plots 2D Chromatic Histogram
function plot_hist2d(cr, cb)
hist2d = zeros(256);
cr = round(cr);
cb = round(cb);
for i = 1:length(cr)
   hist2d(cr(i),cb(i)) = hist2d(cr(i),cb(i))+1;
end
surf(hist2d)
%**************************************************************************
%Following fnction fit the statistics of sample chrominance values into
%2D Gaussian model and returns the parameters of model
function [rmean,bmean,rbcov]=make_model()
[cr1, cb1] = get_crcb('sampleset/1.jpg');
[cr2, cb2] = get_crcb('sampleset/2.jpg');
[cr3, cb3] = get_crcb('sampleset/3.jpg');
[cr4, cb4] = get_crcb('sampleset/4.jpg');
[cr5, cb5] = get_crcb('sampleset/5.jpg');
[cr6, cb6] = get_crcb('sampleset/6.jpg');
[cr7, cb7] = get_crcb('sampleset/7.jpg');
[cr8, cb8] = get_crcb('sampleset/8.jpg');
[cr9, cb9] = get_crcb('sampleset/9.jpg');
[cr10, cb10] = get_crcb('sampleset/10.jpg');
[cr11, cb11] = get_crcb('sampleset/11.jpg');
[cr12, cb12] = get_crcb('sampleset/12.jpg');
[cr13, cb13] = get_crcb('sampleset/13.jpg');
%concatenate all values
cr = [cr1 cr2 cr3 cr4 cr5 cr6 cr7 cr8 cr9 cr10 cr11 cr12 cr13];
cb = [cb1 cb2 cb3 cb4 cb5 cb6 cb7 cb8 cb9 cb10 cb11 cr12 cb13];
rmean = mean(cr);
bmean = mean(cb);
rbcov = cov(cr,cb);
jointchart = zeros(256);
for r = 0:255
   for b = 0:255
     x = [(r - rmean);(b - bmean)];
      jointchart(r+1,b+1) = [power(2*pi*power(det(rbcov),0.5),-1)]*exp(-0.5* x'*inv(rbcov)* x);
   end
end
subplot(4,3,1);
plot_hist2d(cr,cb)
title('2D Chromatic Histogram Model')
subplot(4,3,2);
surf(jointchart)
title('Joint Gaussian Distribution Model')
%**************************************************************************
%Following function convert original image into normalized skin likelyhood image
function[likely_skin]=get_likelyhood(filename,rmean,bmean,rbcov)
img = imread(filename);
imycbcr = rgb2ycbcr(img);
[m,n,l] = size(img);
likely_skin = zeros(m,n);
for i = 1:m
   for j = 1:n
        cr = double(imycbcr(i,j,3));
        cb = double(imycbcr(i,j,2));
        x = [(cr-rmean);(cb-bmean)];
        likely_skin(i,j) = [power(2*pi*power(det(rbcov),0.5),-1)]*exp(-0.5* x'*inv(rbcov)* x);
     end
  end
lpf= 1/9*ones(3);
likely_skin = filter2(lpf,likely_skin);
likely_skin = likely_skin./max(max(likely_skin));
subplot(4,3,3);
imshow(img, [0 1])
title('Original RGB Image')
subplot(4,3,4);
imshow(likely_skin, [0 1])
title('Skin Likelyhood Image')
%**************************************************************************
%Following function take likelyhood image as input and segment it into binary image by
%setting the threshold adaptively
function [binary_skin,opt_th] = segment_adaptive(likely_skin)
[m,n] = size(likely_skin);
temp = zeros(m,n);
diff_list = [];
high=0.55;
low=0.01;
step_size=-0.1;
bias_factor=1;
indx_count=[(high-low)/abs(step_size)]+2;
for threshold = high:step_size:low
   binary_skin = zeros(m,n);
   binary_skin(find(likely_skin>threshold)) = 1;
   diff = sum(sum(binary_skin - temp));
   diff_list = [diff_list diff];
   temp = binary_skin;
end
[C, indx] = min(diff_list);
opt_th = (indx_count-indx)*abs(step_size)*bias_factor;
binary_skin = zeros(m,n);
binary_skin(find(likely_skin>opt_th)) = 1;
subplot(4,3,5);
imshow(binary_skin, [0 1])
title('Skin Segmented Image')
%**************************************************************************
%Following function applies erosion to binary image and mark the regions
function[labelBW]=label_regions(binary_skin)
[m,n] = size(binary_skin); 
% %Apply Minimal Erosion to open up some small holes
% se0 = strel('disk',2); 
% erodedBW0=zeros(m,n);
% erodedBW0= imerode(binary_skin,se0);
%Fill the regions
filledBW=zeros(m,n);
filledBW = imfill(binary_skin,'holes');
se2 = strel('disk',10); 
erodedBW=zeros(m,n);
erodedBW = imerode(filledBW,se2);
subplot(4,3,6);
imshow(erodedBW)
title({'After Erosion';'(disk size: 10)'})
se1 = strel('disk',8); 
dilateBW=zeros(m,n);
dilateBW=imdilate(erodedBW,se1);
dilateBW = immultiply(dilateBW,binary_skin);
subplot(4,3,7);
imshow(dilateBW)
title({'After Dilation';'(disk size: 8)'})
labelBW=zeros(m,n);
[labelBW,num] = bwlabel(dilateBW,8);
color_regions=zeros(m,n);
color_regions= label2rgb(labelBW, 'hsv', 'black', 'shuffle');
subplot(4,3,8);
imshow(color_regions)
title({'Labeled Regions';['(',num2str(num),' regions)']})
%**************************************************************************
%Following function filters regions which has at least one hole
function [eulerBW]=euler_test(labelBW)
e = regionprops(labelBW,'EulerNumber');
eulers=cat(1,e.EulerNumber);
holes=1-eulers;
region_index = find(holes>=1);
[m,n]=size(labelBW);
eulerBW=zeros(m,n);
for i=1:length(region_index)
     [x,y] = find(bwlabel(labelBW) == region_index(i));
     bwsegment = bwselect(labelBW,y,x,8);
     eulerBW=eulerBW+bwsegment;
  end
  subplot(4,3,9);
  imshow(eulerBW)
  title({'After Euler Test';['(',num2str(length(region_index)),' regions)']})
%**************************************************************************
%Following function determines whether the region aspect ratio is within
%range of being a face region
function [aspectBW]=aspect_test(eulerBW)
[m,n]=size(eulerBW);
filledBW = imfill(eulerBW,'holes');
se1 = strel('disk',3); 
growBW=zeros(m,n);
growBW=imdilate(filledBW,se1);
[labels,num] = bwlabel(growBW,8);
[aspect_ratio]=get_aspect(labels);
region_index = find(aspect_ratio<=3.5 & aspect_ratio>=1);
aspectBW=zeros(m,n);
for i=1:length(region_index)
      [x,y] = find(bwlabel(filledBW) == region_index(i));
      bwsegment = bwselect(filledBW,y,x,8);
      aspectBW=aspectBW+bwsegment;
  end
  subplot(4,3,10);
  imshow(aspectBW)
  title({'After Aspect Ratio Test';['(',num2str(length(region_index)),' regions)']})
%**************************************************************************
%Following function returns image regions which pass template matching test
function [template_passed]=template_test(aspectBW,originalRGB,template)
imgray=rgb2gray(imread(originalRGB));
imtemplate=imread(template);
[labels,num] = bwlabel(aspectBW,8);
[m,n]=size(aspectBW);
orient = regionprops(labels,'Orientation');
angles=cat(1,orient.Orientation);
c = regionprops(labels,'Centroid');
centroids=cat(1,c.Centroid);
template_passed=zeros(m,n);
gray_matched=zeros(m,n);
for j=1:num,
[x,y] = find(labels == j);
bwsegment = bwselect(aspectBW,y,x,8);
oneface=immultiply(bwsegment,imgray);
cx1=centroids(j,1);
cy1=centroids(j,2);
p=regionprops(bwlabel(bwsegment),'BoundingBox');
boxdim=cat(1,p.BoundingBox);
regw=boxdim(3);
regh=boxdim(4);   
ratio=regh/regw;
if(ratio>1.6)
regh=1.5*regw;
cy1=cy1-(0.1*regh);        
end
gmodel_resize=imresize(imtemplate,[regh regw],'bilinear');
if(angles(j)>0)
gmodel_rotate=imrotate(gmodel_resize,angles(j)-90,'bilinear','loose');
else
gmodel_rotate=imrotate(gmodel_resize,90+angles(j),'bilinear','loose');
end
bwmodel=im2bw(gmodel_rotate,0);
[g,h]=size(bwmodel);
bwmorphed = bwmorph(bwmodel,'clean');
[L,no]=bwlabel(bwmorphed,8);
if(no==1)
bwsingle=bwmorphed;
else
ar=regionprops(bwlabel(bwmorphed),'Area');
areas=cat(1,ar.Area);  
[C,I]=max(areas);
[x1,y1] = find(bwlabel(bwmorphed)== I);
bwsingle = bwselect(bwmorphed,y1,x1,8);
end
filledmodel=regionprops(bwlabel(bwsingle),'FilledImage');
bwcrop=filledmodel.FilledImage;
[modh,modw]=size(bwcrop);
gmodel_crop=imresize(gmodel_rotate,[modh modw],'bilinear');
cenmod=regionprops(bwlabel(bwcrop),'Centroid');
central=cat(1,cenmod.Centroid);
cx2=central(1,1);
cy2=central(1,2);
mfit = zeros(size(oneface));
mfitbw = zeros(size(oneface));
[limy, limx] = size(mfit);
   startx = cx1-cx2;
   starty = cy1-cy2;
   endx = startx + modw-1;
   endy = starty + modh-1;    
    startx = checklimit(startx,limx);
    starty = checklimit(starty,limy);
    endx = checklimit(endx,limx);
    endy = checklimit(endy,limy);
    for i=starty:endy,
     for j=startx:endx,
       mfit(round(i),round(j)) = gmodel_crop(round(i-starty+1),round(j-startx+1));
      end;
    end;
gray_matched=gray_matched+mfit;
crosscorr =corr2(mfit,oneface);
if(crosscorr>=0.6)    
    template_passed=template_passed+bwsegment;
end;
subplot(4,3,11);
imshow(gray_matched,[0 255])
title('Template Matching')
end;
function [ratiolist] = get_aspect(inputBW)
major = regionprops(inputBW,'MajorAxisLength');
major_length=cat(1,major.MajorAxisLength);
minor = regionprops(inputBW,'MinorAxisLength');
minor_length=cat(1,minor.MinorAxisLength);
ratiolist=major_length./minor_length;
function newcoord = checklimit(coord,maxval)
    newcoord = coord;
    if (newcoord<1) newcoord=1; end;
    if (newcoord>maxval) newcoord=maxval; end;

Pawe? 2011-11-4

移动：DGM 2023-12-29

Thank you wery much. Now i know what i was doing wrong. I have 1 more question how to change the centroid to rectangle? Is there a easy way to do it ?

Walter Roberson 2011-11-4

移动：DGM 2023-12-29

You can use the rectangle() function to plot rectangles.

请先登录，再进行评论。

Problem with defining an argument (face detection)

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

回答（1 个）

4 个评论
显示 2更早的评论隐藏 2更早的评论

另请参阅

类别

标签

Community Treasure Hunt

Problem with defining an argument (face detection)

0 个评论 显示 -2更早的评论隐藏 -2更早的评论

回答（1 个）

4 个评论 显示 2更早的评论隐藏 2更早的评论

另请参阅

类别

标签

Community Treasure Hunt

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

4 个评论
显示 2更早的评论隐藏 2更早的评论