Homework 3 Eye Detection

Transcription

1 Homework 3 Eye Detection This homework purposes to detect eyes in a face image by using the technique described in the following paper Accurate Eye Center Location and Tracking Using Isophote Curvature". 1. Reading the Image and Finding Faces by Using OpenCV (2.4.5) Face Detector Main function opens image and tries to find faces by using detectfacialfeatures function: #include <stdio.h> #include <conio.h> #include <stdlib.h> #include <opencv\cv.h> #include <opencv\highgui.h> #include <opencv\cvaux.h> #include <opencv2\highgui\highgui.hpp> #include <opencv2\imgproc\imgproc.hpp> using namespace cv; CvHaarClassifierCascade *cascade,*cascade_e,*cascade_nose,*cascade_mouth; CvMemStorage *storage; char *face_cascade="haarcascade_frontalface_alt2.xml"; char *eye_cascade="haarcascade_mcs_eyepair_big.xml"; char *ImageAdresses = "C:\\Users\\Capa\\Documents\\Visual Studio 2010\\Projects\\myNewOpenCv\\myNewOpenCv\\lena.jpg"; char *ImageOutAdresses = "C:\\Users\\Capa\\Documents\\Visual Studio 2010\\Projects\\myNewOpenCv\\myNewOpenCv\\lena_face_eye_out.jpg"; char *ImageEyeAdresses = "C:\\Users\\Capa\\Documents\\Visual Studio 2010\\Projects\\myNewOpenCv\\myNewOpenCv\\eye_out.jpg"; int main( int argc, char** argv ) { /* Variables to take the image in */ IplImage *img,*temp_img; /* Variables to show image locations in the PC */ char image[100]; */ /* load the classifier note that the file is in the same directory with this code storage = cvcreatememstorage( 0 ); cascade = ( CvHaarClassifierCascade* )cvload( face_cascade, 0, 0, 0 ); cascade_e = ( CvHaarClassifierCascade* )cvload( eye_cascade, 0, 0, 0 ); if(!(cascade cascade_e) ) { fprintf( stderr, "ERROR: Could not load classifier cascade\n" ); return -1; } /* Load the Image */ img=cvloadimage(imageadresses); 1

2 } /* Load the temp Image */ temp_img=cvloadimage(imageadresses); if(!img) { printf("could not load image file and trying once again: %s\n",image); } printf("\n curr_image = %s",imageadresses); /* detect face */ detectfacialfeatures(img,temp_img); /* Relase the Haar Classifier Cascade*/ cvreleasehaarclassifiercascade( &cascade ); cvreleasehaarclassifiercascade( &cascade_e ); /* Relase the memory storage */ cvreleasememstorage( &storage ); /* Relase the Image and Temp Image*/ cvreleaseimage(&img); cvreleaseimage(&temp_img); 2

3 detectfacialfeatures function uses cvhaardetectobjects function to detect faces and then it draws a red box for each face. void detectfacialfeatures( IplImage *img,iplimage *temp_img) { float m[6]; double factor = 1; CvMat M = cvmat( 2, 3, CV_32F, m ); int w = (img)->width; int h = (img)->height; CvSeq* faces; CvRect *r; m[0] = (float)(factor*cos(0.0)); m[1] = (float)(factor*sin(0.0)); m[2] = w*0.5f; m[3] = -m[1]; m[4] = m[0]; m[5] = h*0.5f; cvgetquadranglesubpix(img, temp_img, &M); CvMemStorage* storage=cvcreatememstorage(0); cvclearmemstorage( storage ); if( cascade ) faces = cvhaardetectobjects(img,cascade, storage, 1.2, 3, CV_HAAR_DO_CANNY_PRUNING, cvsize(20, 20)); else printf("\nfrontal face cascade not loaded\n"); printf("\n no of faces detected are %d",faces->total); /* for each face found, draw a red box */ for(int i = 0 ; i < ( faces? faces->total : 0 ) ; i++ ) { r = ( CvRect* )cvgetseqelem( faces, i ); cvrectangle( img,cvpoint( r->x, r->y ),cvpoint( r->x + r->width, r->y + r- >height ), CV_RGB( 255, 0, 0 ), 1, 8, 0 ); } printf("\n face_x=%d face_y=%d wd=%d ht=%d",r->x,r->y,r->width,r->height); /* Detect Eyes */ detecteyes(img,r); /* reset region of interest */ cvresetimageroi(img); } if(faces->total>0) { cvsaveimage( ImageOutAdresses, img ); } 3

4 detecteyes function detects eyes and draws a rectangle for eyes: /*Eyes detection*/ void detecteyes( IplImage *img,cvrect *r) { CvSeq *eyes; int eye_detect=0; /* eye detection starts */ /* Set the Region of Interest: estimate the eyes' position */ cvsetimageroi(img, /* the source image */ cvrect(r->x, /* x = start from leftmost */ r->y + (r->height/5.5), /* y = a few pixels from the top */ r->width, /* width = same width with the face */ r->height/3.0 /* height = 1/3 of face height */ ) ); /* detect the eyes */ eyes = cvhaardetectobjects(img,/* the source image, with the estimated location defined */ cascade_e, /* the eye classifier */ storage, /* memory buffer */ 1.15, 3, 0, /* tune for your app */ cvsize(25, 15) /* minimum detection scale */ ); printf("\n no of eyes detected are %d",eyes->total); /* draw a rectangle for each detected eye */ for( int i = 0; i < (eyes? eyes->total : 0); i++ ) { cvsaveimage(imageeyeadresses, img ); eye_detect++; /* get one eye */ CvRect *eye = (CvRect*)cvGetSeqElem(eyes, i); /* draw a blue rectangle */ cvrectangle(img, cvpoint(eye->x, eye->y), cvpoint(eye->x + eye->width, eye->y + eye->height), CV_RGB(0, 0, 255), 1, 8, 0 ); printf("\n eye_x=%d eye_y=%d eye_wd=%d eye_ht=%d",eye->x,eye->y,eye- >width,eye->height); } } 4

5 Original Image is showed in Figure 1. Detected face image is shown in Figure 2: Figure 1: Original Image Figure 2: Detected Face 5

6 Detected Eyes are shown in Figure 3: Result of the process is shown Figure 4: Figure 3: Detected Eyes Figure 4: Result Window 2. Considering the Rectangles Around Eyes After face and eyes are detected, the rectangle around the eyes gives an image in Figure 5: Figure 5: Rectangle Around Eyes To calculate Gradient, Displacement Vectors and Curvedness, the image is taken to the MATLAB environment. All the calculations from this step are done at the MATLAB. 6

7 a. Read The Image % Read The Image I = imread('eye_out.jpg'); % Show Original Image figure, imagesc(i),title('original Image'),impixelinfo,colormap('gray'); b. Convert Image to Grayscale Figure 6: Original Image %Convert image to grayscale J = rgb2gray(i); figure, imagesc(j),title('gray Image'),impixelinfo,colormap('gray'); Figure 7: GrayScaled Image 7

8 c. Divide Image Into Two Parts It is known that eyes are found in the image but their location is not known. It is s also known that there are two eyes in the image. Therefore, image is divided into two parts that every part has one eye. % Image is m x n matrix m=[0 size(i,1)]; n=[0 size(i,2)]; % divide image into two parts. One for each eye. Store it in variable cel %left eye eye{1}=j(1:1:size(j,1),1:1:round(0.50*size(j,2))); %right eye eye{2}=j(1:1:size(j,1),round(0.50*size(j,2)):1:size(j,2)); %create variables for location of eyes x_pixel=zeros(2); y_pixel=zeros(2); %Image matrix elements are converted to double eye{1}=im2double(eye{1}); eye{2}=im2double(eye{2}); Figure 8: Image is divided into 2 parts. Each part has 1 eye. 8

9 d. For Each Eye Start to Calculate the Eye Location Every eye image are taken and firstly smoothed by Gaussian. Following Figures shows grayscaled and smoothed eye images. %for each eye apply the algorithm based on the method specified. for index=1:1:2 % Smooth the image. h = fspecial('gaussian',[5 5],2); eye{index}=imfilter(eye{index},h,'replicate','conv'); % Display the image obtained after smoothing a = {'Image after Grayscaling and Smoothing '}; b = {num2str(index)}; ab = strcat(a, b) figure,imagesc(eye{index},'xdata',m,'ydata',n),title(ab),impixelinfo,colorm ap('gray'); Figure 9: Images are Grayscaled and also images are smoothed with Gaussian 9

10 e. Find The Gradients Of The Image. Then Calculate The Curvedness : Each eye images gradients are calculated. Curvedness is calculates by using formula: %gradients of the image [FX,FY]=gradient(eye{index}); [FXX,FXY]=gradient(FX); [FYX,FYY]=gradient(FY); % Calculate the curvedness Curvedness=sqrt(im2double(FXX.^2 + 2*FXY.^2 + FYY.^2)); % Display the curvedness a = {'Curvedness '}; b = {num2str(index)}; ab = strcat(a, b) figure,imagesc(abs(curvedness),'xdata',m,'ydata',n),title(ab),impixelinfo,c olormap('gray'); Figure 10: Curvedness 10

11 f. Calculate the Displacement Vector Displacement vector is calculated by using formula: % Calculate the displacement vectors Num = FX.^2 + FY.^2; Den = ((FY.^2).*FXX) - (2*(FX.*FXY).*FY) + ((FX.^2).*FYY); Den = - round(num./den); DLX = Den.*FX; DLY = Den.*FY; Num = Num.^(1.5); K= - round(den./num); Disp_Vector=zeros(size(eye{index},1),size(eye{index},2),2); for x=1:1:size(eye{index},1) for y=1:1:size(eye{index},2) Disp_Vector(x,y,1)=DLX(x,y); Disp_Vector(x,y,2)=DLY(x,y); Disp_Vector=round(Disp_Vector); Magnitude_Disp_Vector=sqrt(Disp_Vector(:,:,1).^2 + Disp_Vector(:,:,1).^2); % Create the weight array weight=zeros(size(eye{index},1),size(eye{index},2)); for x=1:1:size(eye{index},1) for y=1:1:size(eye{index},2) weight(x,y)=abs(curvedness(x,y)); minrad = 4; maxrad = 40; % Create the mapped image that will store the result Mapped = zeros(size(eye{index},1),size(eye{index},2)); Used_Disp_Vector = zeros(size(eye{index},1),size(eye{index},2),2); 11

12 for x=1:1:size(eye{index},1) for y=1:1:size(eye{index},2) if((disp_vector(x,y,1)~=0) (Disp_Vector(x,y,2)~=0)) if((y+disp_vector(x,y,1)>0) && (x+disp_vector(x,y,2)>0)) if((y+disp_vector(x,y,1)<=size(eye{index},2)) && (x+disp_vector(x,y,2)<=size(eye{index},1)) && (K(x,y)<0)) if((magnitude_disp_vector(x,y)>=minrad)&&(magnitude_disp_vector(x,y)<=maxra d)) % use only those displacement vectors which are within the % specified range Used_Disp_Vector(x,y,1)=Disp_Vector(x,y,1); Used_Disp_Vector(x,y,2)=Disp_Vector(x,y,2); Mapped(x+Disp_Vector(x,y,2),y+Disp_Vector(x,y,1)) = Mapped(x+Disp_Vector(x,y,2),y+Disp_Vector(x,y,1)) + weight(x,y); % plot the used displacement vectors a = {'Displacement Vector '}; b = {num2str(index)}; ab = strcat(a, b) quiver(used_disp_vector(:,:,1),used_disp_vector(:,:,2),0),title(ab); Figure 11: Displacement Vectors 12

13 g. Create the Mapped Image % smooth the mapped image Mapped=imfilter(Mapped,h,'replicate','conv'); % plot the mapped image obtained after smoothing a = {'Mapped Image after Blurring with Gaussian '}; b = {num2str(index)}; ab = strcat(a, b) figure,imagesc(mapped,'xdata',m,'ydata',n),title(ab),impixelinfo,colormap(' gray'); Figure 12: Mapped Image After Blurring with Gaussian 13

14 h. Find the maximum isocenter % Find the maximum isocenter [y_pixel(index),x_pixel(index)]=find(mapped==max(mapped(:))); Mapped(Mapped<max(Mapped(:)))=0; % plot the result a = {'Result '}; b = {num2str(index)}; ab = strcat(a, b) figure,imagesc(mapped,'xdata',m,'ydata',n),title(ab),impixelinfo,colormap(' gray'); Figure 13: Maximum Isocenters are found in each image 14

15 i. Show the result. This result gives the location of eyes and shows them in the original image: x_pixel(1)=x_pixel(1)+m(1); y_pixel(1)=y_pixel(1)+n(1); x_pixel(2)=x_pixel(2)+m(1)+0.50*size(j,2); y_pixel(2)=y_pixel(2)+n(1); % Display the resulting image. figure('name','result Image - Eye Locations Are Found'), imshow(i,[],'initialmagnification','fit'),title('result Image - Eye Locations Are Found'),impixelinfo,colormap('gray'); hold on; plot(x_pixel(1),y_pixel(1),'g*'); plot(x_pixel(2),y_pixel(2),'r*'); x_pixel(1),y_pixel(1) x_pixel(2),y_pixel(2) hold off; Figure 14: Result Image: Eye locations are found 15

16 Figure 15: Left eye is at (57,38) and Right eye is at (116,37) Left eye is found at (58, 38). Right eye is found at (116, 37). Ground truth shows that: Left eye is at (49, 32), Right eye is at (111, 33). Other Image Results: 16

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