OpenCV IS Report No Report Medical Information System Labratry

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1 OpenCV IS Report No Report Medical Information System Labratry

2 Abstract OpenCV OpenCV

3 OpenCV ( )

4 1 1.1 OpenCV OpenCV Intel C C++ Python Java Windows Linux BSD (OSS) C++ OpenCV GUI( ( ) ) 4 imgproc( ) highgui( ) objdetect( ) core( ) (pixel) R( )G( )B( ) (255, 0, 0) (0, 0, 255) (255, 255, 255) RGB 4 1) 2

5 2 while 1 #include "opencv2/objdetect/objdetect.hpp" 2 #include "opencv2/highgui/highgui.hpp" 3 #include "opencv2/imgproc/imgproc.hpp" 4 #include "opencv2/core/core.hpp" 5 6 using namespace std; 7 using namespace cv; 8 9 main 10 int main( int argc, const char** argv ) 11 { 12 // Definition 13 CvCapture* capture; 14 Mat frame; // Load the cascades (Only for face detection) 17 if(!face_cascade.load(face_cascade_name)) 18 { 19 printf("error loading1\n"); 20 return -1; 21 }; // Capture from camera and set size 24 capture = cvcapturefromcam( -1 ); 25 cvsetcaptureproperty( capture, CV_CAP_PROP_FRAME_WIDTH, 640 ); 26 cvsetcaptureproperty( capture, CV_CAP_PROP_FRAME_HEIGHT, 480 ); // Processing the video and show it 29 if( capture ) 30 { 31 while( true ) 32 { 33 Mat dst, gray_img; 34 Mat dst2(frame.rows*0.5, frame.cols*2.0, frame.type()); 35 vector <Rect > faces; frame = cvqueryframe( capture ); 38 //Processing functions

6 // char c = cvwaitkey( 50 ); 42 if( c == 27 ) break; 43 imshow( "output_window", frame ); 44 } 45 } 46 return 0; 47 } highgui.hpp 2. Load the cascades 4. Processing the video and show it std::vector<rect> faces; 1. cvcapturefromcam(-1) cvsetcaptureproperty 3. cvqueryframe cvcapturefromcam while 4. cvwatikey ms () frame page per second fps 5. if( c == 27 ) break; char c = cvwaitkey( 50 ); esc while 4

3 2 Processing 3.1 RGB R G B NOT 1 dst = frame; Fig. 3.1.1 (a) (b) Fig.

7 3 2 Processing 3.1 RGB R G B NOT 1 dst = frame; Fig (a) (b) Fig ( ) flip(frame, dst, 0); Fig

8 3.4 3 (a) (b) (c) (d) Fig ( ) 1 resize(frame, dst2, dst2.size(), 0.5, 2, cv::inter_cubic); Fig (a) (b) Fig ( ) 3.4 6

9 GaussianBlur(frame, dst, Size(5, 5), 10, 10); Fig (a) (b) 5 (c) 11 Fig ( ) (3.1) f(x, y) = 1 2πσ 2 exp( x2 + y 2 2σ 2 ) (3.1) (3.1) σ Fig Y σ 5 X σ bilateralfilter(frame, dst, 5, 50, 100); Fig f(i, j) g(i, j) (3.2) 7

10 (a) 3 3 (b) 5 5 Fig ( ) (a) (b) 5 (c) 11 Fig ( ) 8

11 3.5 3 g(x, y) = w w n= w n= w f(i + m, j + n) exp( m2 + n 2 ) exp( w w n= w n= w 2σ 2 1 exp( m2 + n 2 2σ1 2 ) exp( (f(i, j) f(i + m, j + n))2 2σ2 2 ) (f(i, j) f(i + m, j + n))2 2σ2 2 ) (3.2) w σ 1 σ 2 σ cvtcolor(frame, dst, CV_BGR2GRAY); Fig (a) (b) Fig ( ) 2 RGB Y (3.3) Y = 0.299R G B (3.3) 3.6 ( )

3.6 ( ) 3 3.5 gray img 2 1 threshold(gray_img, dst, 0, 255, cv::thresh_otsu); 2 Fig. 3.6.1 (a) (b) Fig. 3.6.1 2 ( ) t 2 n 1 m 1 σ 1 n 2 m 2 σ 2 n t m t σ t σw 2 σ 2 b σ 2 w = n 1 n 1 + n 2 σ 2 1 + n 2 n 1 + n 2 σ 2 2 (3.

12 3.6 ( ) gray img 2 1 threshold(gray_img, dst, 0, 255, cv::thresh_otsu); 2 Fig (a) (b) Fig ( ) t 2 n 1 m 1 σ 1 n 2 m 2 σ 2 n t m t σ t σw 2 σ 2 b σ 2 w = n 1 n 1 + n 2 σ n 2 n 1 + n 2 σ 2 2 (3.4) m t σ 2 b = n 1(m 1 m t ) 2 + n 2 (m 2 m t ) 2 n 1 + n 2 = n 1n 2 (m 1 m 2 ) 2 (n 1 + n 2 ) 2 (3.5) (3.5) m t = n 1m 1 + n 2 m 2 n 1 + n 2 (3.6) σ t σ 2 b = n 1n 2 (m 1 m 2 ) 2 (n 1 + n 2 ) 2 (3.7) σ 2 t = σ 2 b + σ2 w (3.8) 10

13 3.7 3 σb 2 σw 2 = σ2 b σ 2 t σ2 b (3.9) σ t σ b 2 n 1 n 2 (m 1 m 2 ) 2 (3.10) t n m (3.10) 3.7 OpenCV 2.5 gray img Haar-like 1 face_cascade.detectmultiscale(gray_img, faces, 1.1, 2, 0 CV_HAAR_SCALE_IMAGE, Size(200, 200)); 2 dst = frame; 3 for( size_t i = 0; i < faces.size(); i++ ) 4 { 5 Point center( faces[i].x + faces[i].width * 0.5, faces[i]. y + faces[i].height * 0.5 ); 6 ellipse( dst, center, Size(faces[i].width * 0.5, faces[i]. height * 0.5), 0, 0, 360, Scalar(255, 0, 255), 4, 8, 0 ); 7 } Fig (a) (b) Fig ( ) 11

14 3.7 3 OpenCV Fig Haar-like AdaBoost Learning images Input image Feature value Feature value Learning Recognition Result for learning Input image Fig ( 2) ) Fig Fig ( ) Fig Fig Haar-like 12 AdaBoost AdaBoost 12

15 3.7 3 Edge features Line features Center surround feature Fig Haar-like ( 2) ) 13

16 1),.., ) slideshare 10 CV OpenCV. 14

2 Eclipse 2.1 Eclipse 1. Eclipse 2. 3 ( 4) E:Yworkspace OK 3 Eclipse 4 3. Eclipse 5 5 Eclipse Eclipse 2. 7 C 2

2 Eclipse 2.1 Eclipse 1. Eclipse 2. 3 ( 4) E:Yworkspace OK 3 Eclipse 4 3. Eclipse 5 5 Eclipse Eclipse 2. 7 C 2 3: 1 1. PC 2. Phantasys 201209 ( 1) 1 PC 2 Windows 3. Windows ID ( 2) 4. Web (Firefox Internet Explorer) 5. A 6. http:// http://www.cv.ics.saitama-u.ac.jp/yosinori/lecture/seminar/ Web 1 7. USB PC USB