Classification and recognition of simple KNN under Opencv-python

KNN is a simple algorithm used to classify data mining, which is used to realize the simple recognition of 4 flowers in this cluster.

Environment: PYTHON2.7+OPENCV3.0+WINDOWS10

Principle: After the use of KNN function to extract 4 flower feature points, the need to identify the image to extract the signs points, and the gallery of 4 flowers, compared to the category of the most matching points are considered similar.

Code:

Read in image data:

1 2IMG =cv2.imread (name)3   4Q_img=[1]*105Q_img[0] = Cv2.imread ("images/qiangwei1.jpg")6Q_IMG[1] = Cv2.imread ("images/qiangwei2.jpg")7Q_IMG[2] = Cv2.imread ("images/qiangwei3.jpg")8Q_IMG[3] = Cv2.imread ("images/qiangwei4.jpg")9Q_IMG[4] = Cv2.imread ("images/qiangwei5.jpg")Ten    OneX_img=[1]*10 AX_img[0] = Cv2.imread ("images/xinghua1.jpg") -X_IMG[1] = Cv2.imread ("images/xinghua2.jpg") -X_IMG[2] = Cv2.imread ("images/xinghua3.jpg") theX_IMG[3] = Cv2.imread ("images/xinghua4.jpg") -X_IMG[4] = Cv2.imread ("images/xinghua5.jpg") -  -T_img=[1]*10 +T_img[0] = Cv2.imread ("images/taohua1.jpg") -T_IMG[1] = Cv2.imread ("images/taohua2.jpg") +T_IMG[2] = Cv2.imread ("images/taohua3.jpg") AT_IMG[3] = Cv2.imread ("images/taohua4.jpg") atT_IMG[4] = Cv2.imread ("images/taohua5.jpg") -  -Y_img=[1]*10 -Y_img[0] = Cv2.imread ("images/yinghua1.jpg") -Y_IMG[1] = Cv2.imread ("images/yinghua2.jpg") -Y_IMG[2] = Cv2.imread ("images/yinghua3.jpg") inY_IMG[3] = Cv2.imread ("images/yinghua4.jpg") -Y_IMG[4] = Cv2.imread ("images/yinghua5.jpg")

To get a grayscale image:

Gray =Cv2.cvtcolor (Img,cv2. Color_bgr2gray) Q_gray=[1]*10Q_gray[0]=Cv2.cvtcolor (Q_img[0],cv2. Color_bgr2gray) q_gray[1] = Cv2.cvtcolor (q_img[1],cv2. Color_bgr2gray) q_gray[2] = Cv2.cvtcolor (q_img[2],cv2. Color_bgr2gray) q_gray[3] = Cv2.cvtcolor (q_img[3],cv2. Color_bgr2gray) q_gray[4] = Cv2.cvtcolor (q_img[4],cv2. Color_bgr2gray) X_gray=[1]*10X_gray[0]=Cv2.cvtcolor (X_img[0],cv2. Color_bgr2gray) x_gray[1] = Cv2.cvtcolor (x_img[1],cv2. Color_bgr2gray) x_gray[2] = Cv2.cvtcolor (x_img[2],cv2. Color_bgr2gray) x_gray[3] = Cv2.cvtcolor (x_img[3],cv2. Color_bgr2gray) x_gray[4] = Cv2.cvtcolor (x_img[4],cv2. Color_bgr2gray) T_gray=[1]*10T_gray[0]=Cv2.cvtcolor (T_img[0],cv2. Color_bgr2gray) t_gray[1] = Cv2.cvtcolor (t_img[1],cv2. Color_bgr2gray) t_gray[2] = Cv2.cvtcolor (t_img[2],cv2. Color_bgr2gray) t_gray[3] = Cv2.cvtcolor (t_img[3],cv2. Color_bgr2gray) t_gray[4] = Cv2.cvtcolor (t_img[4],cv2. Color_bgr2gray) Y_gray=[1]*10Y_gray[0]=Cv2.cvtcolor (Y_img[0],cv2. Color_bgr2gray) y_gray[1] = Cv2.cvtcolor (y_img[1],cv2. Color_bgr2gray) y_gray[2] = Cv2.cvtcolor (y_img[2],cv2. Color_bgr2gray) y_gray[3] = Cv2.cvtcolor (y_img[3],cv2. Color_bgr2gray) y_gray[4] = Cv2.cvtcolor (y_img[4],cv2. Color_bgr2gray)

Get Keypoints,descriptor:

Detect = Cv2.xfeatures2d.SIFT_create (800) Kp,des=Detect.detectandcompute (gray,none) Q_KP=[1]*10Q_des=[1]*10Q_kp[0],q_des[0]=Detect.detectandcompute (q_gray[0],none) q_kp[1],Q_DES[1] = Detect.detectandcompute (q_gray[1],none) q_kp[2],Q_DES[2] = Detect.detectandcompute (q_gray[2],none) q_kp[3],Q_DES[3] = Detect.detectandcompute (q_gray[3],none) q_kp[4],Q_DES[4] = Detect.detectandcompute (q_gray[4],none) X_KP=[1]*10X_des=[1]*10X_kp[0],x_des[0]=Detect.detectandcompute (x_gray[0],none) x_kp[1],X_DES[1] = Detect.detectandcompute (x_gray[1],none) x_kp[2],X_DES[2] = Detect.detectandcompute (x_gray[2],none) x_kp[3],X_DES[3] = Detect.detectandcompute (x_gray[3],none) x_kp[4],X_DES[4] = Detect.detectandcompute (x_gray[4],none) T_KP=[1]*10T_des=[1]*10T_kp[0],t_des[0]=Detect.detectandcompute (t_gray[0],none) t_kp[1],T_DES[1] = Detect.detectandcompute (t_gray[1],none) t_kp[2],T_DES[2] = Detect.detectandcompute (t_gray[2],none) t_kp[3],T_DES[3] = Detect.detectandcompute (t_gray[3],none) t_kp[4],T_DES[4] = Detect.detectandcompute (t_gray[4],none) Y_KP=[1]*10Y_des=[1]*10Y_kp[0],y_des[0]=Detect.detectandcompute (y_gray[0],none) y_kp[1],Y_DES[1] = Detect.detectandcompute (y_gray[1],none) y_kp[2],Y_DES[2] = Detect.detectandcompute (y_gray[2],none) y_kp[3],Y_DES[3] = Detect.detectandcompute (y_gray[3],none) y_kp[3],Y_DES[4] = Detect.detectandcompute (Y_gray[4],none)

To match using the KNN match class:

BF =Cv2. Bfmatcher () q_matches=[1]*10Q_matches[0]= Bf.knnmatch (des,q_des[0],k=2) q_matches[1]= Bf.knnmatch (des,q_des[1],k=2) q_matches[2]= Bf.knnmatch (des,q_des[2],k=2) q_matches[3]= Bf.knnmatch (des,q_des[3],k=2) q_matches[4]= Bf.knnmatch (des,q_des[4],k=2) X_matches=[1]*10X_matches[0]= Bf.knnmatch (des,x_des[0],k=2) x_matches[1]= Bf.knnmatch (des,x_des[1],k=2) x_matches[2]= Bf.knnmatch (des,x_des[2],k=2) x_matches[3]= Bf.knnmatch (des,x_des[3],k=2) x_matches[4]= Bf.knnmatch (des,x_des[4],k=2) T_matches=[1]*10T_matches[0]= Bf.knnmatch (des,t_des[0],k=2) t_matches[1]= Bf.knnmatch (des,t_des[1],k=2) t_matches[2]= Bf.knnmatch (des,t_des[2],k=2) t_matches[3]= Bf.knnmatch (des,t_des[3],k=2) t_matches[4]= Bf.knnmatch (des,t_des[4],k=2) Y_matches=[1]*10Y_matches[0]= Bf.knnmatch (des,y_des[0],k=2) y_matches[1]= Bf.knnmatch (des,y_des[1],k=2) y_matches[2]= Bf.knnmatch (des,y_des[2],k=2) y_matches[3]= Bf.knnmatch (des,y_des[3],k=2) y_matches[4]= Bf.knnmatch (des,y_des[4],k=2)  

Record and filter the matching points:

sum1=0 sum2=0 sum3=0 sum4=0 forIinchRange (5):       forM,ninchQ_matches[i]:ifM.distance < 0.55*N.distance:sum1=sum1+1 forIinchRange (5):       forM,ninchX_matches[i]:ifM.distance < 0.55*n.distance:sum2=sum2+1 forIinchRange (5):       forM,ninchT_matches[i]:ifM.distance < 0.55*n.distance:sum3=sum3+1 forIinchRange (5):       forM,ninchY_matches[i]:ifM.distance < 0.55*n.distance:sum4=sum4+1

return Result:

ifMax (SUM1,SUM2,SUM3,SUM4) = =sum1:return "Rose"      ifMax (SUM1,SUM2,SUM3,SUM4) = =sum2:return "Apricot Blossom"      ifMax (SUM1,SUM2,SUM3,SUM4) = =sum3:return "Peach Blossom"      ifMax (SUM1,SUM2,SUM3,SUM4) = =SUM4:return "Sakura"

GUI uses a simple page developed using Wxformbuilder+wxpython

Final file:

As follows:

The recognition rate is not very high because the picture gallery is small and the algorithm is simple.

Classification and recognition of simple KNN under Opencv-python