opencv object detection

face and eye detector.First we need to load the required XML classifier. The input image or video is then loaded in grayscale format.Import NumPy as NP Import = Cv2. Cascadeclassifier ('haarcascade_frontalface_default.xml'= Cv2. Cascadeclassifier ('haarcascade_eye.xml'= cv2.imread (' Sachin.jpg'= Cv2.cvtcolor (img, Cv2. Color_bgr2gray)Now we detect the face in the image. If a face is detected, it returns the rectangle where the face is locatedRegion Rect (X,Y,W,H). Once we have this position,

Reference: Pedestrian detection using hog features and SVM Classifier:Http://blog.csdn.net/carson2005/article/details/7841443 Hog + SVM has excellent Pedestrian detection effects due to its characteristics, but it also has good effects on other targets. Here we will expand the scope. Carson2005's blog article describes how to use opencv to implement sample trai

transformation matrix. And the online about findhomography introduction is relatively few, so will let people misunderstand findfundamentalmat will calculate the transformation matrix.Try to return the matrix with the Findhomography function, in the template image, the object is already marked with a green box outline, according to the object's four boundary points, and the transformation matrix, you can get the transformed

Opencv 2.4 implements the DPM program in C ++. The main difference between it and the previous C version is that it can detect multiple targets at the same time. During use, you can put the trained model in a folder, and put the image to be detected in another folder for detection. Unfortunately, the accelerated content is not considered. Latent SVM regression ¶ Discriminatively trained Part Based Models f

Document directory 1) load the cascade classifier 2) read Video Streams 3) use this classifier for each frame 4) display the target The target detection method supported by opencv is the classifier training based on the Haar feature of the sample to obtain the cascade boosted classification ). Note: In addition to haar features, the new C ++ interface can also use the HSV features. First, we will in

; - }Wuyi } theprintf"min Distance:%f", mindist); - Wu for(inti =0; i ) { - DoubleDist =matches[i].distance; About if(Dist 1.5*mindist,0.02)) { $ Goodmatches.push_back (Matches[i]); - } - } - ADrawmatches (IMG1, Keypoints_obj, Img2, Keypoints_scene, Goodmatches, Akazematchesimg, Scalar::all (-1), +Scalar::all (-1), vectorChar>(), drawmatchesflags::not_draw_single_points); theImshow ("Good match result", akazematchesimg); - $Waitkey (0); the ret

the //Open operation (expansion corrosion operator) +Mat out; -Mat element = Getstructuringelement (Morph_rect, Size (3,3)); $Erode (Gray_diff, out, Element); $Dilate ( out, out, Element); - - //Find Outlines theMat DST = Mat::zeros ( out. Size (), cv_8uc3); -Vectorcontours;WuyiVectorhierarchy; theFindcontours ( out, contours, hierarchy, Cv_retr_ccomp, cv_chain_approx_simple); - intindex =0, Largestcomp; Wu DoubleMaxarea =0; - for(; index >=0; index = hierarchy[index][0])

The effect is still a bit of a problem, I hope we discuss togetherFindrotation-angle.cpp: Defines the entry point of the console application. FindContours.cpp: Defines the entry point of the console application. #include "stdafx.h" #include This is the original implementation of the Code of the blog post:http://blog.csdn.net/wangyaninglm/article/details/41864251Reference documents:http://blog.csdn.net/z397164725/article/details/7248096http://blog.csdn.net/fdl19881/article/details/6730112http://b

[OpenCV Getting Started Guide] Article 7 Line Segment Detection and Circle Detection The Section 5 contour detection in [OpenCV Getting Started Guide] and section 6 contour detection in [OpenC