opencv object recognition

Handwritten Digit Recognition [QT + OpenCV], handwritten digit qtopencv [Description] There are many ways to implement Handwritten Digit Recognition. This article tries to simplify it so that you can quickly understand how to implement a dynamic system. 【] [Idea] 1. Feature Extraction Divide the image into 5x5 areas, and then calculate the proportion of black

K-means algorithm is a well-known clustering algorithm, not only easy to achieve, and the effect is good, the training process without manual intervention, is a pattern recognition and other areas of the home must good products ah, today take this algorithm to practice practiced hand. Dynamic clustering of indirect clustering methods in unsupervised learningProcess:1. Random selection of K points in a sample as a cluster center2. Calculate the distanc

are in the same order.*/int index = 0;for (int i=0;i{PData = mask.ptrfor (int j=0;j{if (pdata[j]==0)rst[index++]= ' 0 ';Elserst[index++]= ' 1 ';}}return rst;}void Photofingerprint::insert (Mat src,string val){String strval = HashValue (src);M_hashmap.insert (paircout}void Photofingerprint::find (Mat src){String Strval=hashvalue (SRC);Hash_mapif (It==m_hashmap.end ()){coutElsecout/* Return *it;*/}int Photofingerprint::D istance (String str1,string str2){if ((Str1.size ()!=64) | | (Str2.size ()!=

Well-written SVM + hog Classifier Training Class mysvm: Public cvsvm {public: int get_alpha_count () {return this-> sv_total;} int get_sv_dim () {return this-> var_all;} int get_sv_count () {return this-> decision_func-> sv_count;} double * get_alpha () {return this-> decision_func-> alpha;} float ** get_sv () {return this-> Sv;} float get_rov () {return this-> decision_func-> rho;}}; void train () {char classifiersavepath [256] = "C: /pedestrianDetect-peopleFlow.txt "; string positivepath =" E

OpenCV Handwriting Selection quiz (b) Character recognitionThe choice question basically only need to recognize ABCD and empty five content, theoretically should recognize the rate is higher, the identification code refers to the online search code, because the reference URL is more, now also can not clear is the reference where the code, here does not thank each.Basic steps:First, the recognition function

This article was reproduced from: http://blog.csdn.net/ap1005834/article/details/51340831 First, preface This article refers to the automatic license plate recognition project in the deep understanding of OPENCV practical Computer Vision Project analysis, and then carries on the practice after understanding the method. The difficulty of accurately identifying the sequence of the characters in the license p

system becomes higher.4.2 uses the nearest neighbor. That is, the selection of the smallest European distance image as the result of the current test image.In the actual system, it is often necessary to use K nearest neighbor, that is, choose the smallest k Euclidean distance, determine which class they belong to and determine the current numerical results.For reasons of simplification, the correct rate of digital recognition is not very high. But th

information of the keyboard The following function is to identify the coordinates of the cipher keyboard by means of an image recognition method. The password keyboard encountered is shown in the following figure There is also the contents of the input box, I have to intercept. Solution idea: 1, image recognition, and then click the coordinates 2, development cooperation, to the specific app package, lif

It can be said that from the beginning of this article, the end of the basic image recognition, came to the second stage of learning. In peacetime processing two value image, in addition to some of the morphology of the operation, there is a section of the contour connected area perimeter mark, there is one of the most common is the filling of the hole, OpenCV here become diffuse water filled, in fact, can

, limit2, color): row_num, Col_num= Card_img_hsv.shape[:2] XL=Col_num XR=0 YH=0 yl=row_num Row_num_limit= 21Col_num_limit= Col_num * 0.8ifColor! ="Green" ElseCol_num * 0.5#Green with gradient forIinchRange (row_num): Count=0 forJinchRange (col_num): H=Card_img_hsv.item (i, J, 0) S= Card_img_hsv.item (I, J, 1) V= Card_img_hsv.item (I, J, 2) ifLimit1 and andV:count+ = 1ifCount >Col_num_limit:ifYL >I:yl=IifYH I:yh=I forJinchRange (col_num): Count=0 forIinchRange (row_num): H=Card_im

],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=[

call in OpenCV is the following function, which is convenient(1) It is usually used to increase the global contrast of many images, especially when the contrast of useful data of an image is quite close.(2) Brightness can be better distributed on the histogram. This can be used to enhance the local contrast without affecting the overall contrast, histogram equalization by effectively extending the common brightness to achieve this function.(3) It is

This is a Raspberry Pi run, opencv3.OPENCV provides a handwritten digital picture to us, as shown, as a sample library for identifying handwritten numbers.0 to 9 A total of 10 numbers, each number has five lines, a row of 100 digits. First of all, we need to intercept these 5,000 numbers.The picture size is 1000*2000, and each number block size is 20*20.1. Intercept samples and storeThe following code is the process of intercepting the above numbers a

First, preface This article refers to the automatic license plate recognition project in the deep understanding of OPENCV practical Computer Vision Project analysis, and then carries on the practice after understanding the method. The difficulty of accurately identifying the sequence of the characters in the license plate area is to realize the exact location of the license plate area. So the final

Special Collection AnalysisThe dataset is Letter-recognition.data, with a total of 20,000 data, separated by commas, the data instance is shown below, the first column is the letter mark, and the remainder is a different feature. t,2,8,3,5,1,8,13,0,6,6,10,8,0,8,0,8Learning methods1. Read in the data and remove the separator number2, the first column of data as a marker, the rest of the training data3. Initialize the classifier and train with training data4, the use of test data to verify the acc

Busy for the most part of the semester fingerprint, palm processing, here a little to sort out someGoal Find the center point and Triangle Point of fingerprint image, count the number of interval ridge lines, complete the fingerprint classification. Function The processing that needs to be done includes: Segmentation foreground and background (using gradient map, direction field realization); Enhanced fingerprint image (through equalization, Edge convergence, smoothin

(New_right_point) point_limit (Heig Th_point) Point_limit (left_point) car_img= Dst[int (left_point[1]): Int (heigth_point[1]), int (left_point[0]): Int (new_right_point[0]) car_imgs.append (car_img)elifLEFT_POINT[1] > Right_point[1]:#Negative AngleNew_left_point = [Left_point[0], heigth_point[1]] Pts2= Np.float32 ([New_left_point, Heigth_point, Right_point])#characters are only highly needed to changePts1 =Np.float32 ([Left_point, Heigth_point, Right_point]) M=Cv2.getaffinetransform (Pts1, pts

The first parameter, the Inputarray type SRC, the input image, namely the source image, fills the Mat class the object to be able. The second parameter, the Outputarray type of DST, the output image, and the source picture have the same size and type. The third parameter, the const size type of dstsize, is the size of the output image, with the default value of size (), which is calculated by size (src.cols*2,src.rows*2) By default,

thresholds is discussed in the extension), according to the high threshold is worth an edge image, such an image contains very few false edges, but because the threshold is high, the resulting image edge may not be closed, the problem is not resolved to use another low threshold value. Popular: Is in the edge detection, or to use the filter to reduce noise, first through the horizontal and vertical direction of a partial derivative, the amplitude and direction of the gradient, so that each poin

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,